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Multi-Programming Sets of Languages. How Actuarial Modelling Centres Information Security Models to service such a Language Universe

Written by Thomas Mutsimba, an Enterprise Risk Management Professional, with specialist endowment in advanced actuarial analysis and advanced forensic analysis©

Visit ThomasActuarialanalysisworld Blog for more cutting edge insights @ https://actuarialanalysisworld.finance.blog/

Business and Information Systems environments may become convoluted and or multiplexed by using multi-sets of programming languages. This is especially in dynamic information processing environments where the demand for utilities become critical to drive business analogies of different datum structures. It is important to note that programming is an information systems’ engineering tenet that sets the pedestal of jargonated architectures hidden behind user interfaces. The sets of programming languages de-sensitized via de-jargonation methodologies serves to ensure security. This security is information security. It is not just an end product of information systems engineering, but it is a fundamental tenet that through modelling sets, become the centre stage of servicing the information and communication systems environment.

Multi-programming sets of languages are now being used because of highly transformative business environments. These are used to engineer data formation tablets. These data formation tablets are a tenet of actuarial modelling. In this article, I explore how actuarial modelling centres information security models to service a multi-programming language universe. Programming is the definitive degeneration of a set of instructions that compute algorithm set motion dynamics. This programming sets the tone of execution of algorithmic instructions using indexed sequencing and motion sensory dynamics.

Actuarial modelling in Information security models

Actuarial modelling comes in via the information security models. The former refers to the input fundamentals formation degenerating the sector-merged formation of a variety of components in a system. Actuarial modelling is convoluted in the sectoral datum structures and it is discernible through actuarial formation analytics. The thrust of this article is to expound on the actuarial formulants analytical datum perspiratory dynamics as they are deployed, via centerage de-jargonates in the algorithm sensory motion dynamics. Entities seldom expound the multi-programming language sets actuarial formation analytics because of de-sensitization points or points of interest. Points of interest is where jargonates of programming datum structures convoluted in tenets of indexing, looping, concomitant error jargonated analyticals centre information security structural systems.

The Knowledge Index

This article is an uncouth article, expounding knowledge convoluted in actuarial forensics formation dynamics of programming science that any information security risk or model manager would have advantage over peers.

Drivers of Multi-language programming analytics

Via a centerage knowledge index programming language analytic, the following are drivers of multi-language programming proffering actuarial index central formation knowledge base extrapolated to a multi-programming sets:

Programming centerage identification sets.
Gentric Actuarial Distributive formation.
Programming Engineering Datum analytical formation.
Center of Index Hierarchical programming.
Formulants of multi-programming assurance structures.
Information Security-Multi-programming language conclusionary assurance.

Programming Centerage Identification Sets

Formulation program indexing formation refers to the programming release construction of indexing. Indexing refers to the formation of datum grouping methodologies, convoluted using set quantum fixed framework identification formulants. This formation centres the various and or multi-programming powered datum pinned structures de-jargonated under information system structural systems. The formulation program indexing formation in a multi-programming environment is a key depicter of common parsing language identification sets. Why is that so? It is so because of the following:

Indexing is an advanced actuarial degenerate format.
Program indexing ferments the information security models to compact link different datum structures.
Program index formation is a sensitized datum-node structure utility. What is a sensitized datum node structure utility? A sensitized datum-node structure refers to decryption of information systems structural systems that are convoluted by information security as a model of nodal linkage between the information system and the technological universe. Program indexing, a tenet of actuarial formation in multi-programming environments copulating and or serving the centre of the program. Information security program delineation enables the identification of structures. These stratums linked via node datum de-jargonation technically provides a basis of actuarial measurement of pervasiveness of information systems structural depictors of common language parsing identification formulants.
Program indexing formation in multi-programming environments deployment of actuarial formation is a key driver of information security modelling centerage. Information security modelling centerage in this case is a sequestration technic formulator. Jargon centric stratums are looping technics meant to link different functionaries. Programming in multi-programming environments results in formulator-need sequestration mechanisms. These are advanced actuarial formation technics that cause information security sensitivity analytics to commonality formulate to cause a differential quotient measurement base. What is a differential quotient measurement base? It is information security model deployment technics using formulants built on five (5) de-jargonates. The five quotient de-jargonates are:

1.Programming sensory parsing measurement.

2.Programming language performance indicator measurement.

3.Programming datum structure meshing efficiency code.

4.Programming frameworks: the centerage efficiency identifier.

5.Programming languages indexing centre.
Identification sets of the parsing stratum must be an Ajuncture trait decipherable. What is an ajuncture trait decipherable? This is a line-by-line sensory language notation . How is this notation constructed? It is constructed using language tabular symbols or language quotient notation identifiers. Using utility core modular language parsing mergers, a combination construction interface is de-populated over commonality identification sets. The Actuarial formation utility will extricate analytics over the directory building utility. The directory-built utility forms the program binary base index linkage to analytics linked to the directory building utility. The Actuarial analyst of datum structures in the multi-programming environment creates profiling analytics that are directory input language commonality parsing identification sets.

Centre of Programming parsing utility

What is the centre of Programming parsing utilities? This refers to the centerage of a component of a programming tenet hosting a utility, a program, an enabler of the extrication of actuarial quantum director. Where is this centre located in a multi-programming environment?

Information security becomes critical as its actuarial formatics must centre the jargonated architectures along this centre. In a program, this centre is hosted via an interpreter desensitizer of the parsing utility language. The interpreter translates the extrication formulants via commands that are built through the command line prompt. The command line prompt envisaging or built to set jargonate combiners of multi-programming environments ridden with multi-registry datum structures, parsing utilities can enter at the actuarial formation of information security components datum perspiratory encryption dynamics.

The Actuarial formation of centerage analytics

The actuarial formation of parsing centerage analytics are real. They are performed using key analogue analytic record breaks created each time a parsing command is executed via the centerage command line prompt. The centerage command line prompt directs the record hits via actuarial squared deviation of binary formation base indexing. The squared deviation lists or ranges or notates record breaks using a notation language-built tab, built in the command line parsing enabler.

Jargonated at actuarial squared deviation aesthetics binary language hits, concomitant formulants correlate via extrapolation of binary language base mathematical in formulant correlator. What is this? This is the mathematical efficacy set interpreter of centerage analytics factorial lead indicators.

How is this mathematical efficacy set interpreter of significance in multi-programming language environments?

This is of significance as it attenuates the effective programming bits (binary bound) towards the actuarially formatted combining of multi-programming language towards the centerage index. The centerage index will be a key actuarial measure of information security amplification of key datum structure jargonation to protect multi-server environments where data base registers are held. In formulant reporting of assurance over information security will be reported using this Centerage Assurance Index. This is the age of multiple programming environments that is at bay. The Actuarial knowledge I write is advanced techno-actuarial analysis built in actuarial forensics formation.

Utility Index Relational Identifier

Utility Index Relational Identifier is an actuarial measurement technic that sequestrate information security model requirements via creation of utility built in functional protagonistic datum index contributory identifier. It is a de-basing mechanism that is mathematically deciphered.

How is the Utility Index Relational Identifier mathematically deciphered?

Utilities are used in a variety of programming languages. Multi-programming environments with a quest of centring information security models drive programming language binary formulation centre index. Using advanced actuarial formation analytics, we dexterize the binary datum formatting index. Dexterization is done as follows:

Utility censorship mode dexterizes at datum process of extrapolation deciphered at extraction command line.
Dexterization is a formulant actuarial extrapolation exponentiation using binary language indexing. Binary language indexing is used as an actuarial input fundamental via information security centerage. The index is a bit recessionary expansionary laxative range formulator set to attenuate dexterization extrapolated quest. Nudging sets of utility indexing relational identification fosters the mathematical deciphering tenet.
Utility relational quotiency is a software algorithm acu-frequency maximization tenet. What does it mean? It means utility formation of actuarial technics used to de-jargonate datum-pinned structural relational functional denominatory is deciphered via this quotiency. Quotiency of this nature is also an information security de-jargonated analytical. The actuarial formation of the components of the centerage index pinned actuarial technics decentralizes the mode formation and or movement dynamics.

Control of this utility tenet moves in tandem with registries’ hive record counts analyticals. What does it mean? It means registry hive pinned analytics are essential for utility information systems analytics impact on the multi-programming environment. The multi-programming environment stands as a utility hosting conduit that must facilitate information security peripheral analytics. What does this mean? Information system peripheral analytics refer to the study and determination of information security deciphered datum pinned perspiratory input and output dynamics with an impact analytic of this nature on the multi-programming environment.
Dexterization is actuarially centred and multiplexed. This refers to the use of the determined actuarial binary indexed actuarially centred de-jargonation technics. The de-jargonation technics here unravels the quest for centring information security in multi-programming environment.

Deviationary Program Inconsistencies

This is synchronization-asynchronized actuarial formatic input variant analytics. Information security models measured at the entrance denturity pervasion datum-pinned structures affect the multi-programming environments. Inconsistencies posture themselves at the rate of development algorithm index. The development algorithm index is a multi-programming environment network structured and deciphered actuarial formation movement dynamics.

Organizations or utilities today must format the sensationalization de-jargonation input fundamental algorithm indexing sequencing emanating from deviationary program inconsistencies that arrive via parsing breaks inconsistent concomitant inefficiencies. Parsing breaks inconsistent concomitant inefficiencies refer to the following:

Parsing formulation sets are sequestrated at language index combination factors which are distributed in multi-programming environments at centerage index actuarial analytical formulatory utility decipherables. The sets being referred to here are sets of record break extrication mechanisms. Record break extrication mechanisms of the parsing algorithms are released in the multi-programming merging utilities using an actuarial technic known as Censor form de-jargonator. A Censor form de-jargonate can decipher frameworks used to implement parsing algorithms within a multi-programming environment.
Actuarial technicalization of parsing co-variants. What are parsing co-variants in multi-programming environments? Parsing co-variants are multi-programming language environments extrication leverages that are used to release algorithm de-jargonation analyticals. What does this mean? De-jargonation analyticals as an input into the information security model or models of such an environment require parsing co-variants. Under actuarial formation co-variants is a dual binding efficient frontier built on language formation index.
Parsing breaks-The analytical consummation set. The analytical consummation set refers to the role that the envisaged and or formatted, deployed information security model plays in securing the parsing utilities. In multi-programming environments parsing utilities are susceptible to dysfunctionality injectors. Dysfunctionality breach injectors refer to malware and or malicious code that works with de-sensitizing indexes of datum-pinned structures.

Depictors of Program de-sensitization Indexes

De-sensitization indexes in multi-programming environments refer to areas and or datum pinned indexed structures where there is an ability to introduce fault lines that can tamper with the de-jargonation analytical functionaries. Formatting information security models in such an environment may be a mammoth task.

Using actuarial techniques there are depicters of de-sensitization indexes. Program structures run against multi-programming environments operating systems registries structures may indicate comparative analytics of movement dynamics as the multi-programming environment is formatted.

Sensationalized Amplified Algorithm index partitioning datum

This refers to plotted algorithm trace of breakage in looping formatics that are actuarially determined at entrance of meshing indexed datum pinned structures. Using actuarial and forensic analysis merged technics, the amplification mode is deciphered using the following methods:

Amplification of indexed stratum record breaks.
Actuarial deviation technics of datum-pinned indexed structures from different programming environments.
Command line extraction code of actuarially de-sensitized datum indexed structures.
Record extraction and formation analytics.

The Depicters of Program de-sensitization indexes are:

The Leverage formation actuarial analytics.
Censorship mechanism of algorithm interchange or interchange between multi-programming environments.
Information security model actuarial formation mode.
The troughing and centring of trend indexed datum-pinned structures.
Actuarial formation centerage trend analytics.
Actuarial modelling of information security sensationalization.
Datum Governance centerage technics.
Data build banks.

The Looping conundrum directional formulator and or depicter

This section deals with the formulators of the looping conundrum. The Looping conundrum represents challenges experienced in a multi-programming environment. The looping conundrum in such an environment comprises the following:

Structurally damaged data banks.
Looping utilizer of utilities.
Program structures indexes and desensitization indexes overpopulation.
Programming source code meshing in multi-programming environment.
Actuarial formation of looping technics.
Hierarchical datum base structures.

The Looping conundrum directional formulators refers to the actuarial formation input enablers of information security model pinned director of looping. Looping is a de-sensitizer formatic mode. What does this mean in light of multi-programming environments? It means that looping is done at actuarial leverage centre formation of de-jargonated input and output fundamentals. The formulators serve as de-jargonates of formulants. The formulants being referred to here are the information security structural systems combination outcomes that are calculated using built in actuarial analysers.

Today’s Organizations or Entities

Today’s organizations and or entities serve as the sequestration site where coding is done at data banks information security formatted structures. The format here is deciphered using deep actuarial laboratory sets hosted in registry structures built using intelligence of utilities co-listing registry hives to test the formatic input decipherables.

The looping conundrum formulators include:

Sets of perversion of program indexed structures destined for the multi-programming environment.
Actuarial centerage efficiency buoyancy.

Sets Meshing Methodologies

Sets meshing methodologies are key programming identification methodologies. Since this item is being elucidated to emphasize the importance of combining or merging multi-programming environments, sets are identified as information security structural systems. One cannot exclude the importance of security. The sets meshing methodologies differ. It is not a one size fits all mechanism. Some of the programming sets meshing methodologies include:

Registry meshing utility analytics.
Environmental datum structures commonality meshing.
Algorithm meshing analyser.
Standardised actuarial formation notation of information security models.

Gentric Actuarial Distributive Formation

Gentric actuarial distributive formation refers the datum sensitivity-pinned notation of key input and output fundamentals of the multi-programming environment distributive systems. In a previous section, I covered the centerage identification sets. Without these centerage identification sets one would not be able to de-base to Gentric actuarial distributive formation. There are many gentric actuarial distributive formation mechanisms.

These mechanisms are built on the partitioning index of information security models and actuarial formation. The partition index is used in advanced actuarial formatic compartmental efficiency dynamics. These efficiency dynamics are used in multi-programming environments. The formatics of compartmental efficiency in multi-programming environments include:

1. Program motion velocity centerage.

2. Algorithm denturity indexed structures.

3. The generation of a multiplexed multi-programming environment registry structures.

4. The genetic code of multi-programming environment.

5. Formation efficiency datum line-centerage index.

Program Motion velocity centerage

This is the measurement index in the multi-programming environments that is used to de-jargonate movement dynamics that are a capitulation of distributive formation analytics. Gentric actuarial distributive formation can determine the velocity centerage. Velocity centerage is determined by a myriad of different factors some of them attributable to information security models at hand. The factors include:

Velocity quantum formulants of velocity centerage.
Actuarial formation program datum structures linkage.
Actuarial measurement bases used to decipher the motion dynamics in multi-programming.
Actuarial formation of datum perspiratory dynamics.

Algorithm Denturity Index Structures

Algorithms not appropriately written are measured using Denturity index structures. Using standardized efficiency frameworks denturity index lists factorial lead indicators that need to posture dentures. Information security models use de-sensitization indexes.

The Generation of multiplexed multi-programming Registry Structures

Gentric actuarial distributive formation also noted in multiplexed multi-programming registry structures refers to inter-twining and interweaving registry structures. How are such registry structures deciphered? This requires gentric actuarial formation technics hence the reason we look at actuarial distributive formation. Actuarial distributive formation comes in a variety of formats. Listed below are the formats:

1. Information security distributive centerage of actuarial deciphers.

2.Language centerage index formation.

3. Actuarial formation of programming language “comma”.

4.Separators utilities de-jargonates.

Information security distributive centerage of actuarial deciphers

This is a gentric actuarial formation that crystallizes in multi-programming environments. The focus is on the distributive mechanism. Information security distributive centerage of actuarial deciphers refers to the rectum deciphered channel of penetrative datum pinned structures that sets the stage for a malcontent baseline structure that redirects the mode or direction of the multi-programming environment. The centerage of actuarial deciphers is deciphered via identification of information security factorial lead indicators. A centre of excellence framework-built database of these indicators is photo-stated live at the multi-programming environment distributive monitoring. The dashboard takes host to the following:

Actuarial centerage centre of formulatory data comprising of modelling object pinned datum structures.
Censorship technics of monitoring factorial lead indicators.
Frequency of program denturity along the computed trendline extrapolated over the entire multi-programming universe.
Actuarial formation analytical factors which differ with each multi-programming environment.

Language Centring Index Formulation

Programming languages used in multi-programming environments are a key component in multiplexity of registry structures. The Registry for each programming environment servicing as an input is a jargonated architecture component of an information security model. These programming languages are employed in what is known as Language Centring Indexing Formation.

Language centring index is a language used to build programs and or an algorithm. The components of these languages must be partitioned using the language utility index de-jargonation.

How does the language utility de-jargonation work?

It is built with the programming database linguistics of the advanced algorithm reader. Built for multi-programming environments actuarial technics, formation sectors are built to aggregate, analyse, and evaluate as well as monitor and report. An index designed, written using actuarial mathematics centerage circulator or iterator, runs throughout the multi-programming environment plotting datum trend performance. Centerage of the languages is a categorised mathematical algorithm built to accentuate compilation of reportables. The centerage is calculated using Variance Stochastic modelling. What is variance stochastic modelling?

Variance Stochastic Modelling

This is Actuarial modelling used in multi-programming environments to format censorship of information security distributive centerage. Constructed via a benchmark programming language list of common and unique registry structures, behavioural trends and performance as registry structures, be it meshing or pervading are deployed into the multi-programming environment, each injection identifier of the security built in decipherable is allocated and or scored at a framework whose factors are squared notated characteristics.

The squared component superscript is a dual functionality actuarial fundamental identifier of key input and output fundamentals as noted by the information security model of components or information security structural systems.

Stochastic Model Sensitization Sectors of Programs

What are these sensitization sectors? Information security models are convoluted in program storage memorability. RAM conscientizer track constantic merging of sector rotation efficiencies. What is this? Multi-programming environments host multiple servers deployed to the production server. The Production server in turn hosting hardware storage capacities has a capability via program development utilities to develop and deploy a stochastic model sensitization sector.

This sensitization sector serves the de-juncture multi-programming conversion index. A de-juncture multi- production for conversion index is measured at collectibles of RAM-hard disk rotation access juncture points of interactions in the multi-programming environments. The points of interest are hits of interactions between the main servers hosting multi-programming environment data. On each hard disk sector, the stochastic sensitization sector data recorded in operating system registries are a myriad of classes which also includes RAM access, additions, deletions, alterations, registry structures, formatic additions, rearrangements and restructuring tenets. Measured at an index broken down into a framework, scores are allocated at each hit, points of interest are iteratively plotted at development milestone intervals. These specific points plotted at certain points are compared against the stochastic modelling target and or forecasted model objectives

Actuarial Formation of Programming Language “Comma”

What is the actuarial formation of programming language “comma”? This refers to the information security modelling components unique characteristics. The comma refers to misplaced program object structures. This occurs in highly advanced program structures in multi-programming environments.

Separator Utilities de-jargonates

Separator utilities used in multi-programming environments refer to information security modelling actuarial formation used to separate different program object structures. The separator utilities serve as an actuarial formation decipherable mechanism technic These in multi-programming environments are critical algorithm de-jargonators.

Programming Engineering Datum Analyticals Formation

These are datum analyticals formation structures focusing on engineering programming in a multi-programming environment. Datum analyticals in such environments are built on actuarial formation structures. An Actuarial analyst must know the actuarial fundamentals of programming engineering that are input into the modelling of datum analyticals. The fundamentals I am referring to are as follows:

Program code sector analysis.
Datum structures pinned on object orientation.
Structural analytics using gentric outlying orientation factors.
Programming analytics at each multi-programming environment.
Actuarial centerage indexing as a key datum analytical formation.
Actuarial formation of analyticals de-basing to key input and output fundamentals in a multi-programming environment.
Period driven program sector resource and Sector driven analytics and reporting.

Centre of Index Hierarchical Programming

This is an actuarial formation technic built on advanced information security modelling centre of indexing hierarchical programming. This is Programming in multi-programming environment that follows programs and or software model hierarchies that are formatted using actuarially plotted centerage. The actuarially plotted index centerage is plotted and or formatted based on program structures being built.

Why is this of significance to multi-programming sets of languages?

The centre of index hierarchical programming is meant to ensure optimization of a chosen information security model and or models. This is a relegation of de-basing actuarial formation perspiration dynamics meant to centre the index hierarchy. Organizations and or entities involved in use of multi-programming sets of languages must employ actuarial technics to centre information security to service such a language universe. The factorial lead identifiers of the centre of index hierarchical programming that direct the modus operandi include the following:

Hierarchical objects set up frequencies using actuarial notation to classify and or categorize.
The notation methodology of information security modelling tenets.
Sense structure of actuarial technics motion built in sensory utility de-jargonation.
Mathematical relational identifiers built on binary formation index of data structures at different levels and or hierarchies.
RAM sectors identified at actuarial formatic input.
Formulants structural analysis of multi-language sets.

Formulants of Multi-programming Assurance Structures

Multi-programming sets of languages used to build multi-programming environments employed or supported by actuarial models that centre information security use a certain modus operandi. The modus operandi being referred to are formulants or key input methodologies that de-jargonate the assurance structures. This becomes imperative to give assurance or provide multi-programming assurance structures. This is a gap that always exists in Board, Audit and Compliance Committees due to technically convoluted jargonated architectures that must be broken down and or unravelled for the benefit of Governance structures.

There is an amplified assurance structure jargon. This is frame worked multi-programming structure, assurance structure that is sensationalized methodology. Sensationalized is used here to formulate the importance of period-banked analytical datum frequencies. Data is always a critical component or input for assurance structures. Stated below are the formulants of multi-programming assurance structures:

Jargonated architectures of multi-programming languages.
Key programming language indicators as deciphered via the centerage index.
Formulated reportables built from actuarial formation of information security models.
Risk Modelling built via languages securitization.
Denturity of language sector analytics.
Formulants indexed analytical structure.
Assurance structures modelling tenets.

Information Security-Multi-programming sets of languages assurance

This is an alignment tenet, conclusionary in nature that depicts how the multi-programming sets of languages gives assurance to the multi-programming quest through centring executed by actuarial formation technics. This assurance is built on core-modular tenets that must give satisfaction to the entities Board, Audit and Compliance Committees. The four core modular tenets are:

Information security model actuarial formation testing.
Key Actuarial Index indicators.
Gentric Analytics. Centerage reportables.
Multi-programming sets of languages performance indicators.

This is the information security model centring of actuarial formation as a base formation that supports of use multi-programming sets of languages in a multi-programming environment. Organisations and or entities lag in this area effectively perspiring gaps in information security multi-programming environments assurance centring for the Boards and their governance committees.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

THE GENTRIC ACTUARIAL MODELLING OF INFORMATION SECURITY IN HIGHLY TRANSFORMATIVE TECHNOLOGICAL ENVIRONMENTS

Written by Thomas Mutsimba, an Enterprise Risk Management Professional, with specialist endowment in advanced actuarial analysis and advanced forensic analysis©

Visit ThomasActuarialanalysisworld Blog for more cutting edge insights @ https://actuarialanalysisworld.finance.blog/

Fast paced, high velocity sensory information communication technology environments are a caricature of deficiencies mirrors on the earth’s information systems. It is not the proliferation of new technologies that stand alluminary to permeating information security threats, but it is the design modelling of information security. Entities are investing huge sums of money in information security due to information and data breaches that are formulated at a rate of porous information security models. The information security models I refer to are not information security models per se, but are simplistic defence mechanisms, control convoluted in protecting enterprise assets and core node architecture. Engineering systems in the information technology arena must keep pace with the jargonated information security fundamentals.

Information security, as critical as it is, stands as a core-formation node between the technological universe and the process elemental drivers within entities. In this article I unravel and showcase skilling formation technics. Skilling formation is the posture of gentric actuarial modelling of information security within entities of various types and or industries. Serving markets, I desensitize archaic architectures or weak information security models that are regarded current when they are becoming irrelevant and unreliable due to permeating security architectures.

Information security refers to the secure actuarial formation of deluging data analytical structures at a pace and rate of sensitization formation algorithms, building formation centers of security layered structures of information systems. It is gentrified through actuarial-scientific technics of correlation of data program hierarchical structures that relegate data objects entering the information to certain or particular indexed structures of sequestration deciphered at core modular analytic utilities that de-jargonate modelling hierarchies of sensitization technics. In this article actuarial modelling stands out as the only correlator of mathematical effective integerization of binary bits of programming at a deverbing structural never seen and never heard language specifics only decipherable by algorithm technologies from the Higher order dimension.

Information Security Modelling Actuarial Technics

What is this? This refers to actuarial fundamental formulators of different simulated and deployed programming sequencing, indexing convoluted, linked via hierarchical object looping sensory language defined technics. It is this tenet that is at the bay of the exposureto perversion of information security models. The following modus populative datum deciphered actuarial stages format the methodology of modelling of information security:

Information security model centership identified
Jargonate architectures
Actuarial Information security index structural systems
Modelling types and de-sensitization indexes
Formulation of information security actuarial analytics
Denture Index technology of information security
Selective formulants of security languages
Directors of information security assurance
Development of Actuarial information security algorithm

1.Information security model centership identified

Every information security model has a centership guard. What is this centership guard? It is the jargonate modular alluminary to threat intelligence perceptibles permeating the information communication technologies arena. Centership guard de-jargonates threats or permeating risk factors at controllables and uncontrollable stratums. These stratums de-jargonated at datum structures, programmed applications enter the fray to de-sensitize motion dynamics that are releasing security modelling functionaries. Security modelling functionaries are standing the ground formation attacks convoluted in hidden indexed structures. Convoluted indexed hidden datum and program object structures are alluminary attempting to circumvent information security models linkage nodes. These linkage nodes, sapphired under actuarial information security objects mesh or repel introducing digitally deranged paths in the modelling sensory nodes. Today entities speak of information security models, but the models seldom enact the essentialities of actuarial formation centership technics of the de-jargonated fundamentals. Centership is the core of the information security stratum.

Centership of an information security model is pinned on the following factors:

Operating System Registry Structures.
Data Structures linkage identifiers.
Cyber formatic defence mechanism.
Sense tenure of convoluted alert commands.
Registry analyticals of de-jargonated security models.

2. Jargonate Architectures

Jargonate architecture is the encrypted information data architecture that directs the actuarial formation of the information security model. Jargonate refers to the sensitive and protected architecture of the information systems architecture environment. Jargonate architectures are formatted actuarially in a variety of ways:

2.1 Jargonate Architecture actuarial modelling directors

These are actuarially determined sets of information systems resources and related program sets of objects that are ranked at sensitization and peripheral exposure to actuarial formatted modes of attacks. Organizations and or entities are exposed to these variant indexed structures.

In advanced information security, variant indexed structures are asymptomatic agile pinned datum and jargonated infrastructure of information systems whose nodes in their design, vary based on changes in the functional abilities. Why is that so? It is so because of program sequence of looping and variant integerization of structural sets of relational data structures. Because information security deals with sets of information systems resources set or laid at different models or related via program integer spasms.

2.2 Data integer spasms

Data integer spasms are the deverberating and reverberating formatted actuarially set commands whose mathematical sequestration at various levels of separation, looping and consequence integer variants move in tandem with the command input and output decipherables. What is this? This is the acceleration and deceleration of information security model commands built via command line prompt with a tenure language-built command thereby extracting the indexed data structures that are hierarchically set at data motion dynamics.

The set formulants of actuarial formation of information security models are deciphered via RAM-hard disk sectors movement dynamics that are capitulated to the driver instructions feeding from information security model data sources. Actuarial technics are employed at highly advanced security mathematical security co-deverbers. Mathematical security co-deverbers are data algorithms whose transmission via command set instructions that are grouped according to the information security model.

2.3 Integer Factorage sector actuarial model

Integer factorage sector actuarial model is an actuarial fundamental input into the information security model. Mathematically computed range of integers are listed or range set to represent a factorization of extrapolation of the model over different data objects. It is fortitude built to serve through different hierarchies of data objects and or structures.

The hard disk storage motion dynamics capitulates over integer factorage factorization. The actuarial technic is detention-based formulator set at a rate of functionality sapphired information security.

2.4 Disk Sector size convolution

Disk Sector size convolution is a forensically fostered size nudging effect. Size nudging effect is the electro-magnetic data current reader command. What is this? This refers to the disk sector size limit hierarchy capitulation movement quotient. This actuarial technic is a scintillating factor. The factor is programmed via application built-in sensitizers.

2.5 Disk Sector size actuarial efficiency frontier

What is disk sector size actuarial efficiency frontier? This refers to the computing resource disk sector constructible actuarial technics. Disk sector size is linked to the datum line of skewness of plotted size serialization over different ranges of recording capabilities. These data recording capabilities are built on sector utilization which is a factorized quotient of information security model tenacity induced factors. These tenacity induced factors are RAM access, additions, deletions, and alterations statistics.

How to actuarially format the efficiency frontier?

Thereto alluded the disk sector sizes RAM recorded access logs stratums. Actuarial formation is built on key log analytics. These key log analytics are centerage sequestrated at an attenuation factor formulation index.

The attenuation factor formulation index

This refers to the disk sector size rotation scintillation mean indexed at an extrapolatory denominatory attenuation formulatory commands. These commands induced formulate the stratum formatted datum line of skewness.

The efficiency frontier is identified based on datum line of skewness technics of scintillating order of elevation from one low tier trend to medium and higher tier trend. Under actuarial formation this efficient frontier milestone centerage must be structured, built in a framework de-jargonation phasing of disk sector size actuarial technics.

Formulatory Data nodes

Formulatory data nodes are identifiable using the information security linked data nodes. The layered elevation structure of data structure nodes is notated using an actuarial basis of measurement. This basis of measurement uses data strings construction efficiencies. Data strings construction efficiencies reveal if strings of data objects interlinkage are re-engaged and dis-engaged compartmental efficiency of these formulatory data nodes.

3. Actuarial Information Security Index Structural Systems

Information Security Models built on information security structural systems are indexed. What does it mean? It means that the elementary components we are alluding to are a range phased, combined, and set as different sets that are exposed to information security actuarial formation of indexes.

What is the use of these indexes? These indexes are jargonated quantum frameworks meant to measure the performance of information security model components in response to permeating unwarranted objects, data defined and or system defined. Information security structural systems are tenure formatted and control. Why is that so? It is so because information security models operate on active time and passive time. Why does it mean so? It means so because of the lost index tenacity quotient. How is the lost index tenacity quotient measured?

Lost /Passive Index Tenacity Quotient

This is a measure of passivity duration algorithm in providing security identifiers and or detectors on a system functionality that has already been achieved. Lost index tenacity is an information system structural system that is a de-jargonation of the information system tenacity. As information systems consume the received meshing structures and or permeating structures, the information system lost tenacity quotient, a two factor denominatory strategy convolutes use of information security model as it is deployed across multiple information system structures. An information security model sieves through attacking antigen or malware loaded onto the operating system.

To compute the Lost Index tenacity quotient, one has to create a myriad of factors contributing to the lost index. Some of the factors include:

General Index Stratums: a collective inventory of measurement indexes stratified by information systems component resources.
Index Partitioning. Partitioning of the Lost Tenacity Quotient is necessary in order to deal with different measurement components that is different information systems architecture and resources that consume information security formatted technics decipherables These decipherables include
Application modular input and output controls.
Audit utilities linked to the information security model assurance function.
Sequestration tenet of module performance.
Efficiency processing frontiers.
Information security model processes balancing dynamics
Process correlation connectors of different object nodes
Modelling analytics of index jargonation.
Index efficiencies of stratum movement dynamics.
The tenacity quotient of the lost nature is a measurement technic instituted via actuarial formation of the above-mentioned factors against each of the strategic actuarial determinants that optimizes the actuarial formation technic.

Active Index Tenacity Quotient

What is Active Index Tenacity Quotient? This is current information systems structural indexes tenacity, a reflection of a measurement decipherable of the ability of the information structural tenacity buoyancy as data structures, actuarially convoluted are populated over the information security model during the operating system registry and information security model interaction. The active index security tenacity quotient is measured differently. It is measured using denominatory jargonation decipherables. The decipherables articulated in information security terms are as follows:

Index logging frequencies of registries data structures.
Information security deciphering of levels of operating system level security breaches as deciphered under registry levels allocations.
Information system architecture actuarial stratums identification as operating systems registries are combined, convoluted, and analyzed as infrastructure resources are added at any particular point in time.
The quotient is calculated at ranking of its data, the aforesaid data above; jargonated risk factor event and factor ranked according to information security framework. Ranking is not easy but with a clear framework whose actuarial formatic input, one ranks with a fundamentalistic information security model input sensitivity testing. Investment in these technics are required. Enterprises are required to invest in actuarially astute intelligent systems and manpower. To support quotient floatation dynamics, the information security model requires such support.
Gentric analytical quotient limits. These are limits of actuarial information security model inputs. What are these limits? There limits refer to the information system security resources that encompass the capacity of the program to remain agile, in other words addition of expansionary strings, program language limits, gentric analyzers of algorithm length and its ability to attenuate model floatation dynamics. These are expressed using different bases of measurements. In other instances, some limits are set by manufacturer standardized limits for devices and in other instances software and or program developer defined limits that stand to direct floatation attenuating velocity and factorial directors of the information security model technics. Examples of limits are:
Disk sector storage capacity.
Device and input utilities processing speed.
Attenuation speed and power dynamics.
Gentric identifiers of risk factor dynamics.

4. Modelling Types and De-sensitization Indexes

Information security has many models, for it is not a one size fits all. In this section I expand modelling types and de-sensitization indexes. De-sensitization indexes are points in information security frameworks as deluged populatively in the information security models where indexes of actuarial modelling formation are able to create and or cause fault lines along the designed model. De-sensitization is a technic of rendering the information security model weak and calling for agile development and or improvement envisaged as the information system environment grows bigger and larger.

De-sensitization indexes are used in conducting information security model vulnerability tests or stress asymmetry tests to give impetus to the information security model. De-sensitization is actuarially formatted by de-jargonation of various input fundamentals. Organization and or entities face the challenge of not knowing information security model de-sensitization indexes. That is if the small to medium enterprises even know how to de-sensitize or even have a robust actuarially formatted information security model.

An actuarially formatted information security model is a sensitized information security model that withstands clone attacks at points of interest and or tenacity. This is an impetus tenet-based mode of testing of the input fundamentalized functionality inputs. An impetus-based tenet gives velocity driven quotient technics of ensuring the veracity and strength of information security model. The actuarial modelling approach is a supporter of this impetus-based tenet mode of testing of the input fundamentalized functionality inputs.

Joint several notation

What is Joint several notation? Joint several notation, is a technic of actuarial formation and or modelling of information security. Information systems and or models, components or resources are identified and information system wise notated using dual-functionality effective degenerate center. The dual-functionality effective degenerate incorporates the input and output fundamentals that are laxatively adjusted at information security modelling technics to notate the points or line of weakness. These points and or lines of weakness are set at a Prestige anti-locking datum censorship.

Prestige Anti-locking Datum Censorship

Prestige anti-locking datum censorship is a method of actuarial formation or modelling of information security where hierarchical datum input and output levels identifiers are plotted under dual functionality notated information system components. The intermediary utilities of input and output are excluded. Why are the intermediary components excluded when de-sensitization indexes may be postured here thereto? It is because intermediary utilities are zones of information security that alter formation actuarial technics.

Formation mode actuarial technics format the entrance analytics at interlockery injection methodology. Interlockery injection methodology refers to the threat and or vulnerability entrance results into the input and output midpoint actuarially notated index. Measuring actuarial notation of the input and output datum input identifiers is carried out using plottation centerage of dual functionality notated information system components.

Using centerage in the nudging prestige anti-locking datum centership, the identifiers aforementioned migrate the centerage inputs as the information security model furthers the de-jargonates of the security breach. The de-jargonates of security breaches are centerage indexing of the prestige Anti-locking datum centership. Diversions are plotted at security breaches notated identifiers. How is this useful?

These are useful actuarial analytics using framework indexing factorage identifiers. Information Security models require organizations and or entities to invest in actuarial modelling actuarial technics that give real time, input and output fundamentals that are a true representation of information security breaches.

Modelling Proportional Information Security Identifier

Information security models must be built on actuarial information security identifiers. The model I elucidate now is known as the proportional information security identifier. This is a model built on each proportional information security identifier. A proportional information security identifier is a deluge predictive analytic of actuarial information systems plotted and notated as information security identifiers that tenure control that via de-jargonate algorithms. De-jargonate algorithms are written at a language known as denturistic index sequence of proportional information security model identifier.

Denturistic Index Sequence of Proportional Information Security Model Identifier

This is an information security breach-incident population index sequencing using a centerage of alluminary global datum structures that are ranked by global mapping of types of operating system registries and industrial usage fundamentals of those certain types of operating registries. Why is this model global?

It is built on topographical illustration of economic mapping of information and data jargonates as they are distributed worldwide. This model can be extrapolated to sensitive centers via the worldwide web. Dentures or information security breaches are indexed , collated at a rate of their proportion perversion of the information security identification structures.

A mathematical sequencing algorithm is written that attenuates the order of rank in the proportion of the information security model. The model is definitive of mathematical skill sets of jargonate language parsing points in the formation of actuarial walkthrough. The sensational methodologies used in formatting actuarially pinned information security models are set at sequestrated layers of defence.

Jargon fanatical boards of formation are used. Information security models today lack the use of advanced actuarial technics in formatting information security breaches real time.

What needs to be done?

For the information security sector to thrive actuarial formation technics are required using sequencing indexing jargonation technics that serve to allocate or notate levels of breaches and an acu-centership of incident response mechanics to information security incidence.

Stated below are some of the information security models that may be used and that I will cover in another issue:

Information Security sensor-lorge model
Bystic scintillatory factorial sequestration information security model.
Multiplexed algorithm formation information security model.
Actuarial quantum analytical information security model.
Mapping censorship key indicatory information security model.
Industrial sailing data information security model.
Quantum Financial formulators information security model.
Energy Industry Information security model.

5. Formulation of Information Security Actuarial Analytics

Information security broken down or de-jargonated can produce centerage pinned actuarial analytics. The formulation of information security actuarial analytics refers to the formation of controllables and uncontrollables. Components of datum nature, convoluted , converted, and conversed to format the security mode beams on which the totality information security model take center stage. An information security model takes center stage at a rate of actuarial analytics formation architecture. Many information security models in entities or organizations are just blanket models whose formation analytics cannot be undertaken let alone are not known.

What are Actuarial Analytics of Information Security?

These refer to the de-jargonation of nodal input and output fundamentals jargonated in the information security model using attack formation mode partitioning index systems written using high value and high data velocity censorship modelling that brings to the fore indexing looping and hierarchical elevation jargonates sequestrating the modelling tenet that elevate datum centerage plottation over the universe. The universe I am talking about is the universe of extremely sensitive information security devices and relation programs correlated at information security model potency pervasiveness as the model is deployed from one compartment of the universe to another compartment of the universe. This advanced knowledge that lags a great deal postures itself through the emergence of never seen information security breaches and dysfunctional information security utilities and or related applications.

The subject of information security in many organizations becomes very difficult to assure Boards, Audit and Compliance committees. Why is that so? It is so because of the inability to formulate information security actuarial analytics. These skills are and will be in high demand in realms of information security to come or demanded by the emerging highly transformative technological environments that may essentially be made redundant. Organizations and or entities must gird up their technology tentacles as the rate of growth of the need for actuarial analyticals of information security will be a platform for information security assurance-Board and Governance linkage identifiers.

How are Information Security Models Actuarial Analytics formulated?

There are countless ways of formulating actuarial analytics. Why is that so? It is so because of dynamic information security models that are not known. When one formulates actuarial analytics, the following risk factor de-jargonates must be considered:

Formulation policy and formulation of information security model.
Information security model flowcharting and floatation of de-jargonation variables.
Indexing technics that decipher actuarial formatics.
Actuarial datum centerage identifiers.
Direction of the model and how it fits into the strategic mix.
Actuarial mathematical potency and efficacy of model decipherables using de-jargonation mechanisms.
Knowledge of information technology environmental components.
Strategic reportables using dynamic efficiency identifier.
Actuarial technics location points on information security model.
Knowledge of actuarial data analytics at formation base indexing.

6. Denture Index Technology of Information Security

Denture Index Technology of Information security refers to the determination of plottation centerage index line of sensitivity jargonated by points of weakness and points of interest. The denture index jargonated at technology peripherals analyticals. What does it mean?

It means that information security modelling is an engineering tenet sapphired under techno-actuarial formation technics. Techno-actuarial formation is de-jargonated using the acu-centerage specificity index.

Acu-Centerage Specificity Index

This is an Actuarial siphoning-phase permeated utility of de-jargonation that stands to asynchronate actuarial inverse relationship synchronization differentials. These differentials are alluminary postured as antigen or oppositionary sets of datum pinned scintillating different hierarchical indexed plotted points of specificity. This acu-centerage specificity index includes the following floatation points of interest that must be taken into account:

Center jargonate identifier.
Plotted dentures set at squared deviation frequency analysis.
Gentric formation indexing of dentures.
Technology fundamentals of input and output decipherable actuarially powered tenets.
Sequestration of modelling inputs.
General formulants secured under formation technics of actuarial analysis.
Dentures technology alluminary postures.
Data phasing indexing sequencing.

Factorial Leads under Denture Index Technology

The factorial leads I refer to here under this section are directors and or formulators of Denture index technology. Information security set over jargonated index of dentures enables the veracity scintillating factor. The veracity scintillating factorage is a tenet of formulatory leads. Using the above formulatory leads. Illuminating denture index technology of information security are the following factorial leads:

Information assurance structural jargons.
Pervading commonality structures.
Gentric actuarial formative structures.
Analytical structural data algorithms.
Censorship mode of evolution.
Data scintillation exclamators.

7. Selective Formulants of Security Languages

What is the significance of selective formulants of security languages? How do these selective formulants impact actuarial modelling of information security models? Since actuarial models of information security models must be part of them, there are programming languages used. These programming languages are used to toggle information security models at selective formulants. What does this mean?

This means the security languages center the index lineage of actuarial formation modes of model de-jargonates. Centering the index lineage of actuarial formation gives impetus to the measurement dynamics of the information security models. The question is how many organizations and or entities are able to document let alone explore selective formulants of security languages using actuarial techniques? The question can be answered by a study of international information security models whose data or formation architecture is deciphered via selective formulants of security languages.

What are these selective formulants of security languages? The selective formulants are:

The parsing mechanism or proportion deverberator utility.
Security language duration formation.
The algorithm constructor ad interpreter.
The tenacity of actuarial formatted points in security languages.
Longitudinal datum plottation analytics.
Security language pervasiveness over multi-programming environments.

8. Directors of Actuarial Information Security Assurance .

Information Security assurance is not just about attesting to the information security deluge of modelling components tenacity as pervasion structures attempt to format and change datum pinned structures. There are actually actuarial formation decipherables. These decipherables ameliorate the denturity of information security assurance due to directors. The directors are:

Actuarial information technology possibilities.
Governance-Cyber risk linked identifiers pointing towards information security assurance.
Detailed registry analytics.
Network topography knowledge and mapping.
Network data analytics.
Design of formulatory antagonistic controllables feeding via points of interest plotted at centerage index sequencing.
Hectafon. What is it? A Hectafon refers to the de-jargonation devices plugged into automated information security modelling utilities. They decipher the formation of model registry structures and plot centerage weak points indexes. This can be covered in another issue.

9. Development of Actuarial Information Security Algorithm

There are five key structural centerage mechanisms that must be focused on regarding the development of information security algorithms. The mechanism are:

Censorship tenet of evolution.
Indexing sequencing technics.
De-jargonation centerage plottation utilities.
Sense tenure control-the velocity conundrum.
Research and Development of Higher dimension revelatory hidden datum systematic information security models.
Judged Formation modes of Actuarial information security models.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

CYBER RISK ASSURANCE STRUCTURES ACTUARIAL MODELLING. A DEVERBING MODULUS OF ACTUARIAL MODELLING REALMS

Written by Thomas Mutsimba, an Enterprise Risk Management Professional, endowed with advanced actuarial analysis and advanced forensic analysis©

Visit ThomasActuarialanalysisworld Blog for more cutting edge insights @ https://actuarialanalysisworld.finance.blog/

Cyber risk, a degenerate of cyber-attacks, is a tenet of unexpected and uncertain events perpetrated to compromise information and communications technologies paving way for foreign parties’ exploitation of information communication technology resources and related infrastructure. It is not an uncommon tenet, but it is well known in the business arena and information technology stratified structures. This article serves to demystify the assurance conundrum persisting in the markets due to the proliferation of cyber-attacks, populative in nature, deluging operating systems and applications. Boards and Governance committees are striving to bridge the gap been themselves and senior management of entities in various industries and markets of different sizes. It is not that assurance structures do not exist in most cases, but it is because of deficiencies syndromes in the modelling tenet. What is it about the modelling tenet?

It is Cyber risk actuarial modelling of assurance structures that is at the bay of exposure to dentures created by cyber-attacks. Governance management formulators stand alluminary to the perversion of datum pinned attacks meant to deform the cyber sequestrated assurance structures. I serve in this quest to demonstrate the use of actuarial modelling of assurance structures in the data governance and systems security governance tenet.

Using actuarial technics modus of the Higher order revelation this article opens an uncouth realm of cyber technics pinned actuarial modelling of assurance structures.

What is Cyber risk modelling of assurance structures?

This refers to the use of datum and sensory design of perspiratory dynamics in the cyber tenet universe of input and output fundamentals set at an actuarial formation base indexing. The actuarial formation base indexing is the technics laden listing of fundamental cyber-attack incident formation base in an algorithmically built knowledge base or repository capable of plotting psychographic analytical postures that serve to trigger population of cyber defence mechanisms. The actuarial formation base indexing can be programmed using data sensory algorithms. These data sensory algorithms are not the application developed algorithms that drive security features on applications, but they are high velocity sensory sectors nudged on data centers hosting application data and network data. The build-up or architectures of these assurance structures come in different formation mechanisms. The question that comes from world entities or organizations’ cyber environments is how to quality assure cyber reportables to Risk, Audit and Governance committees. It is not so much about the voluminous library of cyber-attacks hitting the cyber security walls, but it is about the actuarially constructed attack formation modes perspiratory dynamics in the applications and network architecture that via information security will be matrixed and risk deluging factors factorized at the attenuated effect of sensory formulatory dynamics. Here you note that the earth’s cyber security management technologies lag or are not where they are supposed to be.

In my view, nudged by the Higher order dimension effective nature of new realms of Cyber security management technologies, the earth is rotating or the cyber security fraternity is pinned on one spot, not developing, but turning are the cyber attacks’ perspired dynamics. Earth’s palaces of education and the so-called cyber astuteness institutes have no paradigm shift. I write this as a messenger wielding never seen and never heard desensitization cyber-attack realms analogies, articulating, via Higher order dimension impartation of never seen and never heard technologies.

Cyber risk Actuarial formation base indexing

Cyber risk actuarial formation base indexing as I defined earlier is an adjudicated formatic scintillating deluge of binary data programming language of indexing and looping fanatical data. This indexing tenet, built on the sacrophant tenet of cyber assurance structures modes, the modes aforesaid are as follows:

Indexing detention separation structures.
Data structures of cyber formatic input.
Sequestration cyber attack structural dynamic efficient formulator.
Governance-cyber assurance linkage identifier.
Objective mapping centerage of cyber assurance reportables efficiency.

Indexing Detention Separation Structures

Indexing detention separation structures is an extraction actuarial indexing sequencing of cyber attacks formatting structures. Cyber attacks formatic structures are operating system registry hive structure changes that change with changes in attack formation modes impacting the operating system critical file indexes. This indexing built on detention separation structure results in a clout of impending cyber attacks detention.

Registry formation datum line of skewness is a deluger of asymptomatic cyber attacks formation analytics. The actuarial assurance at this stage is built on employable risk ranking data sensory methodologies. These risk ranking data sensory methodologies are autonomous and automated at cyber attack dentures in an entity’s wholesome strategic line of security weakness.

How do you see this Strategic line of security weakness?

This Strategic line of security weakness in accumulated and built, plotted using what is known as Core modular indexing structural sequencing. The linkage to enterprise wide cyber risk is done via actuarial extrapolatory sequencing of formulatory data. Using de-jargonated analytics correlational structural strategic risk, correlated risk threats and or factors are grouped into polarized factorial lead indicators of dysfunctional business units disabled as a result of disfigured indexing structures.

Data structures of Cyber formatic input

The actuarial formation of data structures built in cyber formatic input is a gentric data sieving actuarial technic. Technicalized at a gentric phasing technic of data structures, the formatic input is a gentric cyber attack formation mode that must be extricated at a jargonation “Z-X” effect. What is this “Z-X” effect? This is a never seen and never heard algorithm linked re-arrangement technic caused through injection of looping formulas in the permeating data formatic injected input. The “Z-X” is an integer mathematical correlation efficiency measurement that is actuarially calculated, attenuated at sector of storage memory technics of data recording capabilities. The integer mathematical correlation efficiency is calculated at two factor authentication jargonate of input factorization. The integer is a set limit serialization of memory dual sector limit size of attack mode formation. I will demonstrate practical insights into background microscopic cyber-attack actuarial modelling using examples in my next issue.

Sequestration Cyber-attack Structural Dynamic Efficient Formulator

Sequestration is an actuarial modelling of cyber-attack structural formulators. These formulators are strategic risk factors, stratum factorized using threat intelligence vulnerability methodologies. But at a high-level methodology of cyber-attacks, the sectoral formulatory methods are jargonates of vulnerability topographical structures in the entity or organization.

The cyber attack and or security policy is a populative formulatory jargon index. Indexing is a technic built to phase and or sequestrate layers of defence. Layers of defence in organizations and or entities differ sequentially and in metamorphosis as the systems security environment changes. As the organization sets the cyber-attack response plans, the sequestration structural dynamic efficiency formulator identifies input serialization of modus of formation. Boards and Governance committees’ knowledge on cyber security actuarial modelling of assurance structures is built on elementary knowledge of the basics of cyber risk assurance structures. To synchronous formulate cyber risk assurance structures with a Board policy on cyber risk assurance, the dynamic efficiency formulators are a starting point. Why is that so? It is so because of the rate of development of applications that mesh with operating systems of the organizations is increasing at an unprecedented pace. The dynamic efficiency formulators are:

Cyber-attack efficiency index measured at a rate of input stratification identifiers of attack formation modes, known and unknown.
Strategic threats and intelligence efficiency for factorized threats and vulnerability library.
Overall net-off gentric phasing approach to actuarial cyber assurance opinion.
Data sensory indicators, a key formation technic of the overall data inspired sectors of cyber risks.
Centerage index based on strategic identifier of cyber risks.
Cyber-attacks data actuarial stratum indexed formulators partitioning.
Optimization of cyber-attacks modus actuarial screening device formulators; data sensors.

Governance-Cyber assurance linkage identifiers

The critical formulator of cyber assurance measurables is the Governance-Cyber attack linkage identifiers. These linkage identifiers serve as strategic cyber risk actuarial modelling technics. The identification component serves as a feeding channel to Board and Governance committees concerning cyber risk assurance. Determined by a number of factors strategically sapphired via strategic cyber attack assurance structures, the flow sets the linkage identifiers. Structuring the Governance-cyber attack linkage reportables will be legible or visible for the Board and Governance committees. A clear elucidation of cyber attacks pinned cyber risk assessment and modelling gives impetus to the veracity of the technic. Some of the linkage identifiers are as follows:

Elementary strategic imperatives formation base.
Key risk indicators of strategic imperatives.
Actuarial formulators of Cyber-attacks database hubs.
Operating systems’ registries indexed structures.
Dashboard mapping results of cyber assurance results to the Boards, Audit and Risk committees.
Gentric actuarial correlation factors.
Board strategic cyber risk matrix outlook.

Objective Mapping Centerage of Cyber Risk Assurance Reportables Efficiency

Cyber Risk assurance reportables efficiency is a tenet de-jargonated at an objective mapping technic. The technic, built on actuarial formulatory directors, is set at a technic of plotting of the centerage. The centerage is an indexed structure of data sensory monitoring capabilities. Organizations and or entities are formatted along the centerage of reportables efficiency.

How is this Cyber Risk Assurance Reportables Centerage deciphered?

It is built at factorization of threat intelligence and vulnerabilities risk factors strategically cast across all strategic risk categories of the entity and it will postulate a datum plot of centerage of reportables efficiencies. Organizations must not engage in haphazard reporting of cyber risk assurance reportable centerage. Using actuarial technics, datum stature of an entity will not be able to decipher the skewness of vulnerabilities and or threats. The assignment of threats and or vulnerabilities factors is a deverberation of data bits values convoluted in figury number representation movement dynamics reflected in strategic movement dynamics of statistical distribution of populative capability architecture of cyber risk assurance structures over a serialized periodic value driven postures of cyber-attacks.

Factors to consider in Cyber risk assurance reportables centerage:

Datum formulation center partition.
Cyber-attack/risk assurance process compartment efficiency dynamics.
Factorial lead programmatic mathematical structures.
Registry Programming Analytics.
Cyber risk actuarial information data algorithms.
Centerage indexing sequencing of process compartment cyber risk.
Robust Information security centerage of deficiency factors.
Overall Board committee stratified cyber risk structural analytics.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

INTEREST RATE MANAGEMENT IN A DIAMETRIC UNIVERSE. A HIGHER ORDER DIMENSION INTEREST RATE MANIPULATION OF MARKET DETERMINATIONAL FORCES AND A DEBUNKER THAT INTEREST RATES ARE OUT OF ENTITIES HANDS. AN INTEREST RATE TECHNOLOGY VIEW!

Written by Thomas Mutsimba, an Enterprise Risk Management Professional, endowed with advanced forensic analysis and advanced actuarial analysis©

Interest rates are causing havoc today in the markets let alone international markets. It is not the interest rates per se, but it is the flotational dynamic mechanism that is astounding and difficult to predict or trace. Humanity operating in these markets ridden with interest rates merely use interest rate and economic theory developed and or proffered by those who study business and economic fanatical data. The mantra for the entities is interest rates are market determined therefore there is not much that can be done.

While it is true that interest rates are market determined, it is not true that interest rates that are market determined cannot be controlled let alone be arrested to steer corporations operating in the universe to uncharted compartments. Why am I saying so? I am saying so because interest rates themselves have technological tenets that are dynamically not known. What are interest rates theological dynamic tenets? This in totality is the theoretical formulation and formation technics that are entrenched in the interest rate theory that is being learnt or studied repeatedly by those seeking accoladed knowledge in the field of business and economic management sciences.

What is stated today is the theory of interest rates and how their determinant theory laden factors, where convoluted, design or populative architecture technics broken were brought to the fore of the world. Today interest rates are a topical discussion driving business boards of governance discussions spanning sustainability and profitability tenets let alone revenue maximization initiatives. I write this article as an elucidating technic of never seen and never heard methodical analytical technics built on actuarial modelling. I also serve to debunk the interest rates pervasiveness is stopping markets strategic motion dynamics. Strategic motion dynamics are diametric monument interest revenue matrixed systemic stratums that are hidden in the interest rate diametric universe. What am I saying here?

I am saying organisations and or entities are not operating their survival tentacles partitioned from the interest rate hidden matrixed stratums. Interest rate matrixed stratums are jargonates of the interest rate theory that is rigid and locks corporations in the mantra of there is not much that can be done. I will use a market stratified technic, demonstrating, tweaking regulatory environments by demonstrating inundating actuarial technics. In the era that we are in, the so called COVID-19, corporations’ strategic velocity of development is locked up, proffering inability to de-jargonate the interest rate conundrum.

Interest rates are built on five modes of actuarial deverberations. The actuarial inference here refers to the input fundamentalist conundrum that posture the diametric asymmetrical layered and multiplexed datum skewness locking corporations to stay stagnant in revenue. Regulations of markets stand in corporations’ way to block the ability of entities to break the cycle. Regulations by market boards and or authorities remain a stumbling block. The five modes of actuarial interest rate deverberation are as follows:

1.Interest Rate formulant jargonate

This is the convoluted conundrum of interest rates universe preventing corporations’ quest to arrest the interest rates velocity quotient and quantum diametric pinned compartments. The real question that always come up is how one can manipulate interest rates when they are not controlled by a single entity or corporation, but by market forces.

1.1 Jargonate Centerage Sensitivity Formulator

The jargonate centerage sensitivity formulator is the center of interest rate formulations and determinants. In world markets, corporations are faced with the market forces which are market determinants of the interest rate level and buoyancy efficiency dynamic suspensive tantrum set data. The interest rate level is at a prerogative of the buoyancy efficiency dynamic suspensive formulant. The buoyancy efficiency dynamic suspensive formulant is determined by five factorial actuarial technics built centerage formulators. These are:

Quantum quotient formulation sense.
Interest rate sensory motion monitoring capability.
Regulatory laws data pinned decision directory formulator.
Interest rate universe controlling decision technic by market determinants.
De-jargonated regulation controllables.

Quantum Quotient Formulation Sense

The quantum quotient formulation sense of interest rates is a critical contributor to the jargonate sensitivity formulator. Why is that so? It is so because the interest rate movement dynamics are quantum input fundamentalized at a rate of the so-called market forces factorial leading indicators of supply and demand. Arresting this quantum formulation sense requires the peaks and troughs desensitization postulated on changes that must be made to strategic objectives of the organization. Theoretically organizations ascribe to the deverberated quantum and market forces bound interest rate indicators. Now in my view this is a problem. Why is that so? It is because while the organization is busy changing its strategic imperatives with movement in interest rates, this quantum quotient formulation sense perspires the directional sensitive diametric interest rates that the market postures. At the negotiating table of financial services boards, banks and central banks, there are motions that service strategic interests. The Quantum quotient formulation sense and base still remains perspiring the trajectory of the direction of the market.

What does an entity do in this case? How can an entity arrest interest rates in its universe? These are the sort of questions that are met with an answer; nothing can be done.

What to do in the context of quantum quotient formulation sense?

This is measured at strategic world and or regional markets decisions dynamics encapsulated in the demand and supply principle. The organization of today must move to actuarial sensory technics that target the most interest rate dynamism. Formulating a sensory policy is key, but not just reverting to leaving external consultants to perform analytics to drive decision making. The quotient is a product of enumerative denominatory risk factors that follow the trajectory formulation sense of the interest rates. De-jargonated using quotient relational potency of the higher order of actuarial mathematics that does not focus on what happened 30 to 50 years ago or any other period range, but focusing on interest rate sensitization formation technics and or systems that are futuristic, past experiences are debunked and boards will innovatively start moving with innovation of advanced actuarial technology. There is more but I will elucidate in another issue the actuarial technology fundamentals.

Interest Rate Sensory Motion Monitoring Capability

Interest rate sensory motion monitoring capability refers to the datum quotient movement dynamics that are seen and recorded over the strategic line of sensitivity of any organization. Here I am not talking about quantum levels of the absolute interest rate at any particular point in time, but I am talking about sensory minute datum fundamentalistic to the interest rate theory. What does it mean in light of debunking the hands-off approach to interest rates? It means monitoring of interest rate motions is an adjudication of convulsivity of the level of interest architecture de-jargonate analyticals. What are these? These refer to seeing beyond movement dynamics of interest sensory database algorithms built at gap convulsivity identifier. Gap convulsivity identifiers are actuarial indicators of supply and demand factorial lead and lag indicators. Lag indicators are duly indicative of the formation modus pock of interest rate dynamism and risk universe. This in effect is the perceptory built interest rate technology of algorithms. To demonstrate why some corporations that reside in other nations suffer, it is because of non-proximity to de-jargonated sensory motion dynamics. Here, I am not referring to physical and or geographical proximity, but I am referring to information and data algorithm realms that secure and or arrest the interest rate sensory motion monitoring capability. Of note is the fact that many organizations lack in their proximity to information data algorithms until such a time when organizations start operating in the information data algorithms realms.

Sensory of interest rate velocity realms

What is the sensory of interest rate velocity realms? This refers to the factorial interest rate theory potency of correlation co-efficiency. How does it work? It works based on multiplexed interest rate velocity environmental factors. These environmental factors are deciphered via the jargonate mechanics of interest sensitivity analyses. Interest rate sensitivity analysis is built on sixteen sensitivity indexes that are not known and never seen. These indexes are:

Interest rate formulators of recessionary correlational mathematics.
Interest rate jargonate systems of market forces sensory dimension of strategic interest equilibrium technics.
Formulators of actuarial interest rate technics.
Actuarial sensitivity partitioning of compartmental economic and business science population stratums of strategic decision making.
Forms of interest rate desensitization indexes.
Actuarial Datum line of sensitivity.
Information Datum Algorithms of interest rate partitioning.
Data analysis built on interest rate management algorithms.
Formats of interest rate sensitivity dentures.
Market forces debunked indexing analytics.
External data sensitization of market forces interest rate sensitizers.
Factorial Data Technics of interest rate technicalities.
Strategic interest rate management efficiencies.
Interest rate technology jargonation.
Interest rate policy partitioned by Technology.
Conclusionary formulators of Actuarial interest rate Technology.

Regulatory Laws Data pinned decision directory Formulator

This is a decision directory pinned on regulations and or laws formulated by the strategic broader universe-wide motions. Regulatory laws and data determine the synchronised elementary entities that are in the value chain. Why is that so? It is so because regulations are not built or constructed for a single entity in wider macroeconomic policy but they are built for the studied value chains on whom the interest rates usurp value creating momentary movement in interest rates bits we alluded to. This together with other several factors create the regulatory bit directional formulator in the interest rates universe.

Interest rates universe controlling decision technics by market determinants

Interest rates universe being part of the jargonate centerage sensitivity formulator; a control decision technic by market determinants. What does it mean? It means interest rates are an input formulator whose controlling tentacles are a factorial lead market determinant of market forces. How does it help the buoyancy efficiency dynamic suspensive formulant?

Interest rates are built on what are known as de-compression bits. These bits are interconnected and broken, interconnected, and flattened by the strategic risk universe as they are convoluted as de-conscientizers of figury numbery data movement dynamics. Market determinants come to the fore of decision technics as they become part of the numbery data movement dynamics. Which ever way you tweak these, technics of data movement dynamics will be consumed by some decisions that come at entities Boards.

For example, interest rates change by five basis points from 10 basis points to 5 basis points. One question would be; why is it that since interest rates have changed from 10 basis points to 5 basis points it is only the quantum absolute movement of 5 basis points most talked about and how it impacts the strategic imperatives of the entity. Interest rates left to the autonomy of market forces are explained in terms of only interest rate theory but instead there are other intervening factors and new knowledge of interest rate tenets in the business universe. The quantum suspensive buoyancy dynamics are where interest rates are built on. Until corporations are able to drill to these imperatives, then interest rates will be debunked and arrested through buoyancy efficiency dynamic suspensive formulators.

I will continue in my next issue on the five modes of actuarial interest rate deverberations as this article is longer due to the detail laden new interest rate technology.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

ARE COVID-19 INSPIRED RISKS SYNONYMOUS WITH EXISTING AND OR FORECASTED ENTERPRISE RISKS?

Written by Thomas Mutsimba, an Enterprise Risk Management Professional, endowed with advanced forensic analysis and advanced actuarial analysis©

Visit ThomasActuarialanalysisworld Blog for more cutting edge insights @ https://actuarialanalysisworld.finance.blog/

This short-paper addresses whether COVID-19 inspired risks are the same as existing and or forecasted enterprise risks. In light of the prevailing risk universe in the form of COVID-19, questions may be posed by Boards and their Committees regarding the reliability and relevancy of the enterprise risk assessments. In this paper I explore what Risk management committees, Chief Risk Officers, Risk Managers, Chief Executives and Risk Officers may do to enable buoyancy in adjudicating the existing risk management processes. I use what is known as the Gap Identification Measurement technique. The Gap identification measurement techniques using the intensity-extremity quotient that permeates the risk diametric velocity as risk management processes are suspended and deployed to counter COVID-19 inspired threats. Risk universes, either COVID-19 or enterprise specific move along the strategic line of sensitivity impacted by the Strategic objectives of the organization. Organizations must dwell on the pre-cursor six principles. Challenges organizations face in the permeating risk universes are the stagnation threat of innovation in risk analysis and assessment techniques. However, innovation does not only cover reinvent the wheel, but it covers compartmental efficiency dynamics along the degenerative risk assessment processes. The following is a list of six principles that any organization may focus on:

Wheel of spiky data formulation;
Data algorithmic meaning;
Risk factor quotient rate convulsive at rate of velocity of gap measurement;
Nine-factor authentication measurement;
Generating denominational quotient;
Statistical distribution efficient factor;

I expound the principles to demonstrate the Gap identification measurement technique:

1. Wheel of spiky data formulation

The wheel of spiky data phrase and or metaphor out posturing refers to the sensitivity element of risk data that is creating forms of gaps in the identified, approved or official risk assessment measurement techniques. Data; at least not all of it is useful but data formulated and channeled as the lesser concomitantly postulated strategic objectives may result in emerging COVID-19 inspired risks filtering through the universe without the risk analysis sieving effect. However here we note that perennial risk analyses are stagnant risk assessment techniques that never evolve in tandem with the dynamic risk universe. Wheel of spiky forms data for risk quality actualization. Here I represent the effective nature of COVID-19 inspired risks on the risk assessment processes. It may not be COVID-19 but other sudden or immediate risk assessment in waiting, risks that require the entity and its Board attention. This attention is not attention to the risks per se but attention to the quality of risk analytics to be able to identify and measure risks. Many organizations today like to play it safe rather adopt a conservative approach in the risk analyses imperatives. The result of a conservative approach widens the gap quotient created by the quality of risk analytics.

2. Data Algorithmic meaning

What is data algorithmic meaning? This refers to the data meaning driving methodology as the data, particularly moves along the line of sensitivity in the risk universe. How is your entity making use of key data and assumptions the universe is emitting? There is data that has certain qualities and there is data with another set of qualities. Therefore, the modus pock or initial data meaning populative capability must be imperialized in order to ensure that risk universe exposure to the official risk assessment. What is now the significance of COVID-19 data algorithmic meaning? Organizations must answer this question. This question will be answered in the organization’s contextualized key data and assumptions it employs in carrying out a COVID-19 inspired risk analyses.

3. Risk Factor Quotient rate convulsive at rate of velocity of gap measurement rate

Risk factor quotient rate: what is it? This is the risk analytic deficient compartmental efficiency gap that postures itself as the risk(s) perspires factorial event lead indicators. The lead indicators I am talking about are convulsed at the voluminous impactive nature of the datum line of skewness of where the strategic imperatives are heading. This dissected at risk quantum analysis enables the organization to qualitatively and quantitatively quality assure the risk assessment and or risk analysis rate. Formulation of risk analyses does not happen because risk trigger event(s) such as COVID-19 have occurred, but it happens because COVID-19 arrests the velocity quotient. This velocity quotient is the one that is encapsulated in the analytics deficient compartmental efficiency gap.

The velocity of gap measurement rate is quite critical to the risk analyses methods. Why is it critical? It is critical because of the asymptomatic populative difference between the existing and or forecasted enterprise and the COVID-19 inspired risk analyses. The convulsivity of the Risk factor quotient measured at the gap measurement rate is a subject of the formula proportional dynamics. What does this mean? Here in this paper I am elucidating quantum risk dynamics that may be applied within a small to medium enterprise as well as the large enterprises. The complexity of this risk knowledge is not the complexity or the big worded type of element but what is complex is the availability and or the quality-sapphired applicability of COVID-19 inspired risk analyses in the quest to close the risk analyses gap measurement.

4. Nine Factor authentication measurement

The nine-factor authentication measurement is indeed a nine-step veracity checking mechanism. Voluminously nine steps require a separate paper to explain the nine step dynamics that must be looked at. However, the thrust of this authentication measurement is to elucidate the fluidity and or buoyancy of the designed risk analytics to usurp qualities of the metrics extracted from the measures to accost the outcomes. What is being accosted here? It is the quantum quality traits of the analyses that inform decision making. How many organizations today are applying such quality dynamics? The question remains and to be answered through examination of the organization’s floatation mechanism for risk analyses. For organizations today yearn for quantitative quantum risk measurement techniques that reveal the encapsulated organization quality decision making.

5. Generating denominational quotient

First we need to define generating denominational quotient. This refers to the perspiration dynamics of the envisaged and selected risk analyses motion sensory technique in an entity’s data science. The denominational quotient is a datum calculative portion centered deviation of movement from an existing risk infrastructural analysis to the desired strategic objectives which are denoted by the mean co-efficient posturing the bi-variance of risk factors measured at the existing and or forecasted enterprise risk analyses against the COVID-19 inspired risk analysis. The quotient formulae are as follows:

From the above denominational evident formula, the thrust is to depict motion movement from the current enterprise risk analysis to the COVID-19 inspired risk analysis. But one would ask a question : how does one use the generating denominational quotient and what is the meaning of the result of the above formula? Since the denominational quotient dynamics measures the movement quotient dynamics in the analytics measurement base, one can use it by development of an indexing framework that will combine each of the following:

Enterprise risk analysis factorized lead indicators.
Total factorized universe risk factors.
COVID-19 inspired factorized lead indicators.

The indices developed for each of the above factors must be built on commonality degenerative factors and indicators to ensure succinctness and tenacity of the quotient formulae. The above formulae may not be applicable to all industries, but it serves as an implementation guidance that may be considered in changing one model of risk analytics to another model of risk analytics.

Statistical Distribution Efficient factor

This is a data analytics efficiency factor that is used to posture the compartmental efficiency dynamics in the risk analyses populative difference between the enterprise risk analysis and the COVID-19 inspired risk analysis. This statistical distribution efficient factor is measured using different methods and or sets of indicators. Some of the methods are as follows:

Datum line of skewness population.
State of dentures in the risk analysis methodology.
Stochastic modelling stress tests
Efficiency dynamics as a percentage of rate of change of data algorithmic meaning velocity.
Formulation tenacity: it is measured at the degenerative capability of data from the modus pock.

This paper has focused on the six principles to demonstrate methodologies that may be employed in the deployment of another methodology in the enterprise. This paper demonstrates methodologies for determining if enterprise risks are synonymous with COVID-19 inspired risks.

Stated below is an example of questions one may ask, decisions that may be made before ensuring that dual risk analyses; enterprise risk analyses and COVID-19 inspired risk analyses are quality assured.

1. Identify COVID-19 inspired risks:

Is the identified COVID-19 inspired risk synonymous with existing and or forecasted enterprise risk?
If the answer to bullet 1 is yes, identify key risk factors for the existing and or forecasted enterprise risk impacted by COVID-19.
Identify, validate and evaluate assumptions and or key data supporting the aforesaid key risk factors.
Map risk factors that are common for both existing and or forecasted enterprise risks and COVID-19 inspired risks thereto. Mapping involves linking the risk factors to the COVID-19 inspired risks synonymous or identified with existing and or forecasted enterprise risks. Here make a decision on whether the COVID-19 risk assessment is a baseline, tactical, project-based, regulatory or issue- based. [This is known as key data and or assumptions meshing method.

2. If the COVID-19 inspired risks are not the same as existing and or forecasted enterprise risks, perform the following:

Identify key risk factors severally impacting COVID-19 inspired risks.
Identify, validate and evaluate key data and or assumptions supporting the identified key risk factors (risk factors impacting COVID-19 inspired risks)
Map identified risk factors to COVID-19 inspired risks, map identified risk factors to each of the existing and or forecasted enterprise risks.
Decide whether the risk assessment for COVID-19 inspired risks and or existing and or forecasted enterprise risks is an issue-based risk assessment or other types of risk assessments such as baseline, regulatory, project risk-based or tactical

3. After having performed the above steps, perform risk assessment based on the following:

COVID-19 inspired risks are not synonymous with existing and or forecasted enterprise risks.
COVID-19 inspired risks are synonymous with existing and or forecasted enterprise risks.
There are no COVID-19 inspired risks but there are issues inspired by COVID-19 inspired events.

Example: designed by Thomas Mutsimba

NB. Support decision steps from measurement of differences between COVID-19 inspired risk analysis and existing and or forecasted enterprise risk analysis using the Gap Identification Measurement technique that supports risk analyses in the COVID-19 era.

The thrust of paper is to direct considerations in the assessment of COVID-19 inspired risks as COVID-19 is a new event permeating the risk diametric universe of the enterprises. This guideline is not a cast in stone but provides immediate considerable foundation to circumvent the impending COVID-19 inspired risk universe.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

HOW TO MEASURE CORONAVIRUS MUTATION USING DATA MODELLING TECHNIQUES

Written by Thomas Mutsimba, an author, an Enterprise Risk Management Professional endowed with advanced actuarial analysis and advanced forensic analysis©

The Coronavirus, dubbed “COVID-19” by Health researchers, medical laboratories and medical specialists has wreaked havoc as it has mutated a great deal killing a great number of people. What is challenging to the world is because there has not been sufficient research concerning this scourge. Data and related analysis have been based on few accumulated fundamentals that have insufficiently provided the markets worldwide with sufficient information. I have watched since an announcement was made on media platforms concerning this scourge, the coronavirus.

The revelation coming to me as I watched how the national governments of the world grapple with communicating the right and valid information concerning the velocity of mutation of the coronavirus. It is a nutty issue. Enterprising to me is my gift of advanced actuarial analysis and advanced forensic analysis calling me to showcase the knowledge embedded in this gift.

The coronavirus mutates and or develops from unknown realms as on this planet our judgment cannot find a cure for it. A need arises from an enterprise risk management perspective, wholesomely to configure and set nations for orderly amassing, recording, evaluation and analysis, monitoring and reporting of coronavirus research and experiment data to ensure the right, valid and accurate information is communicated to the economies at a rate of stage of development index.

Data modelling techniques are required, in fact there is deficient data management regarding the coronavirus mutation. This article seeks to cover risk dimension diametric concerning the mutation of the coronavirus in this world in the current status quo of things. I will expound data modelling covering the mutation of the so-called coronavirus. First of all, mutation of infectious diseases requires organized and orderly management of research data and or diagnosis that must outpace the stage of development of the diseases. Because some economies in Africa are already grappling with funding needed to monitor, research and deploy treatment of infectious diseases such as this one. This cannot happen without proper data management.

Coronavirus is not spared either. There are ten measurement bases which can be used, embedded in data modelling to deal with the corona virus mutation. Data modelling is the use of characteristic pattern to tenure or time when, how, where and for whom certain patterns posture themselves. Hence in this advent of “COVID-19” data modelling seeks to compartmentalize the disease stage of mutation and or development and establish the record management of disease to aid national governments Health departments in resource allocation and management. Hence the ten measurement bases are:

[1] Origination data.

[2] Mutation analytics using sensory motion.

[3] Risk diametric stage of development indicators .

[4] Generation classification indices.

[5]Analytics algorithm development.

[6] Disease mutation scientific indicators.

[7] Generic mutation indexing development.

[8] Disease categorical structures of data blocks interaction.

[9] Tenure of disease data block chain.

[10] Monitoring and reporting structures.

Now we will unpack the measurement bases listed above using data modelling techniques to track coronavirus mutation.

Origination Data

Origination data is a set of bits of pieces of inception patterns of the corona virus. This origination data must be known in terms of the actual year and or location where the corona virus was first detected. As it stands right now the data is limited. Why is it limited? It is because the disease emanates from unknown realms. The origination data is crucial for the following five reasons:

Setting the measurement mutation foundation base mathematically notated to show the variant standard deviation of stage of mutation of disease characteristic patterns.
Gives impetus to mutation categorization and or classes of characteristics for analysis,
Origination data is important because it shows the origination hot spots where targeted focus may be set on. Hot spots are crucial for studying mutation of diseases. This is because hot spots show where coronavirus originated from. The characteristics of hotspots are crucial for building disease origination mutation factors.
Origination data builds disease knowledge database for future reference when establishing trends for disease mutation.
Disease mutation does not operate or cannot be effectively studied without proper data, let alone origination data.

Mutation Analytics using Sensory motion

Data relating to coronavirus mutation must be subject to what is known as mutation analytics using sensory motion. Diseases are not stagnant but instead due to their genetic corrosive action of either bacteria parasites or minute active parasites that populate under specific conditions or conditions that are promoted by more dominating disease conditions dominating agents. Mutation analytics are used by sensory motion. What is “sensory motion”? Sensory motion is the stage of development reengagement from one level to another. The reengagement being referred to is the bio-chemical reactive mutation of disease parasitic behavior that stand at a rate of development risk factor indexing.

How do you design the disease risk factor development index? To demonstrate this in the context of the coronavirus, I will use advanced actuarial data analysis that employs some advanced forensic analysis. To begin the illustration, we need to take note of the following scientifically bio-chemically notated conditions and or assumptions:

[1] Coronavirus research is limited

[2] The risk factors have to be identified by regional or location characteristics [disease-corona virus] mutation. Since these risk factors are not cast in stone but are notated using bio-chemical reactive analysis of environmental conditions that posture the mutation analytics behavioral patterns, it stands as a notation key.

[3] Genetic mutation of diseases is not a stand-alone consideration? Why is that so? It is so because controlled and uncontrolled conditions bio-scientifically posture mutation lead factors and lag factors. The lead factors are positive transfiguration tenets while the lag factors recede the ability of the disease to gravitate towards the origination environment where origination data was amassed.

[4] Coronavirus dubbed COVID-19 is a new trendsetter in killing factors. What is a killing factor? A killing factor is a factor that increases the probability of terminating life. How can that be measured or at least use risk analytics modelling to project life termination point?

[5] The center of focus of a disease. What is a center of focus? Every disease has a center of focus. It refers to the human nucleic center. The human nucleic center is the nervous system functional tenacity. How can the center of focus be determined? Research techniques are critical as the disease mutates at a rate, sometimes that cannot be detected early enough.

[6] Gravitationally, a disease like “COVID-19” hugely understood as a bad flu requires real time advanced research techniques. Health centers and or medical laboratories have to act as providers of real-time powerful health business intelligence.

[7] Technology synchronous component. Here we are not talking about business intelligence, but disease database designing must be synchronous. Algorithms written to proffer sensory motion of disease mutation must afford nations and or governments with simultaneous enablement of enactment of technology law and data sets that render mutation interrelationships of minute parasites as the jargonated medium of transmission and transfiguration.

[8] Consider profiling assumption and or risk factor-based indexing methods. These can be tested at a rate of mutation nucleic loss indices. What is a nucleic loss index? This refers to microscopic laboratory recording of test samples harboring coronavirus nucleic mutation centrage. This and its existence will be indicative of the behavioral virus. Actuarial extrapolation techniques may be used.

Example

Considering South Africa has recorded approximately a range of 2 to 4 cases of coronavirus. Topographical analysis of the regional location of the patients infected by the virus is key. Let’s say in ten provinces there is an average infection of 3 to 4 persons in each of the provinces.

To design data models for coronavirus mutation, the following can be considered:

[1] Stage of development tenet.

[2] Stage of development compartmentalization indexing.

[3] Mutation indexing focusing on tenacity index of coronavirus nucleic mutation.

[4] Gentric phased actuarial phasing of mutation characteristics.

[5] Scorecard using risk rating metrics.

[6] Budgetary resource ranking for Health actuarial funding reserve.

[7] National Statement of accounts for actuarial reserving associated with COVID-19 funding related reserving.

[8] Stage of completion of treatment reserving.

[9] National monitoring and reporting of infectious disease data analytics results.

[10] Risk response mechanisms for coronavirus or infectious diseases in economies [for instance developed and developing economies]

[1.1] Stage of Development

We will illustrate the simple example. Economies must look at the stage of development of the coronavirus and or other infectious diseases This is done using actuarial data amassment. What is this actuarial data in the health context? This refers to the characteristics of the coronavirus that are indicative of the stage of development. These characteristics according to our example are identified in each of the provinces of South Africa. As we have said there can be an average of 3 to 4 infections in each province over a certain tenure. Those characteristics that are indicative of the stage of development are postured to notated characteristic statements linked to the categorized stages of development. Bio-chemical reactive analysis done in samples taken into laboratories for testing will attest the characteristics recorded over a period of time. However, this actuarial data amassment is critical. The use of mathematical notation may be embedded into systems designed to record, store and monitor the mutation motion of disease stage of development characteristics.

However, with the current status quo in economies of the world investment in such is hardly organized infectious disease management and is a no feature. But we also note that inadequate advanced data management and monitoring to be specific gives impetus to the lack of trust by masses in public health insurance and management systems. Why is that so? It is so because resources are not linked to organized and informed decision making. Politics of leaders always wreak an ill-equipped platform for coronavirus mutation management.

[2.1] The Stage of Development compartmentalization indexing

Again, the fact that we are now talking about the coronavirus and or other infectious diseases stage of development compartmentalization is a testament to much needed employment of actuarial input fundamental techniques. What are we talking about here? We are talking about identification of key disease [coronavirus or other injection disease] actuarial data. There is a certain need to compartmentalize the stage of development of a disease such as coronavirus of that nature. It is not worth to wholesomely look at the stage or just look at face value, but compartmentalization may focus on the following questions to measure and analyze as linked to stage of development:

Compartment portion of the disease that populates the entire stage of development. In terms of coronavirus, it could be the type of food eaten, the hygiene standards where the food originated from, virus nucleic contamination rate, generic parasitic behavior patterns, factors that are unknown which the health professions can’t decipher.

The stage of development entire population mechanism.

The stage of development motion mechanism. The disease mutation motion mechanics is different from the population mechanism. The former refers to the parasitic bio-chemical reactive mutation as microscopic laboratories sensory view detects. The latter refers to the disease topographical location at or in a stage of development. These variables must be known and developed. Statements by health officials must not be political but they are given credence by such organization of stage of development compartmentalization techniques.

Compartmentalization measurement techniques. What are these? These refer to the quotient dynamics factored by the most critically ranked factorial lead indicators of the emerging quotient. As a virus moves from one stage of development compartment to another, lead indicators evolve, and they change depending on a number of factors. Factors postured here are italically degenerated at velocity of mutation requiring sensory motion detection techniques powered by fast paced data banks of recording motions.

Stage of development is based on a technique called stage dentures lacerated frontiers. What is this? It means every virus at each compartment in the stage of mutation depicts dentures lacerated frontiers. These frontiers posture how extravenous, reactive decapitating atoms of treatment cause mutation to fail. But it does not mean mutation fails; what fails is the failure of the virus nucleic center to keep producing perversion antigens. This coronavirus is also susceptible to stage denture lacerated frontiers. These lacerated frontiers are formed by four virus compounds that are interlinked by and through environmental conditions formation methodology.

Stage of development nurtured at virus loading capacity indexing venerated for data origination skewness. What is this? Virus loading capacity indexing is the averaging of statistically notated characteristics of virus formation from one stage of development compartment to another compartment. Coronavirus disease data must be amassed at the loading capacity index. Standard deviation factorization against the origination data compartment mean is analyzed using actuarial statistical techniques. These scientific biological chemo analysis methodologies must be employed for analysis at stage of development compartments. Compartments against stage of development drill down to the basics and or fundamental inputs that give impetus or affect the mutation dynamics.

Danger risk as a specific risk. This is a specific risk that should be reassessed at each compartment of the stage of development of the disease. The risk assessment should focus on risk factors; these are the compartment higher probability risk trigger events posing greatest danger risk.

[3.1] Mutation Indexing focusing on Tenacity Indexing of Coronavirus Mutation

Mutation indicing is the degenerate disease transformative indicators at a much-detailed level of measurement. But this indicing is propped up by digital data analytics using algorithms written specifically for Health insurance methodologies. Mutation indicing focusing on Tenacity indicing of coronavirus mutation is an advanced medical laboratory knowledge base. It works on the following manner that can also be used:

Mutation indexing identifies three tri-partial components of the coronavirus mutation. These components are recorded at component exponential factor. The component exponential factor is a quantum disease risk posture that is based on microscopic medical actor that acts with bodily fluids interacting with the virus. How is this possible? Medical laboratories test for virus actors. These virus actors are the ones that will drive mutation antio-pathogens acting to exponentially multiply the factors to be used in the indexing. This is advanced medical laboratories statistical analysis that can be used conjoined with advanced actuarial analysis embedded in data modelling techniques.

One of the tri-partial components venerates the pyramidal hierarchical elevation technique. This is a data analytic technique that focuses disease elevation danger based on mutation trends. Coronavirus is not spared; in other words, it is covered by this component. Virus of the corona nature is still new, at least regarded as a new virus. There could be an opportunity there to utilize it to develop data modelling monitoring techniques of elevation by mutation. Medical Health schemes and Risk data scientists must be able to employ some of these techniques acoustically as planning price and or fee modelling of medical aid contributions, employing these methodologies in a medical scheme benefit modelling and provide the tri-partial component for disease impact on medical schemes profiling of product development initiatives. One wonders how this is being done today.

The third tri-partial component is indice notation and how the populative wholesome indice is eventually computed. Notation labelling is based on viral characteristic behavior pattern induced as a component to form the mutation indicing. Several techniques and methodologies may be used. Indicing is based on minute component that can be recapitulated as mutation environments evolve over a long period of time. Nudging to me is the mathematical probability modelling . This is the probabilistic tenet of mutation. National governments should be able to employ this critical component to forecast disease mutation capabilities. It is a definite issue that national governments lack such innovative datum banks for coronavirus mutation management. This may not be coronavirus only but other infectious diseases . Where are we as nations, concerning this?

[4.1] Gentric Phased Actuarial Phasing of Mutation Characteristics

What is this Gentric Phased Actuarial phasing of mutation characteristics? This sounds complicated and jargon laden. But this refers to the asymptomatic employment of data modelling actuarial techniques of phasing mutation characteristics posturing survival behavioral tendencies of a killer disease. Data would have been amassed at a rate of notated disease [coronavirus in this case] characteristics phasing. Phasing being referred to is actuarial data phasing. Phasing involves withdrawal of component measurement basis from a bank of characteristics using actuarial data modelling. But how does it happen?

First it makes use of equilibrium and disequilibrium analysis dynamics. The bank of coronavirus characteristics has positive and negative contributory antio-pathogens. What are antio-pathogens? These are actors in viral load tenacity indice in each of the positive and negative characteristics. To ensure that this works best, equilibrium refers to the match equality between positive and negative balance bank characteristics while a disequilibrium can go either way where positive contributory antio-pathogens are greater than negative contributory antio-pathogens and vice-versa. However, a definition for coronavirus must be made to determine what it effectively means.

Data formulation of the coronavirus study: What is it under this tenet. It refers to how you make decisions for coronavirus treatment decisions. But where does it come from? It comes from a variety of sources concerning the aggregated stage of development of the disease.

[5.1] Scorecard using Risk rating metrics

Results of coronavirus risk assessments whether done at stage of development level or compartment level within the stage of development level requires recording, computation and evaluation or analysis. All of these processes of coronavirus effective nature are bound by recording . Recording may be done on designated scorecards using metrics identified from the coronavirus and other infectious diseases. Because a scorecard is widely used in other fields, at this phase of development it is nurtured at a bio-medical understanding of the effectual nature of the coronavirus.

[6.1] Budgetary Resource Ranking for Health Actuarial Funding Reserve

The virus, coronavirus dubbed COVID-19 is here and it is mutating asymptomatically. Funding can be an issue for National governments and that may boggle their minds. But why is budgetary resource ranking for Actuarial funding, a reserve measurement base? It is a measurement base because the health insurance and investment bill require mutation stage of development allocation. The National governments must look at how the virus funding is determined .

More so it is not just about the funding, but it is also about budgetary resource ranking to posture funding votes. Posturing funding votes in National governments is not just about supporting funding release but it is about the actuarial reserving of such funds. In most cases National governments set up actuarial reserving through consulting actuaries’ prescriptive direction.

It is crucial to reserve funding actuarially, at degenerate input fundamentals deceleration and acceleration. Determining the velocity of approach of the coronavirus requires acceleration of the virus treatment funding using acceleration techniques in the coronavirus data models deciphered as new and continuous flows of data are experienced. The deceleration also of the virus resource driven input fundamentals will ensure that mutation is managed using resource bound modelling techniques.

[7.1] National Statement of Accounts for Actuaries Reserving associated with COVID-19 Funding related reserving

Nations, as they grapple with the scourge of the coronavirus also almost simultaneously are concerned with the impact of COVID-19 funding related reserving on National Statement of Accounts. I pose two questions as follows:

Why is budgetary resource ranking critical for Health Institutions?
How is the budgetary resource ranking linked to coronavirus funding-related reserving?

Budgetary resource ranking is critical for the stage of development of the coronavirus, mutation to be specific. Using data metrics of the disease impacts and effects, resources which may be needed in financial form may be ranked. National governments: if truth be told must rank budgetary resources for allocation using actuarial techniques that stand to venerate the criticality of the stage of development of the coronavirus. Mutation supported by such quantum driven analyses stand to be tackled together. Budgetary resource ranking stands to be linked to the coronavirus reserving. How? It does so through identified data actuarial input deceleration and acceleration that we spoke about. The health care and health departments must improve on modelling of approaches to mutation of coronavirus or other infectious diseases.

[8.1] Stage of Completion of Treatment reserving

The stage of completion of Treatment reserving refers to the mutation fundamental treatment resource allocation. It is reserving actuarially for that matter, that is done at the quest to complete treatment. Data is amassed at a rate that actualize compartmental data amassment of

the disease origination data to the stage of completion. Treatment reserving is a specialized amassing of resources that are deployed to the field using actuarial reserving techniques. The technicalities of stage of completion of Treatment reserving are:

[8.1.1] Life cycle of disease [coronavirus] mutation.

[8.1.2] National Health department tenders’ specificity.

[8.1.3] Risk indicators conclusive assessment.

[8.1.4] Treatment acupuncture methodologies.

[8.1.5] Treatment progress measurement tools.

[8.1.6] Economic impact assessment tools.

[8.1.7] The relegation of disease adverse effects to non-critical zones.

[8.1.8] Coronavirus or other infectious diseases stature.

[8.1.9] New rehabilitation measures of societies in other words post treatments measures.

[9.1] National Monitoring and Reporting of infectious diseases

This is a measurement base in its own right that focuses on monitoring and reporting of infectious diseases. Degeneration of a disease can happen at a fast-paced environment. This activity is generically important in the process of managing mutation using data modelling techniques. It is a fast-paced populated mechanism. Monitoring and evaluation of mutation of national monitoring and reporting of infectious diseases. This phase comprises of continuous amassing of data mutation data as specific points. These are disease mutation compartments that are of a remnant nature that come after the official initially diagnosed characteristic patterns.

[10.1] Risk Response Mechanisms for Coronavirus or Infectious diseases in economies

After everything is said and done, National governments and or Health care boards, Authorities should have a raft of risk response measures for mutation technicalities and or threats identified that may already be wreaking havoc. Risk response mechanisms for coronavirus and other infectious diseases are of a various nature. They are not cast in stone, but the most important issue is for anyone to know how to get there. Mutation equilibrium and disequilibrium analysis dynamics will point to a raft of measures. Risk events and factor analyses that are dominating the mutation compartments will probably give impetus to the tenure of the disease on mutation diametric sensitivity path. Nurturing the mutation path on diametric dimension.

There is a lot I could cover on this article, but it is imperative I cover piecemeal nuggets that expose locations in the mutation of the coronavirus that can be exploited to deal with it once and for all.

For this revelation comes to me in unimaginable proportions espousing advanced actuarial analysis and advanced forensic analysis . In my next issue I will contribute to deal with the measurement bases for coronavirus mutation.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

VENERATED DATA. HOW DATA IS BUILDING THE CRYPTO BLOCK. EMERGING FRONTIERS THAT HAVE NEVER BEEN SEEN

Written by Thomas Mutsimba, an author, an Enterprise Risk Management professional endowed with advanced actuarial analysis and advanced forensic analysis ©

Data, proportionally growing and disproportionally populating the digital platforms and their resources bound components. Industrially data is used in a myriad of functions. Data is venerated , it is building functional blocks of asymptomatic tenets. Data is asymptomatic. What do we mean by this and why are we saying this? It is because it is genetically altered behind the scenes only to be postured as information necessary for various uses. This article seeks to explore and expound on how data is building the crypto block. There are frontiers emerging that have never been seen. First and foremost, it is imperative to drill to the basics of definitions of data and the crypto block. The purpose being to provide the reader of this article with a formulation of the build up of this very important aspect of data.

Data refers to the bits of raw, structured and unstructured pieces of characteristic patterns of various genetic parts of information that are converted to structured sets of information. The difference between data and information is that the former is degenerated to another form, a structured one for that matter to serve a certain purpose. The Crypto block is the mode of data build up used to structure and filter information for various transaction processing systems. Crypto refers to the sensory structural data deliberations meant to drive certain meanings. The Crypto block mostly noted in the crypto currency nature of functionary business is emerging. This Crypto block regarded as built from data is essential for explaining how the Crypto block works and how it is giving rise to new structural data development programs. The Crypto block used to build various monetary currencies that are not backed by any value is emerging and is introducing new notions of legal tender. There are characteristics relating to the Crypto block, but these characteristics are important to formulate a channel base of formation of data building up the Crypto block. But why is it known as crypto block . Crypto here refers to the block tenure interconnection when ever a kind of a transaction is required. The characteristics aforesaid mentioned include:

Block building techno-science in information communication technology.
Block chain reverberation as an emerging frontier.
How block chain started off from document sharing and distribution in a networking architecture.
Scientific data models and how they can be built up; improved to give impetus to the data formulation of the crypto block.
Judgmental data algorithms.
Venerated data, the genesis.
Formulation algorithm of the Crypto block.
Algorithm binary dimensionary data.

Block building Techno-science in Information communication technology

Block building is also an information communication technology tenet. Why is it so? It refers to the crypto cloning data sets build up using data designing modular technology. Block building is a techno-science field. Why is there a science component captioned in the block? It is because the crypto block building is generated from the principles of data science. The principles of data science proffer data modelling as an important aspect.

Block building degeneration principle

The Block building degeneration principle is built on the triangular intertwining and interweaving formation principle. What is this? This refers to the following:

[1] Pyramid structure one

[2] Overarching supporting structures two

[3]The Algorithmic triangular centering focus

We explore the above as follows:

Pyramid structure one

The Pyramid structure brings to the fore the pyramid shape like formation that is built on three core principles. These core principles venerates the data modular terse degeneration velocity driven algorithmic tenets. Fostered at a rate of innovation it stands to develop the crypto block in the background. Out postured information formulate the benefits of crypto block building. The speed of processing of information without the intervening intermediaries is one such benefit. But how are the data blocks structured? The principles cause the parsing of hashes to signature-censored central processing unit . What nurtures the block to interconnect another block hashed to represent a call in transactionary request? It is the block duration of the transaction embedded in the block processing unit. Each block linked to the processing unit through index referencing ensure that the index referencing assigns hashes for each transaction. These hashes and or codes ensure that no intermediary or persons sees where the requests originates. The pyramid structure is hierarchically centered. Hence the pyramid shape with the three core principles.

The next question would be how the hashes are eliminated as new transactions come in from a different location? These hashes are eliminated in three stages:

[1] Transactionary requirement elimination.

[2] Conclusive favorable deal identification

[3] Tenure requirements fulfillment

2.1 Overarching supporting structures two

Overarching supporting structures two is the pyramidal outlook structure. This pyramidal structure is coded “two”. Why is that so? It is so because the supporting tenet stands as a flagged ladder by ladder elevation of blocks. These are building blocks of crypto structural alleviation techniques. Why do we mean by this? We mean it is venerated as at the level of this principle and the advantages and or merit of this are:

The two represents the structural algorithm binary movement of data monitoring of transactions formulated into two lags indexed in the background.

It is posturing mastery central location recording against remote lagging revealing a type of a ledger distribution network that is not conventional but is a unique facilitation of transactions.

3.1 The Algorithmic triangular centering focus

This refers to the formulation series of the block building exercise. What is this block building exercise? This refers to the formation hinge of the crypto block. It is an advanced formation basis set to constellate the five pillars of triangular centering. The five pillars bring to the fore crypto-technology building that has never been seen. Calculated at mathematical binary language of computer processing, it is postured on the five hinges:

[1] The pillar of transaction identification indexing.

[2] The pillar of formulation mechanisms of crypto as developed from the document distributed system.

[3] The pillar of creating a decentralized transaction platform.

[4] The pillar of history record.

[5] The pillar of monitoring and reporting

Two formats are known under this centering focus. The formats are:

Crypto transcribing language.

Crypto modus pock.

The former refers to the transmission modular tenets while the latter refers to the crypto technology deployment.

Block Chain reverberation as an emerging frontier

Block chain technology is an emerging technology frontier in world markets transaction systems. It is a characteristic that we should all see, but not secretly as block chain platforms have proliferated. But on this characteristic, what exactly are we referring to? Block chain reverberation is block chain in its raw, uncouth form. We are referring to the out posturing of this technology at an unprecedented pace. But what are the symptoms of block chain today? It is disruptive technology and the need to pervade new frontiers of transactions and mode of payments. The six sigmatic penetration of block chain technology is the dimensional design of transactions in the markets, let alone the global markets. The six sigmatic dimensions are as follows:

[1] Block chain algorithm formatting.

[2] Block chain tenet conjoined with world transactions and or world transaction processing systems.

[3] The use of block chain and its compatibility with transaction processing operating systems as well as generic enterprising operating systems.

[4] Report generation and SQL-language scripts.

[5] Venerated data analysis algorithms.

Using the above six sigmatic efficient frontier quotient, block chain reverberation can be measured at a reverberation rate turned into a penetration rate calculated and defined compounded in an index to consider its disruptive effect in industrial markets. The market dimensional effective nature of block chain embedment in current architectures of the business environment can be reliably measured. Industries today grapple with an inability to determine the impact of disruptive technology in current business architectures. An index can be designed using the reverberation rate hinge on the sigmatic dimensions alluded to above. The reverberation rate can be calculated as follows:

Sigmatic dimension quotient risk factors are identified, and an exponent is embedded in each factor reverberation rate. How do you measure the factor reverberation rate. It is measured as follows:

[1] Assign a risk factor for each market transaction operating system. What are the factors? Variables are measured using mathematically notated bank of characteristics postured risk factors. These characteristics are exponentially envisaged to grow at a rate of transaction bottlenecks; inefficiencies staggered or effected by impending structures of block chain. Designing a tabular bottleneck characteristic and assigning quants and attempting to measure their populative impact on the total enterprise disruptive technology. These quants are not just assigned but careful intelligence is required from the enterprise. An example would be as follows:

A retail enterprise stock distribution system with clients in various locations. Because the clients for the retailing enterprise now prefer changing their mode of payments via a block chain e-wallet. Because the e-wallet is not accommodated by the retail enterprise marketing system. Where in the market there is a competitor whose enterprise information systems are configured to integrate with a block chain platform. Now how do you decipher the block chain reverberation rate on the entity whose enterprise systems are not aligned and or configured to mesh with the emerging block chain e-wallet of clients in various locations?

First and foremost, identify the risk factors. The risk factors are as follows:

[1] Emerging irrelevancy and unreliability of the enterprise information system.

[2] Operating system incapacitation.

[3] Retail distribution network is susceptible to business alienation as a result of irrelevancy and unreliability.

To provide the data of block chain (emerging architecture on) the existing business, one can design a rating index based on synchronated bottlenecks appearing in the enterprise information systems.

The sigmatic dimensions are cast against each of the listed risk factors. How you rate the sigmatic dimensions against each of the risk factors depends on the rate of block chain reverberation on the enterprise information system.

How Block chain started off from document sharing and distribution in a networking architecture?

Since block chain has been documented to have started off from document sharing and distribution networking architecture. It works based on a transaction distributed ledger system that does not have an intermediary. But this is not all and it is a limited view. So much can be done on it. It is not all because the current block chain view is not just it. I am going to expand more on the deep technology that has not been touched on by any research institute and or center regarding the block chain tenet.

The genesis that has never been seen

Block chain is built on five core principles not withstanding the document sharing system origins. The five core principles are as follows:

Principle 1: The data required in a block chain environment is clysmically formed. What does it mean? It means the data and or documents [as outlined in origins] is built on the best available statistically tested permeating binary formulation procedures. This is data science of data capacitation and distribution within the block chain development algorithms built on programming language that is used. It is used to tenure permeate architectures that can no longer fit within the scheme of things.

Principle 2: Built legally at a rate of formulation datum. What is this? There exist limitations to the build up of block chain algorithms. Why is that so? It is so because of jurisdictions regulations in which the block chain algorithms operate in. Since there are jurisdiction limitations that cause the current status, development algorithms are limited. Full blown block chain foundations must come in without industrial limitations.These can only be revealed if entities or nations do not impose industrial regulations due to fear of new frontiers eroding existing architectures.

Principle 3: Data algorithms can run at “Base 10” programming language . What is “Base 10 programming language?”. Base 10 programming language is language built up, exponentially extrapolated at mathematical models that are not known in existing business structures. It is built on exponentiation extrapolation driven parsing algorithms. The question is does base 10 exist? The answer is no, it does not exist in current language development programs, but it exists in an impending dimension. This impending dimension can only be seen by enlightened minds, minds that are not limited by a limited earth world view, minds whose gifts know no boundaries and are not formed by the supposed advanced technology as it is described in the current business architectures. This is revelation coming to me in unimaginable proportions venerated in my gift of advanced actuarial analysis and advanced forensic analysis.

Principle 4: Genetics, the mutation of Block chain

What is this principle referring to? It is referring to the stagnant development of block chain because of industrial fear and protectionism. World economies are not ready for new architectures of business fundamentals. The reason is not because the business has not done adequate baseline risk assessments. A loud no in my view but the reason is because of fear of losing revenues; higher opportunity costs that may be incurred at a rate of loss index. What is the rate of loss index? This refers to the genetic mutation of block chain against receding business architectures of the current status quo meant to protect equity dividend sustainability.

Principle 5: Data mutation censorship monitoring against block chain effectiveness and effectiveness. Again, here we consider the data mutation censorship monitoring resultant loss index against the effectiveness of block chain effectiveness and efficiency. This principle must be deciphered or considered at length.

Scientific Data models and how they can be built up, improved to give impetus to data formulation of the Crypto block

Scientific data models play an important if not venerated role in the marvelous formulation of the crypto block. Data generated at scientifically notated ratios definitely, will allow development of the Crypto block. Without data, quality assured, the crypto block will cease, slowing its usefulness in generation of fast paced transaction volumes. The willingness of industrial players to provide their data plays a critical role. That begs the question! Data availability? Where are we as the industry? Who is willing to provide their data, integrate their data with other industrial players? Scientific data models are robust data models. They set the pace, interlocking data centers, cloud driven, creating the opportunities for block chain.

Judgmental Data Algorithms

What are these? These are methodical algorithms that create platforms for analysis of data under the block chain train using artificial intelligence. Judgmental data algorithms work best under the block chain train. A serial bus of algorithms that work in these back, jagged hinging on instructions for opinion formulation. This however is a critical characteristic of block chain technology. Judgmental data algorithms in fact are the future copulated with powerful business intelligence.

Venerated data, The Genesis

Venerated data is data that is most robustly used by a variety of platforms. An example would be the ability to use data from multiple sources. Block chain ushers in a train of venerated data. The aforesaid example covers the use of integration of wallets from various commercial banks to facilitate the transfer of funds expended on transactions of a different nature. However, that’s not all as venerated data goes beyond the use of wallets in the block chain technology arena. It reaches the horizons of dimensional interweaving and intertwining transaction platforms.

Formulation Algorithm of the Crypto

The formulation algorithm of the Crypto block is the Crypto block source code. This posture the construction of the ladder asynchronated codes that promote the hashing algorithms used to identify entries in the venerated distributed ledger code of the crypto currency nature. The formulation algorithm is now highly secured at this stage. This is attested to by the fact that no one knows who owns the venerated Crypto currency trading platforms and the non-disclosure of traders on the platforms becomes an issue. Questions are posed on these esthetics about the development of the Crypto block for various industries.

Algorithm Binary Dimensionary Data

Algorithm dual core serving data is another name that we may give to the algorithm binary dimensionary data serving a characteristic that fosters the study of plausible and or non-plausible relationships between and or amongst data banks that are particularly growing over the Crypto block at a speed that has never been seen. This binary dimensionary data is not extinct but it still exists as relationship characteristic of data may be examined for various purposes.

The Crypto block is growing but there is not much research knowledge out there to give impetus to the need to out post its growth demeanor. I will cover measurement dimensions of characteristics of the Crypto block in my next issue. Data is venerated in its use at the speed of thought under the Crypto technology.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

PHASING TECHNIQUES IN ACTUARIAL RESERVING

Written by Thomas Mutsimba, an author and an Enterprise Risk Management Professional endowed with advanced forensic analysis and advanced actuarial analysis

Actuarial modelling has been the hyphen of focus in the series of modelling issues I have addressed in the past. There are a number of techniques that actuarial modelling proffers through its innovation. Innovatively, actuarial modelling is what I birth out through this nudging gift that seeks to introduce efficient frontiers in strategic management and of course de-campaigning promoting business decisions on archaic foundations of business that run the risk of adjudicating irrelevancy and unreliability.

In this issue I focus on Phasing techniques employable in actuarial reserving . One would ask what phasing techniques are in actuarial reserving? However, it’s a question to be concerned with as I expound on functional fundamentals that algorithmically perspire phasing techniques. Phasing techniques are quantum techniques or tools or methodologies that are used in the reserving tenet of actuarial planning. Reserving is the process of acceleration and deceleration of actuarial fundamentals at financially compliant equitable retention of benefits of an entity at adjudicated intervals through employing decelerated and accelerated indexed input fundamentals to maintain sustainability of an entity within the ambit of its strategic objectives.

Reserving here is a critical component of actuarial modelling. This article seeks to produce or denote phasing techniques in actuarial reserving. This may be postured in staggered withdrawal of funds to allocate them towards a particular tenet of reserving. Phasing here is demonstrated in the ladder approach. The ladder acceleration approach or the ladder deceleration approach stand as a demonstration of phasing.

Phasing Technique 1: Actuarial Reserving formulation

The Reserving formulation is a critical component. How is it a critical component? Reserving, as we have alluded to involves the acceleration and deceleration of fundamentals. From a Financial statements’ perspective reserves are captured in the Statement of changes in equity and reserves but these reserves reflect accounting accumulation of retention profits in the financial period or over the financial period. Inferences may be drawn with actuarial reserving but for the purposes of showing mainly the differential perspiring of reserving through the phasing method. The Phasing method nudgingly shows how the actuarially determined reserves are harnessed-used over various purposes. Formulation of actuarial reserving becomes the starting point. Actuarial reserving has nine characteristics. The characteristics are as follows:

[1] Financial statement analysis of key reserve impact components.

[2] Component testing of key reserve perspiration.

[3] Equilibrium efficiency and its tenet.

[4] Characteristic identifier of reserving components.

[5] Chain reserving method-a component.

[6] Formulation analysis and quantification of risk.

[7] Quantum risk equilibrium analysis.

[8] Decision based reserving.

[9] Formulation analytical.

[10] Reserve retention and reformulation.

1.1 Financial Statement Analysis of Key reserve impact components

What is the impact of Financial Statement analysis? In Actuarial reserving financial statement analysis is important as it is an exercise of debasing the base of accounting values of line items in the financial statements. What is debasing? Debasing is a process of breaking down financial items value formation using degenerate actuarial input fundamentals to reflect the real values as opposed to nominal values. Financial statement analysis is a key tenet in reserving because of the following:

1.1.1 Analysis identifies key line items on financial statements formation critical for sustainability review and formulation

1.1.2 It allows mapping of value items to key input fundamentals.

1.1.3 Financial statement analysis postures reserving injection tenets . These reserving injection tenets are principles to be applied when reserving.

1.1.4 Reserving is formed in two factorial lead indicators.

1.1.3 Financial statement analysis forms correlations between account balances.

2.1 Component Testing of Key reserve perspiration

This refers to the section of the reserves used formulate an actuarial reserve. The components are marked by input fundamentals . Testing of reserve components has 6 components .The components are as follows:

[1] Reserving adjudication principles.

[2] Reserving components divisional factors.

[3] Reserving analytical factors .

[4] Reserving impact input fundamentals (Regulatory)

[5] Reserving cumulative balances

2.1.1 Reserving adjudication principles

What are reserving adjudication principles? Reserving adjudication principles are the principle-based deliberations of reserving components that are generated at enterprise financial statements level. Actuarially, reserving is apportioned at financial statement account balance and equitable distribution reserves. This forms the basis of apportioning through formulas mathematically and actuarially driven. The following are characteristics of reserving adjudication principles:

Reserving should be based on a technique defined by organizational policy and industrial board regulatory requirement.

Reserving identifies equitable distributable resources as a source of funding.

It is a co-efficient technically accounted for retention of profits.

Reserving is driven by Industrial Board requirement of maintenance of capital adequacy.

Reserving is a tenet that runs on continuous bound quantum risk portion to a dedicated account.

Reserving is an actuarial technique at most to keep the entity on sustained levels.

Reserving will be explained in detail under the phasing methodology.

2.1.2 Reserving Components divisional factors

Reserving components are postured in some determined factors. These factors are known as divisional factors. What are divisional factors? Divisional factors are classes of reserves that are presented in various entities financial statements and chosen for the phasing techniques. These various classes that are depicted as divisional factors can use the division or apportioning of reserves over various periods using the phasing techniques. Divisional factors of reserves present themselves in five modes. The modes are:

[1] Reserve magnitude factorization.

[2]Acceleration methodology characteristics.

[3] Reserving technique-specific reasons and unique quantum risk component.

[4] Divisional factors efficiencies.

[5] Reserving phasing region or compartment of efficient frontier.

2.1.3 Reserving analytical factors

Reserving analytical factors are lead indicators of trend line requirements of actuarial reserving system. Since we have spoken about the effective factorial methodologies or modes of reserving components divisional factors it becomes imperative at this stage to determine the analytical factors. It is not just about the determination of analytical factors, but it is also about expounding the tenet, the reserving tenet. The analytical factors are as follows:

[1] Analyze the reserving withdrawal component.

[2] Determine the compounding reserving factorial component.

[3] The formula for reserving is built on the “Calculative standard reserving accounting principle’’ that results in the reserve lag released to the Income Statement as a payment of claims in the case of insurance entities or financial services funds.

[4] Reserving is built on a variety of methods such as The Chain ladder method and or Bornhuetter ferguson method as well as trendline reserving techniques.

[5] Reserving postures Gordon-stated principles. What are the Gordon-stated principles? The Gordon-stated principles are principles built on actuarial visionary man who had visualized the field of actuarial accounting and reserving in the old age.

[6] Actuarial principals development phases.

[7] Factorial leads of actuarial phasing.

Gentric component phasing’

The Gentric component phasing, proportionally calculated at accelerated and decelerated phasing approach is a critical component of the reserving phasing technique. This together with several tested components acquired from various sources that may be internal and external may result in the phasing motion desensitization. Postured at calculated sensitivity indexing we expound on the gentric component phasing work.

Formulation of gentric component phasing

It starts off with the identification of actuarial reserving components. The Actuary or the Actuarial analyst must be provided with a set of financial statements and related disclosures for an entity. As a starting point this allows the Actuary or Actuarial analyst to reserve structural presentation right from reporting to the actual fundamentals. The component phasing formulation requires four modes. The modes are as follows:

[1] List all components according to ranking of usage of the reserve in the component.

[2] Component that can be indexed with Industrial standardization must be identified.

[3] Is the Industrial Standardization a benchmark or a guidance framework? This needs to be answered. The question must be answered by looking at the available information at the entity.

[4] General mathematical formulation tenet. What is this? This is the generic reservation summation serialization that determines how transfers are made between revenue statements and reserving and equity schedules. Usually mathematical calculations that are computative can be presented in the following ways:

[4.1] Efficient Frontiers of component phasing co-efficiency is crucial. How do you present component efficiency frontiers as it marks the mathematical deliberations of behavioral movements in reserves over a period of time? Measurements may be done through a number of bases; rather measurement bases. These measurement bases include the following:

[4.1.1] Standard co-efficiency series analysis.

[4.1.2] Extended data phasing extrapolation.

[4.1.3] Reserving frequency standard deviation from reserving base amount. The base amount refers to the fundamental capital reserve at inception of the reserving phasing that is used to estimate sustainable reserve phasing over a certain number of periods.

[4.1.4] Co-efficiency measures the dual and or binary performance of two factor stages. What are two factor stages? Two factor stages refer to the fundamental input relationships that reserve phasing may create. As an entity engages reserve phasing, this results in creation of a set of performance indicators in the tenet of sustainability. Sustainability measured at postured periodical phased motions of reserving is crucial for the breaking of actuarial advisory conundrum.

Tenure Calculation techniques

Tenure is crucial in actuarial studies and or actuarial work. What does it refer to? It refers to determination of alluded period or time series in which one wants to extrapolate the actuarial performance of the reserving phasing motion. There are five tenure computation techniques. These techniques are as follows:

[1] Period calculated or determined based on populative behavior of reserve phasing. What does it mean? It means the tenure of reserves can hinge on period determination methodology.

[2] Tenure population using tenure extrapolation index of series factorized by the number of reserves made per period.

[3] Using the bipolarization of factors depicting the tenure index computed.

[4] Derelict number of years a reserving method is using. This is also a tenure computation index. As simple as it maybe it shows the extremity or stress asymmetry degenerated fundamentals.

In my next article on reserve phasing I will expound on the tenure determination techniques as well as application and analysis of reserve phasing using industrial actuarial examples.

Disclaimer: All views expressed in this article are my own and do not represent an opinion of an entity whatsoever, with which I have been, am now or will be affiiliated with.©

THE IMPACT OF NUCLEAR ENERGY ON COMPARTMENTAL EFFICIENCY OF MANUFACTURING SYSTEMS TODAY

Written by Thomas Mutsimba, an author, an Enterprise Risk Management Professional. Thomas is an author endowed with advanced actuarial analysis and advanced forensic analysis

Nuclear energy has become a topical “top dog” of discussion today because of world energy variations and deficiencies. These energy variations and deficiencies are noted in shortages, imbalances between supply and demand of energy in the markets; market shocks and economic crises in the world. Nuclear, dangerous, hazardous, centrifugal harvested cells of fumigative cylinders of energy storage that are kept at highly secured sites, cautioned sites and highly regulated industry.

What is nuclear energy? Nuclear energy is a source of electromagnetic current converted from chemically compounded uranium enriched deposits that are extracatory from chemical composition degeneration for the purpose of availing a source of electricity generated power. It is a very sensitive source of energy in today’s markets because of the regulation that is high and closely monitored by authorities let alone by international regulatory bodies. Nuclear consists of five components in its structurally formatted compounded atomic layers. These five components are as follows:

[1] Nuclear atomic outer compounding layer;

[2] Uranium cellulite formation;

[3] Nuclear formation atomic formation;

[4] Explosionary centrifugal force penetration;

[5] Cubic enrichment and decomposition balancing dynamics;

1. Nuclear atomic outer compounding layers

Nuclear is an energy source but it starts with uranium as an ore, a source of radioactive material or extraction that is critical for harvesting the uranium compound. It comprises of atomic particles whose compounds are generated at the altitude deep bed where geological rock formations meet or alternatively friction postured rock structures are genetically made. What do we mean by this? It means it is not easy to discover uranium deposits because of the geological formation of the earth where deposits require deep exploratory technologically powered instruments that explorers use.

What is the nuclear atomic outer layer? The outer layer is compounding atoms that give effect to the process sieving through the ore which is the uranium layer. The outer layer festered through enrichment ready ore nucleic chemical component structure.

1.1 How does the Ore result in the harvest?

The ore extracted at gigantic pipe instrumentation using injection molding of channel dug through compressed air under high health and safety measures. Because of the corrosive element meaning, it gives rise to oxidizing carbonated dangerous gases. The harvest is not done when the ore is harvested by centrifugal cylinders made that store the extracted ore periphables. Nurtured through advanced nucleic physiological chemical engineering analysis, harvesting through grafting into the centrifugal cylinders stands as a propelling nuclear energy drive. Harvesting is for most stated elevation of extracted uranium periphables to the next stage of genetics of nuclear energy.

1.2 Neurological Analysis perspired techno-division separation of chemical compounds

What is this? This refers to nucleic core dangerous chemical-physiological analytical chemistry. Stated in deep advanced exploratory work, nuclear energy, built and feared for potential reverberating years of gentric radioactivity levels that through history have been touted as being dangerous and believed to cause cell human body:-cell mutation decomposition. Cellulite is an extractible color-coded chemical that advances human fallibility that is the flesh voltaic reception of external hazardous bodies. Neurological analysis causes dangerous brain-surgery discovered variations using standard deviation of results analysis extrapolated on a rail like posture of distributions causing variations to be plotted hypo graphically. Why are we talking about this? We are talking about this because the drive for nuclear energy to be used must be backed by, of course a business case and of course studied formulants. What are formulants? Formulants are atio-atoms that are identified through deep analytical physio-chemistry. They are atomic explosions discoverable through advanced chemistry bomb manufacturing technology. Dense populative particles that are highly charged with positive antigens (excrete laden periphables) are active; in fact, highly active and because dangerous data plotted upheaval graphical posture of their characteristics.

I write this article from genetic glory ridden visualizations of the highest order. I neither studied nuclear physics nor applied to an institution of higher learning nor did I buy or open any book. But through a gift imparted by High echelons of glory I write. Highly blessed I cannot keep this knowledge to myself, but it beckons, in fact my purpose beckons over and over again plunging me into higher order glory of knowledge. I ooze with this advanced knowledge that comes to me in unimaginable proportions, never seen, never heard. Built in a myriad of gifts espoused through advanced forensic analysis and advanced actuarial analysis I serve the world; I serve the business fraternity through this gift imparted by Higher echelons of glory that has this earth under Glory’s creative ability. Enterprise Risk Management is my life espoused in the never seen and never heard revelations of knowledge of the highest order.

Back to the morphosis of this article-chartered topic, “Nuclear energy impact on compartmental efficiency of manufacturing systems today”. Genetically we have expounded more on the processes involved in the extracatory perseverance of nations to use nuclear energy. A number of nations have had a thought of extracting nuclear energy. Why is it a critical energy source? It is a critical energy source because of the following:

Nuclear energy is extracatory because of its explosivity as a result of nuclear reactor ridden atomic reverberation explosion;
Nuclear energy as a long-term energy source and not short-term energy source;
Nuclear energy is degenerated at minute levels but through processing it or transforming it , it becomes high output;
Nuclear energy is energy of the century. What does it mean? It means with the permeating dimensions of energy scarcity what the world calls renewable, clean energy sources do not meet the total Global energy needs. Hence nuclear energy becomes the energy of the century due to its populative nature and high output;
Nuclear energy generated at temperature based centrifugal cylinder-based enrichment processes is a corrosively dangerous source of weaponry;
Alloys do not give much power output. What are we referring to when we speak of alloys? We are referring to chemical-compounded mineralistic tendencies of earth endowments that may also be harvested through technology-driven chemical compounding processes;
There are safer methods of using nuclear energy, but these have not been discovered;
Nuclear energy is energy formulation based. What does it mean? It means nuclear energy is based on formulation methodology. Its criticality as far as the formulation gives impetus to the degenerate atomic architecture;

2. Uranium Cellulite formation

Uranium cellulite formation is color coded chemical component. We explained that cellulite is a color code extractible component that is cellulite portion of the extractive chemical component that has various degenerative effects to the human health.

Cellulite formation postures chemical component as it has a core-atomic building effect. That’s the reason it is a degenerate; its core-building attribute separates what is regarded and what is not regarded. Cellulite formation corrosive elements. The four corrosive elements are:

Cellulite genetic base foundation;
Base numbery tabular contamination index;
Genetic formula struck nuclear atoms;
Cellulite pervasion and disproportionate components. The disproportion agent.

3. Nuclear Formation Atomic formation

Why are we dwelling on nuclear formation atomic formation? It is the septic agent catalytic nature of the uranium atomic architecture as far as the extraction of the uranium sables are interweaving and intertwining minute binding node link atoms. Its atomic formation is needed to study the destructive chemical architectures that are highly oxidized and carbonated through atomic perspiration of charging atoms that race to consuming the atoms that are yearning to be reverberated and reduce to posture the fatherly or parent-child relationship atomic binding atoms which perspire the degenerate populative architecture.

4. Explosionary Centrifugal Force penetration

What is this? This refers to the charging atomic architecture that causes the explosionary centrifugal genetic degenerator. This is advanced nuclear physics driven by chemical analytics component driven.

5. Cubic enrichment and decomposition balancing dynamics

The cubic enrichment and decomposition balancing dynamics post volume-pressure metrics that extricate the energic effective nature of uranium enrichment. Uranium enrichment, much talked about in industrial quantum molecular physics is the degenerate oculent. What is an oculent? An oculent is the formation basis of atomic pervasion in optical physics postulate. Optical physics postulate zooms in to identify the tiny realms of degeneration of atoms.

Manufacturing Compartmental efficiency. The Incoming Nuclear energy uses and how Industrial Manufacturing systems are impacted by it?

Compartmental efficiency; defined in Manufacturing industries as the disproportion portions of the production line that consume or posture consumption of inputs at a certain rate of motion calculated in that compartment. Today, manufacturing industries are divided into compartments on their production lines. But these compartmental engineering dynamics are not much talked about. In this section we focus on Nuclear energy. Its sensitive and highly charged source of energy may be used in a variety of industries or manufacturing industries. Why manufacturing industries? It is so because manufacturing may be a high consumer of energy at any particular point in time.

Nuclear energy populated in manufacturing materials engineering due to the scientific and technological nature of manufacturing materials efficiency.

How does this energy source impact compartmental manufacturing efficiency?

Compartmental manufacturing efficiency is dis-aggregated at a rate of slow rate of disproportion indexing of efficiency. What is this indexing of efficiency? This is nuclear energy that may be injected into the manufacturing materials formation. Formulant energy cannot be seen, neither can it be measured with precision and accuracy when it is injected into physical matter. Is this true? Yes it is true because energy transmission happens at a certain rate of deceleration of value or quantity such that when it reaches a certain target it will be in disproportionate levels. Now, manufacturing compartmental efficiency is impacted by Nuclear energy in the following ways:

a. Nuclear energy penetration index measurement

This is an index calculated using volume-pressure effect as energy is transmitted to compartments of the production lines for usage. This index uses a five-factor authentication component. This authentication component consumes the energy quantity in the compartment. Measured using the reactive ability of nuclear energy, materialistic atoms will nurture efficiency reduction index calculated at a certain rate of input energy factor as a basis point of the output factor. This sounds complicated isn’t it? It is not but if you use advanced actuarial models computed through use of energy efficiency model indexes, one will be able to actuarially fundamentalize input proportions of nuclear energy. Compartmentalization helps energy efficiency in different ways. Some of the ways are:

Photo-light essentials posturing voltaic abilities of materials at reception of compartment;
Generic energy efficiency index as a proportion of compartmental efficiency;
Energy efficiency has many variables. Energy efficiency has many co-efficients. Co-efficiency postures a gravitational force that pulls unseen atomic postures that are deverberated in project outcomes measurement;

b. Energy efficiency is not nuclear energy efficiency

Why is that so and what does it mean? Energy efficiency measurement is standard notation based on atmospheric pressures measurement based but nuclear energy efficiency is based on exclusive nuclear viscosity energetic characteristics. We note the difference as having the qualities of laboratories amplification of the highly charged energy. Different measurement bases give impetus to the differences we are referring to.

c. Genetic notation used to standardize nuclear energy input fundamentals is generally calculated fundamentals using industrial actuarial modelling

Calculated through the use of advanced actuarial models’ genetic mutation can be industrial mathematics of calculating the mutation of efficiency over compartments of efficiency of the manufacturing engineering industries.

d. How to energize an efficiency frontier ?

Since the production line efficiency symbol is one frontier that comes with nuclear energy. Nuclear energy can energize the efficiency frontier. How does it do it? It is based on the balancing of atomic velocity of the enriched uranium molecules being delivered for nuclear energy consumption motion. In the compartmental efficiency energization fosters the production of disposables. These nuclear energy disposals are in simple terms centrifugal enrichment cylinders that have gone past the maturity stage of development to the next stage. In this case the next stage may be the next stage of usage of production efficientization.

Generally formulated standard efficiency. How it comes up? The standard efficiency of nuclear energy comes in various formats during the production line compartments of the manufacturing entity. It lines up in the following ways:

Efficiency is measured at the rate of decomposition of centrifugal tubes;
Standard formulation notation is mathematical and actuarially calculated through value analysis of engineering processes set to consume nuclear energy;
Gentric phased approach analyzed by actuarial determination base of standard efficiency of fundamentals. Standard efficiency fundamentals include nuclear enrichment status, its atomic effective production on product or materials being produced, rate of deceleration of fundamentals tweaking produce desired result results. Standard efficiency notated by standard deviation equilibrium analysis of inputs.

d. Genetically modified approach to Nuclear energy

The Nuclear energy unknowns and their postures in efficiency compartments of manufacturing efficiency compartments.

My next article continues with Nuclear energy driving the revenue alternatives for the energy industry.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with. ©

STRUCTURING ENGINEERING FINANCE MODELS: A RISK MODELLING VIEW

Written by Thomas Mutsimba, an author, an Enterprise Risk Management Professional. Thomas is an author endowed with advanced actuarial analysis and advanced forensic analysis

Engineering finance has become a scapegoat for the failing economies of the world. Engineering has become the driving input factor for worldwide infrastructural projects. Economies pinned on massive growth of populations has induced a need to rethink engineering and what it brings to the Gross Domestic Products (GDP) of nations. Why is engineering so important in today’s economies? Engineering is important as it is a design tenet of various statuses or outlook of nations in the world. This article focuses on structuring of engineering finance models for use by project finance professionals and the world at large. A risk modelling view or approach is being used to posture how engineering finance models affect the outlook of nations let alone institutions engaged in funding of engineering projects. What is meant by structuring of engineering finance models? Engineering finance models structuring is the generation of engineering components needs analysis in a formation fashion to posture project finance as a generic input into the risk modelling input. But how does finance affect the engineering tenet? As no business or project thrives without an initial outlay. Engineering projects are modeled under finance structural adjustments. A methodology let alone methodologies are available to prop up the structural modeling exercise. Situational deverberation of the modus operandi of the tenets of structural engineering finance model is made using a situational approach. Why is this approach used? It is used because it aims at discounting theoretical approaches to solving problems but instead promote applied engineering models.

Scenario 1

Engineering finance models are structured using the following:

[1] Nut factory shaped finance population mechanism

[2] Finance needs requirements tenet

[3] General Factory component architecture

[4] Rolled-up reserving system

[5] Stress Asymmetry test-case model

[1] Nut factory shaped finance population mechanism

This is a nut factory shaped finance population mechanism. This scenario is used to demonstrate the finance population or degeneration mechanism. This factory is highly equipped with machines set up to manufacture edible nuts. In other words, the engineering mechanism used to set up the factories gives impetus to the need to know how to structure finance models either at inception or at a quest for ongoing engineering improvement. Why is it called a nut factory shaped? It is so because of the designing of the factory rather the arrangement of engineering components. We will posture the following as engineering components:

[1] Raw nuts receiving sensor.

[2] General calculative timer for the receiving bay.

[3] Peeling censorship motion detectors.

[4] Generic quality algorithm-based inspection modulator.

[5] Mixing or optimization of peeled nuts with a combined injector of flavoring portions

instrument.

[6] Rowing motor belts that propels production lines.

[7] Minute engineering components that fastens the machine set-up and design components.

How is the engineering finance model of the nut-shaped factory structured?We posture this solution. We postured this solution using risk modelling actuarial techniques.

Where F is financial

F = es + rb + ps + gq + on + pi + b + g

All the above components are cost input fundamentals in the model of the financing component. In order to populate the nut shaped factory mechanism, this is a process that calls for five generic housing fundamentals for the population of the engineering finance model.

Fundamental 1: Factory populative architecture degenerated in the efficient frontier of the Factory

The efficient frontier of the factory is a weighted average index of the populative architecture degeneration of a financing model. The efficient frontier of the nut factory may be measured by 5 bases. The five bases are as follows:

Base 1: General indexing cost fundamentals aligned to the world engineering standardization indexes

Base 2: Populative architectural data driven factory motion of costs

Base 3: Factory standard processing efficiencies

Base 4: Aluminium postured factory metals indexes

Base 5: The Genesis of Technology-The base here is alignment through alignment through benchmarking with technology powered factories.

We will cover illustrative computations of the five bases in my next article.

Fundamental 2: Efficiency dynamics using quantum risk modelling

Efficient dynamics are variations of efficiencies that are scenario based built in quantum-based risk modelling. How do they work? Risk models are used to calculate efficiency ratios at postured risk models. The test cases for each of the risk models will calculate the efficiency ratios.

Efficiency ratios take various forms from materials, machine efficiency comparison to product model optimization indexes, general material usage rate, general processing (in this case, the nut factory), efficiency rate. However, risk models here are actuarial based. Engineering technicians will be apprised of the actuarial model outputs required for the entity to achieve investment optimization through structuring of engineering finance models. This is advanced analysis using actuarial modelling techniques.

Fundamental 3: Cost utilization mix nullifier of abnormal loss index

What is this fundamental? This refers to the defined set of costs for instance in the nut factory. When costs are utilized it means the factory processes are consuming normal costs at a certain rate. But the utilization mix is a nullifier. What does it mean? A nullifier is a cost allocation in a factory process that amplifies the efficiency of product optimization mix therefore rendering the losses from a factory process null and void. In other words, there is a reduction in the abnormal losses , nullified or reduced to very minute levels or even eliminated.

This cost utilization mix is based on indexing factors such as employed raw materials, materials or inputs that are divided using a utilization mix designed or recommended by Industrial Engineers, Chemical Engineers or Food processing engineers in a particular format. Generally speaking, this is a huge area of factory operations management or operations techniques. Cost utilization mix will be expounded more on the use of actuarial techniques to nullify abnormal loss indexes.

Fundamental 4: General Technology powered division of roll-over efficiency losses

What are roll-over efficiency losses? In a nut factory, each production or processing process has compartments of efficiency depending on the stage of the process. What inspires compartment efficiency? Compartments efficiencies are inspired by trigger input effects as new additives or modifier injection processes alter the form of the product from one level to another . Therefore, from an engineering perspective one notes that efficiency compartments are necessary, and they give a realistic view of efficiency motion. Therefore, efficiency losses are index indicators of the level of investment required in each compartment. Actuarially at each compartment we can generate fundamental cost pricing input to determine the model fundamentals. A large number of start-ups or small to medium engineering or production firms are not aware that production line efficiencies can be compartmentalized using actuarial techniques.

Fundamental 5: General seismic bound ablution of losses conjoining with cost perspiration in the factory set-up and design

General seismic bound ablution of losses conjoining with a cost perspiration in factory set-up and design. What is this? This refers to the elimination or rejection losses using a systematic motion method where costs are incurred in tandem with factory set-up and design. Therefore, this general seismic bound is needed in the nut-factory. It is not just a process of injecting costs or investing without structuring the engineering finance models in a systematic way. Seismic as a word represents a gigantic treatment of abnormal losses in the factory using factory design and set-up rearrangement techniques. This is a technique that certainly works but requires operations research methodology entangled in factory operations management.

These are the five fundamentals, but we will expound more in a later issue.

[2] Finance needs requirements tenet

Drawing up finance needs simplistically is not difficult. In this case we are dealing with a nut factory. Finance needs requirements needs understanding of the set-up and design of the factory. Factories are different, factory designs are different therefore needs requirements must focus on the technicalities of engineering components of the factories that feature or are relevant to the production process. Investment cost risk engineers here, have a lot of work to do. Needs requirements is populously hidden in the architecture of the factory setting. These needs may be imputed in an actuarially built model that will power cost-build up which can also in turn be improved over time through cost motion fundamental rearrangements and reorganizations.

[3] General Factory Component architecture

General factory component architecture is the build-up or set-up of the factory and also by design. Why is it critical to the structure of engineering finance models? Because the architecture is the dissipator of critical factory efficiency frontiers driven by the set-up, by design.

Factory component architectures by those who do engineering drawings will give a full picture of all components and how they interweave and intertwine. Factory component architecture has two or more component architecture presentation. These are as follows:

Engineering drawing architectural design;
Engineering drawing efficiency frontiers design;

The above mentioned two components are very important. Efficiency frontiers are crucial as they give the Factory investor of where decompartmentalization of efficiency frontiers connect costs-build up at different stages of production

[4] Rolled-up reserving system

The Rolled-up reserving system is mentioned here because it is important to ensure reserves; in fact, sufficient reserves are available. Reserves in the form of retained earnings to fund continual or perpetual working capital needs. Roll-up reserving system is synonymous with capital preservation techniques that seek to channel doses of resources at intermittent intervals depending on the nature of the production line. However, a rolled-up reserving system is all billed up against return on investment. Modelling of a reserving system for engineering structuring of finance takes employment of actuarial techniques in various ways. Because the running of engineering factory components may be done on a basic platform or basis. Many are oblivious of the benefits of being stringent with maintaining reserves that foster traction of momentum of the production line. My next issue will demonstrate the rolled-up reserving system of engineering structuring of finance models.

[5] Stress Asymmetry Test case model

Structuring Engineering finance models from a risk modelling view also involves or explains stress asymmetry for each of the factory engineering scenario.

But how useful is Stress asymmetry? Stress asymmetry under engineering finance models ensures that tensions recognized in production bottlenecks ranging from financial to non-financial are withstood by shock process. Since an actuarial model would have been used, structuring the engineering finance model becomes a tenacity tenet issue. How does it become one? Product optimization mixes are the bench marking fundamental input into the Stress asymmetry. Organizations today should promote the use of actuarial techniques.

Disclaimer: All views expressed in this article are my own and do not represent the opinion of an entity whatsoever, with which I have been, am now, or will be affiliated with.