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Journal articles on the topic 'Risk – Mathematical models'

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Published: 4 June 2021

Last updated: 10 March 2023

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1

Prokopjeva, Evgenija, Evgeny Tankov, Tatyana Shibaeva, and Elena Perekhozheva. "Behavioral models in insurance risk management." Investment Management and Financial Innovations 18, no. 4 (October 21, 2021): 80–94. http://dx.doi.org/10.21511/imfi.18(4).2021.08.

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Behavioral characteristics attributed to consumers of insurance services are a relevant factor for analyzing the current situation in the insurance market and developing effective strategies for insurers’ actions. In turn, considering these characteristics allows the insurer to be more successful in the highly competitive field, achieving mutual satisfaction in interacting with the customer. This study is aimed to develop cognitive models of the situation (frame) “Insurance”, taking into account the specifics of the Russian insurance market and systemic factors affecting participants’ behavior in the market. In this regard, the study involves systemizing risks at various levels of the economic system, generalizing factors for the motivation of insurance consumers, developing descriptive and economic-mathematical models for the behavior of economic entities in risky situations.The results obtained represent a behavioral model of interactions among insurance market entities, which determines opportunities for efficient and mutually beneficial coordination of their activities. The developed model includes the following elements: structured individual and institutional frames “Insurance”; a professional index of interest in insurance presented in the form of a mathematical model; methodology for governing the relationships among insurance participants in the digital environment.The recommendations enable predictions of the situation in the insurance market and allow most accurately defining the consumer needs in the conditions of market changes.

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2

Yarygina, I. Z., V. B. Gisin, and B. A. Putko. "Fractal Asset Pricing Models for Financial Risk Management." Finance: Theory and Practice 23, no. 6 (December 24, 2019): 117–30. http://dx.doi.org/10.26794/2587-5671-2019-23-6-117-130.

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The article presents the analysis findings of the problems and prospects of using the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The aim of the article is to find out the features of value of bank assets and to develop recommendations for assessing financial risks based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used to achieve the aim. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a volatile market. It was proved that using financial mathematics in banking contributes to the stable development of the economy. Mathematical modeling of the price dynamics of financial assets is based on a substantive hypothesis and supported by an adequate apparatus of fractal pair pricing models in order to reveal specific market relations of business entities. According to the authors, the prospects of using forecast models to minimize the financial risks of derivative financial instruments are positive. The authors concluded that the considered methods contribute to managing financial risks and improving forecasts, including operations with derivatives. Besides, the studied fractal volatility parameters proved the predictive power regarding extreme events in financial markets, such as the bankruptcy of Lehman Brothers investment bank in 2008. The relevance of the article is due to the fact that the favorable investment climate and the use of modern financing methods largely depend on the effective financial risk management.

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3

&NA;. "Biologically Based Mathematical Models of Lung Cancer Risk." Epidemiology 4, no. 3 (May 1993): 193–94. http://dx.doi.org/10.1097/00001648-199305000-00002.

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4

Park, Colin N. "Mathematical models in quantitative assessment of carcinogenic risk." Regulatory Toxicology and Pharmacology 9, no. 3 (June 1989): 236–43. http://dx.doi.org/10.1016/0273-2300(89)90062-7.

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5

Antucheviciene, Jurgita, Gang Kou, Vida Maliene, and Egidijus Rytas Vaidogas. "Mathematical Models for Dealing with Risk in Engineering." Mathematical Problems in Engineering 2016 (2016): 1–3. http://dx.doi.org/10.1155/2016/2832185.

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6

Ifrim, Ana Maria. "Mathematical Models in Quality Engineering." International Journal of Innovation in the Digital Economy 8, no. 3 (July 2017): 18–34. http://dx.doi.org/10.4018/ijide.2017070102.

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The present paper deals with the factors that contribute to assuring the quality of the processes involved in project management. The novelty of the approach consists in the fact that the project management processes are analysed with the help of quality indicators in case of time variance. By studying the numeric variable for the proposed economic phenomenon, a smaller discrete interval is obtained, which accounts for the numeric variable being treated as a continuous variable. The practical application of such an analysis is that a risk management plan can be designed based on the parameters which define the quality of the management process.

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7

Bondareva, Irina Olegovna, Sabina M. Sidagalieva, and Evgeniya T. Nesterova. "MATHEMATICAL MODELING OF RISK MANAGEMENT IN TRANSPORT LOGISTICS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 2 (April 30, 2021): 75–88. http://dx.doi.org/10.24143/2072-9502-2021-2-75-88.

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The article considers the business processes at the transport logistics enterprises as a chain of clear regulations, where noncompliance or delay of one of them results in disruption of the whole process. Risk management is one of the key tasks requiring the development of modeling tools and prevention of undesirable situations. There has been shown a structural model of the risk of failure to achieve the strategic goal of a cargo port, supplemented by several levels of consideration. The tree of goals of the transport logistics enterprise was built. Failure to achieve a particular goal is considered as a risk situation, or a risk. A set of factors for assessing its implementation is opposed to each goal, formulas for calculating the indicators used are given. A model of scenarios of all existing significant risks has been developed. A multi-level hybrid logical-probabilistic model, a cascade logical-probabilistic model and a multi-level cascade hybrid logical-probabilistic model of the risk of failure to achieve the main strategic goal of the port/transport enterprise are proposed. The main idea is the need to link together the technology of formalizing risks using the constructed logical-probabilistic models and simulation, where the interpretation of the results is possible using logical and probabilistic models and scenarios. The proposed models make it possible to carry out a comprehensive analysis of the risk of failure to achieve the strategic goal of a cargo port based on the scenario formalization of risks of various levels of management, as well as to simplify the process of interpreting the results of simulation modeling taking into account external factors of influence. The integrated use of all these models is the basis for the development of timely management decisions. Particular attention is paid to the description of the technology for constructing logical, probabilistic and scenario models of various types.

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8

Gosling, John Paul. "Harnessing mathematical models and uncertainty in toxicological risk assessments." Toxicology Letters 229 (September 2014): S160. http://dx.doi.org/10.1016/j.toxlet.2014.06.551.

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9

Kodell, Ralph L. "The use of mathematical models in carcinogenesis risk assessment." Mathematical and Computer Modelling 11 (1988): 146–51. http://dx.doi.org/10.1016/0895-7177(88)90470-0.

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10

Voina, O. A., and A. O. Voyna. "Mathematical Models of Risk Control for Regenerating Markov Processes." Cybernetics and Systems Analysis 55, no. 5 (September 2019): 817–27. http://dx.doi.org/10.1007/s10559-019-00192-x.

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11

Сычев, Михаил, Mikhail Sychev, Владимир Минаев, Vladimir Minaev, Александр Фаддеев, and Aleksandr Faddeev. "Seismic risk assessment in tourist-recreational areas: mathematical models." Servis Plus 9, no. 2 (June 15, 2015): 25–34. http://dx.doi.org/10.12737/11309.

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The article highlights the problem of evaluation of seismic risks in the tourism and recreational areas. To this end, the grounded and practically being tested mathematical model for evaluating the seismic stability of tourist-recreational area on the example of the Black Sea, the Caspian and the Mediterranean region. This model combines the influences of disturbances associated with the anomalous gravity field (vertical component), and takes into account modern crustal movements (horizontal component), calculated according to the space geodesy. The physical model of the geological environment in the form of a closed homogeneous isotropic elastic space in the form of "plates" with as averaging the values of the density, shear modulus and Young´s modulus is suggested. The geological environment is considered in the framework of Newtonian rheology, that is without taking into account the seismic deformation of energy dissipation. Experimental calculations show good agreement with the results of modeling really occurred in the historically-sky aspect of the catastrophic earthquakes in the study area. Suggested an additional testing model by comparing the orientation of the vectors of the horizontal displacements at the surface, resultingfrom mathematical modeling, containing information on contemporary movements of the earth´s crust according to the space geodesy. The analysis shows that the greatest seismic risk are generally a characteristic of those places of the study area, where the vectors of horizontal displacements in opposite directions, characterized by a helical orientation. The prospects of using the model are describe, if it is considered according to Maxwell rheology of the medium, which allows to take into account the effect of the relaxation of stresses and strains in the rate of accumulation in the subsurface. This approach may allow a quantitative estimation of the seismic deformation energy dissipation, which is very significant in terms of the forecast estimates ofseismicity in the time aspect.

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12

De Wrachien, D., and S. Mambretti. "Mathematical models for flood hazard assessment." International Journal of Safety and Security Engineering 1, no. 4 (December 31, 2011): 353–62. http://dx.doi.org/10.2495/safe-v1-n4-353-362.

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13

Alazzam, Malik Bader, Abdulsattar Abdullah Hamad, and Ahmed S. AlGhamdi. "Dynamic Mathematical Models’ System and Synchronization." Mathematical Problems in Engineering 2021 (November 19, 2021): 1–7. http://dx.doi.org/10.1155/2021/6842071.

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We created the equilibrium, which includes sickness outcomes, health and risk behaviors, environmental factors, and health-related assets and delivery systems, and it should be incorporated in system Dyc (dynamic) modelling of chronic disease prevention. System Dyc has the ability to model a variety of interconnected illnesses and dangers, as well as the interaction between delivery systems and afflicted people, as well as state and national policies. This paper proposes a unique idea. Hybrid synchronization utilizes four positive LYP (Lyapunov) exponents based on state feedback management with two identical systems of the Lorenz system 6D HYCH system.

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14

Stroev, S. P. "Mathematical models for insolvency risk management of an industrial enterprise." CONTINUUM. MATHS. INFORMATICS. EDUCATION, no. 2 (2021): 89–98. http://dx.doi.org/10.24888/2500-1957-2021-2-89-98.

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15

Liu, Xuan, Gerrit Cornelis Kooten, and Jun Duan. "Calibration of agricultural risk programming models using positive mathematical programming." Australian Journal of Agricultural and Resource Economics 64, no. 3 (March 8, 2020): 795–817. http://dx.doi.org/10.1111/1467-8489.12368.

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16

Euhus, David M. "Understanding Mathematical Models for Breast Cancer Risk Assessment and Counseling." Breast Journal 7, no. 4 (July 2001): 224–32. http://dx.doi.org/10.1046/j.1524-4741.2001.20012.x.

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17

Seigneur, Christian, Akula Venkatram, Don Galya, Paul Anderson, David Liu, Donna Foliart, Rudolph von Burg, Yoram Cohen, Thomas Permutt, and Leonard Levin. "Review of mathematical models for health risk assessment: I. Overview." Environmental Software 7, no. 1 (January 1992): 3–7. http://dx.doi.org/10.1016/0266-9838(92)90018-y.

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18

Ivanyo, Yaroslav, Nina Fedurina, and Zhanna Varanitsa-Gorodovskaya. "Mathematical models of agricultural production management in high risk environments." E3S Web of Conferences 222 (2020): 01018. http://dx.doi.org/10.1051/e3sconf/202022201018.

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The paper presents an algorithm for modeling the production of agricultural products in the formation of agrometeorological events. Stochastic models of variability of downpours, early snow-fall and crop yields are constructed to assess the likelihood of extreme events. Based on a probabilistic assessment of crop bio-productivity by a normative method, economic losses from agrometeorological events are determined. A model for optimizing crop production taking into account natural risks was built and implemented for an agricultural organization. The results were obtained according to data of the Irkutsk district.

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19

Urbina, Angel, and Thomas Paez. "Statistical Validation of Structural Dynamics Models." Journal of the IEST 46, no. 1 (September 14, 2003): 141–48. http://dx.doi.org/10.17764/jiet.46.1.f430423634885g67.

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There is an increasing reliance in the engineering community on the use of mathematical models to characterize physical system behavior. This is happening even though mathematical models rarely simulate real system behavior perfectly. Due to this reliance, we require objective, well-founded mathematical techniques for model validation. This paper develops a formal approach to the validation of mathematical models of structural dynamics systems. It uses a probabilistic/statistical approach to the characterization of an important measure of behavior of dynamic systems subjected to random excitations, and seeks to validate a mathematical model in a statistical sense. An example is presented.

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20

Khanlarzadeh, Sarvinaz. "Mathematical Modeling of the Risk Reinsurance Process." WSEAS TRANSACTIONS ON MATHEMATICS 21 (June 20, 2022): 447–60. http://dx.doi.org/10.37394/23206.2022.21.52.

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This paper presents a method for assessing financial risks and managing them to optimize the decision-making process. It is shown that the type of economic entity at risk and its activities in the financial market affect the specifics of financial risk management, which can be classified into three main groups: hedging, diversification, and insurance. The main instruments used for this purpose are also identified. Special attention is given to the dynamic properties of financial flows arising from the simulation of artificial financial instruments, as well as to their influence on the results of financial risk management when taking into account errors in estimating parameters of mathematical models. The purpose of our study is to create a mathematical model that can be used to assess the risk reinsurance process. We will create a mathematical model of the risk reinsurance process using the following steps: 1. Identify all of the relevant variables in our analysis. 2. Determine how these variables interact with each other and come to a conclusion about how they influence each other's values. 3. Find equations that represent these relationships between the variables and solve for their values with those equations. 4. Test these models against real data from known cases in order to ensure that they work as expected, then use them for future studies or applications requiring this type of modeling technique.

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21

Drissi, Ramzi. "Mathematical Risk Modeling: an Application in Three Cases of Insurance Contracts." International Journal of Advances in Management and Economics 8, no. 6 (October 30, 2019): 01–10. http://dx.doi.org/10.31270/ijame/v08/i06/2019/1.

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Risk is often defined as the degree of uncertainty regarding the future. This general definition of risk can be extended to define different types of risks according to the source of the underlying uncertainty. In this context, the objective of this paper is to mathematically model risks in insurance. The choice of methods and techniques that allow the construction of the model significantly influence the responses obtained. We approach these different issues by modeling risks in three base cases: basic insurance of goods, life insurance, and financial risk insurance. Our findings show that risk modeling allowed us to better measure certain events, but did not allow us to predict them accurately due to a lack of information. Therefore, good modeling of the risk determinants makes it possible to modify the probability associated with the occurrence of a risk. While it cannot predict exactly when a risk will occur, it can help make decisions that will reduce its effects. Keywords: Basic insurance, Life insurance, Mathematical models, Financial risk, Biometric function.

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22

Kostyuchenko, Mikhail, Volodymyr Gogo, Boris Kobilyansky, Oleg Kruzhilko, Ihor Yefremov, Kyrylo Hriadushchyi, and Oleksandr Tkachuk. "ANALYSIS OF PRODUCTION RISK ON EXAMPLES OF MINERS ‘LABOR." JOURNAL of Donetsk Mining Institute, no. 2 (2021): 159–75. http://dx.doi.org/10.31474/1999-981x-2021-2-159-175.

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Objective: Based on the review of the array of publications to analyze the methods and models of general risk assessment, the nature of industrial risks and management processes on the examples of labor of coal miners. Propose a classification of mathematical models of industrial risk and identify the most appropriate model for the work of miners in the stochastic system “man-machine-environment”. Methodology: Applied to the use of situational analysis, qualimetry, probability theory and risk theory, methods of classification of occupational risks. Results: Based on a systematic analysis of multifactorial risks of emergency situations, the essence of industrial risks and management processes on the examples of coal miners, the dominant causes of industrial risk in the ergatic system (“man – machine – environment”), models and methods of risk research. Scientific novelty: For the first time on the basis of the analysis of the reasons, dynamics and consequences of industrial risks the classification of mathematical models of risks which are adapted to ergatic systems of mine production is offered. Practical value: The need for adequate practical application of risk methods and models for the assessment and measurement of industrial hazards has been proven.

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23

Pavlovska, M. O. "BLOOD PRESSURE, HYPOCHONDRIA AND DEPRESSION: MATHEMATICAL MODELS OF RELATIONSHIP." International Medical Journal, no. 4(104) (December 24, 2020): 12–20. http://dx.doi.org/10.37436/2308-5274-2020-4-2.

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Modern clinical diagnostics has standards and medical systems for the diagnosis of hypertension, advanced information technology. Mathematical models of the relationship between systolic blood pressure and psychological indices of hypochondria and depression have been described. Methods of mathematical statistics were applied as follows: factor, cluster, discriminant, regression analyzes, Markov chains, polynomial splines and neural networks, they were implemented in software products, such as NeuroModelDBPM, "Monitoring", VerMed. The presented model of interaction of systolic arterial pressure, Hs−hypochondria, D−depression confirms an importance of these states at an initial stage of arterial hypertension and allows the allocation of four options of psychosomatic relations in patients: organ and system somatic defeats of psychosomatic character, somaticized psychiatric reactions, reactions of exogenous type. It has been shown that disharmonious personality traits, risk factors, disorder of chronobiological structure of blood pressure, left ventricular hypertrophy and its diastolic dysfunction contribute to the formation of nosogeny in hypertension. Their development is hindered by harmonious personality traits, keeping a healthy lifestyle, minimal changes in the chronobiological structure of blood pressure, a slight degree of left ventricular hypertrophy and its diastolic dysfunction. The leading cardiovascular risk factors in patients with hypertension are stress, burdened heredity, low physical activity, carbohydrate abuse, higher education and high socioeconomic status. Nosogeny in hypertension should also be considered as a risk factor, as well as should be taken into account in the stratification of the overall cardiovascular risk and accomplishing a proper adjustments. Key words: arterial hypertension, mathematical statistics, arterial pressure, hypochondria, depression, information technology.

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24

Orlov, A. I. "Mathematical methods for studying risks (resumptive article)." Industrial laboratory. Diagnostics of materials 87, no. 11 (November 21, 2021): 70–80. http://dx.doi.org/10.26896/1028-6861-2021-87-11-70-80.

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We define risk as an unwanted opportunity and divide risk theory into three stages — risk analysis, risk estimation, risk management. Safety and risk are directly related to each other, being like a «mirror image» of each other which necessitates developing both the general theory of risk and particular theories of risk in specific areas. General risk theory allows for a uniform approach to the analysis, estimation and management of risks in specific situations. Currently, three main approaches to accounting for the uncertainty and describing risks are used — probabilistic and statistical approach, fuzzy sets, and the approach based on interval mathematics. The methods of risk estimation primarily based on probabilistic and statistical models are considered. The mathematical apparatus for estimating and managing risks is based on nonparametric formulations, limit relations, and multi-criteria optimization. Asymptotic nonparametric point estimates and confidence limits for the probability of a risk event are constructed on the base of binomial distribution and the Poisson distribution. Rules for testing statistical hypotheses regarding the equality (or difference) of two probabilities of risk events are proposed. An additive-multiplicative risk estimation model based on a hierarchical risk system based on a three-level risk system has become widespread: private risks — group risks — final risk. For this model, the role of expert estimation is revealed. The prospects of using (in the future) the theory of fuzzy sets are shown. The article deals with the main components of the mathematical apparatus of the theory of risks, in particular, the mathematical support of private theories of risks related to the quality management, innovations and investments. The simplest risk assessment in a probabilistic-statistical model is the product of the probability of a risk event and the mathematical expectation of the accidental damage. Mathematical and instrumental methods for studying global economic and environmental risks are discussed.

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25

Stikhova, Olga. "Mathematical Estimation Methods and Models for Industrial Companies." EPJ Web of Conferences 248 (2021): 03001. http://dx.doi.org/10.1051/epjconf/202124803001.

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The collateralized debt obligations and credit default swaps applications are shown in this paper. The industry obligations secondary market risk estimation methods are considered in this work. The new methods taking into account statistically significant parameters for industrial credit derivatives portfolio are offered for single-name investment risks numerical experiments realization. The mathematical estimation of tranche were shown. The single and multiple name default obligations necessary mathematical modeling methods and formulae for the industrial materials manufacturers derivative credit tools market are shown. It is determined that the portfolio of synthetic debt tools is made of the given parameters. The task of a loss derivative tranches mathematical estimation is solved. Late defaults raise the equity tranches payment required sums with high spreads, early defaults reduce. Also the functional characteristics required for an estimation huge debts problem solving are partly considered in this paper. The problem of the default modeling for market tools and numerical simulation of the obligations influence on conditions of current bistability mode are shown here. Some credit derivatives of industrial manufacturers are demonstrated in the modeling process of default as an example. It is found that the model is an additional factor help us to estimate the default opportunity.

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26

Batz, Kevin, Adrian Gallus, Benjamin Lucien Kaminski, Joost-Pieter Katoen, and Tobias Winkler. "Weighted programming: a programming paradigm for specifying mathematical models." Proceedings of the ACM on Programming Languages 6, OOPSLA1 (December 8, 2022): 1–30. http://dx.doi.org/10.1145/3527310.

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We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weights can be numbers but also other objects like words from an alphabet, polynomials, formal power series, or cardinal numbers. We argue that weighted programming as a paradigm can be used to specify mathematical models beyond probability distributions (as is done in probabilistic programming). We develop weakest-precondition- and weakest-liberal-precondition-style calculi à la Dijkstra for reasoning about mathematical models specified by weighted programs. We present several case studies. For instance, we use weighted programming to model the ski rental problem — an optimization problem. We model not only the optimization problem itself, but also the best deterministic online algorithm for solving this problem as weighted programs. By means of weakest-precondition-style reasoning, we can determine the competitive ratio of the online algorithm on source code level.

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27

Minaev, V. A., A. O. Faddeev, T. R. Akhmetshin, and T. M. Nevdakh. "MATHEMATICAL MODELS OF GEODYNAMIC RISK ESTIMATION AT THE LITHOSPHERE PROCESSES RESEARCH." Technology of technosphere safety 82 (2018): 40–47. http://dx.doi.org/10.25257/tts.2018.6.82.40-47.

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28

Stiehl, Thomas. "Using mathematical models to improve risk-scoring in acute myeloid leukemia." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 12 (December 2020): 123150. http://dx.doi.org/10.1063/5.0023830.

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29

Seigneur, Christian. "Review of mathematical models for health risk assessment: VI. population exposure." Environmental Software 9, no. 2 (January 1994): 133–45. http://dx.doi.org/10.1016/0266-9838(94)90005-1.

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30

Liu, David. "Review of mathematical models for health risk assessment: VII. chemical dose." Environmental Software 9, no. 3 (January 1994): 153–60. http://dx.doi.org/10.1016/0266-9838(94)90027-2.

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31

Kortcheva, A., V. Galabov, J. Marinski, V. Andrea, and C. Stylios. "New approaches and mathematical models for environmental risk management in seaports." IFAC-PapersOnLine 51, no. 30 (2018): 366–71. http://dx.doi.org/10.1016/j.ifacol.2018.11.333.

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32

Ghamami, Samim. "Static models of central counterparty risk." International Journal of Financial Engineering 02, no. 02 (June 2015): 1550011. http://dx.doi.org/10.1142/s2424786315500115.

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Following the 2009 G-20 clearing mandate, international standard setting bodies (SSBs) have outlined a set of principles for central counterparty (CCP) risk management. They have also devised formulaic CCP risk capital requirements on clearing members for their central counterparty exposures. There is still no consensus among CCP regulators and bank regulators on how central counterparty risk should be measured coherently in practice. A conceptually sound and logically consistent definition of the CCP risk capital in the absence of a unifying CCP risk measurement framework is challenging. Incoherent CCP risk capital requirements may create an obscure environment disincentivizing the central clearing of over the counter (OTC) derivatives transactions. Based on novel applications of well-known mathematical models in finance, this paper introduces a risk measurement framework that coherently specifies all layers of the default waterfall resources of typical derivatives CCPs. The proposed framework gives the first risk sensitive definition of the CCP risk capital based on which less risk sensitive non-model-based methods can be evaluated.

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33

Kumar, Rajiv, A. K. Gupta, and M. Naveen. "Compartment Fires: BFD Curve and Mathematical Models." Journal of Applied Fire Science 17, no. 1 (January 1, 2007): 73–95. http://dx.doi.org/10.2190/af.17.1.e.

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34

Minaev, V. A., R. O. Stepanov, and A. O. Faddeev. "Problem of Mathematical Model Adequacy in Assessing the Seismic Risk." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 4 (137) (December 2021): 93–108. http://dx.doi.org/10.18698/0236-3933-2021-4-93-108.

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The problem of mathematical models' adequacy in assessing seismic risk is considered. It is demonstrated that the currently used methods of testing such models make it possible to assess only the consistency of simulation and real data by counting the number of earthquake epicenters that appear in the areas with increased values of the fields with various indicators. The paper proposes a fundamentally new approach to testing adequacy of the seismic risk assessment models based on examining statistical hypotheses. Application of this approach is considered in a seismic risk assessment model for the territory of Armenia and the adjacent regions. Practical implementation of the proposed approach and the results obtained convincingly confirm that the tested mathematical model is adequate. Normality of the seismic risk values general set distribution calculated by the probabilistic model for Armenia and the adjacent territories is presented. Correlation coefficients of theoretical and empirical frequencies distribution are 0.75--0.99. It is shown that adequacy of the seismic risk assessment probabilistic model should be checked taking into account the earthquake abyssal levels. Conclusions are provided on operability and possibility of further use of the considered method in checking adequacy of assessing the seismic risk mathematical models

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35

Mamunts, D., S. Sokolov, A. Nyrkov, S. Chernyi, M. Bukhurmetov, and V. Kuznetsov. "Models and Algorithms for Estimation and Minimization of the Risks Associated with Dredging." Transport and Telecommunication Journal 18, no. 2 (June 1, 2017): 139–45. http://dx.doi.org/10.1515/ttj-2017-0013.

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Abstract There are a lot of models and algorithms to minimize risks during dredging operations and they are not without drawbacks. The paper describes the authors’ approach to solving this problem. Mathematical models are proposed and on their basis software is developed. Methods of the risk theory are used to minimize the risks. In this paper a consequence of influence refers to the deviation from the goal expressed in the expected results and the deviation of certain criterion factors. In this case, we mean any measure of quality. In its turn, risk factors reduce criterion factors. These factors are divided into categories - general transportation risks and risks of transporting ground. In these categories, one may derive the following risks - incidents at transport resulting from the impact of a set of random factors including the human one. For risk analysis and management, in addition to identifying critical chains of risk situations, the stochastic model for evaluating the chains is set forth. In order to implement this algorithm, the mathematical package Maple is used, which allows for conducting the required calculations with a software package including the Graph Theory. The paper presents fragments of the code listing.

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Zirka, Andrii, Mariia Zirka, and Natalia Kadet. "FEATURES OF RISK ASSESSMENT IN THE CREATION OF UAV FOR VARIOUS PURPOSES." Science-based technologies 51, no. 3 (October 28, 2021): 193–204. http://dx.doi.org/10.18372/2310-5461.51.15994.

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One of the perspective directions of the development to modern aviation is connected with designing and producing unmanned aerial vehicles (UAV) of various functionalities for applying in military and civilian spheres. The syntheses of UAV control systems, regardless of their type and purpose presumes the creation of adequate mathematical models, first of all adequate aerodynamic mathematical models. In the paper results that forms and justify the aerodynamic mathematical model and as well as the results of building a general mathematical model of the longitudinal movement of the perspective UAV are presented. In the article on the basis of the analysis of the reasons of involvement in performance of research and development works the basic risks of the indirect factors and possible negative scenarios of performance of projects of creation of samples of aviation equipment are defined. Based on the results of the analysis of risk-forming factors, the risk indicators of the projects of creation (modernization) of aircraft models are substantiated. A methodical approach to the criterion assessment of risk indicators at the stages of research and development work on the development (modernization) of aircraft is proposed

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alsalem, Abdulaziz Fhad Abdulaziz, Zaid Mohammad Alqahtani, Anas Ageel Alshammari, Hassan Rashed Alzamanan, Saad Mohammed Alsarhan, Suyuf Ahmed Alwallah, Rizq Saleh Alismail, and Hassan Mohammed Atiah. "Evaluating the Risk of Type 2 Diabetes Mellitus Using Artificial Neural Network." International Journal Of Pharmaceutical And Bio-Medical Science 02, no. 11 (November 29, 2022): 546–51. http://dx.doi.org/10.47191/ijpbms/v2-i11-13.

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To identify risk factors, neural network analysis is used to create disease prediction models, including diabetes. The goals of this study were to identify diabetes risk factors and determine their relative contribution using artificial intelligence as a mode of prediction. The current investigation was led by breaking down the dataset, as shown below. We chose a dataset from Kaggle. The diabetes dataset was from India. It has 763 female members, 497 of whom have no diabetes and 266 who have type 2 diabetes. We used neural network analysis to create mathematical models and visualize the distribution of diabetic risk factors. The significance level was set at 0.05. The current study found that the following risk factors were ranked in order of importance: Diabetes Pedigree Function, age, glucose, skin thickness, blood pressure, BMI, insulin, and number of pregnancies. When combined, neural network analysis is effective in developing mathematical models that can predict disease risk factors.

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Pitcher, Ashley B., and Shane D. Johnson. "Exploring Theories of Victimization Using a Mathematical Model of Burglary." Journal of Research in Crime and Delinquency 48, no. 1 (February 2011): 83–109. http://dx.doi.org/10.1177/0022427810384139.

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Research concerned with burglary indicates that it is clustered not only at places but also in time. Some homes are victimized repeatedly, and the risk to neighbors of victimized homes is temporarily elevated. The latter type of burglary is referred to as a near repeat. Two theories have been proposed to explain observed patterns. The boost hypothesis states that risk is elevated following an event reflecting offender foraging activity. The flag hypothesis, on the other hand, suggests that time-stable variation in risk provides an explanation where data for populations with different risks are analyzed in the aggregate. To examine this, the authors specify a series of discrete mathematical models of urban residential burglary and examine their outcomes using stochastic agent-based simulations. Results suggest that variation in risk alone cannot explain patterns of exact and near repeats, but that models which also include a boost component show good qualitative agreement with published findings.

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Brown, David W., and Robert F. Anda. "Risk Factors for Disease Risk Factors and Attributable Risk Calculations: Are There Mathematical Limits?" Open Epidemiology Journal 3, no. 1 (January 7, 2010): 1–2. http://dx.doi.org/10.2174/1874297101003010001.

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The Adverse Childhood Experiences (ACE) Study, a collaborative effort between Kaiser Permanente (San Diego, CA) and the Centers for Disease Control and Prevention (Atlanta, GA), was designed to examine the long-term relationship between adverse childhood experiences (ACEs) and a variety of health behaviors and outcomes in adulthood [1]. ACEs include childhood emotional, physical, or sexual abuse and household dysfunction during childhood. The ACE Study, based on chronic disease prevention and control models, proposes that ACEs influence social, emotional, and cognitive impairments which in turn increase the probability of adopting health risk behaviors that have been documented to influence the subsequent development of disease, disability, social problems, and ultimately premature death. We use the ACE pyramid to depict this concept (see www.cdc.gov/nccdphp/ace/pyramid.htm).

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Minaev, V. A., N. G. Topolsky, A. O. Faddeev, R. O. Stepanov, and D. S. Grachev. "Risk assessment models with functionally different influences on natural and technical systems." Technology of technosphere safety 89 (2020): 8–19. http://dx.doi.org/10.25257/tts.2020.3.89.8-19.

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Introduction. The complex combination of natural and technogenic factors that lead to dangerous threats to the health and life of the population, as well as to material values, creates a need to develop special mathematical models for risk assessment in the relevant territories. Herewith it is important to take into account the significant differences between these factors. The new areas of research are models that describe natural and technogenic risks using differential equations that reflect different types of functions. The article presents the development of this research area. Goals and objectives. The goal of the article is to create a model for risk assessment in natural and technical systems (PTS), based on taking into account the influences of different natural and technogenic factors on them. Objectives include justification, construction and practical implementation of the mathematical model of risk assessment in the form of differential equations system. Methods include interpretation of the considered influences on PTS in terms of risks and assessment of the dynamic interaction of natural and technogenic factors in the form of inhomogeneous differential equations. Results and discussion. Solutions for models of assessing complex natural and technogenic risks in relation to two cases that differ in NTS are found: functionally different external natural and technogenic influences on PTS, which are understood as their type, in which the effects of both natural and technogenic factors are described by different mathematical functions. Conclusions. The first model considers parabolic (reflecting threats whose intensity gradually decreases with distance from the epicenter) and linear types of influences (reflecting sudden threats). The second model considers parabolic and hyperbolic (reflecting threats, the intensity of which decreases sharply over time) types of influences. It is concluded that it is necessary to create a special computer album of complex influences on the PTS in order to prevent "replay" of various situations and develop the most effective response to emerging dangers from the EMERCOM units and other structures. Key words: model, assessment, natural and technogenic risks, functionally different influences, counteraction, EMERCOM units.

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Shuryak, Igor. "Enhancing low-dose risk assessment using mechanistic mathematical models of radiation effects." Journal of Radiological Protection 39, no. 4 (September 27, 2019): S1—S13. http://dx.doi.org/10.1088/1361-6498/ab3101.

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Ryan, Patrick. "Review of mathematical models for health risk assessment: IV. Intermedia chemical transport." Environmental Software 8, no. 3 (January 1993): 157–72. http://dx.doi.org/10.1016/0266-9838(93)90012-7.

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Venkatram, Akula, and Christian Seigneur. "Review of mathematical models for health risk assessment: II. Atmospheric chemical concentrations." Environmental Software 8, no. 2 (1993): 75–90. http://dx.doi.org/10.1016/0266-9838(93)90018-d.

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Brown, Stephen L., and Brad Schwab. "Review of mathematical models for health risk assessment: VIII. dose/response relationships." Environmental Software 9, no. 3 (January 1994): 161–74. http://dx.doi.org/10.1016/0266-9838(94)90028-0.

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ZUBAREV, I. S. "USE OF BANKRUPTCY MATHEMATICAL MODELS TO ANALYZE FORECAST PAYMENT CAPACITY." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1, no. 1 (2021): 29–32. http://dx.doi.org/10.36871/ek.up.p.r.2021.01.01.005.

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The article deals with forecasting the risk of financial insolvency, which is an integral part of the financial and economic analysis of the organization and helps investors and creditors to identify the stability of any enterprise.

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Nilsen, Vegard, and John Wyller. "QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single-Hit Dose-Response Models." Risk Analysis 36, no. 1 (January 2016): 145–62. http://dx.doi.org/10.1111/risa.12389.

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Nikulina, E., V. Severin, and D. Lukinova. "Mathematical Models for Investigation of WWER-1000/320 Transients." Nuclear and Radiation Safety, no. 1(77) (February 19, 2018): 18–23. http://dx.doi.org/10.32918/nrs.2018.1(77).03.

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The paper presents mathematical models of the reactor WWER-1000/320, which are designed to investigate non-stationary operating modes of the reactor. The models in relative state variables include a point model of neutron kinetics with six groups of delayed neutrons and models of thermal processes, gradual heat generation, change in xenon concentration. The effects of reactivity on the movement of control rods and changes in reactor power, the effects of reactivity on changes in fuel and coolant temperatures, effect of change in the concentration of xenon are taken into account. The values of the constant parameters of the models are given for the start of stationary fuel loading.

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Shain, Kenneth H. "Mathematical Models of Cancer Evolution and Cure." Blood 126, no. 23 (December 3, 2015): SCI—55—SCI—55. http://dx.doi.org/10.1182/blood.v126.23.sci-55.sci-55.

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You cannot cure what you do not understand. So how can mathematical modeling address this pressing issue? The advances in therapeutic success in multiple myeloma over the last decades have hinged on an an army of researchers identifying a critical genetic, epigenetic and biochemical signaling factors within of MM cells as well as the tumor microenvironment (TME). Unfortunately, despite these large scale efforts we do not yet offer our patients curative intent therapy. The inability to provide curative therapy, especially in the setting of HRMM, is characterized by evolving resistance to lines of sequential therapy as a result of alternating clonal dynamics following the failure of initial therapy to eradicate minimal residual disease (MRD). Recent results underline the importance of tumor heterogeneity, in the form of pre-existing genotypically (and phenotypically) distinct sub-populations that translate to drug-resistant phenotypes leading to treatment failure. This phenomenon of “clonal tides”, has been well characterized using contemporary molecular techniques demonstrating that clonal evolution progresses by different evolutionary patterns across patients. Thus, resistance to therapy is a consequence of Darwinian dynamics- influenced by tumor heterogeneity, genomic instability, the TME (ecosystem), and selective pressures induced by therapy. Such evolutionary principles can be analyzed and exploited by mathematical models to personalize therapeutic options for patients with MM. Currently available clinical decision support tools and physician acumen are not able to account for the shear amount of information available. Mathematical models, however, provide a critical mechanism(s) to account of the large number of aspects to help predict and manage MM- accounting for what we do not know. Models can be designed with the specific intent of characterizing intra-tumoral heterogeneity, changing ecosystems, and clinical parameters over time to create patient-specific clinical predictions much like hurricane prediction models. This can only be achieved by creating mathematical models parameterized by longitudinal data of a number of parameters. The novel application of mathematical models based on Darwinian dynamics can be imputed with data to 1) predict progression events (risk of progression to from smoldering to active MM), 2) relapse, and 3) predictions of clinical response of MM patients for the optimizing therapeutics for cure or optimal control of MM; thus, providing invaluable clinical decision support tools. Disclosures: Shain: Celgene: Consultancy , Speakers Bureau ; Amgen/Onyx: Consultancy , Speakers Bureau ; Takeda: Consultancy , Speakers Bureau ; Signal Genetics: Consultancy , Research Funding.

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Brannigan, Vincent, and Carol Meeks. "Computerized Fire Risk Assessment Models: A Regulatory Effectiveness Analysis." Journal of Fire Sciences 13, no. 3 (May 1995): 177–96. http://dx.doi.org/10.1177/073490419501300302.

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Computerized Fire Risk Assessment Models have been proposed for regulatory use. The models normally are used to examine alternative designs to determine whether they are equivalent to standard code approved Structures. However, mathematical equivalence in a model may not constitute social or technical equivalence, if the assumptions and methods used in the model are not property specified. Regulatory Effectiveness Analysis is a tool which can be used to determine whether the model satisfies the regulator's legal requirements, and whether it is properly responsive to public judgments of fire safety. Model builders should expect detailed examination of the specifications and data used in the model, and be prepared to show competent verification and validation of the model results.

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SCHAFFNER, DONALD W. "Utilization of Mathematical Models To Manage Risk of Holding Cold Food without Temperature Control." Journal of Food Protection 76, no. 6 (June 1, 2013): 1085–94. http://dx.doi.org/10.4315/0362-028x.jfp-12-424.

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This document describes the development of a tool to manage the risk of the transportation of cold food without temperature control. The tool uses predictions from ComBase predictor and builds on the 2009 U.S. Food and Drug Administration Model Food Code and supporting scientific data in the Food Code annex. I selected Salmonella spp. and Listeria monocytogenes as the organisms for risk management. Salmonella spp. were selected because they are associated with a wide variety of foods and grow rapidly at temperatures >17°C. L. monocytogenes was selected because it is frequently present in the food processing environment, it was used in the original analysis contained in the Food Code Annex, and it grows relatively rapidly at temperatures <17°C. The suitability of a variety of growth models under changing temperature conditions is largely supported by the published literature. The ComBase predictions under static temperature conditions were validated using 148 ComBase database observations for Salmonella spp. and L. monocytogenes in real foods. The times and temperature changes encompassed by ComBase Predictor models for Salmonella spp. and L. monocytogenes are consistent with published data on consumer food transport to the home from the grocery store and on representative foods from a wholesale cash and carry food service supplier collected as part of this project. The resulting model-based tool will be a useful aid to risk managers and customers of wholesale cash and carry food service suppliers, as well as to anyone interested in assessing and managing the risks posed by holding cold foods out of temperature control in supermarkets, delis, restaurants, cafeterias, and homes.

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