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Journal articles on the topic 'Risk assessment - Mathematical models'

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Published: 4 June 2021

Last updated: 26 January 2023

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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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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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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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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 &#34;plates&#34; with as averaging the values of the density, shear modulus and Young&#180;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&#180;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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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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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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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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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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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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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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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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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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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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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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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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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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Sivakova, Lenka, Anna Zubkova, and Witalis Pellowski. "Application of a Priori and a Posteriori Estimate on Risk Assessment." Communications - Scientific letters of the University of Zilina 20, no. 2 (June 30, 2018): 56–61. http://dx.doi.org/10.26552/com.c.2018.2.56-61.

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The problem of setting the values and interconnections between elements of the models in the safety, protection and security field, appears as the biggest obstacle in taking crisis management decisions. The article attempts to represent a mathematical approach to modify the expected values and interconnections that can occur in the models describing the protected system in order to minimize errors caused by subjectivity. Here presented procedures are described in the examples of their potential use. The main idea is to focus on improving estimates for better response to reality, then to find new estimates, since those would still be weighed down by the subjectivity caused errors. Based on this premise this article attempts to characterize application of mathematical methods on minimizing the subjectivity caused errors in the models in risk assessment.

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Kodell, Ralph L. "Bioassay Designs for Validating Biologically Based Mathematical Models of Carcinogenesis for Risk Assessment." Drug Metabolism Reviews 28, no. 1-2 (January 1996): 219–23. http://dx.doi.org/10.3109/03602539608994001.

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Brandt, Jan-M., Márton Benedek, Jeffrey S. Guerin, and Jörg Fliege. "Reliability-as-a-Service for bearing risk assessment investigated with advanced mathematical models." Internet of Things 11 (September 2020): 100178. http://dx.doi.org/10.1016/j.iot.2020.100178.

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Fasching, Peter A., Mayada R. Bani, Carolin Nestle-Krämling, Tim O. Goecke, Dieter Niederacher, Matthias W. Beckmann, and Michael P. Lux. "Evaluation of mathematical models for breast cancer risk assessment in routine clinical use." European Journal of Cancer Prevention 16, no. 3 (June 2007): 216–24. http://dx.doi.org/10.1097/cej.0b013e32801023b3.

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Minasyan, V. В. "Risk Assessment Models of the Companies Implementing R&D Projects." Finance: Theory and Practice 23, no. 1 (February 27, 2019): 133–46. http://dx.doi.org/10.26794/2587-5671-2019-23-1-133-146.

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Companies implementing R&D projects face their unique features. There is the need for large capital investments, long-term implementation, high growth potential, low probability of success, and diffculties in fnancing among them. Implementation of such projects is associated with high risks. This leads to underfunding as uncertain results deter investors. The problem of assessing the risks arising from the implementation of such projects has not yet been suffciently studied at the level of mathematical analysis models. The objective of the article is to develop a model allowing to explore the risks arising from implementing R&D projects. The author has developed a risk assessment model using the VaR measure modifed for this application. The formulas have been obtained to calculate this measure. They have been adjusted to simple analytical expressions assuming the balanced distribution of cash ﬂow from the project, or triangular distribution. The model considers the most important causes of risks in R&D projects. It can be used in a real-case scenario if a preliminary risk assessment of a project is done before its implementation and a decision is made on risk-based implementation. Moreover, this methodology can be used to standardize the decision-making process for the R&D projects implementation considering the “risk appetite” using the VaR risk measure.

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Rozhina, Viktoria D. "CREDIT RISK ASSESSMENT MODELS AT THE BEHAVIOR-SCORING STAGE." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 7/1, no. 127 (2022): 128–40. http://dx.doi.org/10.36871/ek.up.p.r.2022.07.01.013.

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Credit risk management is one of the most important tasks in banking risk management. Credit risk is the possibility of financial losses if the borrower fails to fulfill its obligations in a timely manner and in full, in particular as a result of a delay or non-repayment of a payment on a banking product. This article is devoted to the search for optimal methods for assessing the main component of the credit risk model - the probability of default of the borrower (PD) at the stage of building behavioral models. The paper considers two blocks of mathematical models - quantitative and classification. The object of the study is a portfolio of homogeneous loans of a commercial bank, the subject is the dynamics of the exit of the considered contracts into default. As an effective approach, the author proposes an integrated method for introducing the forecast result obtained using migration matrices as a new variable for the scoring model. Namely, revealing the depth of delinquency, upon reaching which borrowers do not improve the quality of the loan, as a separate predictor that helps to improve the scoring result.

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Purchase, I. F. H. "Risk assessment. Principles and consequences." Pure and Applied Chemistry 72, no. 6 (January 1, 2000): 1051–56. http://dx.doi.org/10.1351/pac200072061051.

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Risk assessment is an important tool in deciding how to allocate resources to controlling risks. In most cases it is based on hazard data derived from animal experiments and on exposure data from an assessment of the likely or actual exposure of the population of interest. Recent advances have improved the understanding of the use of the no adverse effect level (NOAEL) and safety factor for risk assessment by providing a scientific justification of the 100-fold safety factor. Concern about the risks of exposure by various routes simultaneously (aggregate exposure) and the risks of exposure to mixtures (cumulative risk assessment) have lead to new approaches to these issues. For many years, risk assessment of genotoxic carcinogens has relied on low-dose extrapolation using mathematical models. Recently, these methods are being reconsidered and, in some cases, replaced with the NOAEL/safety factor approach combined with all information on the mechanism of action and the magnitude of the response. It is vitally important to ensure that risk assessment provides accurate and unbiased estimates of risk of exposure so that appropriate measures can be taken to control the risks.

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Gavaghan, David, Alan Garny, Philip K. Maini, and Peter Kohl. "Mathematical models in physiology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1842 (March 22, 2006): 1099–106. http://dx.doi.org/10.1098/rsta.2006.1757.

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Computational modelling of biological processes and systems has witnessed a remarkable development in recent years. The search-term ( modelling OR modeling ) yields over 58 000 entries in PubMed, with more than 34 000 since the year 2000: thus, almost two-thirds of papers appeared in the last 5–6 years, compared to only about one-third in the preceding 5–6 decades. The development is fuelled both by the continuously improving tools and techniques available for bio-mathematical modelling and by the increasing demand in quantitative assessment of element inter-relations in complex biological systems. This has given rise to a worldwide public domain effort to build a computational framework that provides a comprehensive theoretical representation of integrated biological function—the Physiome. The current and next issues of this journal are devoted to a small sub-set of this initiative and address biocomputation and modelling in physiology, illustrating the breadth and depth of experimental data-based model development in biological research from sub-cellular events to whole organ simulations.

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Ge, Hui Lin, Shu Shen Liu, Bing Xia Su, and Zhi Xu. "Mathematical Derivation of Concentration Addition, Independent Action and Effect Summation Models." Applied Mechanics and Materials 361-363 (August 2013): 1054–57. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.1054.

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The assessment of the combined effects of substances is usually based on concentration addition (CA), independent action (IA) or effect summation (ES) models. Both concepts are founded on different modes of actions of substances, but the knowledge about their relationship is rare. In this paper, we derived a series of inequalities for CA, IA and ES models, and proposed two novel models i.e., ES with the exponent e (ESE) model and ES with the power of the number of components n (ESN) model to evaluate the mixture effect. Our results may have certain significance in mixture risk assessment practices.

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Victorov, Alexey, and Veronika Kapralova. "RISK ASSESSMENT BASED ON MATHEMATICAL MORPHOLOGY OF LANDSCAPE MODELS (CASE STUDY OF THERMOKARST PLAINS)." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 6, no. 2 (June 1, 2013): 63–71. http://dx.doi.org/10.24057/2071-9388-2013-6-2-63-71.

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Victorov, Alexey, and Veronika Kapralova. "RISK ASSESSMENT BASED ON MATHEMATICAL MORPHOLOGY OF LANDSCAPE MODELS (CASE STUDY OF THERMOKARST PLAINS)." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 6, no. 2 (June 14, 2013): 63–71. http://dx.doi.org/10.15356/2071-9388_02v06_2013_06.

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BROWN, MARTYN H., KENNETH W. DAVIES, CHRISTELLE M. P. BILLON, CAROL ADAIR, and PETER J. McCLURE. "Quantitative Microbiological Risk Assessment: Principles Applied to Determining the Comparative Risk of Salmonellosis from Chicken Products." Journal of Food Protection 61, no. 11 (November 1, 1998): 1446–53. http://dx.doi.org/10.4315/0362-028x-61.11.1446.

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Ensuring microbiological safety requires identification of realistic hazards and the means of controlling them. The risk assessment framework proposed by Codex Alimentarius allows the impact of raw materials and processes to be appreciated, and the output can be used for risk management and communication. Mathematical models allow numerical information to be processed by a Computer and interpreted to give quantitative or comparative risk assessments. In this example, models have been put together according to the Codex Alimentarius principles, providing a quantitative risk assessment (QRA) of salmonellosis from frozen poultry products. This model-based QRA takes into account three types of information: occurrence and distribution of the agent, sensitivity of populations to infection (e.g., normal or susceptible), and the effect of cooking (in the factory or home) on concentration of the agent and hence risks of infection after product consumption. It only demonstrates the impact of a single-process step (heating) and the effect of changes in population sensitivity, raw material quality, and cooking regime on the final risk. The effects of growth and recontamination are not considered. To aid risk communication, the models have been visualized by means of displays and slider Controls on a Computer screen because effective communication is essential to encourage manufacturers and their product designers to assess the effect of changes in processing or materials on risk.

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Mitrovic, S., and V. P. Suits. "A methodology for applying modern information technologies in the economic analysis of organization's risks." Economic Analysis: Theory and Practice 19, no. 2 (February 28, 2020): 268–85. http://dx.doi.org/10.24891/ea.19.2.268.

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Subject. The article examines theoretical, methodological and practical aspects of applying modern methods and information technologies to improve the economic risk analysis of organizations. Special attention is focused on internal audit and control, in particular, on the self-assessment procedure, being an innovative tool for monitoring and assessing risks to identify and minimize them within the corporate governance system. Objectives. The purpose is to study possibilities for improving the methodology for economic risk analysis of organizations through modern information technologies, innovative tools, and mathematical models. Methods. The study rests on methods of quantitative, qualitative analysis, popular scientific and empirical research in the field of economic sciences, including comparative, structural and functional methods of economic analysis, the desk study, methods of expert assessments, situation analysis, statistical assessment, testing parameters, etc. Results. The offered methodology is aimed at risk assessment and risk analysis of organizations. The results of the study contribute to the implementation of new tools of internal control, audit and analysis, in particular, the self-assessment procedure and the coefficient of reliability of results, which have a positive impact on efficient risk management and the enhancement of investment appeal of organizations. Conclusions. The automation of the risk analysis process and selected procedures and techniques should create conditions for a logical transition to more accurate (as compared to currently prevailing in practice) quantitative measurement methods, and for building the mathematical models that make it possible to predict and obtain results based on empirical data rather than on expert estimates.

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Lovell, David P., and Gail Thomas. "Quantitative risk assessment and the limitations of the linearized multistage model." Human & Experimental Toxicology 15, no. 2 (February 1996): 87–104. http://dx.doi.org/10.1177/096032719601500201.

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1 Quantifying carcinogenic risk is an important objec tive for assisting in the assessment and management of risks from chemical exposure. The most widely used ofthe many mathematical models proposed for extrapolation of carcinogenicity data from animal studies to low dose human exposures is the linearized multistage (LMS) model. This has, in effect, become the default approach for much of Quantitative Risk Assessment (QRA). The practical properties of this model have been investigated. 2 Analysis of simulated data using the LMS model showed (i) that the Maximum Likelihood Estimate (MLE) of the low dose slope, q1, was unstable and extremely sensitive to small changes in the data; (ii) the 95% Upper Confidence Limit (UCL) estimate, q1*, preferred by the US Environmental Protection Agency (EPA) was insensitive with only small changes in values being obtained for large changes in the data; (iii) data sets where there was no statistical significance could give risk estimates similar to those obtained from data sets with clear dose-related effects; (iv) the size of the values of the Virtually Safe Dose (VSD) obtained did not necessarily relate to the biological interpretation of the data sets; (v) the value of q1* obtained was closely related to the top dose used in the study. 3 Limitations of the LMS model were illustrated by examples of its use in assessing the carcinogenicity of 2, 3, 7, 8-TCDD leading to the conclusion that the existing models are not suitable for routine use in the estimation of the risk from chemical carcinogens. The use of the LMS model has been justified in part by its original derivation from a mathematical model based upon a multistage model of carcinogenesis. However, estimates of the parameters of the model used to provide estimates of low dose risk to humans have no direct relationship to specific biological event in carcinogenesis. Further developments in mathematical models and increased understanding of the biological events underlying carcinogenesis will lead to more biologically plausible QRA methods which would then justify serious consideration of QRA by regulatory authorities throughout the world.

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Kruzhilko, O., N. Volodchenkova, V. Maystrenko, B. Bolibrukh, V. Kalinchyk, A. Zakora, A. Feshchenko, and S. Yeremenko. "Mathematical modelling of professional risk at Ukrainian metallurgical industry enterprises." Journal of Achievements in Materials and Manufacturing Engineering 1, no. 108 (September 1, 2021): 35–41. http://dx.doi.org/10.5604/01.3001.0015.4797.

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Purpose: To develop a more advanced methodology, the application of which will provide an informational and computational and analytical basis for planning and implementing effective preventive measures aimed at minimizing occupational risks with limited resources, as well as in the absence of organizational and technical capabilities to create absolutely safe working conditions Design/methodology/approach: For the study, statistical data were used that obtained from enterprises of the metallurgical industry of Ukraine. Research methods: analysis and generalization of known scientific results, methods of statistical analysis, mathematical modelling, expert assessments and decision theory. Findings: The results of experimental studies have confirmed the possibility of an objective assessment of various options for the OSH management strategy, which allows justifying the allocation of funds for OSH in the required amounts. It is shown that professional risk management strategies are characterized by different efficiency in the use of available financial resources, and the most effective strategy is one that allows you to minimize the level of risk (in comparison with other strategies) with the same amount of funding. Research limitations/implications: The study focuses on enterprises of the metallurgical industry in Ukraine. Practical implications: The application of the developed mathematical models demonstrates the effectiveness of financing certain preventive and protective measures, and stimulates the head to ensure industrial safety. Originality/value: The developed mathematical models allow justifying the allocation of funds for OSH in the required amounts.

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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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Singh, Amrik, and K. R. Ramkumar. "Risk assessment for health insurance using equation modeling and machine learning." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (July 26, 2021): 201–25. http://dx.doi.org/10.3233/kes-210065.

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Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

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Tymchuk, Oleg, Maryna Iepik, and Artyom Sivyakov. "Information Security Risk Assessment Model Based on Computing with Words." MENDEL 23, no. 1 (June 1, 2017): 119–24. http://dx.doi.org/10.13164/mendel.2017.1.119.

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The basis for company IT infrastructure security is information security risks assessment of IT services. The increased complexity, connectivity and rapid changes occurring in IT services make it impossible to apply traditional models of quantitative/qualitative risk assessment. Existing quantitative assessment models are time-consuming, at the same time, qualitative assessment models do not take into account the subjective expert assessments and the uncertainty of risk factors. This paper presents the new information security risk assessment model for IT services based on computing with words. The model methodology is based on OWASP risk rating methodology for web applications. To evaluate risk factors, it is proposed to use dictionary consisting of 16/32 granular terms (words). Problems of uncertainty in perceptual assessments of risk factors are taken into account using methods of the theory of discrete interval type-2 fuzzy sets and systems.

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Giordano, Marie, Rebecca Zale, Betsy Ruffle, Elizabeth Hawkins, and Paul Anderson. "Review of mathematical models for health risk assessment: V. chemical concentrations in the food chain." Environmental Software 9, no. 2 (January 1994): 115–31. http://dx.doi.org/10.1016/0266-9838(94)90004-3.

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Jin, Peng Kang, Xing Wang Ning, Xin Jin, and Xian Bao Wang. "Health Risk Assessment of Wastewater with Carbinol in Gas Field." Applied Mechanics and Materials 178-181 (May 2012): 424–28. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.424.

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The carbinol in wastewater from inhalation and direct contact present more risk to human health, as well as cause hazard to ecological environment through waste water emission directly and soil infiltration. In this paper, health risk assessment of wastewater with carbinol in gas field is performed using mathematical models of non-carcinogenic health risk assessment. The results show that the non-carcinogenic health risks caused by wastewater with carbinol are in the range of 6.33×10-5 to 9.20×10-5, which are much higher than the acceptable risk level 7.00×10-5. Through the calculation of the threshold of the carbinol concentration in the wastewater, the carbinol concentration in the wastewater is less than 24500mg/L, not on human body risks. However, the carbinol concentration in the wastewater is much than 24500mg/L, it is a health risk to human body, which provides the scientific basis for the prevention and mitigation of wastewater with carbinol health risks.

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Mikhalevich, V. S., P. S. Knopov, and A. N. Golodnikov. "Mathematical models and methods of riks assessment in ecologically hazardous industries." Cybernetics and Systems Analysis 30, no. 2 (March 1994): 259–73. http://dx.doi.org/10.1007/bf02366429.

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Fu, Hui, Yahong Zhang, Rama Subbareddy, and Thanjai Vadivel. "Mathematical modeling on workplace violence hazard assessment and security analysis using Optimized Grey Dynamic System Theory." Work 68, no. 3 (March 26, 2021): 835–43. http://dx.doi.org/10.3233/wor-203417.

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BACKGROUND: Employers must provide their workers with a safe working environment. Violence at the workplace is considered to pose risks for mental health. However, it is rarely investigated whether or not violence at the workplace in a setting can further increase the risk of mental disorders among employees. Risk assessment of workplace violence is still a major challenge for law enforcement, mental health, and other professionals. These critical and specific evaluations need an innovative approach. OBJECTIVES: In this paper, the Optimized Grey Dynamic System Theory (OGDST) is used to analyze work-related incidents and hazard assessment. The forecasting model is built using annual data sets of work-related incidents. RESULTS: Research shows that aggressive psychological behavior often precedes the physical abuse of the workplace, whereas employers often ignore signs of warning even when identified by employees. Effectiveness tests demonstrate the efficiency of these suggested models. CONCLUSION: The results convey information supporting the conceptualization and assessment of models of workplace violence as a phenomenon arising from negative physical and psychological experiences of individuals at the workplace.

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Wang, Shun Qing, and Hai Yan Chen. "Research on Information Security Risk Assessment Based on Discrete Dynamic Bayesian Network." Applied Mechanics and Materials 608-609 (October 2014): 295–99. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.295.

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Being important in the field of information security, it is essential to study the major process and the mathematical models of information security risk assessment.This paper proposed amodel based on the discrete dynamic Bayesian network and further studied the major process of the assessment.The experiment which works on the implementing platform shows the results are reasonable and feasible. The model proves that it iseffective to calculate the probability of the risk.

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COSER, ALEXANDRU, MONICA MIHAELA MAER-MATEI, and CRISAN ALBU. "Predictive Models for Loan Default Risk Assessment." ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH 53, no. 2/2019 (June 16, 2019): 149–65. http://dx.doi.org/10.24818/18423264/53.2.19.09.

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Kazaryan, R., N. Galaeva, R. Avetisyan, and Sh Aliev. "Building lifecycle management based on 4D modelling as the main workspace for building risk assessment." E3S Web of Conferences 224 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202022402023.

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The use of information technology in the management of construction projects has become a decisive factor for the successful completion of projects, taking into account time, quality and financial costs. There are challenges in visualizing the planning process and integrating information between stakeholders. The paper considers some aspects of the development trend of information technology in construction. The element base, including the risks arising during the design and construction, associated with the direct use of computer and mathematical models of the object in the design is considered. The generated information models will provide basic information for the participants in the design process, which ultimately will be the basis of the element base of an effective tool for ensuring project life cycle management. The following methods were used: system analysis, logical-mathematical modelling, systems theory, economic-visual modelling, research methods of operations, economic and mathematical methods. A basic block diagram of 4D modelling is presented to minimize the occurrence of risks during design. The model allows considering the possibility of assessing the duration of the project, the level of labour productivity, as well as visualizing the construction process. The presented analysis indicates the importance of using 4D modelling in relation to the life cycle of the object in order to prevent the possibility of construction risks.

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Atoyev, Konstantin, and Pavel Knopov. "Assessment of Environmental, Social, Governance and Technogenic Components of Investment Risks." Cybernetics and Computer Technologies, no. 3 (November 29, 2022): 37–45. http://dx.doi.org/10.34229/2707-451x.22.3.4.

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Introduction. To assess the investment attractiveness (IA) and development opportunities for investment objects (IO), non-financial factors characterizing the environmental, social, governance and technogenic (ESGT) features of objects of possible financing have been increasingly used recently. The purpose of this data analysis is to establish how the ESGT-parameters of IO may reflect their financial health and performance prospects in a rapidly changing world. Having built an ESGT risk profile with the help of mathematical models, the IA of the object of study and the strategy of practical measures to increase it are determined. When modeling these processes, one should consider the growing uncertainty of the modern world due to the emergence of new risks; a large number of systemic links between the structures of the modern technosphere; the power-law nature of the distribution of the probability density of catastrophe damage, which decreases more slowly than the Gaussian dependence. In addition, the efficiency of complex production systems is largely determined by the balance of their individual links. Therefore, to assess investment risks, new methods are required to formalize the dependence of IA on ESGT-factors for the integrated management of the level of credit, market, insurance and operational risks under conditions of uncertainty. The purpose of the article is to develop mathematical methods for quantifying IA and determining real costs to improve the management, social and technological structure of IO, and minimize environmental pollution. Results. A mathematical model has been developed for assessing the environmental, social, managerial and technogenic leaving risks of investment, which makes it possible to determine the optimal strategies for increasing the IA of a possible IO. For a comprehensive risk assessment, methods of the theory of singularities of smooth reflections (TOGO) and the method of analysis of hierarchies (MAH) are used. The following algorithm for estimating IA is proposed: 1) determining the indices of the ESGT-components of risk; 2) calculation of bifurcation index values; 3) determination of the weakest links, which are associated with a decrease in IA; 4) identification of priority measures to prevent the reduction of IA or restore it to a predetermined level and minimize the negative impacts of extreme events and ensure sustainable development. Conclusions. The obtained results show that mathematical models based on the use of TOGO and MAH methods are an important tool for estimating IA under conditions of uncertainty. They allow: 1) to calculate the degree of approximation of the parameters characterizing the functioning of the object to their critical values ??when the IA changes; 2) to determine effective controls to minimize the risk of losing IA or minimize the time and losses for returning IA; 3) to consider the uncertainty factor associated with the features of the decision-making process. The development of this work is aimed at creating an information system for assessing IA for the integrated management of the level of credit, market, insurance and operational risks in the face of uncertainty and determining effective scenarios for minimizing investment risks. Keywords: mathematical modeling, system analysis, investment risks.

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Cheng, Jianwei, Xixi Zhang, and Apurna Ghosh. "Explosion risk assessment model for underground mine atmosphere." Journal of Fire Sciences 35, no. 1 (December 14, 2016): 21–35. http://dx.doi.org/10.1177/0734904116676495.

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In the coal mining industry, explosions or mine fires present the most hazardous safety threats for coal miners or mine rescue members. Hence, the determination of the mine atmosphere explosibility and its evolution are critical for the success of mine rescues or controlling the severity of a mine accident. However, although there are numbers of methods which can be used to identify the explosibility, none of them could well indicate the change to the explosion risk time evolution. The reason is that the underground sealed atmospheric compositions are so complicated and their dynamical changes are also affected by various influence factors. There is no one method that could well handle all such considerations. Therefore, accurately knowing the mine atmospheric status is still a complicated problem for mining engineers. Method of analyzing the explosion safety margin for an underground sealed atmosphere is urgently desired. This article is going to propose a series of theoretical explosion risk assessment models to fully analyze the evolution of explosion risk in an underground mine atmosphere. Models are based on characteristics of the Coward explosibility diagram with combining mathematical analyzing approaches to address following problems: (1) for an “not-explosive” atmosphere, judging the evolution of explosion risk and estimating the change-of-state time span from “not-explosive” to “explosive” and (2) for an “explosive” atmosphere, estimating the “critical” time span of moving out of explosive zone and stating the best risk mitigation strategy. Such research efforts could not only help mine operators understand the explosibility risk of a sealed mine atmosphere but also provide a useful tool to wisely control explosive atmosphere away from any dangers. In order to demonstrate research findings, case studies for derived models are shown and are also used to instruct readers how to apply them. The results provide useful information for effectively controlling an explosive underground sealed atmosphere.

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Del Ponte, Emerson M., Cláudia V. Godoy, Marcelo G. Canteri, Erlei M. Reis, and X. B. Yang. "Models and applications for risk assessment and prediction of Asian soybean rust epidemics." Fitopatologia Brasileira 31, no. 6 (December 2006): 533–44. http://dx.doi.org/10.1590/s0100-41582006000600001.

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Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

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Levin, S. F. "Inadequacy for mathematical models of measurement objects and risk calculations according to ISO/IEC 17025-2019." Izmeritel`naya Tekhnika, no. 7 (2020): 13–21. http://dx.doi.org/10.32446/0368-1025it.2020-7-13-21.

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The problem of inadequacy of mathematical models of measurement objects is considered in connection with the problem of “definitional uncertainty of measurement” and the need for risk management in accordance with GOST ISO/IEC 17025-2019 “General requirements for the competence of testing and calibration laboratories”. The prehistory of the problem is described: from the formation of moment and compositional approaches to estimating accuracy to the introduction of a special term “inadequacy error for mathematical model of measurement object”. The negative impact of hopelessness of conceptual and terminological transformations in metrology and critical contradiction of the applicability estimates for “statistical methods” of GOST R ISO/IEC 31010-2011 “Risk management – Risk assessment techniques” and “Guide to the expression of uncertainty in measurement” is noted. It is shown that taking into account the inadequacy of probabilistic models in risk calculations is a necessary condition for results reliability.

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Villars, Monique, Mark Gerath, and Don Galya. "Review of mathematical models for health risk assessment: III. Chemical concentrations in surface water, groundwater and soil." Environmental Software 8, no. 3 (January 1993): 135–55. http://dx.doi.org/10.1016/0266-9838(93)90011-6.

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Ihekwaba, Adaoha E. C., Ivan Mura, Pradeep K. Malakar, John Walshaw, Michael W. Peck, and G. C. Barker. "New Elements To Consider When Modeling the Hazards Associated with Botulinum Neurotoxin in Food." Journal of Bacteriology 198, no. 2 (September 8, 2015): 204–11. http://dx.doi.org/10.1128/jb.00630-15.

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Botulinum neurotoxins (BoNTs) produced by the anaerobic bacteriumClostridium botulinumare the most potent biological substances known to mankind. BoNTs are the agents responsible for botulism, a rare condition affecting the neuromuscular junction and causing a spectrum of diseases ranging from mild cranial nerve palsies to acute respiratory failure and death. BoNTs are a potential biowarfare threat and a public health hazard, since outbreaks of foodborne botulism are caused by the ingestion of preformed BoNTs in food. Currently, mathematical models relating to the hazards associated withC. botulinum, which are largely empirical, make major contributions to botulinum risk assessment. Evaluated using statistical techniques, these models simulate the response of the bacterium to environmental conditions. Though empirical models have been successfully incorporated into risk assessments to support food safety decision making, this process includes significant uncertainties so that relevant decision making is frequently conservative and inflexible. Progression involves encoding into the models cellular processes at a molecular level, especially the details of the genetic and molecular machinery. This addition drives the connection between biological mechanisms and botulism risk assessment and hazard management strategies. This review brings together elements currently described in the literature that will be useful in building quantitative models ofC. botulinumneurotoxin production. Subsequently, it outlines how the established form of modeling could be extended to include these new elements. Ultimately, this can offer further contributions to risk assessments to support food safety decision making.

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