Статті в журналах: "Risk assessment mathematical models; fermentation mathematical models"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

Petruzzi, Leonardo, Daniela Campaniello, Maria Rosaria Corbo, Barbara Speranza, Clelia Altieri, Milena Sinigaglia, and Antonio Bevilacqua. "Wine Microbiology and Predictive Microbiology: A Short Overview on Application, and Perspectives." Microorganisms 10, no. 2 (February 11, 2022): 421. http://dx.doi.org/10.3390/microorganisms10020421.

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Predictive microbiology (PM) is an essential element in food microbiology; its aims are the determination of the responses of a given microorganism combining mathematical models with experimental data under certain environmental conditions, and the simulation a priori of the growth/inactivation of a population based on the known traits of a food matrix. Today, a great variety of models exist to describe the behaviour of several pathogenic and spoilage microorganisms in foods. In winemaking, many mathematical models have been used for monitoring yeast growth in alcoholic fermentation as well as to predict the risk of contamination of grapes and grape products by mycotoxin producing fungi over the last years, but the potentialities of PM in wine microbiology are underestimated. Thus, the goals of this review are to show some applications and perspectives in the following fields: (1) kinetics of alcoholic and malolactic fermentation; (2) models and approaches for yeasts and bacteria growth/inactivation; (3) toxin production and removal.

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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

Kuzin, P. I., A. P. Boyko, V. N. Oberderfer, E. I. Kuzina, and A. V. Bukharin. "IMPROVEMENT OF MATHEMATICAL MODELS OF FIBER-OPTIC LINEAR PATH." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 212 (February 2022): 26–31. http://dx.doi.org/10.14489/vkit.2022.02.pp.026-031.

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The article describes the main problem of making a decision about the possibility and effectiveness of the use of fiber-optic communication facilities on communication lines with random parameters. As an example, fiber-optic communication lines of a trusted operator or an adversary are considered. The main reason is indicated, which is the lack of methods that allow predicting the possibility and expediency of using specific fiber-optic communication facilities in given environmental conditions. The necessity of developing and applying a probabilistic mathematical model of a fiber-optic linear path is explained as the basis of the methodology for evaluating the effectiveness of the use of fiber-optic communication means. An analysis of existing mathematical models is given and their shortcomings and imperfections are indicated. A probabilistic mathematical model of the fiber-optic linear path has been developed, which allows calculating the numerical characteristics of the quality indicators of the fiber-optic linear path: Optical Signal-to-Noise-Ratio (OSNR) and Bit Error Ratio (BER), based on the known technical data of the communication means used. The mathematical expectation of a random variable is used as a numerical characteristic, as an idealized average of OSNR and BER. Based on the mathematical expectation, other numerical characteristics are calculated, which are also used when making a decision as a risk measure, and allow us to judge its magnitude and significance. The developed probabilistic mathematical model is proposed as the basis of the efficiency assessment methodology for evaluating the effectiveness of work on communications.

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28

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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29

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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30

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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31

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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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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33

Hubal, E. A., P. S. Fedkiw, and J. S. Kimbell. "Mass-transport models to predict toxicity of inhaled gases in the upper respiratory tract." Journal of Applied Physiology 80, no. 4 (April 1, 1996): 1415–27. http://dx.doi.org/10.1152/jappl.1996.80.4.1415.

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Mass transport (the movement of a chemical species) plays an important role in determining toxic responses of the upper respiratory tract (URT) to inhaled chemicals. Mathematical dosimetry models incorporate physical characteristics of mass transport and are used to predict quantitative uptake (absorption rate) and distribution of inhaled gases and vapors in the respiratory tract. Because knowledge of dose is an essential component of quantitative risk assessment, dosimetry modeling plays an important role in extrapolation of animal study results to humans. A survey of existing mathematical dosimetry models for the URT is presented, limitations of current models are discussed, and adaptations of existing models to produce a generally applicable model are suggested. Reviewed URT dosimetry models are categorized as early, lumped-parameter, and distributed- parameter models. Specific examples of other relevant modeling work are also presented.

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34

Zentou, Hamid, Zurina Zainal Abidin, Robiah Yunus, Dayang Awang Biak, Mustapha Zouanti, and Abdelkader Hassani. "Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment." Biomolecules 9, no. 8 (July 26, 2019): 308. http://dx.doi.org/10.3390/biom9080308.

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Modelling has recently become a key tool to promote the bioethanol industry and to optimise the fermentation process to be easily integrated into the industrial sector. In this context, this study aims at investigating the applicability of two mathematical models (Andrews and Monod) for molasses fermentation. The kinetics parameters for Monod and Andrews were estimated from experimental data using Matlab and OriginLab software. The models were simulated and compared with another set of experimental data that was not used for parameters’ estimation. The results of modelling showed that μmax = 0.179 1/h and Ks = 11.37 g.L−1 for the Monod model, whereas μmax = 0.508 1/h, Ks = 47.53 g.L−1 and Ki = 181.01 g.L−1 for the Andrews model, which are too close to the values reported in previous studies. The validation of both models showed that the Monod model is more suitable for batch fermentation modelling at a low concentration, where the highest R squared was observed at S0 = 75 g.L−1 with an R squared equal to 0.99956, 0.99954, and 0.99859 for the biomass, substrate, and product concentrations, respectively. In contrast, the Andrews model was more accurate at a high initial substrate concentration and the model data showed a good agreement compared to the experimental data of batch fermentation at S0 = 225 g.L−1, which was reflected in a high R squared with values 0.99795, 0.99903, and 0.99962 for the biomass, substrate, and product concentrations respectively.

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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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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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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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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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Mallapur, Shashidhar, and Roland Platz. "Quantification of Uncertainty in the Mathematical Modelling of a Multivariable Suspension Strut Using Bayesian Interval Hypothesis-Based Approach." Applied Mechanics and Materials 885 (November 2018): 3–17. http://dx.doi.org/10.4028/www.scientific.net/amm.885.3.

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Mathematical models of a suspension strut such as an aircraft landing gear are utilized by engineers in order to predict its dynamic response under different boundary conditions. The prediction of the dynamic response, for example the external loads, the stress and the strength as well as the maximum compression in the spring-damper component aids engineers in early decision making to ensure its structural reliability under various operational conditions. However, the prediction of the dynamic response is influenced by model uncertainty. As far as the model uncertainty is concerned, the prediction of the dynamic behavior via different mathematical models depends upon various factors such as the model's complexity in terms of the degrees of freedom, material and geometrical assumptions, their boundary conditions and the governing functional relations between the model input and output parameters. The latter can be linear or nonlinear, axiomatic or empiric, time variant or time-invariant. Hence, the uncertainty that arises in the prediction of the dynamic response of the resulting different mathematical models needs to be quantified with suitable validation metrics, especially when the system is under structural risk and failure assessment. In this contribution, the authors utilize the Bayesian interval hypothesis-based method to quantify the uncertainty in the mathematical models of the suspension strut.

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Markova, Olga Mikhailovna. "Application of mathematical modeling in creating investment portfolio of commercial bank." Vestnik of Astrakhan State Technical University. Series: Economics 2019, no. 4 (December 16, 2019): 112–19. http://dx.doi.org/10.24143/2073-5537-2019-4-112-119.

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The article touches upon the most urgent problem of creating the stock portfolio of a commercial bank, where studying the strategies and tools of the bank’s investment activity and using mathematical models for its assessment help to identify the relationship between profitability and the risk of investing in securities. As a result of applied analysis and modeling of the portfolio structure, the optimal portfolio option is selected, which corresponds to a given level of risk and profitability, as well as to the investment strategy chosen by the bank. There has been analyzed the portfolio structure with specified characteristics of risk and profitability, according to the statistics of previous years. The types of documents have been systematized according to the compliance with the strategy of managing the portfolio of profitability growth, liquidity and risk minimization. Using the models of Markowitz, Tobin and other researchers of probabilistic portfolio assessment through covariance indicators and a correlation coefficient, there have been found the values of return on assets that can change in one direction or have a multidirectional nature, and allow to calculate dependence between the values of return on securities in the portfolio. There have been considered the following models: a portfolio model based on calculating the level of stock returns of LUKOIL JSC, Novatek JSC, Yandex; a portfolio model that includes risk-free assets with the highest level of reliability (government short-term bonds, federal loan bonds); capital asset pricing model which describes dependency between the risk and the required profitability. Based on these calculations there has been inferred the possibility of developing specific areas of the banking business in the field of securities transactions, including: saving funds (providing protection against inflation); capital growth (focus on the securities that have the potential for growth in market value); profitability (purchasing securities in order to obtain dividends on shares and interest on debt securities); liquidity (investments into financial tools that can be sold at any time at favorable prices); risk minimization. The results of the conducted analysis of correlation and regression of the securities portfolio have revealed the most preferred types of securities for growing profitability that are in the bank's portfolio: shares of the Russian oil company LUKOIL, the Russian gas company Novatek, Yandex cIA, as well as the federal loan bonds (based on the terms of calculating history dynamics since May 1, 2018 up to May 1, 2019)

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Niazkar, Majid, Gökçen Eryılmaz Türkkan, Hamid Reza Niazkar, and Yusuf Alptekin Türkkan. "Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey." Computational and Mathematical Methods in Medicine 2020 (November 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/7056285.

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COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham’s prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.

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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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Frolova, Nina, Valery Larionov, Jean Bonnin, Sergey Suchshev, Alexander Ugarov, and Nataliya Malaeva. "Impact database application for natural and technological risk management." Natural Hazards and Earth System Sciences 20, no. 1 (January 13, 2020): 95–106. http://dx.doi.org/10.5194/nhess-20-95-2020.

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Abstract. Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each event, parameters of the simulation models, the range of its application, and residual errors. Each new description included in the database could increase the reliability of the results obtained with application of simulation models. The calibration of mathematical models is the first step to self-learning of automated systems. The article describes the events' database structure and examples of calibrated computer models as applied to the impact of expected emergencies and risk indicator assessment. Examples of database statistics usage in order to rank the subjects of the Russian Federation by the frequency of emergencies of different character as well as risk indicators are given.

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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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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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F. Hasaballah et al., Amany. "Health risk assessment of heavy metals contamination in sediments of the River Nile, Damietta Branch using Mathematical Models." Egyptian Journal of Aquatic Biology and Fisheries 25, no. 2 (March 1, 2021): 947–71. http://dx.doi.org/10.21608/ejabf.2021.170342.

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Dhurve, Priyanka, Ayon Tarafdar, and Vinkel Kumar Arora. "Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics." Journal of Food Quality 2021 (November 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/7739732.

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Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.

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Wiratsudakul, Anuwat, Parinya Suparit, and Charin Modchang. "Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches." PeerJ 6 (March 22, 2018): e4526. http://dx.doi.org/10.7717/peerj.4526.

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BackgroundThe Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.Survey MethodologyIn this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms “dynamics,” “mathematical model,” “modeling,” and “vector-borne” together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were “compartmental,” “spatial,” “metapopulation,” “network,” “individual-based,” “agent-based” AND “Zika.” All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.ResultsWe found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.DiscussionMathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.

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van Wees, Daphne A., Chantal den Daas, Mirjam E. E. Kretzschmar, and Janneke C. M. Heijne. "Double trouble: modelling the impact of low risk perception and high-risk sexual behaviour on chlamydia transmission." Journal of The Royal Society Interface 15, no. 141 (April 2018): 20170847. http://dx.doi.org/10.1098/rsif.2017.0847.

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Risk perception plays an important role in testing behaviour for sexually transmitted infections, but is rarely included in mathematical models exploring the impact of testing. We explored the impact of incorporating sexual behaviour (SB), risk perception (RP) and differential testing uptake in SB–RP groups on prevalence, using chlamydia as an example. We developed a pair model with a susceptible–infected–susceptible structure representing heterosexuals aged 16–26 years. The effect of testing on chlamydia prevalence was compared between a model with only SB (SB model) and a model with SB and RP (SB–RP model). In the SB–RP model, a scenario without differential testing uptake in SB–RP groups was compared to scenarios with differential testing uptake in SB–RP groups. Introducing testing into the SB–RP model resulted in a slightly smaller reduction in chlamydia prevalence (−38.0%) as compared to the SB model (−40.4%). In the SB–RP model, the scenario without differential testing uptake in SB–RP groups overestimated the reduction in chlamydia prevalence (with 4.8%), especially in the group with high SB and low RP (19.8%). We conclude that mathematical models incorporating RP and differential testing uptake in SB–RP groups improve the impact assessment of testing and treatment on chlamydia prevalence.

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