Probability Estimates Research Articles

БАЗА ЗНАНЬ ІНТЕЛЕКТУАЛЬНОЇ ІНФОРМАЦІЙНОЇ СИСТЕМИ ПРОГНОЗУВАННЯ ФАЗОВОЇ СТАБІЛЬНОСТІ ТВЕРДИХ РОЗЧИНІВ

This research work is devoted to the development of a knowledge base for solving the current problem of forecasting the phase stability of solid solutions. Knowledge bases mean a set of facts and inference rules that allow logical conclusion and purposeful processing of information. The most important property of information stored in knowledge bases is the reliability of specific and generalized information in the database and the relevance of the original information obtained using the rules of inference embedded in the knowledge base. The best knowledge bases include the most relevant, reliable and fresh information, have perfect information search systems, a carefully thought-out structure and format of knowledge. The expert system embodies the methodology of adapting the algorithm of successful solutions from one sphere of scientific and practical activity to another. With the spread of computer technologies, it is an identical intelligent computer program that contains the knowledge and analytical abilities of one or more experts in some field of application and is able to draw logical conclusions based on this knowledge, thereby providing a solution to specific tasks. An intelligent information system (IIS) is one of the types of automated information systems, which is a complex of software, linguistic and logical-mathematical tools for the implementation of the main task, which usually consists of data interpretation and forecasting. Data interpretation is one of the traditional tasks for expert systems. Interpretation means the process of determining the content of data, the results of which must be agreed and correct. A multivariate analysis of the data is usually assumed, and the findings from this model form the basis for probabilistic estimates. Forecasting allows you to predict the consequences of some events or phenomena based on the analysis of available data. A parametric dynamic model is usually used in the forecasting system, in which parameter values are set for a given situation. As a result of the development, a knowledge base model was built for predicting the phase stability of solid solutions using a set of production rules, predicates, functions and operators.

A normative model for Bayesian combination of subjective probability estimates

A normative model for Bayesian combination of subjective probability estimates

Probability estimate of the strength reliability of toothed wheel gear

An improved calculation method for the strength reliability of surface-hardened cylindrical gears, based on determining the probability of safe operation of the gear is presented in the paper. The calculation of probability of fault-free operation according to the criterion of depth contact endurance is implemented and the tools of non-parametric statistics are applied. When calculating contact stresses, the twist angle caused by deformations of the shafts gear is taken into account. The reliability of the toothed wheel gear was evaluated using the developed technique.

Comparison Between Bayesian and Frequentist Tail Probability Estimates

Tail probability plays an important part in the extreme value theory. Sometimes the conclusions from two approaches for estimating the tail probability of extreme events, the Bayesian and the frequentist methods, can differ a lot. In 1999, a rainfall that caused more than 30,000 deaths in Venezuela was not captured by the simple frequentist extreme value techniques. However, this catastrophic rainfall was not surprising if the Bayesian inference was used to allow for parameter uncertainty and the full available data was exploited [4]. In this paper, we investigate the reasons that the Bayesian estimator of the tail probability is always higher than the frequentist estimator. Sufficient conditions for this phenomenon are established both by using Jensen’s Inequality and by looking at Taylor series approximations, both of which point to the convexity of the distribution function.

Tail probability estimates of continuous-time simulated annealing processes

<p style='text-indent:20px;'>We study the convergence rate of a continuous-time simulated annealing process <inline-formula><tex-math id="M1">\begin{document}$ (X_t; \, t \ge 0) $\end{document}</tex-math></inline-formula> for approximating the global optimum of a given function <inline-formula><tex-math id="M2">\begin{document}$ f $\end{document}</tex-math></inline-formula>. We prove that the tail probability <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{P}(f(X_t) > \min f +\delta) $\end{document}</tex-math></inline-formula> decays polynomial in time with an appropriately chosen cooling schedule of temperature, and provide an explicit convergence rate through a non-asymptotic bound. Our argument applies recent development of the Eyring-Kramers law on functional inequalities for the Gibbs measure at low temperatures.</p>

Estimating roadkill rates while accounting for carcass detection and persistence using open-population capture–recapture models

Context Accurately estimating wildlife roadkill is necessary to compare different roads, periods, and species, and to plan and assess mitigation effectiveness. We must account for the two main sources of errors associated with carcass sampling – carcass detection and persistence. Open-population models are used to estimate abundance, survival probabilities, and recruitment in living animal populations, accounting for imperfect detection, and they can be used in the context of animal fatalities. Aims The aim of this study was to explore an open-population approach to estimate comparable roadkill rates from carcass capture–recapture data, accounting for carcass detection and persistence. Methods We surveyed carcasses of the white-eared opossum (Didelphis albiventris) and black-and-white tegu lizard (Salvator merianae) on four road stretches using two sampling designs with different number of visits and sampling sessions. Carcasses were marked to be recaptured over visits within the same sampling session, resulting in a capture history for each carcass. Encounter history data were modelled using the superpopulation formulation of the open-population capture–recapture model under Bayesian inference for different datasets. A daily roadkill rate per kilometre was derived from the model entry probability estimate. Key results We estimated a daily roadkill rate with 1501 captures from 447 opossum carcasses and 511 captures from 218 tegu carcasses. For full data, mean carcass detection over the sessions ranged from 0.49 to 0.85 for the opossum and from 0.27 to 0.80 for the tegu, and mean carcass persistence ranged from 0.60 to 0.94 for the opossum and from 0.64 to 0.91 for the tegu. Scenarios with more occasions and captures increased precision of roadkill rates. Conclusions We were able to explicitly estimate roadkill rates using an open-population capture–recapture model under a Bayesian framework. It provides accurate roadkill numbers for a known time frame and road extension, accounting for imperfect detection and its associated uncertainty. Under scenarios of few carcasses, users should consider a higher number of occasions. Implications Not addressing carcass sampling errors or simplistically addressing them (e.g. only once during the study period) could mislead mitigation efforts. The approach used here can be used to estimate fatalities in other locations, such as windfarms and powerlines, for which repeated observations of marked carcasses are an option.

Class-Constrained t-SNE: Combining Data Features and Class Probabilities.

Data features and class probabilities are two main perspectives when, e.g., evaluating model results and identifying problematic items. Class probabilities represent the likelihood that each instance belongs to a particular class, which can be produced by probabilistic classifiers or even human labeling with uncertainty. Since both perspectives are multi-dimensional data, dimensionality reduction (DR) techniques are commonly used to extract informative characteristics from them. However, existing methods either focus solely on the data feature perspective or rely on class probability estimates to guide the DR process. In contrast to previous work where separate views are linked to conduct the analysis, we propose a novel approach, class-constrained t-SNE, that combines data features and class probabilities in the same DR result. Specifically, we combine them by balancing two corresponding components in a cost function to optimize the positions of data points and iconic representation of classes - class landmarks. Furthermore, an interactive user-adjustable parameter balances these two components so that users can focus on the weighted perspectives of interest and also empowers a smooth visual transition between varying perspectives to preserve the mental map. We illustrate its application potential in model evaluation and visual-interactive labeling. A comparative analysis is performed to evaluate the DR results.

Variation in Kidney Transplant Referral Across Chronic Kidney Disease Programs in Ontario, Canada.

Eligible patients with kidney failure should have equal access to kidney transplantation. Transplant referral is the first crucial step toward receiving a kidney transplant; however, studies suggest substantial variation in the rate of kidney transplant referral across regions. The province of Ontario, Canada, has a public, single-payer health care system with 27 regional chronic kidney disease (CKD) programs. The probability of being referred for kidney transplant may not be equal across CKD programs. To determine whether there is variability in kidney transplant referral rates across Ontario's CKD programs. Population-based cohort study using linked administrative health care databases from January 1, 2013, to November 1, 2016. Twenty-seven regional CKD programs in the province of Ontario, Canada. Patients approaching the need for dialysis (advanced CKD) and patients receiving maintenance dialysis (maximum follow-up: November 1, 2017). Kidney transplant referral. We calculated the 1-year unadjusted cumulative probability of kidney transplant referral for Ontario's 27 CKD programs using the complement of Kaplan-Meier estimator. We calculated standardized referral ratios (SRRs) for each CKD program, using expected referrals from a 2-staged Cox proportional hazards model, adjusting for patient characteristics in the first stage. Standardized referral ratios with a value less than 1 were below the provincial average (maximum possible follow-up of 4 years 10 months). In an additional analysis, we grouped CKD programs according to 5 geographic regions. Among 8641 patients with advanced CKD, the 1-year cumulative probability of kidney transplant referral ranged from 0.9% (95% confidence interval [CI]: 0.2%-3.7%) to 21.0% (95% CI: 17.5%-25.2%) across the 27 CKD programs. The adjusted SRR ranged from 0.2 (95% CI: 0.1-0.4) to 4.2 (95% CI: 2.1-7.5). Among 6852 patients receiving maintenance dialysis, the 1-year cumulative probability of transplant referral ranged from 6.4% (95% CI: 4.0%-10.2%) to 34.5% (95% CI: 29.5%-40.1%) across CKD programs. The adjusted SRR ranged from 0.2 (95% CI: 0.1-0.3) to 1.8 (95% CI: 1.6-2.1). When we grouped CKD programs according to geographic region, we found that patients residing in Northern regions had a substantially lower 1-year cumulative probability of transplant referral. Our cumulative probability estimates only captured referrals within the first year of advanced CKD or maintenance dialysis initiation. There is marked variability in the probability of kidney transplant referral across CKD programs operating in a publicly funded health care system.

Probability models for highly parallel image coding architecture

A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.

Almost sure local wellposedness and scattering for the energy-critical cubic nonlinear Schrödinger equation with supercritical data

We study the cubic defocusing nonlinear Schrödinger equation on R4 with supercritical initial data. For randomized initial data in Hs(R4), we prove almost sure local wellposedness for 17<s<1 and almost sure scattering for 57<s<1. The randomization is based on a unit-scale decomposition in frequency space, a decomposition in the angular variable, and – for the almost sure scattering result – an additional unit-scale decomposition in physical space. We employ new probabilistic estimates for the linear Schrödinger flow with randomized data, where we effectively combine the advantages of the different decompositions.

Adjusting conventional FRAX estimates of fracture probability according to the number of prior falls in the preceding year.

Prior falls increase subsequent fracture risk but are not currently directly included in the FRAX tool. The aim of this study was to quantify the effect of the number of prior falls on the 10-year probability of fracture determined with FRAX®. We studied 21,116 women and men age 40years or older (mean age 65.7 ± 10.1years) with fracture probability assessment (FRAX®), self-reported falls for the previous year, and subsequent fracture outcomes in a registry-based cohort. The risks of death, hip fracture, and non-hip major osteoporotic fracture (MOF-NH) were determined by Cox proportional hazards regression for fall number category versus the whole population (i.e., an average number of falls). Ten-year probabilities of hip fracture and major osteoporotic fracture (MOF) were determined according to the number of falls from the hazards of death and fracture incorporated into the FRAX model for the UK. The probability ratios (number of falls vs. average number of falls) provided adjustments to conventional FRAX estimates of fracture probability according to the number of falls. Compared with the average number of falls, the hazard ratios for hip fracture, MOF-NH and death were lower than unity in the absence of a fall history. Hazard ratios increased progressively with an increasing number of reported falls. The probability ratio rose progressively as the number of reported falls increased. Probability ratios decreased with age, an effect that was more marked the greater the number of prior falls. The probability ratios provide adjustments to conventional FRAX estimates of fracture probability according to the number of prior falls.

Cost-effectiveness of implementing a suicide prediction tool (OxMIS) in severe mental illness: Economic modeling study.

Cost-effectiveness analysis needs to be considered when introducing new tools and treatments to clinical services. The number of new assessment tools in mental health has rapidly expanded, including suicide risk assessment. Such suicide-based assessments, when linked to preventative interventions, are integral to high-quality mental health care for people with severe mental illness (SMI). We examined the cost implications of implementing Oxford Mental Illness and Suicide (OxMIS), an evidence-based, scalable suicide risk assessment tool that provides probabilistic estimates of suicide risk over 12 months for people with SMI in England. We developed a decision analytic model using secondary data to estimate the potential cost-effectiveness of incorporating OxMIS into clinical decision-making in secondary care as compared to usual care. Cost-effectiveness was measured in terms of costs per quality-adjusted life years (QALYs) gained. Uncertainty was addressed with deterministic and probabilistic sensitivity analysis. Conducting suicide risk assessment with OxMIS was potentially cheaper than clinical risk assessment alone by £250 (95% confidence interval, -786;31) to £599 (-1,321;-156) (in 2020-2021 prices) per person with SMI and associated with a small increase in quality of life (0.01 [-0.03;0.05] to 0.01 QALY, [-0.04;0.07]). The estimated incremental cost-effectiveness ratio of implementing OxMIS was cost saving. Using probabilistic sensitivity analysis, 99.96% of 10,000 simulations remained cost saving. Cost-effectiveness analysis can be conducted on risk prediction models. Implementing one such model that focuses on suicide risk in a high-risk population can lead to cost savings and improved health outcomes, especially if explicitly linked to preventative treatments.

Twin SVM for conditional probability estimation in binary and multiclass classification

In this paper, we estimate the conditional probability function by presenting a new twin SVM model (CPTWSVM) in binary and multiclass classification problems. The motivation of CPTWSVM is to implement the empirical risk minimization on training data, which is hard to realize in traditional twin SVMs. In each subproblem of CPTWSVM, it measures the empirical risk and outputs the corresponding probability estimate of each class, which eliminates the problems of inconsistent measurement in twin SVMs. Though an additional discriminant objective function is introduced, the optimization problem size of each subproblem is smaller than conditional probability SVM, and is solved by block decomposition algorithm efficiently. In addition, we extend CPTWSVM to multiclass classification by estimating the conditional probability of each class, and maintaining the above properties. Numerical experiments on benchmark and real application datasets demonstrate that CPTWSVM outputs the estimate of probability and the data projection well, resulting in better generalization ability than some leading TWSVMs communities, in terms of binary and multiclass classification.

791. ICU Provider Overestimation of Ventilator-Associated Pneumonia Diagnostic Probability: An Opportunity for Diagnostic Stewardship

Abstract Background Overdiagnosis of ventilator-associated pneumonia (VAP) is common in intensive care units (ICUs). Because VAP is a clinical diagnosis without an easily accessible confirmatory diagnostic test, understanding how ICU physicians practice diagnostic reasoning in suspected VAP is vital to informing diagnostic stewardship efforts. We aimed to assess the accuracy of ICU practitioner estimates of VAP diagnostic probability. Methods We conducted a survey of physicians at Michigan Medicine ICUs. Providers were presented with clinical vignettes (Figure 1) in which mechanically ventilated patients developed clinical signs suggestive of VAP and then had radiographic/microbiologic tests performed. Providers were asked to estimate the probability of VAP before and after each presented clinical sign or test result, which were then used to impute provider-estimated likelihood ratios (pLRs) for each clinical sign and diagnostic test. Provider-estimated probabilities and LRs were compared to evidence-based VAP probabilities and LRs (eLRs) derived from systematic reviews and meta-analyses. Survey Case Example. Results Of 102 survey respondents, 54% felt moderately to extremely confident in their ability to accurately diagnose VAP. Providers significantly overestimated the pre and post-test probability of VAP in all clinical scenarios (provider-estimated VAP diagnostic probabilities are visualized using density plots relative to evidence-based diagnostic probability in Figure 2). Median pLRs for nearly all VAP clinical signs and diagnostic tests were also significantly higher than eLRs (Figure 3), most notably for isolated purulent endotracheal secretions (pLR 1.54 vs eLR 0.76, p&amp;lt; 0.01), positive chest radiograph (pLR 6.0 vs eLR 3.55, p&amp;lt; 0.01), and positive bronchoalveolar lavage culture (pLR 5.7 vs eLR 1.37, p&amp;lt; 0.01). Density Plots of Provider-Estimated vs Evidence-Based Diagnostic Probability of Ventilator-Associated Pneumonia. Provider-Estimated vs Evidence-Based Diagnostic Likelihood Ratios for Clinical Signs and Test Results Related to Ventilator-Associated Pneumonia. Conclusion Physicians routinely overestimated the diagnostic probability of VAP and the positive LRs of possible VAP clinical signs, as well as VAP diagnostic radiographic and microbiologic tests. Educational initiatives addressing and calibrating ICU providers’ perceptions of VAP diagnostic probability warrant investigation as an antimicrobial stewardship tool. Disclosures Owen Albin, MD, Charles River Laboratory: Advisor/Consultant|Cipla Pharmaceuticals: Advisor/Consultant|Shionogi Inc: Advisor/Consultant.

223. Development and Analysis of a Novel DOOR Endpoint for Complicated Intra-abdominal Infections (cIAI) Using 10 Registrational Trials for Antibacterial Drugs

Abstract Background Desirability of outcome ranking (DOOR) uses an ordinal ranking system to evaluate global outcomes in clinical trial participants by incorporating safety and efficacy assessments into a single endpoint. In this study, we developed and applied a DOOR endpoint for cIAI clinical trials. Methods We reviewed 10 Phase 3 noninferiority trials for cIAI with electronic patient-level data (n=5473 participants) submitted to the FDA between 2005-2021. Extending previous work [CID. 2019:68(10):1691-8)], we developed an expanded cIAI-specific DOOR endpoint based on clinically meaningful events captured in trial datasets and those that were unique to patients with cIAI. Using this DOOR endpoint, we assigned each participant a DOOR rank, estimated the probability that a participant in the study treatment arm in each trial would have a more desirable DOOR rank than if assigned to the comparator arm, and analyzed individual components of clinical experience in each trial. Results Based on analysis of available data, we noted heterogeneity in definitions of “indeterminate” clinical outcomes, and significant diversity and increased incidence of infectious complications (ICs), serious adverse events (SAEs), and surgical/percutaneous procedures in participants without clinical cure. These informed the expansion of the DOOR endpoint for cIAI to include clinical efficacy outcomes, ICs, SAEs, and additional procedures (Table 1). The DOOR distributions between treatment and comparator arms in all 10 trials were similar. DOOR probability estimates for the 10 trials ranged from 44.5% to 50.3% but were not nominally statistically significant. Component analyses in two trials showed that the study treatment was nominally statistically inferior to the comparator with regard to SAEs and clinical failure, respectively (Fig. 1b, 1c). Table 1.cIAI-Specific DOOR EndpointFigure 1.Forest plot listing the DOOR probabilities and probability for each DOOR component from 3 trials. Trial 1 has no significant differences between the treatment arms in the component analysis (A). The study treatment arm was shown to be nominally statistically inferior for SAEs in Trial 2 (B) and for clinical failure in Trial 3 (C). Conclusion We developed a cIAI-specific DOOR endpoint to better elucidate the events that participants experienced in these trials. The component analysis allowed more nuanced evaluation of the factors that contributed to the composite DOOR probability estimate and provided a visual display of the risk-benefit assessment of a study treatment vs. the comparator. Our study was limited by its retrospective approach and trial design heterogeneity. Disclosures Deborah Collyar, B.Sci, Apellis Pharmaceuticals, Inc.: Advisor/Consultant|Kinnate Biopharma: Advisor/Consultant|M2GEN: Advisor/Consultant|Maxis Clinical Sciences: Advisor/Consultant|Parexel: Honoraria|Pfizer: Honoraria|Roundtable Analytics, Inc.: Ownership Interest Sarah B. Doernberg, MD, MAS, Basilea: Clincal events committee|Genentech: Advisor/Consultant|Gilead: Grant/Research Support|Regeneron: Grant/Research Support|Shinogi: Clincal events committee Scott R. Evans, Ph.D., M.S., Abbvie: DSMB|Akouos: DSMB|Apellis: DSMB|AstraZeneca: Advisor/Consultant|Atricure: Advisor/Consultant|Becton Dickenson: Advisor/Consultant|Breast International Group: DSMB|Candel: DSMB|ChemoCentrix: Advisor/Consultant|Clover: DSMB|DayOneBio: DSMB|DeGruyter: Editor|Duke University: DSMB|Endologix: Advisor/Consultant|FHI Clinical: DSMB|Genentech: Advisor/Consultant|Horizon: Advisor/Consultant|International Drug Development Institute: Advisor/Consultant|Janssen: Advisor/Consultant|Lung Biotech: DSMB|Neovasc: Advisor/Consultant|NIH: Grant/Research Support|Nobel Pharma: Advisor/Consultant|Nuvelution: DSMB|Pfizer: DSMB|Rakuten: DSMB|Roche: DSMB|Roivant: Advisor/Consultant|SAB Biopharm: DSMB|SVB Leerink: Advisor/Consultant|Takeda: DSMB|Taylor &amp; Francis: Book royalties|Teva: DSMB|Tracon: DSMB|University of Penn: DSMB|Vir: DSMB Thomas L. Holland, MD, Aridis: Advisor/Consultant|Lysovant: Advisor/Consultant Henry Chambers, MD, Merck: DSMB member|Merck: Stocks/Bonds|Moderna: Stocks/Bonds Vance G. Fowler, Jr, MD, MHS, Affinergy: Grant/Research Support|Affinergy: Honoraria|Affinium: Honoraria|Amphliphi Biosciences: Honoraria|ArcBio: Stocks/Bonds|Basilea: Grant/Research Support|Basilea: Honoraria|Bayer: Honoraria|C3J: Honoraria|Cerexa/Forest/Actavis/Allergan: Grant/Research Support|Contrafect: Grant/Research Support|Contrafect: Honoraria|Cubist/Merck: Grant/Research Support|Debiopharm: Grant/Research Support|Deep Blue: Grant/Research Support|Destiny: Honoraria|Genentech: Grant/Research Support|Genentech: Honoraria|Integrated Biotherapeutics: Honoraria|Janssen: Grant/Research Support|Janssen: Honoraria|Karius: Grant/Research Support|Medicines Co.: Honoraria|MedImmune: Grant/Research Support|MedImmune: Honoraria|NIH: Grant/Research Support|Novartis: Grant/Research Support|Novartis: Honoraria|Pfizer: Grant/Research Support|Regeneron: Grant/Research Support|Regeneron: Honoraria|Sepsis diagnostics: Sepsis diagnostics patent pending|UpToDate: Royalties|Valanbio: Stocks/Bonds Sumathi Nambiar, MD MPH, Johnson and Johnson: Stocks/Bonds Helen W. Boucher, MD, American Society of Microbiology: Honoraria|Elsevier: Honoraria|Sanford Guide: Honoraria.

Proposal of STRIM Improving Rule Induction Method and its Application to Datasets Generated via Partial Correspondence Hypothesis

Various data mining models and/or methods have been proposed to date. A statistical test rule induction method (STRIM) has been proposed as one of them, that induces if-then rules hidden in a dataset known as the decision table generated based on a simple hypothesis. This study improves the previous data generation model using a hypothesis similar to human rating and the rule induction method to adapt to real-world datasets. Specifically, 1) the hypothesis is expanded from a complete correspondence hypothesis to a partial correspondence hypothesis. 2) The previous rule induction method is developed into a Bayesian STRIM, that infers and/or explores the causes based on the results. The applied rule induction method’s validity and usefulness are confirmed using a verification system. The relationship and difference between Bayesian STRIM against a maximum a posteriori probability estimate and a Bayesian network method are also studied in the rule induction problem.

О некоторых моделях оптимизации производства растениеводческой продукции в условиях биологических рисков

The aim of the work is to build and use problems of parametric and stochastic programming to solve problems of optimizing the production of agricultural products under conditions of biological risks. To achieve the goal, two tasks were solved: determining the patterns of variability of some series characterizing possible risks in crop production, and building mathematical models to optimize crop production, taking into account the damage caused by plant pests. The paper proposes models for optimizing the production of crop products under conditions of biological risks. Some indicators of such tasks are described using probabilistic estimates. To optimize the production of agricultural products under the influence of biological risks, mathematical models with probabilistic estimates, as well as a parametric programming model, were built. Examples of approbation of models in the planning of production of crop production are given. The work used materials on the number of rodents, invasions of locust pests for the long-term period 2009-2020. according to the Irkutsk region. In addition, information on the yield of agricultural crops in the municipalities of the southern territory of the region, as well as data on the activities of CJSC Irkutsk Semen, located in the Irkutsk region, are involved. To model series on the number of rodents, areas of distribution of acridoids and the number of their larvae per unit area, methods of probability theory and mathematical statistics were used (technologies for constructing probability distribution laws, regression analysis and assessment of the quality of models). The variability of agricultural crop yields was assessed using the methods of constructing trends and factor dependencies. When optimizing the production of crop products, methods for constructing and solving extreme problems were used. Models of parametric and stochastic programming are proposed. At the same time, the experience of developing applied models for optimizing the production of agricultural products with and without taking into account risks was applied.

Seismic collaborative reliability evaluation of slopes using subset simulation via support vector machine

The computational effort places a limit on the low failure probability estimate of geotechnical engineering exposed to seismic stimulation. In this research, a novel method is proposed for the seismic reliability assessment of complicated geotechnical engineering slope structures with low failure probability. First, an improved subset simulation (SS) approach is provided for the low-failure events with high-dimensional indirect cross-correlation parameters. This method expands the usefulness of SS by using the support vector machine (SVM) method. Then, in order to reduce computing effort, the seismic collaborative reliability evaluation (SCRE) framework for seismic reliability assessment based on SS and the response conditioning method (RCM) is constructed. Finally, the accuracy of SCRE is verified by comparison with the standard Monte Carlo simulation based on 2D slope. The results show that SCRE's computational effort is approximately 2% that of Monte Carlo simulation. Meanwhile, SVM does a good job of preserving the significant indirect cross-correlation that occurs in the subspace samples that were split by SS. The time-saving benefit of SCRE is further enhanced when the aforementioned situations are expanded to 3D, and the findings demonstrate that the failure probability and sliding surface of the slope are different in 2D and 3D.

Stochastic perturbation of tendencies and parameters of parameterizations in the global ensemble prediction system based on the SL-AV model

AbstractAlgorithms for stochastic perturbation of parameters and tendencies of physical parameterizations for subgrid-scale processes are implemented into the ensemble prediction system. This system is based on the global semi-Lagrangian atmospheric model SL-AV with the resolution of 0.9 × 0.72 degrees in longitude and latitude, respectively, 96 vertical levels, and our implementation of the Local Ensemble Tranform Kalman Filter (LETKF). The use of stochastically perturbed parameterizations allows to generate ensembles with a significantly larger spread compared to one obtained with the method of static parameter perturbation. An improvement in the probabilistic estimates of the ensemble forecast for different seasons is shown.

Probabilistic Structural Model Updating with Modal Flexibility Using a Modified Firefly Algorithm.

Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder-Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification.