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No prior behavioral science research has delved into the impact of gamble presentation (horizontal or vertical) on individuals' utilitarian behavior, despite evidence suggesting that such choices can be influenced by comparing attributes like probability and money in gambles. This article addresses this gap by exploring the influence of gamble presentation on utilitarian behavior. A two-factor independent measures design was employed to explore the influence of the type of gamble presentation and age on participants' utilitarian decision-making preferences. The findings showed a reduced likelihood of participants choosing the non-utilitarian gamble with vertically presented gambles compared to horizontal ones. Consequently, participants' utilitarian behavior was influenced by between-gamble comparisons of available attributes, with utilitarian choices (e.g., choosing Gamble A) being more prevalent in vertical presentations due to a straightforward comparison on the probability attribute. Furthermore, the results also revealed that older participants take more time than their younger counterparts when making utilitarian errors. We attribute this to their abundant knowledge and experience. Future research should explore the comparative psychological processing used by participants in risky decision-making tasks. [ABSTRACT FROM AUTHOR]

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources. [ABSTRACT FROM AUTHOR]

Upwelling, which mixes deep and surface waters, significantly enhances the productivity of surface waters and plays a critical role in marine ecosystems, especially in key fishing areas like Zhoushan. This study utilized merged sea surface temperature data and an upwelling edge detection algorithm based on temperature gradients to analyze the characteristics of upwelling in Zhoushan and the Yangtze River Estuary over the past 28 years. The results indicate that upwelling in Zhoushan begins in April, peaks in July, gradually weakens, and disappears by October. The phenomenon is most pronounced during the summer months (June to August), with significant spatial distribution differences in April and September. Notably, high probability values of upwelling centers and core areas are mainly concentrated near Ma'an Island, Zhongjieshan Island, and Taohua Island. In these areas, upwelling remains stable during the summer, forming a unique "footprint" distribution pattern, and these are also the locations of the Zhoushan National Marine Ranch. From April to August, the extent of the upwelling area gradually decreases and stabilizes. These findings emphasize the frequent upwelling activity around Zhoushan and its significant contribution to the formation of local fisheries. Additionally, considering that the formation of natural upwelling in the East China Sea depends on the southern monsoon, the study suggests establishing artificial upwelling systems during periods unfavorable for natural upwelling, based on high probability areas, to enhance fishery yields and support the development of local fisheries. [ABSTRACT FROM AUTHOR]

Volkswagen Technical Development (TE) is responsible for all prototype development and prototype production for the Volkswagen brand and has its own logistics department (TE-Logistics). In the logistics of prototype parts in the automotive industry, new versions of prototype parts (henceforth referred to as updating parts) are repeatedly assembled in finished prototype vehicles. These updating parts are stored in warehouses and provided to an assembly site to ensure a timely assembly of the associated prototype vehicles. As the internal warehouse on the company site is not large enough for the high variety of parts, an additional external warehouse in the logistics network is needed. However, since prototype parts are unique, the allocation of the parts in suitable warehouses is particularly important. Currently, the various warehouses and the short-term demands repeatedly lead to reactive transshipments between the warehouses. To this end, we developed an approach for proactive transshipments based on a machine learning forecast and a mixed-integer linear programming model for planning proactive transshipments of parts between the warehouses to minimize transport costs. The model is based on a probability estimation of future demands to anticipate the expected optimal warehouse. After the model had revealed high improvement potential through a case study with real-world data in terms of costs and availability time compared to the current reactive process, we derived decision rules and developed a rule-based heuristic algorithm that leads to the optimal solution for the industrial use case. We implemented the heuristic with a spreadsheet-based decision support system (DSS) for daily transshipment planning. After successful test implementation, TE-Logistics estimated the annual cost savings for transport to be approximately 10%. [ABSTRACT FROM AUTHOR]

A very important class of models widely used nowadays to describe and predict, at least in stochastic terms, the behavior of many-particle systems (where the word "particle" is not meant in the purely mechanical sense: particles can be cells of a living tissue, or cars in a traffic flow, or even members of an animal or human population) is the Kinetic Theory for Active Particles, i.e., a scheme of possible generalizations and re-interpretations of the Boltzmann equation. Now, though in the literature on the subject this point is systematically disregarded, this scheme is based on Markov Chains, which are special stochastic processes with important properties they share with many natural processes. This circumstance is here carefully discussed not only to suggest the different ways in which Markov Chains can intervene in equations describing the stochastic behavior of any many-particle system, but also, as a preliminary methodological step, to point out the way in which the notion of a Markov Chain can be suitably generalized to this aim. As a final result of the discussion, we find how to develop new very plausible and likely ways to take into account possible effects of the external world on a non-isolated many-particle system, with particular attention paid to socio-economic problems. [ABSTRACT FROM AUTHOR]

Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm running on a uniform 20 km grid over the continental USA and Alaska. This is the first and only high-resolution lightning forecasting model for North America derived from 29-year-long data records. The algorithm consists of a large set of regional logistic equations parameterized on the long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 principal components from a list of 611 potential predictors. Our analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: the temperature and geopotential heights across vertical pressure levels, the amount of low-level atmospheric moisture, and wind vectors. These physical variables isolate the conditions that are favorable for the development of thunderstorms and impact the vertical separation of electric charges in the lower troposphere during storms, which causes the voltage potential between the ground and the cloud deck to increase to a level that triggers electrical discharges. The results from a forecast verification using independent data showed excellent model performance, thus making this algorithm suitable for incorporation into models designed to forecast the chance of wildfire ignitions. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology developed in this study provides a robust way of tracking down specific events in hydraulic fracturing. The innovative workflow produced an effective diagnostics tool. Ultimately, the featured event detection and diagnostics can make it possible to find optimal solutions for oil/gas well completion design, spacing and infill, production management, and wellbore protection. The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit ("frac hit") detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source. [ABSTRACT FROM AUTHOR]

Methane hydrate formation is analytically studied in the presence of the water memory effect using the classical nucleation theory. The memory effect is introduced as a change in nucleation site from a three-dimensional heterogenous nucleation on a solid surface with cap-shaped hydrate clusters (3D-HEN) to a two-dimensional nucleation on the solid hydrate residue surface with monolayer disk-shaped hydrate clusters (2D-NOH). The analysis on the stationary nucleation of methane hydrate under isobaric conditions shows that the memory effect caused an average decrease of 4.4 K in metastable zone width, or subcooling. This decrease can be erased at higher dissociation temperatures (ΔT > 17.2 K) due to a decrease in the concentration of 2D-NOH nucleation sites. Moreover, the probability of hydrate formation is estimated for the purpose of quantifying risk associated with methane hydrate formation in the presence of the memory effect. [ABSTRACT FROM AUTHOR]

In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey. [ABSTRACT FROM AUTHOR]

Process algebra can be considered one of the most practical formal methods for modeling Smart IoT Systems in Digital Twin, since each IoT device in the systems can be considered as a process. Further, some of the algebras are applied to predict the behavior of the systems. For example, PALOMA (Process Algebra for Located Markovian Agents) and PACSR (Probabilistic Algebra of Communicating Shared Resources) process algebras are designed to predict the behavior of IoT Systems with probability on choice operations. However, there is a lack of analytical methods in the algebras to predict the nondeterministic behavior of the systems. Further, there is no control mechanism to handle undesirable nondeterministic behavior of the systems. In order to overcome these limitations, this paper proposes a new process algebra, called dTP-Calculus, which can be used (1) to specify the nondeterministic behavior of the systems with static probability, (2) verify the safety and security requirements of the nondeterministic behavior with probability requirements, and (3) control undesirable nondeterministic behavior with dynamic probability. To demonstrate the feasibility and practicality of the approach, the SAVE (Specification, Analysis, Verification, Evaluation) tool has been developed on the ADOxx Meta-Modeling Platform and applied to a SEMS (Smart Emergency Medical Service) example. In addition, a miniature digital twin system for the SEMS example was constructed and applied to the SAVE tool as a proof of concept for Digital Twin. It shows that the approach with dTP-Calculus on the tool can be very efficient and effective for Smart IoT Systems in Digital Twin. [ABSTRACT FROM AUTHOR]

We review, under a modern light, the conditions that render the Boltzmann equation applicable. These are conditions that permit probability to behave like mass, thereby possessing clear and concrete content, whereas generally, this is not the case. Because science and technology are increasingly interested in small systems that violate the conditions of the Boltzmann equation, probability appears to be the only mathematical tool suitable for treating them. Therefore, Boltzmann's teachings remain relevant, and the present analysis provides a critical perspective useful for accurately interpreting the results of current applications of statistical mechanics. [ABSTRACT FROM AUTHOR]

The persistent decline in infrastructure spending, notably within the transportation sector, raises concerns about governments' capacity to meet the demands of a sustainable growing economy. The incorporation of risk assessment in the analysis and computation of potential cost overruns emerges as an effective and efficient tool, underpinning the economic and financial sustainability of infrastructure expansions. Focusing on the "State Road No. 51 of Alemagna Vittorio Veneto" (SSv-51) variant, this study analyzes and proposes a model to forecast the possible cost overruns of an infrastructure project. The application of the risk assessment tool proposed by the National Anti-Corruption Authority (ANAC) offers valuable insights into potential risks associated with project costs and their valuation. The matrix developed in the current study draws from the ANAC Matrix, which comprises four categories of risk divided into 21 risk types. The selection is based on the project's characteristics, and a matrix is compiled that forecasts the combination of the probability of risk occurrence and the cost impacts on the project. The proposed risk matrix serves as a practical tool for managing uncertainties and estimating potential cost overruns, estimating ex ante a possible cost increase of 7.53%. This projected increase differs only by 1.34% from the final execution costs, mitigating the unforeseen cost overruns not estimated by the initial project. The proposed risk assessment tool emphasizes the importance of integrating risk management into project planning and execution. The research investigated an applied case utilizing an easily adaptable tool, suitable for potential future implementation, further advancement, and broader testing across various project samples in the future. The study provides a framework to assess and mitigate risks linked to cost overruns. As nations navigate infrastructure development complexities, proactive risk management practices are indispensable for efficient resource management, ensuring the economic and financial sustainability of these complex projects. [ABSTRACT FROM AUTHOR]

Istaroxime, an intravenous inotropic agent with a dual mechanism—increasing both cardiomyocyte contractility and relaxation—is a novel treatment for acute heart failure (AHF), the leading cause of morbidity and mortality in heart failure. We conducted a systematic review and meta-analysis that synthesized randomized controlled trials (RCTs), which were retrieved by systematically searching PubMed, Web of Science, SCOPUS, and Cochrane until 24 April 2023. We used a fixed-effect or random-effect model—according to heterogeneity—to pool dichotomous data using the risk ratio (RR) and continuous data using the mean difference (MD), with a 95% confidence interval (CI). We included three RCTs with a total of 300 patients. Istaroxime was significantly associated with an increased left ventricular ejection fraction (mL) (MD: 1.06, 95% CI: 0.29, 1.82; p = 0.007), stroke volume index (MD: 3.04, 95% CI: 2.41, 3.67; p = 0.00001), and cardiac index (L/min/m2) (MD: 0.18, 95% CI: 0.11, 025; p = 0.00001). Also, istaroxime was significantly associated with a decreased E/A ratio (MD: −0.39, 95% CI: −0.58, −0.19; p = 0.0001) and pulmonary artery systolic pressure (mmHg) (MD: 2.30, 95% CI: 3.20, 1.40; p = 0.00001). Istaroxime was significantly associated with increased systolic blood pressure (mmHg) (MD: 5.32, 95% CI: 2.28, 8.37; p = 0.0006) and decreased heart rate (bpm) (MD: −3.05, 95% CI: −5.27, −0.82; p = 0.007). Since istaroxime improved hemodynamic and echocardiographic parameters, it constitutes a promising strategy for AHF management. However, the current literature is limited to a small number of RCTs, warranting further large-scale phase III trials before clinical endorsement. [ABSTRACT FROM AUTHOR]

To promote optimal learning in their students, mathematics teachers must be proficient in problem posing, making this skill a cornerstone in teacher training programs. This study presents a formative action in which pre-service teachers are required to create and analyze a problem involving proportional reasoning within a probabilistic context. For this problem, they must identify the objects and processes involved in its resolution, recognize the degree of algebraic reasoning implied and identify potential difficulties for students. Subsequently, they need to formulate and analyze a new problem with variation, which mobilizes higher-level algebraic activity. Results indicate that prospective teachers struggle to pose problems that engage proportional reasoning, as well as to identify in their analysis which elements of proportional and algebraic reasoning are present in their solutions. Despite this fact, a significant percentage of participants adequately modify the original problem to address higher levels of algebraic reasoning, identifying in these cases the new algebraic objects and potential difficulties that might arise as the degree of generalization required in the solution increases. The study concludes by underscoring the importance of training in problem posing to enhance the knowledge and competences of prospective teachers concerning proportional and algebraic reasoning. [ABSTRACT FROM AUTHOR]

The increasing incidence of wildfires in the Mediterranean region has raised significant scientific and environmental concerns. This study focuses on a retrospective analysis of wildfire ignition and propagation within the context of the Monte Catillo Natural Reserve in Italy. After conducting a comprehensive review of the current state-of-the-art wildfire susceptibility mapping, propagation modeling, probability assessment, forest vulnerability models, and preventive silvicultural measures, we examine the regulatory framework surrounding wildfires in the national context, with a specific focus on prevention, prediction, and active firefighting measures. A geostatistical model of wildfire occurrence was developed, starting with the characterization of the area vegetation and anthropogenic factors influencing wildfire ignition. After that, wildfire observations from the period between 2010 and 2021 were included. The objective is to generate a wildfire hazard map for two distinct vegetation communities. To accomplish this, a statistical analysis was applied using the Poisson Model, assessing its goodness-of-fit by comparing observed frequencies with experimental data through the chi-square test. In conclusion, this model serves as a valuable tool for characterizing wildfire hazards, including ignition probabilities and propagation scenarios, within the Monte Catillo Natural Reserve. The research significantly contributes to enhancing our understanding of wildfire dynamics and plays a crucial role in the development of effective strategies for wildfire risk management. [ABSTRACT FROM AUTHOR]

The Peace–Athabasca Delta (PAD) in northern Alberta is one of the world's largest inland freshwater deltas and is home to many species of fish, mammals, and birds. Over the past five decades, the PAD has experienced prolonged dry periods in between rare floods, accompanied by a reduction in the area comprised of lakes and ponds that provide a habitat for aquatic life. In the Peace sector of the PAD, this likely resulted from a reduced frequency of spring flooding caused by major ice jams that form in the lower Peace River. There is debate in the literature regarding the factors that promote or inhibit the formation of such ice jams, deriving from physical process studies, paleolimnological studies, and—recently—statistical analysis founded in logistic regression. Logistic regression attempts to quantify ice-jam flood (IJF) probability, given the values of assumed explanatory variables, involve considerable uncertainty. Herein, different sources of uncertainty are examined and their effects on statistical inferences are evaluated. It is shown that epistemic uncertainty can be addressed by selecting direct explanatory variables, such as breakup flow and ice cover thickness, rather than through more convenient, albeit weak, proxies that rely on winter precipitation and degree-days of frost. Structural uncertainty, which derives from the unknown mathematical relationship between IJF probability and the selected explanatory variables, leads to different probability predictions for different assumed relationships but does not modify assessments of statistical significance. The uncertainty associated with the relatively small sample size (number of years of record) may be complicated by known physical constraints on IJF occurrence. Overall, logistic regression corroborates physical understanding that points to breakup flow and freezeup level as primary controls of IJF occurrence. Additional influences, related to the thermal decay of the ice cover and the flow gradient during the advance of the breakup front towards the PAD, are difficult to quantify at present. Progress requires increased monitoring of processes and an enhanced numerical modelling capability. [ABSTRACT FROM AUTHOR]

A quantum symmetry called entanglement-assisted invariance, also called envariance, is introduced. It is studied with respect to the process of performing quantum measurements. An apparatus which interacts with other physical systems, which are called environments, exchanges a single state with physical states equal in number to that of the possible outcomes of the experiment. Correlations between the apparatus and environment give rise to a type of selection rule which prohibits the apparatus from appearing in a superposition corresponding to different eigenvalues of the pointer basis of the apparatus. The eigenspaces of this observable form a natural basis for the apparatus and determine the observable of the measured quantum system. It is also discussed how statistical mechanics can be formulated in terms of this symmetry. [ABSTRACT FROM AUTHOR]

The present paper, in the framework of a search for a computer-aided method to detect depression, deals with experimental data of various types, with their correlation, and with the way relevant information about depression delivered by different sets of data can be fused to build a unique body of knowledge about individuals' mental states facilitating the diagnosis and its accuracy. To this aim, it suggests the use of a recently introduced «limiting form» of the kinetic-theoretic language, at present widely used to describe complex systems of objects of the most diverse nature. In this connection, the paper mainly aims to show how a wide range of experimental procedures can be described as examples of this «limiting case» and possibly rendered by this description more effective as methods of prediction from experience. In particular, the paper contains a simple, preliminary application of the method to the detection of depression, to show how the consideration of statistical parameters connected with the analysis of speech can modify, at least in a stochastic sense, each diagnosis of depression delivered by the Beck Depression Inventory (BDI-II). [ABSTRACT FROM AUTHOR]

Quantum Markov chains (QMCs) and open quantum random walks (OQRWs) represent different quantum extensions of the classical Markov chain framework. QMCs stand as a more profound layer within the realm of Markovian dynamics. The exploration of both QMCs and OQRWs has been a predominant focus over the past decade. Recently, a significant connection between QMCs and OQRWs has been forged, yielding valuable applications. This bridge is particularly impactful when studying QMCs on tree structures, where it intersects with the realm of phase transitions in models naturally arising from quantum statistical mechanics. Furthermore, it aids in elucidating statistical properties, such as recurrence and clustering. The objective of this paper centers around delving into the intricate structure of QMCs on Cayley trees in relation to OQRWs. The foundational elements of this class of QMCs are built upon using classical probability measures that encompass the hierarchical nature of Cayley trees. This exploration unveils the pivotal role that the dynamics of OQRWs play in shaping the behavior of the Markov chains under consideration. Moreover, the analysis extends to their classical counterparts. The findings are further underscored by the examination of notable examples, contributing to a comprehensive understanding of the outcomes. [ABSTRACT FROM AUTHOR]

This expository paper advocates an approach to physics in which "typicality" is identified with a suitable form of algorithmic randomness. To this end various theorems from mathematics and physics are reviewed. Their original versions state that some property Φ (x) holds for P-almost all x ∈ X , where P is a probability measure on some space X. Their more refined (and typically more recent) formulations show that Φ (x) holds for all P-random x ∈ X . The computational notion of P-randomness used here generalizes the one introduced by Martin-Löf in 1966 in a way now standard in algorithmic randomness. Examples come from probability theory, analysis, dynamical systems/ergodic theory, statistical mechanics, and quantum mechanics (especially hidden variable theories). An underlying philosophical theme, inherited from von Mises and Kolmogorov, is the interplay between probability and randomness, especially: which comes first? [ABSTRACT FROM AUTHOR]

Background: Physical and mathematical theories have made it possible to generate methods for the characterization and diagnosis of physiological variables such as cardiac dynamics. Therefore, it would be useful to implement them to evaluate the dynamic changes in human physiology during the development of COVID-19, which causes disease, severe respiratory and death. Objective: to establish a method for detecting possible alterations associated with COVID-19 through simulations of adult cardiac dynamics and body temperature using dynamic systems theory, probability, entropy and set theory. Methodology: simulations of cardiac dynamics were generated in subjects with 10 temperature ranges between 32 °C and 42 °C via numerical attractors after their evaluation using entropy proportions. Results: differences were observed in the proportions of entropy that differentiate normal cardiac dynamics and acute myocardial infarction towards progression to fever. Conclusion: the physical mathematical analysis of cardiac behavior in relation to body temperature in people with COVID-19 allowed the establishment of a possible surveillance method for detecting minor alterations. [ABSTRACT FROM AUTHOR]

There is increasing pressure on organisations and countries to manage the financial risks associated with climate change. This paper summarises research on climate change, credit risk and the associated losses, and specifically identifies methods that could contribute to quantifying the causal relationships between climate change and credit risk. We conducted a bibliometric analysis using the Web of Science database to analyse 3138 documents that investigated climate change and credit risk. The key results reveal that climate change has a quantifiable effect on credit risk, and that the most affected industries or sectors are energy, transportation/mobility, agriculture and food, manufacturing, and construction. The prominent methods to quantify causal relationships between climate change and credit risk are regression models, but these are mostly used in preliminary and testing stages. Distance to default and credit risk are the main areas of focus when quantifying climate change and credit risk. Banks are the main type of organisation that have sought to quantify the causal relationship. We identify a research agenda to quantify these causal relationships. [ABSTRACT FROM AUTHOR]

A longstanding issue in the Everettian (Many-Worlds) interpretation is to justify and make sense of the Born rule that underlies the statistical predictions of standard quantum mechanics. The paper offers a reappraisal of Everett's original account in light of the recent literature on the concept of typicality. It argues that Everett's derivation of the Born rule is sound and, in a certain sense, even an optimal result, and defends it against the charge of circularity. The conclusion is that Everett's typicality argument can successfully ground post-factum explanations of Born statistics, while questions remain about the predictive power of the Many-Worlds interpretation. [ABSTRACT FROM AUTHOR]

In Portugal, the rapid growth in housing in and near wildland–urban interfaces (WUIs) increases the wildfire risk to lives and structures. The goal of our study was to assess wildfire hazard in the Central Region of Portugal and in the contact areas of the 60,373 km of WUIs existing in the study area. The degree to which wildfire is a hazard to the landscape and the different urban interfaces areas was assessed using the spatial arrangement of land use/land cover (LULC), topography, and historical incidence of burnt area. The results show that in more than half of the Central Region territory, the wildfire hazard is high or very high; however, most WUIs are in contact with low or very low hazard classes in a total of 87% of the segments. The LULC analysis in the different wildfire hazard classes in WUI contact areas shows that agricultural crops are predominant in the low and very low hazard classes, while in the very high and high hazard classes shrub communities, coniferous and scrub forests dominate, respectively. These results can assist in designing appropriate prevention measures and improving the effectiveness of fire prevention. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates the educational interest of LearningRlab, an educational R package developed for teaching statistics in computer science engineering. The package was developed by final degree project students to be used as an educational environment for statistics students who evaluated the package and provided feedback for future versions. Such a process increases the motivation of both groups of students. This paper presents how the use of the R packages conceived and developed for engineering education can improve the learning process in the computer science engineering bachelor's degree. Two different evaluations, one performed by a group of statistics students, and the other performed by final degree project students, were used to evaluate the impact on the learning process of the first version of the package to develop the second version of the package, which corrects and enhances the first version. The evaluation results show a positive effect on the learning process in both subjects. The analysis of the learning outcomes reflected in the grades of the experimental and control groups demonstrates that LearningRlab can be used as a teaching aid for statistics and final degree project subjects of the computer science engineering degree. The average laboratory grade of the students who used the package (5.76) was noticeably higher than the average laboratory grade of students who did not use it (1.84). [ABSTRACT FROM AUTHOR]

This work makes a contribution to the theory of soft sets. It studies the concepts of soft semi-algebras and soft algebras, along with some operations. Then, it examines the relations of soft algebras set to their ordinary (crisp) counterparts. Among other things, we show that every algebra of soft sets induces a collection of ordinary algebras of sets. By using the formulas (in Theorem 7 and Corollary 1), we present a novel construction, allowing us to construct a soft algebra from a system of ordinary algebras of sets. Two examples are presented to show how these formulas can be used in practice. This approach is general enough to be applied to many other (soft) algebraic properties and shows that ordinary algebras contain instruments enabling us to construct soft algebras and to study their properties. As an application, we demonstrate how elements of the generated soft algebra can be used to describe the weather conditions of a region. [ABSTRACT FROM AUTHOR]

In this article, we analyze the basis of a proposal that allows teaching the notions of reflection, refraction, interference and diffraction from a unified perspective, using Fermat's variational principle, recovered by Richard Feynman in his formulation of the paths sum for quantum mechanics. This allows reconsidering the notions of geometrical and physical optics, using the probabilistic and unified model of quantum mechanics by means of mathematical notions that are accessible to high school students. [ABSTRACT FROM AUTHOR]

This paper is devoted to the evaluation of the hardware resources of computer systems for solving a computationally expensive problem such as the calculation of the probability distributions of statistics by the second multiplicity method based on Δ -exact approximations of samples with a size of 320–1280 characters and an alphabet power of 128–256 characters. The accuracy is Δ = 10 − 5 and the total solution time should not exceed 30 days or 2.592 × 10 6 seconds for 24/7 computing. Owing to the use of the properties of the second multiplicity method, the computational complexity of the calculations can be brought within the range of 9.68 × 10 22 − 1.60 × 10 52 operations with the number of tested vectors at 6.50 × 10 23 − 1.39 × 10 50 . The solution of this problem for the specified parameters of samples during the given time requires hardware resources which cannot be provided by modern computer technology such as processors, graphics accelerators and programmable logic integrated circuits. Therefore, in this paper, we analyze the possibilities of promising quantum and photon technologies for solving the problem with the given parameters. The main advantage of quantum computer systems is the high speed of calculations for all possible parameter values. However, quantum acceleration will not be achieved to calculate the probability distributions of statistics due to the need to check all the obtained solutions. Here, the number of obtained solutions corresponds to the dimension of the problem. In addition, due to the current development level of quantum hardware components, it is impossible to create and use 120 qubit quantum computers for the solution of the considered problem. Photon computers can provide high computation speeds at low power consumptions and require the smallest number of nodes to solve the considered problem. However, unsolved problems with the physical implementation of efficient memory elements and the lack of available hardware components make the use of photon computer technologies impossible for calculating the probability distributions of statistics in the near future (5–7 years). Therefore, it is most reasonable to use hybrid computer systems containing nodes of different architectures. To solve the problem on various hardware platforms (general purpose processors, GPUs and FPGAs) and configurations of hybrid computer systems, we suggest to use the architecture-independent high-level programming language SET@L. The language combines the representation of calculations as sets and collections (based on the alternative set theory of P. Vopenka), the absolutely parallel form of the problem represented as an information graph and the paradigm of aspect-oriented programming. [ABSTRACT FROM AUTHOR]

Modern power engineering is struggling with various problems that have not been observed before or have occurred very rarely. The main cause of these problems results from the increasing number of connected distributed electricity sources, mainly renewable energy sources (RESs). Therefore, energy generation is becoming more and more diverse, both in terms of technology and location. Grids that have so far worked as receiving networks change their original function and become generation networks. The directions of power flow have changed. In the case of distribution networks, this is manifested by power flows towards transformer stations and further to the network with a higher voltage level. As a result of a large number of RESs, their total share in the total generation increases. This has a significant impact on various aspects of the operation of the power system. Voltage profiles, branch loads, power flows and directions of power flows between areas change. As a result of the random nature of RES generation, there are problems with the quality of electricity, source stability issues, branch overloading, voltage exceedances and power balance. The occurrence of various types of problems requires the use of more and more advanced methods to solve them. This review paper, which is an introduction to the Special Issue Advanced Optimisation and Forecasting Methods in Power Engineering, describes and justifies the need to reach for effective and available mathematical and IT methods that are necessary to deal with the existing threats appearing in the operation of modern power systems. It indicates exemplary, current problems and advanced methods to solve them. This article is an introduction and justification for the use of advanced calculation methods and algorithms. Engineering intuition and experience are often not enough due to the size and complexity of power grid operation. Therefore, it becomes necessary to use methods based on artificial intelligence and other advanced solutions that will facilitate and support decision making in practice. [ABSTRACT FROM AUTHOR]

Autonomous healing is a very promising technique in self-healing concrete systems. For capsules to achieve their anticipated performance, they should be able to survive the harsh mixing conditions of concrete, yet rupture upon concrete cracking. At present, there are no standard test methods, either experimental or analytical, for determining the capsule survival rate during concrete mixing. This study investigates the correlation between the capsules' shell properties, concrete rheological properties, the capsules' external forces, and capsule survival rate during concrete mixing. Finite element and statistical modeling techniques were employed to evaluate the capsule performance and predict the survival rate of capsules during concrete mixing, with 68% confidence. The results revealed that the capsules' survivability during concrete mixing is highly influenced by the capsule's radius-to-thickness ratio, the rheological properties of the fresh concrete, the average-paste-thickness (APT) of the concrete mix, the aggregate content and angularity, and the speed of the mixer. In brief, capsules with a radius-to-thickness ratio between 30 and 45 are likely to survive concrete mixing and yet still rupture upon concrete cracking. [ABSTRACT FROM AUTHOR]

This article deals with the problem of loose materials, i.e., wheat grain, when transported by belt conveyors with cover belts. For the purpose of further research, experimental measurements of the deflections of a conveyor belt with loose material were carried out on a self-built laboratory test device. The mechanical contact between the wheat and the belt could be suitably approximated using an elastic foundation. The measured data were evaluated and used to obtain functional relationships for the compressibility moduli of the bilateral Winkler elastic foundation. The obtained relationships were further stochastically processed using the Monte Carlo method. [ABSTRACT FROM AUTHOR]

This work presents a brief review on the modern approaches to data modeling by the methods developed in the quantum physics during the last one hundred years. Quantum computers and computations have already been widely investigated theoretically and attempted in some practical implementations, but methods of quantum data modeling are not yet sufficiently established. A vast range of concepts and methods of quantum mechanics have been tried in many fields of information and behavior sciences, including communications and artificial intelligence, cognition and decision making, sociology and psychology, biology and economics, financial and political studies. The application of quantum methods in areas other than physics is called the quantum-like paradigm, meaning that such approaches may not be related to the physical processes but rather correspond to data modeling by the methods designed for operating in conditions of uncertainty. This review aims to attract attention to the possibilities of these methods of data modeling that can enrich theoretical consideration and be useful for practical purposes in various sciences and applications. [ABSTRACT FROM AUTHOR]

The continuous increase in generation from renewable energy sources, marked by correlated forecast uncertainties, requires specific methodologies to support power system operators in security management. This paper proposes a probabilistic preventive control to ensure N-1 security in presence of correlated uncertainties of renewable sources and loads. By adopting a decoupled linear formulation of the AC load flow equations, the preventive control is decomposed into two subsequent linear programming problems, the former concerning the active power and the latter the voltage/reactive power-related issues. In particular, in the active control problem, the algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method, and Cornish–Fisher expansion to model the forecast uncertainties and characterize the chance constraints in the problem. The goal is to find the optimal phase shifting transformer tap setting, conventional generation redispatching, and renewable curtailment at the minimum cost to assure the probabilistic fulfillment of N and N-1 security constraints on branch active power flows. The second stage solves another linear programming problem, which aims to minimize the adjustments to generators' set-point voltages to avoid violations at node voltages and branch-rated limits due to reactive power flows while meeting generator reactive power constraints. Simulations performed on an IEEE test system demonstrate the effectiveness of the proposed security control method in limiting the probability of violating security limits in N and N-1 state, including voltage/reactive power constraints, in presence of correlated uncertainties. [ABSTRACT FROM AUTHOR]

With the gradual depletion of surface resources, rock instability caused by deep high stress and mining disturbance seriously affects safe mining. To create effective risk management, a rock instability risk field model using microseismic monitoring data is proposed in this study. Rock instability risk was presented visually in 3D visualization. The in-situ microseismic monitoring data was collected and analyzed to make calculation of peak ground velocity (PGV), peak ground acceleration (PGA), energy flux, energy and seismic moment. Indicator weights of PGV, PGA, energy flux are confirmed by using the analytic hierarchy process (AHP) to calculate risk severity. The Copula function is then used to solve the joint probability distribution function of energy and seismic moment. Then the spatial distribution characteristics of risk can be obtained by data fitting. Subsequently, the three-dimensional (3D) risk field model was established. Meanwhile, the established risk field is verified by comparing monitoring data without disturbance and the blasting data with disturbance. It is suggested that the proposed risk field method could evaluate the regional risk of rock instability reasonably and accurately, which lays a theoretical foundation for the risk prediction and management of rock instability in deep mining. [ABSTRACT FROM AUTHOR]

The plastic behavior of strengthened haunched beams utilizing carbon fiber-reinforced polymers (CFRP) was investigated using a probabilistic design that took into account random concrete properties, CFRP properties, and complementary strain energy values, with the reliability index serving as a limiting index, as the proposed method considers a novel method that deals with probabilistic parameters for models with limited plastic behavior designed based on the reliability index. The data used in this research were gathered and evaluated in a recent study on simply supported haunched beams reinforced with carbon fiber-reinforced polymers. The purpose of this research was to use the reliability limitation index for simulated strengthened haunched beams by taking into account randomness in concrete and CFRP properties and the complementary strain energy value, which is considered a plastic behavior controller that provides an illustration of the damage amount within the reinforcement steel bars. The results indicate how randomness affects the behavior of the presented models, which are chosen to have different numbers of CFRP strips. The variable randomness affects load and deflection values where the reliability index value increases as the corresponding load value decrease, reflecting the increased probability of failure in models subjected to higher loading conditions, while tension concrete damage percentages are reflected in the damage pattern presented in the results, showing that as the produced load increases, so does the damage intensity. It is also obvious that the reliability index served as a limitation index while taking concrete characteristics and complementary strain energy as random variables. [ABSTRACT FROM AUTHOR]

Intensity, duration and frequency (IDF) curves are necessary tools for the design and construction of hydraulic projects. However, the pluviographic records needed to determine the IDF curves do not exist or are scarce. This research presents the regionalization of the IDF curves for the department of Boyacá, Colombia, which is made up of 16 municipalities including the provincial capital, Tunja. For the regionalization, the adjustment parameters (u and α) of the IDF curve stations in the study area were used. In the case of regionalization by the parameters found for the construction of the IDF curves, estimation methods with ordinary moments means and maximum likelihood were used. The regionalization and interpolation of the data were performed with Arcgis software. The resulting isoline maps were made in the case of regionalization intensities, and each map is associated with a different return period and duration to construct the IDF curves in the studied area. In the case of the regionalization maps, the parameters associated with each individual parameter were performed last. The results show that the use of IDF curve data is more accurate and reduces errors in the design. With the methods proposed in this study, IDF curves can be constructed for any site of interest that does not have rainfall stations. [ABSTRACT FROM AUTHOR]

Forest fires arise from anthropogenic load and lightning activity. The formation of a thunderstorm front is due to the influence of a number of factors, including the emission of aerosol particles from forest fires. The purpose of this study is mathematical modeling of heat and mass transfer in vegetation firebrand carried out from a forest fire front, taking into account the formation of soot particles to predict forest fire danger from thunderstorm activity. Research objectives: (1) development of a deterministic mathematical model of heat and mass transfer in a pyrolyzed firebrand of vegetation, taking into account soot formation; (2) development of a probabilistic criterion for assessing forest fire danger from thunderstorms, taking into account aerosol emissions; (3) scenario modeling of heat and mass transfer and the formation of a thunderstorm front; (4) and the formulation of conclusions and proposals for the practical application of the developed deterministic–probabilistic approach to the prediction of forest fires from thunderstorms, taking into account aerosol emissions. The novelty of this study lies in the development of a new model of heat and mass transfer in a pyrolyzed vegetation firebrand and a new probabilistic criterion for forest fire danger due to thunderstorm activity, taking into account aerosol emission. The distributions of temperature and volume fractions of phases in a firebrand are obtained for various scenarios. Scenarios of surface fires, crown forest fires, and a fire storm are considered for typical types of coniferous vegetation. Cubic firebrands are considered in the approximation of a two-dimensional mathematical model. To describe the heat and mass transfer in the firebrand structure, a differential heat conduction equation is used with the corresponding initial and boundary conditions, taking into account the kinetic scheme of pyrolysis and soot formation. Variants of using the developed mathematical model and probabilistic criterion in the practice of protecting forests from fires are proposed. Key findings: (1) linear deterministic–probabilistic mathematical model to assess forest fire occurrence probability taking into account aerosol emission and lightning activity; (2) results of mathematical modeling of heat and mass transfer in firebrand taking into account soot formation; (3) and results of scenario modeling of forest fire occurrence probability for different conditions of lightning activity and aerosol emission. [ABSTRACT FROM AUTHOR]

Probabilistic evaluation of the resistance to low-cycle deformation and failure of the critical components in the equipment used in the energy, engineering, metallurgy, chemical, shipbuilding, and other industries is of primary importance with the view towards their secure operation, in particular, given the high level of cyclic loading acting on the equipment during its operation. Until recently, systematic probabilistic evaluation has been generally applied to the results of statistical and fatigue investigations. Very few investigations applying this approach to the low-cycle domain. The present study aims to substantiate the use of probabilistic calculation in the low-cycle domain by systematic probabilistic evaluation of the diagrams of cyclic elastoplastic deformation and durability of the materials representing the major types of cyclic properties (hardening, softening, stabilization) and investigation of the correlation relationships between mechanical properties and cyclic deformation and failure parameters. The experimental methodology that includes the calculated design of the probabilistic fatigue curves is also developed and the curves are compared to the results of the experiment. Probabilistic values of mechanical characteristics were determined and calculated low-cycle fatigue curves corresponding to different failure probabilities, to assess them from the probabilistic perspective. A comparison of low-cycle fatigue curves has shown that the durability curves generated for some materials using analytical expressions are not accurate. According to the analysis of the relative values of experimental probabilities of low-cycle fatigue curves, the use of analytical expressions to build the curves can lead to a significant error. The results obtained allow for the revision of the load bearing capacity and life of the structural elements subjected to cyclic elastoplastic loading in view of the potential scattering of mechanical properties and resistance parameters to low-cycle deformation and failure. In addition, the results enable determination of the scatter tolerances, depending on the criticality of the part or structure. [ABSTRACT FROM AUTHOR]

A singular graph G, defined when its adjacency matrix is singular, has important applications in mathematics, natural sciences and engineering. The chemical importance of singular graphs lies in the fact that if the molecular graph is singular, the nullity (the number of the zero eigenvalue) is greater than 0, then the corresponding chemical compound is highly reactive or unstable. By this reasoning, chemists have a great interest in this problem. Thus, the problem of characterization singular graphs was proposed and raised extensive studies on this challenging problem thereafter. The graph obtained by conglutinating the starting vertices of three paths P s 1 , P s 2 , P s 3 into a vertex, and three end vertices into a vertex on the cycle C a 1 , C a 2 , C a 3 , respectively, is denoted as γ (a 1 , a 2 , a 3 , s 1 , s 2 , s 3) . Note that δ (a 1 , a 2 , a 3 , s 1 , s 2) = γ (a 1 , a 2 , a 3 , s 1 , 1 , s 2) , ζ (a 1 , a 2 , a 3 , s) = γ (a 1 , a 2 , a 3 , 1 , 1 , s) , φ (a 1 , a 2 , a 3) = γ (a 1 , a 2 , a 3 , 1 , 1 , 1) . In this paper, we give the necessity and sufficiency that the γ − graph, δ − graph, ζ − graph and φ − graph are singular and prove that the probability that a randomly given γ − graph, δ − graph, ζ − graph or φ − graph being singular is equal to 325 512 , 165 256 , 43 64 , 21 32 , respectively. From our main results, we can conclude that such a γ − graph( δ − graph, ζ − graph, φ − graph) is singular if at least one cycle is a multiple of 4 in length, and surprisingly, the theoretical probability of these graphs being singular is more than half. This result promotes the understanding of a singular graph and may be promising to propel the solutions to relevant application problems. [ABSTRACT FROM AUTHOR]

Recent studies have shown that it is possible to construct a probability measure from a fractal structure defined on a space. On the other hand, a theory on cumulative distribution functions from an order on a separable linearly ordered topological space has been developed. In this paper, we show how to define a linear order on a space with a fractal structure, so that these two theories can be used interchangeably in both topological contexts. [ABSTRACT FROM AUTHOR]

Related to the letters of an alphabet, entropy means the average number of binary digits required for the transmission of one character. Checking tables of statistical data, one finds that, in the first position of the numbers, the digits 1 to 9 occur with different frequencies. Correspondingly, from these probabilities, a value for the Shannon entropy H can be determined as well. Although in many cases, the Newcomb–Benford Law applies, distributions have been found where the 1 in the first position occurs up to more than 40 times as frequently as the 9. In this case, the probability of the occurrence of a particular first digit can be derived from a power function with a negative exponent p > 1. While the entropy of the first digits following an NB distribution amounts to H = 2.88, for other data distributions (diameters of craters on Venus or the weight of fragments of crushed minerals), entropy values of 2.76 and 2.04 bits per digit have been found. [ABSTRACT FROM AUTHOR]

In this article, the methodology of using probabilistic models of the grinding tool wear process is presented. Probabilistic modeling with empirical data allowed determining the values of other important process features. Among them, the distribution of active grains lifetime or distribution of cumulative attritious wear of the grinding grain apex could be distinguished. The results of modeling and wear analysis of grinding wheels as well as experimental results on peripheral grinding with zoned grinding wheels are presented. The analyzed grinding wheels consisted of three layers: two identical external layers with conventional structure and one internal layer containing the addition of abrasive aggregates. The external layers were profiled by chamfering the edges. As a result, their nominal surfaces were conical. The internal layer had a cylindrical shape and was designed for smoothing the surface after machining with external part. The tools were designed to increase the grinding efficiency and hence a good quality of machined surfaces could be acquired. For the experimental tests, the Ti6Al4V titanium alloy was used. It was found that the change in the shape and position of the grinding zone, as a result of volumetric wheel wear, caused a significant change in fracturing intensity. In the case of multilayer grinding tools, the wear process depends on the physical properties of each layer and their participation during machining of the workpiece. The presented methodology could be applied to a study on the machining process stages, which concerns temporary states and their variability according to the machining time.This makes it possible to reduce the cost of developing new tools dedicated to specific applications. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we considered several theoretical models, a static and four dynamic models without rebounds, of the throw of a fair coin landing on its edge, to demonstrate that the probability of heads or tails is less than 50%, depending on the initial toss conditions, the coin geometry and conditions of the coin and landing surfaces. For the dynamic model with rebounds that is the subject of this second part of the paper, it is found that the probability that a 50 Euro cent coin thrown from a normal height with common initial velocity conditions and appropriate surface conditions will end up on its edge is in the order of one against several thousand. [ABSTRACT FROM AUTHOR]

Considering that a fair coin has two sides and a cylindrical edge, the probability that it would fall on its edge is calculated, yielding the probability of heads or tails of less than 50%. In this first part, the theoretical models for a static case and for five dynamic cases, without rebounds, show that there is a small probability that the coin does not fall on its head or tail, depending on the initial toss conditions, the coin geometry and conditions of the coin and landing surfaces. It is found that the probability that a 50 Eurocent coin thrown from a normal height with common initial velocity conditions and appropriate surface conditions will end up on its edge is in the order of one against several thousand. In the second part of the paper, the dynamic model with rebounds is investigated. [ABSTRACT FROM AUTHOR]

New definitions for fractional integro-differential operators are presented and referred to as delayed fractional operators. It is shown that delayed fractional derivatives give rise to the notion of functional order differentiation. Functional differentiation can be used to establish dualities and asymptotic mixtures between unrelated theories, something that conventional fractional or integer operators cannot do. In this paper, dualities and asymptotic mixtures are established between arbitrary functions, probability densities, the Gibbs–Shannon entropy and Hellinger distance, as well as higher-dimensional particle geometries in quantum mechanics. [ABSTRACT FROM AUTHOR]

Forecasts of precipitation type are of high priority, as they have a large influence on human safety, the economy and the environment. In recent decades, methods of statistical post-processing of numerical weather prediction (NWP) outputs were only applied beside the experience of the operational forecasters. In the last few years, numerical models developed significantly; thus, precipitation type has become a variable directly calculated in some models. In the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system (IFS) model, a new method has been used since 2015 to predict the type of precipitation. In this study, we examine the forecast of the ECMWF IFS ensemble model concerning precipitation type through ensemble verification and a case study on a freezing-rain situation for the territory of Hungary. We put emphasis on the investigation of the usability of ensemble forecasts. We introduce the developed forms of visualization supporting the interpretation of ensemble precipitation-type forecasts. [ABSTRACT FROM AUTHOR]

Surgical treatment in patients with fragility femoral neck fractures often leads to a longer length of hospital stay (LOS) and higher costs. Intensive rehabilitation is one of the choices to reduce LOS, but patient selection criteria are controversial. We intended to develop a clinical score to predict the risk of poor ambulation at discharge. This study was based on a retrospective cohort of patients diagnosed with fragility femoral neck fractures surgically managed from January 2010 to December 2019 at Chiang Mai University (CMU) Hospital. Pre-, intra-, and post-operative factors that affect rehabilitation training were candidate predictors. All patients were categorized into able or unable groups based on their ability to bear self-weight at discharge. Logistic regression was used for score derivation. Five hundred and nine patients were included in this study. Male sex, end-stage kidney disease (ESRD), cerebrovascular disease, psychiatric disorders, pre-fracture ambulation with gait aids, concomitant fracture, post-operative intensive care unit (ICU) admission or ventilator use, and urinary catheter use at second day post-operation were identified as the prognostic factors. The score showed an AuROC of 0.84 with good calibration. The score can be used for risk stratification on the second day post-operation. External validation is encouraged before clinical implementation. [ABSTRACT FROM AUTHOR]

During pandemic times, difficulties and problems related to the health sector are evident as the number of patients coming to health centers is higher compared to normal situations. This increase in the number of patients is typical of the pandemic, due to the high level of contagion in the population. Health personnel have a higher risk of infection, due to their sharing the work of caring for positive patients, so the infection rate is much higher. Hence, it remains necessary to understand the behavior of infection of health personnel, in order to be prepared to deal with the care of patients. Accordingly, in this research, we present a method to estimate different scenarios of infection and assess the probability of occurrence, so we can estimate the infection rate of health personnel. We present a simulation of 21 possible scenarios with 100 workers and a minimum of 80% needed to guarantee patient care. The results show that it is more likely that a 50% contagion scenario will occur, with an acceptable probability of 20%. [ABSTRACT FROM AUTHOR]

The paper aims to capitalize on the new features that are offered by the Microsoft Excel calculation program for reliability modeling, using the Median Ranks estimator that is calculated directly with the BETA.INV function, not estimated by various algebraic estimators, as is generally the case. Starting from this first step, a method of modeling reliability is elaborated through the three-parametric Weibull model that is based exclusively on this software, which is accessible to anyone and can be used even in the case of online learning, which is widespread in recent years due to the pandemic situation. The probability plotting method is applied, using the Median Ranks estimator that is calculated directly with the BETA.INV function for a probability equal to 0.5. A flowchart is made for the proposed method, which could be easily translated into a calculation program. By representing in logarithmic coordinates, we determined the Weibull models for different values that were initially adopted for the location parameter: using as a criterion the coefficient of determination that was obtained using the trendline function for the linear model, it was possible to identify, by successive tests, the optimal value of the location parameter—for which the three-parametric model has a good likelihood. By the proposed method, this value can be found following this iterative process. So, based on the current facilities of the Microsoft Excel program, a precise and easy-to-apply method has been achieved, through which an appropriate three-parametric Weibull model can be identified. [ABSTRACT FROM AUTHOR]