Bayesian Theory Research Articles

Right anterior insula ASL hypoperfusion as a diagnostic biomarker of prodromal and mild dementia with Lewy bodies: preliminary evidence using a Bayesian approach.

Identifying and validating a biomarker with high specificity in early-stage dementia with Lewy bodies (DLB) using a feasible method is crucial to enhance the current suboptimal diagnostic procedure. Previous research revealed abnormalities, including hypoperfusion in the right anterior insular cortex at group level, in prodromal DLB. Exploring hypoperfusion of the right anterior insula, at an individual-level and assessing its relevance as a potential imaging biomarker in early DLB, has, to our knowledge, not been investigated. Our preliminary study aims to assess the feasibility of the technique and to provide a methodological framework for further investigation. We assessed the feasibility and accuracy of the hypoperfusion of the right anterior insula per arterial spin labelling magnetic resonance imaging (ASL-MRI) as a diagnostic biomarker in early DLB and provided rough estimates of its sensitivity and specificity. Defining the region of interest based on previous research, we established the biomarker as the hypoperfusion of the right anterior insula. Discriminative and analytical performances were assessed in comparison to a control group of treatment-resistant depression patients. Bayesian diagnostic reasoning was employed to assess the biomarker diagnostic usability in early DLB in two scenarios: healthy elderly controls and mild cognitive impairment. Additionally, we updated probabilities by integrating data from the Mayo-clinic cognitive fluctuations scale and real-time quaking-induced conversion (RT-QuIC) α-synuclein data. Lastly, a whole-brain perfusion analysis of DLB patients was conducted to identify further brain regions with discriminative abilities. We successfully replicated the right anterior insular hypoperfusion (RAI-Hypo) in all DLB patients at the individual level. The overall sensitivity of the biomarker was 96%, and the specificity was 92%. Bayesian testing revealed the biomarker's highest performance in early-stage DLB with cognitive fluctuations, showcasing a diagnostic potential associated with a high precision and moderate accuracy. In a cognitively non-impaired population, the RAI-Hypo showed a limited usability and lacked in selectivity to qualify as a screening tool. The exploratory whole-brain analysis revealed perfect discriminative capacities in the bilateral anterior insulae and the left inferior parietal lobule. Further studies are needed to confirm our preliminary results. If performance is maintained in subsequent studies and is compared to a more suitable control population, the proposed biomarker may be eventually sufficient to discriminate early-stage DLB from non-DLB.

Preparing Children for Invasive MedicalCancer Treatment with "My Logbook": Preliminary Results of a Pilot Study.

Pediatric cancer is one of the most burdensome chronic diseases, necessitating a variety of severe medical interventions. As a result, the disease and its treatment cause numerous acute and long-term medical, psychological, and socioeconomic strains for young patients and their families. Therefore, psychosocial care using evidence-based interventions (EBIs) before, during, and after medical treatments is essential to ensure that patients receive adequate information and to minimize the adverse emotional and psychosocial impacts such as insecurity, fear, and shame. The present study reports the first promising results of applying cancer-specific psychosocial methods developed in the quality improvement project "My Logbook." The four assessed tools are specifically designed to adequately prepare pediatric cancer patients for surgery, chemotherapy, radiotherapy, and stem cell transplantation. Self and proxy ratings were used to assess the patients' subjective knowledge and emotional well-being before and after each intervention session. The results showed that patient-centered interventions using various creative and developmentally adapted methodologies (e.g., psychoeducation, crafting, games) have the potential to effectively enhance patient health literacy (V = 120.5, p < .001, r = 0.33) and well-being as manifested in more positive (slope = 0.121, p = .016) and less negative (slope = - 0.350, p < .001) or neutral emotions (slope = - 0.202, p = .002). These findings highlight the importance of developing and implementing psychosocial tools in pediatric oncology to prevent psychological overload and negative emotions and to increase subjective control beliefs, autonomy, and empowerment. Moreover, the effective application and systematic evaluation of evidence-based psychosocial tools can facilitate the establishment of standardized guidelines for psychosocial care in pediatric oncology. Thereby, the final goal is to ensure the quality of care and to use education to increase the quality of life for all pediatric cancer patients.Trial registration: ClinicalTrials.gov Identifier: NCT04474678 (July 17, 2020).

Chance, probability, and uncertainty at the edge of human reasoning: What is Knightian uncertainty?

AbstractResearch SummaryFor more than a century, Frank Knight's Risk, Uncertainty, and Profit has significantly influenced entrepreneurship theory development by exploring the nature of uncertainty and the epistemic limits of entrepreneurial action. Knight's work highlights how economic actors cannot fully predict the consequences of their actions. Despite its broad influence, debates persist regarding the nature of Knightian uncertainty. This study addresses these debates through a comprehensive analysis of RUP and Knight's other published and unpublished writings to offer new insights into the nature and meaning of Knightian uncertainty, revealing Knight's holistic theory that integrates “real indeterminism,” “partial knowledge,” and “subjective beliefs.” This analysis provides much needed construct clarity to advance contemporary theories of entrepreneurial action and the role of uncertainty in business venturing processes.Managerial SummaryThis article revisits Frank Knight's foundational work, Risk, Uncertainty, and Profit, a cornerstone in entrepreneurship research for over a century. We highlight Knight's holistic approach to uncertainty, which integrates the concepts of real indeterminism (the inherent unpredictability of future events), partial knowledge (the incomplete understanding of the present and future), and subjective beliefs (individual perceptions and interpretations). The study offers new perspectives on how Knightian uncertainty influences entrepreneurial decision‐making and action, highlighting how this unique type of uncertainty plays a critical role in the business venturing process. These insights provide valuable contributions to contemporary theories of entrepreneurship, emphasizing the complexity and multifaceted nature of navigating uncertainty in business.

Running the enrollment numbers on ophthalmic clinical trials in the United States.

This is one of the first reports to summarize the enrollment metrics for ophthalmology trials completed in the United States (US). This study aimed to describe US ophthalmology clinical trial enrollment metrics to facilitate planning and budgeting of US Food and Drug Administration-regulated ophthalmological drugs trials. A GlobalData PLC search was conducted on or before February 27, 2024, to evaluate the clinical trial landscape for completed ophthalmology clinical trials conducted in the US. The primary search contained only the term "ophthalmology," which was restricted to trials that were completed and were conducted within the US. Trials were classified as multicenter when trials included three sites or more, and when the enrollment search resulted in ≥30 multicenter trials for an individual indication, enrollment data were further broken down by Food and Drug Administration trial phase. The search yielded 2229 trials, which analyzed 980 different drugs produced by 854 different sponsors. The most common indications evaluated in US trials were macular degeneration, glaucoma, macular edema, allergies, and keratoconjunctivitis. Multicenter trials by indication had an overall median enrollment period range of 4.8 to 35.1 months; number of subjects enrollment, range of 36 to 518 subjects; number of sites utilized, range of 4 to 74 sites; and enrollment rate, range of 0.11 to 4.04 subjects/sites per month. There were 17 indications with ≥30 multicenter trials, which allowed for enrollment metric calculation by trial phase. This study provides sponsors with an understanding of the number of subjects and sites needed to complete a trial while also setting realistic enrollment timelines. Although this work represents the US market, more work is needed to better understand other countries given that country-specific guidelines and subject beliefs may impact enrollment metrics.

The correlation between strain rate and earthquake size – A Bayesian probability approach for indicating seismic hazard in the Himalaya and adjacent areas

The correlation between the seismicity and strain rate (SR, in 10-9/yr) is investigated through a combined Bayesian statistical approach to identify the possible locales of seismic hazard in the Himalaya and adjacent areas. The primary result shows that the maximum number of earthquakes in all magnitude (Mw) classes occur in the moderate 30 – 60 SR class. The Bayesian modelled parameter (µ) value for earthquakes in all four SR classes is 0.1315 (0 – 30), 0.1286 (30 – 60), 0.1386 (60 – 90), and 0.1504 (90 – 180). As the µ value is highest in the SR class (90 – 180), the probability of occurrence of larger magnitude event is more. The probability analysis indicates that the future seismic hazard (Mw > 6.0) will be collocated in the highest SR class (90 – 180) with a probability of 35.10 %. This SR class occupies 15 % of the studied area. However, the other SR classes are equally significant for Mw > 6.0 earthquake where the probability varies between 20.55 % (0 – 30), 21.29 % (30 – 60), and 23.06 % (60 – 90) covering 40 %, 30 %, and 15 % of the studied area respectively.

Toward an Efficient Hybrid Clustering and Classification Approach for Fake News Detection in Social Network

Objectives: The spread of fake news on social media has become a pressing issue in recent times. Despite various organizations' efforts to address this problem, it continues to persist, necessitating finding more effective solutions. This study implements a machine learning-based approach for identifying fake news on social media with improved accuracy. Methods : The study's methodology utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to extract semantic features from news articles. Then, a hybrid clustering and classification approach is used, which combines K-means clustering and Artificial Neural Network (ANN) classifiers. Data used is a secondary data consisting of 23481 world news articles obtained from the Reuters.com website and 21417 unreliable news from polifact.com. During the training process, the number of units and layers on the model was tuned to optimize model performance. The model was compared to other baseline models such as KNN, SVM, Decision tree, and Boosted decision tree to establish the best performing model. Findings: The results from the two algorithms were weighted to final classifications using Bayesian probability theory. The proposed approach achieved an accuracy of 99.78%, a sensitivity of 100%, and specificity equal to 99.73%. The model's precision is 99.74%, indicating its ability to identify fake news. The F-score of the approach is 99.87%, indicating that the model strikes a good balance between correctly classifying fake news articles and reliable news articles. The approach outperformed other machine learning classifiers, including KNN, SVM, Decision Tree, and Boosted Decision Tree. Novelty : The study applies a hybrid approach with a classification and clustering algorithm to improve detection of fake news on social media, the approach is tested with varied real-world datasets to establish its robustness under different vocabularies and vocabulary sizes. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Hybrid clustering approach, Classification, CNN, LSTM, Boosted Decision Tree, Social platforms, K-means

Relative-belief inference in quantum information theory

We introduce the framework of Bayesian relative belief that directly evaluates whether or not the experimental data at hand support a given hypothesis regarding a quantum system by directly comparing the prior and posterior probabilities for the hypothesis. In model-dimension certification tasks, we show that the relative-belief procedure typically chooses Hilbert spaces that are never smaller in dimension than those selected from optimizing a broad class of information criteria, including Akaike's criterion. As a concrete and focused exposition of this powerful evidence-based technique, we apply the relative-belief procedure to an important application: . In particular, just by comparing prior and posterior probabilities based on data, we demonstrate its capability of tracking multiphoton emissions using (realistically lossy) single-photon detectors in order to assess the actual quality of photon sources without making assumptions, thereby reliably safeguarding source integrity for general quantum-information and communication tasks with Bayesian reasoning. Finally, we discuss how relative belief can be exploited to carry out parametric model certification and estimate the total dimension of the quantum state for the combined (measured) physical and interacting external systems described by the Tavis-Cummings model. Published by the American Physical Society 2024

Bayesian Joint Probability Approach for Post‐Processing Monthly Precipitation Prediction in Northwest Iran

AbstractThe probabilistic prediction of monthly precipitation events, specifically more‐than‐normal (MN), normal (N), and less‐than‐normal (LN) events, holds significant importance in the field of water resource management. In this paper, we employ the Bayesian Joint Probability (BJP) modeling approach to generate monthly probabilistic forecasts for LN, N, and MN events in Northwest Iran. To model the joint distribution of observed precipitation and model precipitation, we propose the utilization of a log‐sinh transformed multivariate normal distribution. The prior distribution for the parameters of this multivariate normal distribution is established as the normal‐inverse‐Wishart, which is known to be the conjugate of the multivariate normal distribution. Bayesian inference of model parameters and associated uncertainties is conducted using STAN Markov Chain Monte Carlo (MCMC) sampling. The methodology is applied to the outputs of four North American Multi‐Model Ensemble (NMME) models, namely CanCM4i, CCSM4, NEMO, and NASA, spanning the period from 1982 to 2017 for a 4‐month forecast horizon. The accuracy of predictions is assessed through various statistical measures and graphical methods using a 12‐fold cross‐validation strategy. The results demonstrate that (a) BJP probabilistic predictions of precipitation consistently outperform raw predictions across all models, months, lead times. (b) The BJP‐NASA model exhibits superior discrimination and reliability compared to other models. (c) May and November show the least amount of discrimination. This observation may be attributed to the fact that these months serve as transitional periods in Iran. (d) Fall forecasts are the most reliable, while summer forecasts are the least reliable.

The Impacts of Self-Efficacy on EFL Learners’ Speaking Skills

In L2 speaking learning contexts, highly self-efficacious EFL learners are characterised as speakers capable of sharing their ideas confidently with other interlocutors as they have put a higher degree of belief towards their current speaking competencies. In support of self-efficacy, EFL learners are strongly willing to proceed with the complex speaking learning processes as they are prompted to foster their L2 communicative competencies to the utmost level. This small-scale library study was initiated to reveal the potential impacts of self-efficacy on EFL learners’ speaking skills. To achieve the primary study objective, 30 previous self-efficacy studies were profoundly reviewed with a thematic analysis to yield more understandable and trustworthy research results. Based on the obtained research results, it is strongly suggested that second language-speaking learning instructors infuse the robust construction of self-efficacy within EFL learners since they can progressively transform into more successful and confident L2 speakers. In conclusion, a higher degree of self-efficacy growth allows EFL learners to persistently confront various speaking learning impediments due to the successful employment of their speaking learning strategies mutually interlinked with the current speaking learning situations. Keywords: Self-efficacy; Speaking skills; EFL learners.

Explore Bayesian analysis in Cognitive-aware Key–Value Memory Networks for knowledge tracing in online learning

Knowledge Tracing is a crucial aspect of personalized learning that aims to track the evolving knowledge states of students with respect to one or more concepts. However, existing literature either models knowledge state for each predefined concept separately, potentially overlooking the underlying interrelationships between concepts, or lacks the capability to uncover long-term dependencies within a historical sequence of questions. Furthermore, they have limitations in precisely identifying the mastery level of individual learners for specific concepts. In order to address these problems, we propose a novel deep learning-based knowledge tracing model, named Bayesian Cognitive-aware Key–Value Memory Network (BCKVMN). BCKVMN combines the strengths of Dynamic Key–Value Memory Network (DKVMN), Long Short-Term Memory (LSTM), embedding layer, and Bayesian theory. To capture long-term dependencies in question sequences and contextual information, we extend DKVMN by incorporating a standard LSTM module that integrates cognitive processes observed in human learning. Additionally, we employ an embedding layer to uncover the influence of question difficulty on final predictions. Furthermore, we introduce Bayesian theory to enhance parameter interpretation for the model. To investigate complex representation of human learning, our approach considers not only three distinct learning states during the learning process but also the relationships between underlying concepts and students’ mastery levels for each concept. Experimental evaluations are conducted on the three real-world datasets Algebra200, ASSISTMENTS2009 and ASSISTMENTS2017. The results have demonstrated that the proposed model outperforms state-of-the-art methods (achieving nearly 7% improvement in AUC in some cases) while achieving a better balance between performance and interpretability.

Methods for Estimating Parameters of the Common Cause Failure Model Based on Data with Uncertainty

In this paper, we propose a new method to deal with uncertain data in the context of Common Cause Failure (CCF) analysis. Uncertain CCF data refer to the data for which the number of components involved in the failure events is not exactly known. We introduce a new formalism to describe uncertain CCF data to avoid subjective probabilities for the number of failed components in each CCF event that are used in classical methods such as the impact vector method. The parameters of the [Formula: see text]-factor model are estimated using the maximum likelihood method relying on properties of the nested Dirichlet distribution and grouped Dirichlet distribution. A data augmentation technique with an expectation-maximization algorithm is also developed for some schemes of data with uncertainty. Finally, we evaluate the performance of the proposed method through numerical simulations and illustrate its application using an example from the literature.

Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional Standards

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user’s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user’s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

From pre-test and post-test probabilities to medical decision making

BackgroundA central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive.MethodsWe extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making.ResultsWe develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r∗=(1-p)/p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r^{*}=(1-p)/p$$\\end{document} represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r^{*}$$\\end{document} represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians.ConclusionsOur approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.

Structural Disorder by Octahedral Tilting in Inorganic Halide Perovskites: New Insight with Bayesian Optimization

Structural disorder is common in metal‐halide perovskites and important for understanding the functional properties of these materials. First‐principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure‐sampling schemes required to characterize the disorder. Herein, structural disorder in the benchmark inorganic halide perovskites CsPbI3 and CsPbBr3 is computationally studied in terms of the three octahedral‐tilting angles. The subsequent variations in energetics and properties are described by 3D potential‐energy surfaces (PESs) and property landscapes, delivered by Bayesian optimization as implemented in the Bayesian optimization structure search code sampling density functional theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in 3D searches demonstrates the power of active learning and strategic sampling with Bayesian optimization. Further analysis indicates that disorder grows with increasing temperature and reveals that the material bandgap at finite temperatures is a statistical mean over disordered structures.

Uncertain random variables and laws of large numbers under U-C chance space

Uncertainty theory was founded by Baoding Liu for modeling belief degrees about human uncertainty. And chance theory was pioneered by Yuhan Liu based on probability theory and uncertainty theory. In many cases, imprecise risk situation in subjective or objective setting and human uncertainty simultaneously appear in a complex system. To measure uncertain random events in this system, this paper combines uncertainty space with convex non-additive probability space into a new kind of two-dimensional chance space called U-C chance space. Moreover, a new framework for uncertain random variables combining uncertainty theory with convex non-additive probability theory is provided. For applications of this new framework, it can be applied to characterize some kinds of phenomena which possess the characteristics of both imprecise risk situations and belief degrees about human uncertainty in situations of great events, such as financial crisis, major natural disaster, etc. The main contribution of this paper is to derive the types of Kolmogorov and Marcinkiewicz-Zygmund LLNs for uncertain random variables satisfying some conditions under U-C chance space, where the convex non-additive probability is totally monotone. Furthermore, several examples are stated and explained.

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.&amp;#xD;

Analysis of Factors Affecting Emergency Response Linkage in Coal Mine Gas Explosion Accidents

To analyze the influencing factors of the emergency linkage of gas explosion accidents and their causal relationships, a method for analyzing the influencing factors of the emergency linkage of gas explosion accidents is proposed based on a hierarchical holographic model and Bayesian networks. Firstly, based on the hierarchical holographic model to determine the main influencing factors of the accident emergency linkage, we constructed the topological structure model of accident control, secondary disaster, and emergency rescue, and used the triangular fuzzy number to assess the a priori probability and conditional probability of the influencing factors. Next, the most likely factors affecting the accident emergency linkage are quickly identified by combining Bayesian diagnostic reasoning. Finally, sensitivity analysis is conducted to identify the key factors affecting the accident emergency linkage. The results show that the probability of normal operation of gas explosion accident emergency linkage is 78.1%, but when the coal mine environment changes, especially when multiple influencing factors occur simultaneously, the probability of normal operation of accident emergency linkage decreases significantly. Through causal analysis, the degree of influence on the operation of the accident emergency linkage in different situations can be deduced. Through diagnostic analysis, it can be seen that the emergency linkage operation is more sensitive to the two factors of the ventilation and smoke extraction system response and gas over limit, so it is necessary to pay attention to its important role in accident treatment. Meanwhile, the sensitivity analysis shows that the response of the ventilation and smoke extraction system, the accuracy of disaster sharing, the gas over limit, the technical level of the operators, and the team rescue experience are the key factors affecting the emergency response linkage in accidents. This study can provide theoretical guidance for the improvement of the emergency response linkage mechanism of coal mine gas explosion accidents as well as the decision-making of the accidents, minimize the losses of the accidents, and promote the sustainable development of the coal mining industry.

A low-dimensional approximation of optimal confidence.

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter β). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases.

Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.