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In additive manufacturing processes such as laser powder bed fusion, the build orientation and packing of components affect the required support structures, the number of parts in each build, and the surface roughness of the printed parts, among other factors. Maximising the packing density while minimising the build height can increase effective machine utilisation and decrease per-part cost. Yet, the build layout optimisation problem is highly nonlinear and difficult to solve using human intuition, so a systematic algorithm approach is required. Here, we present and demonstrate a voxel-based analysis method with Bayesian optimisation for determining component build orientation in additive manufacturing. We introduce selected case studies incorporating exemplary process attributes of laser powder bed fusion, including the determination of orientation and packing configurations based on support removal and tool-accessibility constraints. [ABSTRACT FROM AUTHOR]

Many theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers' subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches. [ABSTRACT FROM AUTHOR]

Product consumption is an important yet understudied aspect of marketing. Over-the-counter medicines present a unique consumption context because consumers are expected to follow product label instructions. For acetaminophen, a widely consumed over-the-counter drug, the authors study which consumers tend to deviate from the label instructions, why they do so, and the interventions that are most promising to mitigate such deviations. They develop a dynamic structural model of consumption that enables them to investigate the probability of different types of label deviations (e.g., >4 g per day) and drivers of such behaviors. Label deviations are infrequent "tail area" behaviors, and the model uncovers them well. The analysis is based on a unique online consumption diary in which consumers select from two classes of acetaminophen products (single ingredient or a combination of ingredients) to treat their pain or non-pain-related symptoms. Consumers have a higher probability of taking >4 g of acetaminophen when they have multiple symptoms, and a variety of observable factors explain the individual propensity to deviate from the label. The authors propose two interventions—a new label instruction and consumer education—to mitigate the observed label deviations and assess the expected impact of each. [ABSTRACT FROM AUTHOR]

McShane et al.'s (2024) wide-ranging critique of null hypothesis significance testing provides a number of specific suggestions for improved practice in empirical research. This commentary amplifies several of these from the perspective of computational statistics—particularly nonparametrics, resampling/bootstrapping, and Bayesian methods—applied to common research problems. Throughout, the author emphasizes estimation (as opposed to testing) and uncertainty quantification through a comprehensive process of "curating" a variety of graphical and tabular evidence. Specifically, researchers should be encouraged to estimate the quantities that matter, with as few assumptions as possible, in multiple ways, then try to visualize it all, documenting their pathway from data to results for others to follow. [ABSTRACT FROM AUTHOR]

Routines shape many aspects of day-to-day consumption. While prior research has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which the authors define as repeated behaviors with recurring, temporal structures—for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. The authors propose a new approach for doing so, which they apply in the context of ridesharing. They model customer-level routines with Bayesian nonparametric Gaussian processes, leveraging a novel kernel that allows for flexible yet precise estimation of routines. These Gaussian processes are nested in inhomogeneous Poisson processes of usage, allowing the authors to estimate customers' routines and decompose their usage into routine and nonroutine parts. They show the value of detecting routines for customer relationship management in the context of ridesharing, where they find that routines are associated with higher future usage and activity rates, and more resilience to service failures. Moreover, the authors show how these outcomes vary by the types of routines customers have, and by whether trips are part of the customer's routine, suggesting a role for routines in segmentation and targeting. [ABSTRACT FROM AUTHOR]

Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). However, what appears to be an indirect effect in standard mediation analysis may reflect a data-generating process without mediation, including the possibility of a reversed causal ordering of measured variables, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, the authors develop the insight that a statistically reliable total effect, combined with strong evidence for conditional independence of the treatment and the outcome given the mediator, is unequivocal evidence for mediation as the underlying causal model into an operational procedure. This is particularly helpful when theory is insufficient to definitely causally order measured variables, or when the dependent variable is measured before what is believed to be the mediator. The procedure combines Bayes factors as principled measures of the degree of support for conditional independence, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. The authors reanalyze a set of published mediation studies to illustrate how their approach facilitates stronger conclusions. [ABSTRACT FROM AUTHOR]

Standard choice-based conjoint models often ignore or insufficiently approximate consumers' budget constraints, despite the prominent role of budget constraints in economic theory. The authors offer a theoretically motivated improvement to the choice-based conjoint model that is especially appropriate for high-ticket durable goods and develop a Bayesian method for the inference of unobserved budget constraints. The proposed method leverages respondents' stated budget constraints that suffer from measurement error and respondents' financial demographic variables as additional information to reduce the dependency on functional form assumptions in the estimation. The authors show that accounting for budget constraints substantially increases model fit and the accuracy of competitive pricing in an industry-grade discrete-choice experiment on consumer preferences for high-end laptops. The proposed model performs better than the canonical linear price benchmark model, which is not flexible enough to approximate budget constraints. In theory, more flexible utility specifications, such as the nonlinear dummy price model, can approximate consumers' budget constraints. However, they perform poorly when only finite data are available. The authors conclude that applied researchers in industry and academia will benefit from having a better tool for estimating budgets in high-ticket categories. [ABSTRACT FROM AUTHOR]

To fit a set of quantile curves, Bayesian simultaneous quantile curve fitting methods face some challenges in properly specifying a feasible formulation and efficiently accommodating the non-crossing constraints. In this article, we propose a new minimization problem and develop its corresponding Bayesian analysis. The new minimization problem imposes two penalties to control not only the smoothness of fitted quantile curves but also the differences between quantile curves. This enables a direct inference on differences of quantile curves and facilitates improved information sharing among quantiles. After adopting B-spline approximation for the positive smoothing functions in the minimization problem, we specify the pseudo composite asymmetric Laplace likelihood and derive its priors. The computation algorithm, including partially collapsed Gibbs sampling for model parameters and Monte Carlo Expectation-Maximization algorithm for penalty parameters, are provided to carry out the proposed approach. The extensive simulation studies show that, compared with other candidate methods, the proposed approach yields more robust estimation. More advantages of the proposed approach are observed for the extreme quantiles, heavy-tailed random errors, and inference on the differences of quantiles. We also demonstrate the relative performances of the proposed approach and other competing methods through two real data analyses. [ABSTRACT FROM AUTHOR]

An acyclic digraph in which every vertex has indegree at most i and outdegree at most j is called an (i , j) digraph for some positive integers i and j. The phylogeny graph of a digraph D has V (D) as the vertex set and an edge u v if and only if one of the following is true: (u , v) ∈ A (D) ; (v , u) ∈ A (D) ; (u , w) ∈ A (D) and (v , w) ∈ A (D) for some w ∈ V (D). A graph G is a phylogeny graph (resp. an (i , j) phylogeny graph) if there is an acyclic digraph D (resp. an (i , j) digraph D) such that the phylogeny graph of D is isomorphic to G. Lee et al. (2017) and Eoh and Kim (2021) studied the (2 , 2) phylogeny graphs, (1 , j) phylogeny graphs, (i , 1) phylogeny graphs, and (2 , j) phylogeny graphs. Their work was motivated by problems related to evidence propagation in a Bayesian network for which it is useful to know which acyclic digraphs have chordal moral graphs (phylogeny graphs are called moral graphs in Bayesian network theory). In this paper, we extend their work by giving necessary conditions of chordal (i , 2) phylogeny graphs. We go further to give necessary conditions of (i , j) phylogeny graphs by listing forbidden induced subgraphs. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings. [ABSTRACT FROM AUTHOR]

Aim/Introduction: The recent adverse reactions associated with semaglutide have led the Food and Drug Administration (FDA) to issue a "black box warning", and it is necessary to analyze all reports of adverse reactions to improve the safety of its clinical use. Materials and Methods: Statistical analyses and signal mining were performed by obtaining the adverse event reports related to semaglutide in the FAERS database from the first quarter of 2018 to the fourth quarter of 2023. We used disproportionality and Bayesian analysis to examine clinical and demographic attributes, trends reported quarterly, and contrasts between two distinct indications (obesity and type 2 diabetes). Results: We found 10 unexpected adverse signals related to "pancreatic cancer", "intestinal obstruction", "cholecystitis", and "polycystic ovary" and both the two different indications had the same serious adverse reaction events occurring. Conclusions: This study identified many unexpected signals of serious adverse reactions, suggesting the importance of continuous post‐marketing surveillance of semaglutide to understand its potential risks. [ABSTRACT FROM AUTHOR]

Piracy has long plagued the maritime industry, and has led to significant losses of life and goods. The risk of maritime piracy is largely predicted at present based on static analysis. This does not suitably address practical needs because the behavior and activities of maritime pirates are dynamic. Selecting an appropriate strategy for reducing the risk of piracy under a dynamic environment featuring uncertainty thus remains a key challenge. In this study, we propose a two-stage technique for order of preference by similarity to an ideal solution (TOPSIS) model based on the Bayesian network (BN). A data-driven BN is constructed in the first stage of the proposed method to identify the causal relationships influencing the behaviors of pirates. The second stage involves calculating a decision matrix of the strategies by using TOPSIS, where this enhances the strength of risk prediction and dynamic diagnosis by the BN. The main novelty of the proposed model is that it can provide quantitative measurements of strategies for reducing the probability of piracy in a dynamic environment. It provides a decision-making tool for researchers, shipping companies, and national navies to assess the risk of piracy and select effective measures to reduce its likelihood. [ABSTRACT FROM AUTHOR]

Background: Recent trials suggest that more conservative resections such as segmentectomy are non-inferior to more radical approaches. Most segmentectomy can be safely performed using video-assisted thoracoscopic surgery (VATS). The clinical benefits of robotic-assisted thoracoscopic surgery (RATS) remain unclear. We aimed to perform a systematic review evaluating the outcome of open thoracotomy, VATS, and RATS for segmentectomy. Methods: A systematic database search was conducted of original articles exploring the outcome of open versus VATS versus RATS segmentectomy in PubMed, EMBASE and SCOPUS. The primary outcome was 30-day mortality. Secondary outcomes were hospital readmission, air leak, and post-operative pneumonia respectively. Results: 11 studies were included with a total patient sample size of 7280. There were no differences between the three approaches in terms of 30-day mortality, hospital readmission, air leak, and post-operative pneumonia. Conclusion: There are no significant differences between the three approaches in the clinical outcomes measured. While our analysis demonstrates the potential benefits of RATS, it is important to note that the steep learning curve associated with this technique may impact its wider adoption and efficacy in the community. Further randomised control studies are required to compare the short and long terms results of VATS and RATS approaches. [ABSTRACT FROM AUTHOR]

Scholarly literature has demonstrated that hybridity transforms both legacy and new media, but that this change is not even. We treat social media platforms as arenas of remediation, where users share and add their own context to information produced by both media subtypes and compare social media conversations about migration in six European languages that include links to either traditional or new media during 2015–2019. We use a mix of computational and statistical methods to analyze 3.5 million (re)tweets and 500,000 links shared within them. We identify the main differences in agenda setting power, function, and tone present within tweets that include links to legacy or new media. Our results show that discourses are similar across languages but clearly different when remediating legacy and new media. Trust in legacy media is correlated with higher proportion of shared links from legacy media and reversely related to the proportion of shared links from new media sources. Considering the volume and timing of the remediated content, we conclude that legacy media retains its agenda setting power. New media linked content tends to cover migration in association to subjects such as Islam or terrorism and to express strong critical opinions against migrants/refugees. The language used is more toxic than in legacy media linked content. The tweets remediating legacy media articles covered topics like domestic or European politics, causes of refugee arrivals and procedures to give them protection. Thus, legacy and new media remediated content differs in both tone and function: toxicity is low and factuality high for content linking to legacy media, with the reverse being true for new media remediations. [ABSTRACT FROM AUTHOR]

Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones. [ABSTRACT FROM AUTHOR]

ARDS is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored. Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS? We evaluated data from two prior ARDS trials (Higher vs Lower Positive End-Expiratory Pressures in Patients With the ARDS [ALVEOLI] and the Alveolar Recruitment in ARDS Trial [ART]) that compared different PEEP strategies. We classified patients into one of two subphenotypes as described previously. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality. We analyzed data from 1,559 patients with ARDS. Compared with lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in patients with subphenotype A disease in the ALVEOLI study (OR, 1.61; 95% credible interval [CrI], 0.90-2.94) and ART (OR, 1.73; 95% CrI, 1.01-2.98), with a probability of harm resulting from higher PEEP in this subphenotype of 94.3% and 97.7% in the ALVEOLI and ART studies, respectively. Higher PEEP was not associated with mortality in patients with subphenotype B disease in each trial (OR, 0.95 [95% CrI, 0.51-1.73] and 1.00 [95% CrI, 0.63-1.55], respectively), with probability of benefit of 56.4% and 50.7% in the ALVEOLI and ART studies, respectively. These effects were not modified by Pa o 2 to F io 2 ratio, driving pressure, or the severity of illness for the cohorts. We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the most important problems encountered in ceramic tile industry is defective product problem. Defective ceramics lead to loss of income and waste of resources in enterprises. However, it is generally unknown that which factors and production stages cause what kinds of defects. On the other hand, in the literature, the existing modelling studies usually consider the defects seen on industrial ceramics. The defect types of industrial ceramics and those of ceramic tiles are different. This article investigates the reasons behind the defect occurrences on ceramic tiles, along with a comparison between a logistic regression model and a Bayesian network model. The study shows that the Bayesian network model is more successful in estimating the defect types. The constructed Bayesian network model indicates that, in general, the high levels of the production band speed significantly increase the probabilities of all kinds of defects except the deformation defect. Additionally, the high densities of the glaze also increase the occurrence levels of the defects except the deformation defect. Similarly, the high levels of the engobe weight and the engobe density are also among the factors increasing the defect occurrences. [ABSTRACT FROM AUTHOR]

Background: To date, evidence regarding the effectiveness and safety of individualized controlled ovarian stimulation (COS) compared with standard dose COS has been inadequate. Objectives: To evaluate the updated evidence from published randomized controlled trials (RCTs) about the efficacy and safety of individualized COS with different ovarian reserve test biomarkers or clinical experience versus standard dose COS. Search Strategy: Terms and descriptors related to COS, individualized or standard, and RCT were combined to search, and only English language studies were included. Conference abstracts and comments were excluded. Selection Criteria: RCTs with comparison between different individualized COS strategies and standard starting dose strategy were included. Data Collection and Analysis: Two reviews independently assessed the eligibility of retrieved citations in a predefined standardized manner. Relative risk (RRs) and the weighted mean difference (WMD) with 95% confidence intervals (CIs) were pooled using a random‐effects model on R software version 4.2.2. Main Results: Compared with the standard dose COS strategy in pairwise meta‐analysis, the individualized COS strategy was associated with a notable lower risk of ovarian hyperstimulation syndrome (OHSS; 174/2384 [7.30%] vs 114/2412 [4.73%], RR 0.66, 95% CI: 0.47–0.93, I2 = 46%), a significantly lower risk of hyperresponse to stimulation (hyperresponse; 476/2402 [19.82%] vs 331/2437 [13.58%], RR 0.71, 95% CI: 0.57–0.90, I2 = 61%), and a slightly longer ovarian stimulation days (duration of stimulation; WMD 0.20, 95% CI: 0.01–0.40, I2 = 66%). Bayesian network meta‐analysis also found that biomarker‐tailored strategy had a significantly lower risk of OHSS than standard dose strategy (OHSS; RR 0.63, 95% CI: 0.41–0.97, I2 = 47.5%). Conclusion: Compared with standard dose COS strategy, individualized COS strategy could significantly reduce the risks of OHSS and hyperresponse to stimulation, but the duration of stimulation was slightly longer. Trial Registration: PROSPERO: CRD42023358439. Synopsis: Individualized controlled ovarian stimulation (COS) strategy can significantly reduce the risk of ovarian hyperstimulation syndrome (OHSS) and hyperresponse to stimulation, but the duration of stimulation is slightly longer. [ABSTRACT FROM AUTHOR]

Sean Carroll has recently argued that theories predicting that observations are dominated by Boltzmann Brains should be rejected because they are cognitively unstable: “they cannot simultaneously be true and justifiably believed.” While such Boltzmann Brain theories are indeed cognitively unstable, one does not need to appeal to this argumentation to reject them. Instead, they may be ruled out by conventional Bayesian reasoning, which is sufficient to keep Boltzmann Brains at bay. [ABSTRACT FROM AUTHOR]

To cover the problem of dangerous state prediction ahead of vehicle rollover and rescue the vehicle under abrupt cornering condition, a dynamic Bayesian network (DBN) merged robust control is developed to balance the vehicle ride comfort and handling performance. To discretize the automobile state attributes and prepare for the prediction, class attribute contingency coefficient (CACC) is adopted to pre-process the data conveniently and establish the category labels. The key contributions of this paper are efficient rollover prediction with probabilistic and numerical representation, a mapping rule from rollover probabilities to T–S fuzzy membership values, and an intelligent objective switchable control between ride comfort and roll stability. The co-simulation method is adopted to verify the effectiveness of this method with passive suspension, semi-active suspension, and optimal control active suspension. It is shown that the DBN-based robust control is able to reduce the roll angle by more than 27% compared to the passive suspension under double-lane change condition and has the best balancing performance. From the perspective of ride comfort testing on bounce sinusoidal roads, the vehicle DBN incorporating robust controllers can effectively reject vibrations and switch control objectives based on its running conditions. [ABSTRACT FROM AUTHOR]

• This study evaluates the changes in the safety perception of shared automated shuttles and the willingness to re-ride (WTR) it. • Survey data from a shared automated passenger shuttle pilot program in Cary, North Carolina, was used. • Findings reveal that shuttle operations, especially timely arrival and drop-off, significantly affect both perceptional change and WTR. • Individuals with initial perceptions of the SAPS as very unsafe or unsafe showed a higher likelihood of perception change. • Further, the primary motivations for WTR were the enjoyable experience and convenience offered by the shuttle. Recognizing the potential transformative impact on transportation systems, safety perceptions of Shared Automated Vehicles (SAVs) have gained significant attention from researchers in recent years. Yet, the critical factors influencing perception changes and the willingness to re-ride (WTR) have not been extensively studied despite their relevance to SAV operations. This study applied Bayesian Networks (BNs) and Text Network (TN) methodologies to analyze survey data from a shared automated passenger shuttle (SAPS) pilot program conducted between March and April 2023, at Fred G. Bond Metro Park in Cary, North Carolina. Participants in the survey provided feedback on their safety perceptions of the SAPS before and after riding, as well as their willingness to ride again. Key findings reveal that shuttle operations, especially timely arrival and drop-off, significantly affect both perceptional change and WTR. Furthermore, users who accessed the shuttle by walking, biking, or public transportation were more likely to positively change their perception and express a willingness to ride the shuttle again. Also, individuals with initial perceptions of the SAPS as very unsafe or unsafe showed a higher likelihood of perception change. Conversely, older respondents were less likely to experience safety perception changes and WTR. Text network analysis further illuminated that the primary motivations for WTR were the enjoyable experience and convenience offered by the shuttle. The study contributes to the growing body of literature on SAVs by providing practical implications for the future development and testing of SAPSs. These insights are invaluable for policymakers and planners in optimizing SAPS operations, providing a deeper understanding of user experiences and expectations. [ABSTRACT FROM AUTHOR]

The phylogenetic relationships among major lineages of the globally distributed leafhopper subfamily Coelidiinae were reconstructed by analysis of 2903 nucleotide positions from two mitochondrial genes (COI and 16S), four nuclear genes (28S, H3, H2A and Wingless) and 102 discrete morphological characters, compiled for 86 species representing 52 genera within 9 coelidiine tribes broadly representative of the world fauna in addition to 12 outgroup taxa. Maximum likelihood and Bayesian analyses yielded well‐resolved phylogenetic estimates that were highly congruent with most branches receiving strong support. The results indicate Coelidiinae sensu stricto (=Coelidiinae sensu lato without Equeefini and Macroceratogoniini), Thagriini, Tharrini, Tinobregmini and Equeefini are monophyletic. However, the two largest tribes, Coelidiini and Teruliini, are paraphyletic, as well as Youngolidiini and most genera of Oriental Coelidiini. Fossil‐calibrated molecular divergence time analysis indicates that the Coelidiinae sensu stricto originated ca. 149 Ma, prior to the complete separation of the Gondwanan continents, and the tribe‐level lineages diverged between the Lower and Upper Cretaceous (92.77–138.03 Ma). The major lineages of Coelidiinae arose in the Oriental and Neotropical regions, and Oriental Coelidiini arose as a result of dispersal and colonisation from the Afrotropical region. [ABSTRACT FROM AUTHOR]

Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks (NNs) optimized with Bayesian regularization (BR), i.e. NNs-BR, procedure. The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy (TVT) models in terms of the dynamic interactions between the tumor cells free of viruses, tumor cells infected by viruses, viruses, and cytotoxic T-lymphocytes (CTLs). The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells, virus-free tumor cell clearance rate by CTLs, CTLs clearance rate for infectious tumor cells, the natural lifecycle of infectious tumor cells, the natural lifecycle of viral cell, the natural lifecycle of CTLs cells, tumor cells free of viruses' maximum proliferation rate, production of tumor cells with an infection, CTLs simulated ratio for infectious tumor cells, CTLs simulated ratios for virus-free cells and delay in time. The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model. The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10 − 9 to 10 − 1 6 . The efficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics, learning curves on MSE, and error histograms for the dynamics of TVT model. [ABSTRACT FROM AUTHOR]

In the realm of combustion kinetic modeling, the norm involves employing thousands of reactions to delineate the chemical conversion of hundreds of species. Notably, theoretically predicted rate coefficients and branching ratios, derived through the RRKM/master equation (ME) model, play an increasing role in kinetic modeling. Thus minimizing the uncertainty of theoretical prediction across wide working conditions is crucial to refine a kinetic model. The present study takes ethyl (C2H5) + oxygen (O2) reaction system to show that combined forward and reverse uncertainty analysis can be used to further constrain calculated rate coefficients and branching ratios, which were already calculated by high‐level quantum chemistry methods. Forward global uncertainty analysis with the artificial neural network‐high dimensional model representation (ANN‐HDMR) method is employed to select key parameters affecting total rate coefficients of C2H5 + O2 and branching ratios of C2H5 + O2 = C2H4 + HO2 (C1). Reverse uncertainty analysis with Bayesian method was then applied to refine the key input parameters based on experimental data at working conditions selected by sensitivity entropy. Although the target RRKM/ME model system was built on high level theoretical calculations, the combined forward and reverse uncertainty analyses are still able to reduce uncertainties of predicted total rate coefficients of C2H5 + O2 and branching ratios for C1 across a wide range of working conditions. Specifically, the uncertainties of total rate coefficient and C1 branching ratio have been reduced from 1.46 and 1.52 to 1.30 and 1.36 at 298 K and 1 Torr. The analysis process proposed in the present work effectively extrapolates the constraint ability of accurate measured data at one condition to wide working conditions based on the RRKM/ME model. [ABSTRACT FROM AUTHOR]

In this study, we conducted a quantitative analysis of the factors affecting Sentinel-1 interferometric decorrelation in a typical agricultural region in China, where maize and rice, two of the three staple crops in the country, are the primary crop types. Bayesian network and random forest regression analysis were employed to examine the impact of temporal and spatial decorrelation, Doppler centroid differences, and surface cover variations. Among these factors, the temporal baseline is the most significant contributor to decorrelation. This finding opens up the possibility of utilizing Sentinel-1 coherence, which quantifies the degree of decorrelation, as a mapping feature for staple crops. Consequently, we conducted classification experiments for staple crops in this typical agricultural region, utilizing time-series Sentinel-1 coherence data and considering polarization modes. The highest overall accuracy achieved was 98.08 % for the shortest temporal baseline (12 days), with VV polarization outperforming VH polarization. In comparison, we conducted classification experiments using Sentinel-1 intensity backscatter data in VV and VH channels. The combined use of coherence and backscatter data yielded superior results compared to using either data source alone. Furthermore, we conducted classification experiments based on Sentinel-2 optical remote sensing features. To sum up, we compared the classification performance of Sentinel-1 coherence with that of intensity and optical remote sensing features, demonstrating the reliability of time-series Sentinel-1 coherence as a valuable tool for mapping staple crops in China's crucial grain-producing regions. [ABSTRACT FROM AUTHOR]

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167–90, 2004a, Journal of Econometrics, 128(2), 301–23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78–103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena. [ABSTRACT FROM AUTHOR]

In geophysical inference problems, quantification of data uncertainties is required to balance the data-fitting ability of the model and its complexity. The transdimensional hierarchical Bayesian approach is a powerful tool to evaluate the level of uncertainty and determine the complexity of the model by treating data errors and model dimensions as unknown. In this article, we take account of the uncertainty through the whole procedure, thus developing a two-step fully Bayesian approach with coupled uncertainty propagation to estimate the crustal isotropic and radial anisotropy (RA) model based on Rayleigh and Love dispersion as well as receiver functions (RFs). First, 2D surface-wave tomography is applied to determine period-wise ambient noise phase velocity maps and their uncertainty for Rayleigh and Love waves. Probabilistic profiles of the isotropic average VS and RA as a function of depth are then derived at station sites by inverting the local surface-wave dispersion and model errors and RFs jointly. The workflow is applied to a temporary seismic broadband array covering all of Sri Lanka. The probabilistic results enable us to effectively quantify the uncertainty of the final RA model and provide robust inferences. The shear-wave velocity results show that the range of Moho depths is between 30 and 40 km, with the thickest crust (38-40 km) beneath the central Highland Complex. Positive RA (VSH > VSV) observed in the upper crust is attributed to subhorizontal alignment of metamorphic foliation and stretched layers resulting from deformation. Negative RA (VSH > VSV) in the midcrust of central Sri Lanka may indicate the existence of melt inclusions and could result from the uplift and folding process. The positive RA in the lower crust could be caused by crustal channel flow in a collision orogeny. [ABSTRACT FROM AUTHOR]

The P-wave peak distribution in azimuth and takeoff angles, corrected for the distance through empirical attenuation laws, can reveal the radiation pattern amplitude of the source (Tarantino et al., 2019). This piece of information, jointly combined with the available polarities and S-wave peak amplitudes, can provide the full focal mechanism, constraining the solution also when only a few seismic station records are available, that is, in the case of microseismicity. We proposed a new technique, named P-, S-wave amplitude, and polarities (P-SAP), designed to compute the focal mechanism by jointly inverting the P-, S-wave amplitude ratios and P-wave polarities in a Bayesian framework and employing the octree strategy (Fang et al., 1996) to explore the space of possible solutions. The outputs are strike, dip, and rake angles of the most likely triplet (principal and auxiliary planes) with the related uncertainties, as well as other multiple solutions, if present. We tested the methodology to synthetic data, and we applied it to a microseismic sequence that occurred in Irpinia region, southern Italy. A background microseismicity occurs in a volume delimited by the faults activated during the 1980 M 6.9 Irpinia earthquake. This faults system is complex and composed of northwest-southeast-striking normal faults along the Apennines chain and an approximately east-west-oriented strike-slip fault, deep-seated in the Potenza area. A network of three-component accelerometers and velocimeters are currently deployed to monitor the area. The major topic of this work is validating the proposed P-SAP methodology for microseismicity studies. We also inferred the optimum stress tensor of the sequence, confirming that the microseismicity is controlled by the regional stress field and can reveal characteristics useful to highlight behaviors of larger-scale seismicity. [ABSTRACT FROM AUTHOR]

A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process. • A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) method for quality-pattern modelling. • Expectation-maximisation, forward-filtering, and backward-smoothing algorithms for SPDCLV-model learning. • Dynamic causality from external manipulated inputs to latent quality pattern. • Pattern filtering and pattern-based dynamic soft-sensing for quality prediction. • A framework for pattern-based quality prediction and optimisation. [ABSTRACT FROM AUTHOR]

Phosphorus (P) fertilizer use efficiency in potato production is relatively low in semiarid regions, wasting P resources and increasing environmental risks. Therefore, improving P use efficiency (PUE) is critical for sustainable potato production. The critical P dilution curve (CPDC) and P nutrition index (PNI) have proven to be robust diagnostic tools for assessing crop P status and aiding in improving P fertilizer management. Several potato CPDCs have been established, however, few studies have been conducted to establish a CPDC for potato (Solanum tuberosumL.) under ridge planting with drip irrigation, a planting pattern that has been increasingly adopted in semi-arid regions. In addition, the different CPDCs established using the conventional Jestus statistical model cannot discriminate the true variability across scenarios or have become linked to estimation errors. Therefore, the objectives of this study were to (1) establish a potato CPDC based on a Bayesian statisticalmethod and (2) evaluate the relationship between potato yield and PNI. Three years of field experiments with five levels of P2O5 application (0, 80, 160, 240, 320 kg ha-1) were conducted in Inner Mongolia, China. No significant differences were found between CPDCs across the year × site for the assessed scenarios, and thus, a generic CPDC for potatoes in the region was derived as Pc = 0.616 DM-0.296, and it can be used to calculate the PNI. Further analysis showed that at eachgrowth stage, thePNI exhibits a significant plateauing linear relationship with relative potato tuber yield. Thus, it provides a standard for diagnosing the P nutritional status in potatoes and lays a robust foundation for precise P recommendations in the region. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, coxsackievirus (CV) A10 has been associated with increasing sporadic hand, foot, and mouth disease (HFMD) cases and outbreaks globally. In addition to mild symptoms such as pharyngitis and herpangina, CVA10-related complications or even fatality can occur. Currently, systematic phylogenetic studies of CVA10 are limited. Methods: In this study, we first explored the epidemiological and genetic characteristics of CVA10 in Nanchang, an inland southeastern city of China, based on the HFMD surveillance network from 2015-2023. Results: Among 3429 enterovirus-positive cases, 110 (3.04%) were associated with CVA10, with a male-to-female ratio of 1.62. The median age of the CVA10 patients was 2.3 years (interquartile range, IQR 1.0-4.0), with 94.55% (104/110) of the patients aged less than 5 years. Phylogenetic analyses using the full-length VP1, 5'UTR, P1, P2, P3 sequences and near full-length genomes indicated that CVA10 strains (n = 57) isolated in Nanchang belonged to genogroup C; two strains identified in 2017 belonged to C1 subtypes clustered with strains from Vietnam, Madagascar, France and Spain; and the others belonged to C2 subtypes interdigitating with CVA10 isolates from mainland China, the United States and Australia. Through extensive analysis, we identified a rare F168Y mutation in epitope 4 of VP1 in a Madagascar strain of genogroup F and a Chinese strain of genogroup C. Based on Bayesian evolutionary analyses, the average nucleotide substitution rate for the VP1 gene of CV10 strains was 3.07×10-3 substitutions/site/year. The most recent common ancestor (tMRCA) of genogroup C was dated 1990.84, and the tMRCA of CVA10 strains from Nanchang was dated approximately 2003.16, similar to strains circulating in other regions of China, suggesting that the viruses were likely introduced and cryptically circulated in China before the establishment of the HFMD surveillance network. Recombination analysis indicated intertypic recombination of the Nanchang strain with the genogroup G strain in the 3D region. Discussion: Given the shifting dominance of viral genotypes and frequent recombination events, the existing surveillance system needs to be regulated to enhance genomic surveillance efforts on a more diverse spectrum of genotypes in the future. [ABSTRACT FROM AUTHOR]

Background: A multitude of randomized controlled trials (RCTs) conducted in both the initial and subsequent treatment settings for patients diagnosed with metastatic colorectal cancer (mCRC) have provided clinical evidence supporting the efficacy of immunotherapy with the use of immune checkpoint inhibitors (ICIs). In light of these findings, the U.S. Food and Drug Administration (FDA) has authorized the use of several ICIs in specific subpopulations of mCRC patients. Nevertheless, there remains a dearth of direct comparative RCTs evaluating various treatment options. Consequently, the most effective ICI therapeutic strategy for microsatellite-stable (MSS) subgroup and microsatellite instability (MSI) subgroup in the first- and second-line therapies remains undefined. To address this gap, the present study employs a Bayesian network meta-analysis to ascertain the most effective first- and second-line ICI therapeutic strategies. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, EMBASE, Cochrane Library, and Web of Science, with the retrieval date ranging from the databases' inception to August 20, 2024. A total of 875 studies were identified, and seven were ultimately included in the analysis after a screening process. A systematic review and network metaanalysis were conducted on the basis of the search results. Results: This comprehensive analysis, comprising seven RCTs, evaluated first-line and second-line immunotherapy regimens in 1,358 patients diagnosed with mCRC. The treatments under investigation consisted of five initial treatments, including three focusing on MSS patients and two on MSI patients, as well as two secondary immunotherapy regimens, both focusing on MSS patients. A total of 1051 individuals underwent first-line treatment, while 307 received second-line treatment. The application of ICIs proved to offer varying degrees clinical benefits when compared to standard-of-care therapy alone, both in two subgroups of the first and the second treatment phases. Of particular note is the performance of Nivolumab combination with ipilimumab, which demonstrated superior efficacy in improving progression-free survival (PFS) (HR=0.21; 95% CI, 0.130.34),. Moreover, the treatment demonstrated an optimal safety profile, with a relatively low risk of adverse events (OR = 0.33; 95% CI, 0.19-0.56), compared to other first-line treatment modalities for MSI subgroup. Regarding MSS subgroup, the improvement of PFS by Nivolumab plus standard-of-care (SOC) was relatively significant (HR = 0.74; 95% CI, 0.53-1.02). In the realm of second-line therapies for MSS subgroup, the administration of Atezolizumab plus SOC has proven to be an effective approach for prolonging PFS, exhibiting an HR of 0.66 (95% CI, 0.44-0.99). These findings underscore the clinical benefits and safety profiles of ICIs in the treatment of mCRC across various treatment lines. Conclusions: The clinical application of ICIs in both first- and second-line treatment strategies for patients with mCRC yields substantial therapeutic benefits. A detailed assessment in this study indicates that first-line treatment with Nivolumab combination with ipilimumab may represent an efficacious and well-tolerated therapeutic approach for MSI subgroup. In terms of MSS subgroup in first-line therapy, Nivolumab plus SOC may be a relative superior choice. In the context of second-line therapy for MSS subgroup, it is evident that a combination of Atezolizumab and SOC represents a preferable option for enhancing PFS. Furthermore, it is noteworthy that other ICIs treatment regimens also exhibit great value in various aspects, with the potential to inform the development of future clinical treatment guidelines and provide a stronger rationale for the selection of ICIs in both first- and second-line therapeutic strategies for mCRC. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Context Multikinase inhibitors (MKIs) improve the treatment of refractory thyroid cancer, including radioactive iodine-refractory differentiated thyroid cancer (RAIR-DTC) and advanced medullary thyroid carcinoma (aMTC). Objective This study aims to compare the efficacy of MKIs in improving survival outcomes and safety. Data Sources Comprehensive database searches of MEDLINE via PubMed, EMBASE, and Cochrane were performed from inception to December 2023. Study Selection Three independent authors selected these studies. Randomized controlled trials that compared the use of a MKI to other MKIs or placebo were included. Data Extraction and Synthesis This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias was analyzed using the Cochrane risk of bias 2 tool. Bayesian network meta-analysis was performed. Treatments were grouped into common nodes based on the type of MKI. Main Outcomes and Measures Primary outcomes were progression-free survival (PFS) and overall survival (OS). Secondary outcomes included objective response rate, disease control rate, clinical benefit rate, and adverse events. Results Cabozantinib 60 mg/day (CAB60) was associated with the highest prolonged PFS in RAIR-DTC patients, followed by lentivatinib 18 or 24 mg/day (LEN18 or LEN24), and apatinib. PFS was also improved in aMTC patients who received CAB 140 mg/day (CAB140), CAB60, or anlotinib. A significantly greater improvement on the performance of OS was seen in CAB60, LEN24, anlotinib, and sorafenib in RAIR-DTC patients, but in aMTC patients there were lack of statistical differences. Compared with the low-dose MKIs, high-dose MKIs such as CAB, LEN, and vandetanib increased the incidence of adverse events. Conclusion CAB60, LEN, and apatinib are promising topical MKIs with statistically significant primary outcomes in RAIR-DTC patients, while CAB and anlotinib are effective in prolonging PFS in aMTC patients. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The presence of heavy metals, specifically cadmium, in the environment poses significant threats to both ecological systems and human health. However, microalgae have shown potential in addressing this issue through their ability to absorb cadmium and produce valuable biomass, making them a promising solution for bioremediation. Among the various microalgae species, Chlorella vulgaris stands out as a suitable candidate due to its potential for biodiesel production and its capacity to effectively absorb cadmium. Therefore, the main objective of this study is to assess the toxicity of cadmium on Chlorella vulgaris cells using network meta-analysis as a methodology. METHODS: A comprehensive search was conducted on Scopus, Scilit, Google Scholar, and Web of Science to identify relevant studies published from 1 January 1990 to 16 January 2024. Only studies that reported the cell number of Chlorella vulgaris as a result of cadmium exposure were considered for inclusion. The collected data were then subjected to Bayesian frequentist network meta-analysis, utilizing standardized mean difference and a 95 percent confidence interval as measures of effect size. Additionally, a linear regression analysis was performed to examine the dose-dependent impact of cadmium toxicity. FINDINGS: Dose-dependent toxic effects of cadmium on Chlorella vulgaris were evident (R-square of more than 0.90), particularly at a concentration of 1 part per million, deemed as the maximum tolerable threshold. Prolonged exposure revealed a concentration-dependent reduction in cell viability, suggesting potential lifespan shortening. A comparison of growth stimulants, gibberellic acid and brassinolide (standard means differences of 1.7 and 3.8, respectively), in mitigating cadmium toxicity indicated the latter superior effectiveness in sustaining microalgal survivability. The presence of high nitrogen and low phosphorous levels was found to be significantly associated with a reduction in Chlorella vulgaris cells due to cadmium exposure. CONCLUSION: This research has provided conclusive proof of the harmful effects of cadmium on Chlorella vulgaris through the implementation of Bayesian frequentist network meta-analysis, offering valuable insights for environmental management practices. The findings reveal concentration-dependent effects of cadmium toxicity. The survivability of Chlorella vulgaris is determined by the compositions of macronutrients nitrogen and phosphorous. Comparative analyses highlight the superior protective effect of brassinolide over gibberellic acid in mitigating cadmium toxicity. Overall, the findings highlight the potential of Chlorella vulgaris in both bioremediation of heavy metals and biomass production. [ABSTRACT FROM AUTHOR]

Using the complementary strengths of Bayesian networks, decision trees, artificial neural networks (ANNs), and Markov models, this endeavor intends to completely revamp clinical decision-making. In order to provide instantaneous access to image-driven insights and clinical decision support systems (CDSS), want to create a revolutionary framework that merges these cutting-edge methods with cloud-enabled technologies. The proposed framework gives a comprehensive perspective of patient data by merging the probabilistic reasoning of Bayesian networks with the interpretability of decision trees, the pattern recognition abilities of ANNs, and the temporal interdependence of Markov models. This helps doctors to make more educated judgments based on a larger spectrum of information, leading to better patient outcomes. Healthcare workers can get to vital data from any place because to the cloud-enabled architecture's seamless scalability and accessibility. This not only increases the efficiency of decision-making, but also improves communication and cooperation between different medical professionals. This uses cutting-edge modeling strategies and cloud computing to pave a new path in clinical decision-making. This system has the potential to greatly enhance healthcare by integrating image-driven insights with CDSS, to the advantage of both patients and healthcare practitioners. [ABSTRACT FROM AUTHOR]

This study developed a probabilistic determination model with respect to cladding high-temperature burst conditions based on the Bayesian statistical method to reasonably evaluate fuel behaviors under loss-of-coolant accident conditions, including fuel fragmentation, relocation, and dispersal. The candidate models (correlations) were based on the widely accepted empirical model established based on nonirradiated fuel cladding data. Explanatory variables were added to improve the applicability of these models with respect to irradiated materials and generalization performance. The posterior predictive distribution of each candidate model was evaluated using Bayesian estimation comprising 238 sets of high-temperature burst test data. The generalization performance was evaluated using information criteria. The results of model evaluation showed improved predictive performance by considering the effect of hydrogen content. A comparison with burnup as an alternative explanatory variable confirmed that hydrogen content was the better parameter and other burnup-associated effects, such as irradiation hardening of the metal matrix and oxide growth (reduction of the metal matrix), were less dominant under burst conditions. [ABSTRACT FROM AUTHOR]

We present a Bayesian approach for the analysis of rating data when a scaling component is taken into account, thus incorporating a specific form of heteroskedasticity. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are proposed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates of the fitted model and the associated effect measures. An analysis on students' evaluation of a university curriculum counselling service is carried out to assess the performance of the method and demonstrate its valuable support for the decision-making process. [ABSTRACT FROM AUTHOR]

Aims: A recent guideline presented by the ESC Congress in 2022 had indicated a novel therapy targeted at pulmonary artery hypertension, known as pulmonary artery denervation (PADN), which get inspired from a laboratorial trial that could lowering the pulmonary artery pressure through the intervention on the animals. Our aim is to conduct a network meta‐analysis to compare the efficacy and safety of PADN from six aspects with the current conventional therapies. Methods and results: According to the PRISMA guidance, databases including Ovid, ClinicalTrials.gov, Medline, Embase, and PubMed were searched from inception to 22 August 2023, along with a full assessment of the previous five meta‐analyses. Data were extracted and curated for Bayesian network meta‐analysis. The primary outcome was the change in the 6‐min walking distance (6MWD) from baseline with a secondary outcome called change in mean pulmonary artery pressure (mPAP) from baseline. The four safety outcomes included risk of clinical worsening, hospitalization, mortality and severe adverse events (SAEs). The comparison is structured on a contrast model based on 65 randomized controlled trials (RCTs) on PADN and the other conventional mainstream drugs. PADN had a better effect in improving 6MWD than Placebo (−77.76 m, 95% CI: −102.04 to −54.34 m), Macitentan (−65.32 m, 95% CI: −95.34 to −36.1 m), Bosentan (−64.5 m, 95% CI: −94.7 to −35.07 m), Iloprost (−62.66 m, 95% CI: −99.48 to −27.13 m), Oxygen (−62.42 m, 95% CI: −100.01 to −25.78 m), Treprostinil (−62.01 m, 95% CI: −89.04 to −35.61 m), Riociguat (−60.59 m, 95% CI: −86.11 to −35.98 m), Selexipag (−47.2 m, 95% CI: −85.61 to −10.19 m), Sildenafil (−44.92 m, 95% CI: −74.43 to −16.15 m), or Sitaxsentan (−39.53 m, 95% CI: −78.99 to −0.76 m). PADN had a better antihypertensive effect than placebo and showed statistical significant lower risks to induce clinical worsening and re‐hospitalization than treprostinil, riociguat, and placebo groups. No statistically significant difference in risk of mortality and severe adverse events was observed between PADN versus the other interventions. Conclusions: Compared with 16 types of conventional therapies and Placebo, PADN has advantage over nine single therapies and Placebo in improving 6MWD and appears to be better than two types of dual‐drug combined therapies while with no statistical significance. PADN shows a favourable antihypertensive effect on mPAP and has a lower risk to trigger clinical worsening or hospitalization, while its risk on mortality and severe adverse events is still inconclusive. [ABSTRACT FROM AUTHOR]

We develop a new Bayesian approach to estimate noncommon structural breaks in panel regression models. Any subset of the cross-section may be hit at different times within a break window. Break-specific parameters are learned from the cross-section. They reflect whether (i) breaks hit many or few series and (ii) there is a long or short lag between the first and final series hit by a break. In an empirical application to international stock return predictability, the method generates significantly more accurate forecasts than several benchmarks that yield economically meaningful utility gains for a risk averse investor with power utility. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the scalar field dark energy (DE) models within the context of F (T , T G) gravity. Scalar field models are known for their dynamic nature. Parameters in the dynamical equation of state are responsible for the acceleration of the cosmos in recent epochs. We determined that the power-law cosmology fits the OHD and Pantheon data nicely. Using the Bayesian analysis and likelihood function in conjunction with the Markov Chain Monte Carlo (MCMC) method, we examine the three sets of the model parameters H 0 = 6 3. 2 6 − 0. 9 7 + 0. 7 7 q = − 0. 0 3 9 0. 0 0 2 9 + 0. 0 0 2 9 obtained by 57 points of the H (z) data, H 0 = 7 0. 9 3 − 0. 0 0 3 7 + 0. 0 0 2 4 q = − 0. 3 3 8 4 0. 0 0 2 6 + 0. 0 0 2 7 1048 points of the Pantheon data and H 0 = 7 0. 7 6 7 3 2 − 0. 0 0 0 8 8 + 0. 0 0 3 0 q = − 0. 6 4 7 8 0. 0 0 7 5 + 0. 0 0 9 3 by using joint data (H (z) + BAO + Pantheon), respectively. We solved the modified Einstein's field equations by taking the bulk-viscosity component ξ = ξ 0 ρ α . We also establish a correspondence between the F (T , T G) gravity model and scalar field DE models such as quintessence, k -essence, and DBI-essence. The nature of scalar fields and their corresponding potentials are being graphically analyzed. To check the validity of F (T , T G) gravity model, we also analyze the behavior of energy conditions. [ABSTRACT FROM AUTHOR]

Background: There is a growing trend to include non-randomised studies of interventions (NRSIs) in rare events meta-analyses of randomised controlled trials (RCTs) to complement the evidence from the latter. An important consideration when combining RCTs and NRSIs is how to address potential bias and down-weighting of NRSIs in the pooled estimates. The aim of this study is to explore the use of a power prior approach in a Bayesian framework for integrating RCTs and NRSIs to assess the effect of rare events. Methods: We proposed a method of specifying the down-weighting factor based on judgments of the relative magnitude (no information, and low, moderate, serious and critical risk of bias) of the overall risk of bias for each NRSI using the ROBINS-I tool. The methods were illustrated using two meta-analyses, with particular interest in the risk of diabetic ketoacidosis (DKA) in patients using sodium/glucose cotransporter-2 (SGLT-2) inhibitors compared with active comparators, and the association between low-dose methotrexate exposure and melanoma. Results: No significant results were observed for these two analyses when the data from RCTs only were pooled (risk of DKA: OR = 0.82, 95% confidence interval (CI): 0.25–2.69; risk of melanoma: OR = 1.94, 95%CI: 0.72–5.27). When RCTs and NRSIs were directly combined without distinction in the same meta-analysis, both meta-analyses showed significant results (risk of DKA: OR = 1.50, 95%CI: 1.11–2.03; risk of melanoma: OR = 1.16, 95%CI: 1.08–1.24). Using Bayesian analysis to account for NRSI bias, there was a 90% probability of an increased risk of DKA in users receiving SGLT-2 inhibitors and an 91% probability of an increased risk of melanoma in patients using low-dose methotrexate. Conclusions: Our study showed that including NRSIs in a meta-analysis of RCTs for rare events could increase the certainty and comprehensiveness of the evidence. The estimates obtained from NRSIs are generally considered to be biased, and the possible influence of NRSIs on the certainty of the combined evidence needs to be carefully investigated. [ABSTRACT FROM AUTHOR]

Background: Huazhou County has one of the highest rates of hepatitis C virus (HCV) infection incidence and prevalence in Shaanxi Province, northwest China. Understanding the characteristics of HCV transmission patterns in this area could help guide targeted prevention strategies. This study employed phylogenetic analysis and the construction of a molecular transmission network of HCV-infected people in Huazhou County to describe the predominant strains of HCV and identify factors associated with onward transmission. Methods: Whole blood samples were obtained from HCV RNA-positive individuals for sequencing of the non-structural protein 5B region. A maximum-likelihood (ML) phylogenetic tree was constructed to determine HCV subgenotypes, and Bayesian phylogenetic analysis was employed to estimate the evolutionary history. The transmission network was constructed using the ML phylogenetic tree and pairwise distances. Logistic regression was used to identify factors associated with clustering in the transmission network. Results: ML phylogenetic analysis confirmed that the 61 sequences analyzed in the study belonged to subtype 2a. Bayesian phylogenetic analysis showed that the majority of subtype 2a sequences originated in the northwest of China and had descended approximately 8 to 20 years before sampling. Overall, 26.2% of participant sequences were grouped into phylogenetic network clusters. Multivariate logistic regression showed that individuals who had a history of blood transfusions and were living in Shi Village, Huazhou County, were more likely to form clusters within the transmission network. Conclusion: HCV transmission in Huazhou County was predominantly associated with subtype 2a. Having a history of blood transfusions and living in residential Shi Village, Huazhou County, were factors associated with a high risk of HCV infection transmission. Prioritizing targeted interventions for these patient groups may help to prevent further infections. [ABSTRACT FROM AUTHOR]

Summary Seasonal autoregressive (SAR) time series models have been extended to fit time series exhibiting multiple seasonalities. However, hardly any research in Bayesian literature has been done on modelling multiple seasonalities. In this article, we propose a full Bayesian analysis of triple SAR (TSAR) models for time series with triple seasonality, considering identification, estimation and prediction for these TSAR models. In this Bayesian analysis of TSAR models, we assume the model errors to be normally distributed and the model order to be a random variable with a known maximum value, and we employ the g prior for the model coefficients and variance. Accordingly, we first derive the posterior mass function of the TSAR order in closed form, which then enables us to identify the best order of TSAR model as the order value with the highest posterior probability. In addition, we derive the conditional posteriors to be a multivariate normal for the TSAR coefficients and to be an inverse gamma for the TSAR variance; also, we derive the conditional predictive distribution to be a multivariate normal for future observations. Since these derived conditional distributions are in closed forms, we introduce the Gibbs sampler to present the Bayesian analysis of TSAR models and to easily produce multiple‐step‐ahead predictions. Using Julia programming language, we conduct an extensive simulation study, aiming to evaluate the accuracy of our proposed full Bayesian analysis for TSAR models. In addition, we apply our work on time series to hourly electricity load in some European countries. [ABSTRACT FROM AUTHOR]

Uncertainty, complexity, and dynamic changes present challenges for conservation and natural resource management. Evidence-based approaches grounded in reliable information and rigorous analysis can enhance the navigation of the uncertainties and trade-offs inherent in conservation problems. This study highlights the importance of collaborative efforts and evidence-based decision-making, specifically implementing the Resilience-Experimentalist school of adaptive management (RE-ARM), which emphasizes stakeholder involvement, shared understanding, and experimentation. Our goal was to develop an adaptive management framework to reduce the uncertainty around the use of prescribed fire to manage the habitat for eastern black rails (Laterallus jamaicensis jamaicensis) and mottled ducks (Anas fulvigula) in saltmarshes of the Gulf of Mexico. Supported by discussions at a series of workshops, we used a value of information analysis to select a fire management hypothesis to test, developed an influence diagram to represent the system under fire management, used the influence diagram to develop a Bayesian decision network (BDN), and conducted a power analysis to guide management experiments and monitoring. Value of information analysis identified fire return interval as the critical uncertainty. Our BDN provided valuable insight into how managers believe prescribed fire influences vegetation characteristics and how vegetation influences both eastern black rail occupancy and mottled duck abundance. The results of the power analysis indicated that a standard occupancy modeling framework was more useful to compare 2- and 5-year fire return intervals for black rails than two alternative designs (removal and conditional). Our BDN can be used to predict the probability of achieving the desirable vegetative response to increase the occupancy probability of black rails and abundance of mottled ducks, and monitoring data can be used to update the BDN (learn) and improve best management practices for prescribed burns (adapt). Linking the value of information, BDNs, and power analysis enhances our understanding of the system, improves management decision-making, and builds trust among scientists, interested parties, and decision-makers. This approach lays the groundwork for knowledge co-production and adaptive management. [ABSTRACT FROM AUTHOR]

Purpose: The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods: There is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats' local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison. Results: Bayesian network depicted the probabilistic relationship among rat's brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results. Conclusion: The primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology. [ABSTRACT FROM AUTHOR]

The design for large-scale offshore systems like renewable energy systems as well as ship structures represents the key factor for the investigation and application of such devices. The existing guide for design schemes of offshore systems cannot cover novel large-scale design demand for recent offshore systems, as a result of the fast-growing scale of offshore systems but the late update of guides. To this end, this paper proposes a novel risk estimation approach of alternative design schemes for large-scale offshore systems as a basis to support the design scheme determinations. Initially, the risks of design schemes are analyzed by fault tree analysis. Subsequently, Bayesian networks and fuzzy sets are employed to calculate the reliability of alternative designs that comply with, or deviate from, the existing guides. The risk level of alternative designs is assessed to ensure the better performance of alternative design schemes in terms of safety. The Bayesian network approach proposed also accretions the weak links in the alternative designs. The results of this paper contribute to enhancing the survivability of offshore systems, such as renewable energy systems as well as ship structures. [ABSTRACT FROM AUTHOR]