Search

Online reviews provide rich information on customer satisfaction, displaying various numeric ratings as well as detailed explanations presented in written form. However, analyzing such data is challenging due to the unstructured nature of text. This article introduces a novel machine-learning method for identifying interpretable key drivers of star ratings from text reviews, which might vary across segments. By adopting the Ising model prior to account for dependence between words, the model simultaneously achieves segmentation, identifies segment-level key topics (i.e., groups of frequently co-occurring words), and estimates the impacts of the selected words on the ratings. The authors first demonstrate that the proposed model successfully identifies segment-specific key drivers of customer satisfaction using illustrative simulated review data. Then, the authors utilize real-world reviews from Yelp for empirical applications. When applied to online reviews of 5,241 Arizona-based restaurants, the model identifies three distinct restaurant segments, each characterized by three to five important topics. The model's performance is evaluated against six benchmark models, encompassing various topic models and latent class regression with variable selection. The comparison results emphasize the proposed model's unique advantages in prediction, interpretability, and handling heterogeneity. Additionally, the authors demonstrate the applicability of the model in examining customer segmentation for individual restaurants. [ABSTRACT FROM AUTHOR]

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]

The integration of artificial intelligence, machine learning, and deep learning algorithms into machining processes has made them more intelligent, significantly reducing costs, improving production rates, and enhancing product quality by accurately predicting machining responses. In this study, a Bayesian neural network (BNN) is employed to predict tool wear during the milling of Al6061 T6 alloy, showcasing the novelty of BNN in handling uncertainty and providing reliable predictions. Milling experiments were conducted at three different spindle speeds under dry, flood, and minimum quantity lubrication (MQL) strategies. Machinability was evaluated by considering tool wear, milling forces, and surface quality. Unique to this study is the use of force and current signals as input to the BNN model, capturing real-time data to estimate tool wear. The signals were trained and tested to predict tool wear under varying cutting conditions. The results indicated that tool wear in dry conditions was primarily due to adhesion, leading to higher milling forces and poorer surface quality. In comparison, the wet and MQL conditions resulted in 11–21% and 9–13% less tool wear, respectively, than dry conditions, alongside improved surface roughness and reduced machining forces. The BNN model demonstrated its ability to avoid overfitting, providing highly accurate predictions with an error margin of 2–15% when compared to experimental results. Unlike conventional models, the BNN accounts for prediction uncertainty, making it more robust and reliable across different datasets. Thus, the proposed BNN model proves its effectiveness and generalizability in predicting tool wear under various machining conditions, setting a new benchmark for the application of artificial intelligence in machining processes. [ABSTRACT FROM AUTHOR]

Background: A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery. Methods: Experts' opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A 'community of priors' (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed. Results: The most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts' beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism. Conclusion: A Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence. [ABSTRACT FROM AUTHOR]

Foot‐and‐mouth disease (FMD), which is endemic in 77% of countries globally, is a major threat to the global livestock industry. Knowledge of the reproductive number at the population level (i.e., farm level, herd level, or above) for FMD is important to estimate the magnitude of epidemics and design and implement effective control methods. Different methods, based on disparate assumptions and limitations, have been used interchangeably to compute and report reproductive numbers at the population level without a formal comparison between them. This study compares the results obtained when using alternative methods to compute between populations (Rbp) for FMD using one single dataset collected over 10 years (2007–2017) at the commune‐level swine farms in Vietnam. Seven spatial–temporal clusters were identified in the country, and the value of Rbp was computed on each of them using different analytical approaches, namely, epidemic doubling time, nearest neighbor, time‐dependent reproductive number (TDR), sequential Bayesian (SB), and birth–death skyline (BDSKY) analysis in Bayesian evolutionary analysis by sampling trees 2 (BEAST2). Estimated Rbp values were relatively similar across methods ranging from 1.25 to 1.61. For the first time, the results here provide a comparison of different methods used to compute Rbp for FMD. Despite differences in assumptions and limitations, results suggest that different methods produce relatively similar outputs. Additionally, the results here provide foundational knowledge to support the evaluation and control of FMD epidemics in a population. [ABSTRACT FROM AUTHOR]

In this article, we particularly address the problem of assessing the impact of different prognostic factors, such as clinical stage and age, on the specific survival times of men with breast cancer when cure is a possibility. To this end, we developed a quantile regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution in a defective version, which is conveniently reparametrized in terms of the q-th quantile and then linked to covariates via a logarithm link function. This proposal allows us to obtain how each variable affects the survival times in different quantiles. In addition, we are able to study the effects of covariates on the cure rate as well. We consider Markov Chain Monte Carlo methods to develop a Bayesian analysis in the proposed model and we evaluate its performance through Monte Carlo simulation studies. Finally, we illustrate the application of our model in a data set about male breast cancer from Brazil analyzed for the very first time. [ABSTRACT FROM AUTHOR]

The development of the Community Information Database (CID) provides an opportunity to enhance the accuracy of predicting regional seismic damage. The CID includes fundamental attributes of buildings and daily operation information such as maintenance record, loading variation, and structural defect inspection. However, existing models cannot leverage the daily operational information of the CID to calibrate the real-time seismic performance of regional buildings. In this study, a novel Bayesian network (BN) model is proposed to assess the regional seismic damage of buildings based on information provided by the CID. Buildings in an area in Shanghai, China, are selected to investigate the applicability and accuracy of the proposed model. The investigation indicated that the proposed model can synthesize seismic fragility curves and matrices for various building types and calibrate the prediction based on daily operational information such as structural defect, structural alteration, and property management. If the CID has missing building attributes, the proposed model can also utilize an embedded subnetwork to supply the database. Therefore, the proposed model integrates more comprehensive information than the traditional methods to enhance the accuracy of predicting the regional seismic damage. [ABSTRACT FROM AUTHOR]

Traditional manufacturing workshop processes are challenging to change, and passive monitoring methods, such as quality traceability and completion rate monitoring, pose challenges for discrete workshops in achieving real-time and accurate production control. Therefore, combined with the digital twin system, a multi-level production status monitoring model is established to monitor discrete manufacturing workshops actively. This article focuses on discrete manufacturing workshops' modeling and self-evolution production processes. It also proposes an active monitoring architecture for production status in discrete workshops using digital twin technology. A full-information model is constructed for a discrete manufacturing workshop. Then, a multi-node interaction logic is established for the discrete manufacturing workshop. Finally, based on the fuzzy C-means clustering algorithm, production anomalies are monitored, and real-time monitoring of disturbances caused by various production factors, including material status, equipment status, and work-in-progress status, is performed to assess their impact on production. Based on deep Bayesian network learning algorithms, we can monitor production performance and analyze workshop capacity, equipment utilization, bottleneck rhythm, etc. This allows us to actively monitor production performance through the self-evolution of digital twin models. This article aims to proactively predict changes in workshop production status and provide support for further plan completion rate improvement, capacity prediction, and capacity optimization. [ABSTRACT FROM AUTHOR]

Background: Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data's likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness. Methods: We illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random. Results: As expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7–48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI's imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI. Conclusion: The Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR. [ABSTRACT FROM AUTHOR]

AbstractThough previous research yielded inconsistent results, studies suggest an association between frontal alpha asymmetry (FAA), mood, and self-regulation. This inconsistency may be explained by the heterogeneity of experimental protocols and sample characteristics such as the baseline level of distress. The aim of the study was to gain a better understanding of the relationship between FAA and self-report measurements of depression, anxiety, stress, and self-regulation. Using data from 130 participants (Mage = 25.2; SD = 6.8), we conducted correlation analyses and Bayesian statistics to examine these associations. The results revealed moderate-level positive correlations among depression, anxiety, and stress scores measured by the Depression Anxiety Stress Scale, with notable negative correlations observed between self-regulation and depression, as well as between self-regulation and stress. The relationship between FAA and mood was sample-dependent, and effects were restricted to the sample with moderate to extreme levels of distress, revealing consistent patterns within these subgroups. Most importantly, there was a negative correlation between FAA and anxiety. However, there was no clear association between FAA and depression. Consequently, our results show that self-regulation is associated with reduced distress, and that FAA may be a useful biomarker for anxiety in individuals with moderate to high baseline distress levels. [ABSTRACT FROM AUTHOR]

Background and purpose Methods Results Conclusion Several surgical techniques have been used to treat cervical dystonia (CD), however, to date, the optimal surgical technique for CD remains controversial. We therefore conducted the first network meta‐analysis to compare different surgical strategies for CD to inform clinical practice.Electronic databases were searched for surgical strategies for treating CD. The primary outcome was improvement in total Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) score. Subgroup analysis was performed to compare short‐term (< 1 year) and long‐term (≥ 1 year) outcomes. Safety outcomes included surgery‐related adverse events (AEs).A total of 55 trials with 2032 patients employing five surgical strategies were identified, including globus pallidus internus (GPi)/subthalamic nucleus (STN)‐deep brain stimulation (DBS), selective peripheral denervation (SPD), microvascular decompression (MVD) and pallidotomy. All strategies led to significant improvement in total TWSTRS score (mean improvement range 18.65–28.22). GPi‐DBS showed significantly greater enhancement than SPD for the whole dataset (mean difference [MD] 7.03, 95% credible interval [Crl] 1.53–12.56), while both GPi‐DBS (MD 8.05, 95% Crl 2.35–13.80) and STN‐DBS (MD 10.71, 95% Crl 2.22–19.20) exhibited more long‐term improvement than SPD. Regarding safety outcomes, GPi/STN‐DBS and MVD were associated with fewer surgery‐related AEs than SPD (ln odds ratio range −1.68 to −1.41).We conclude that DBS should be the preferred surgical option for CD, and the STN is a promising alternative target choice due to its comparable efficacy with the GPi. However, more direct evidence is still required. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to compare the efficacy of robot-assisted, laparoscopic-assisted and open surgery in the treatment of cholangiocarcinoma, and to evaluate the clinical effect of three surgical methods in the treatment of cholangiocarcinoma by network Meta-analysis. Methods: A systematical retrieval in PubMed and Web of Science was performed for relative literature on the effects of robot-assisted(RA), laparoscopy-assisted(LA), and open surgery(OA) for cholangiocarcinoma in treating cholangiocarcinoma. A literature search updated to September 1st, 2024, was performed. Results: Studies have shown that the length of R0 resection, complication rate, 30-day mortality, Transfusion rate, Lymph Node Metastasis Rate, and hospital stay in RA are superior to LA and open surgery. The relative effectiveness of the three surgical methods in terms of operation time were: open surgery, laparoscope-assisted surgery, and robot-assisted surgery, and there was no significant difference among the three groups. Conclusion: Robot-assisted surgery is safe and feasible in the treatment of cholangiocarcinoma, but more clinical evidence is needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Background: In May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source. Methods: We conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source's emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters. Results: Our models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection. Conclusion: This study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason ≥ 8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa. [ABSTRACT FROM AUTHOR]

Rosavirus is a newly discovered member of the family Picornaviridae that was initially detected in wild rodents and subsequently in children with diarrhoea. Nevertheless, there is a significant gap in our understanding of the geographical distribution, phylogenetic relationships, evolutionary patterns, and transmission of rosaviruses. To address these issues, we analysed 434 rodents and shrews from five different species that were collected in southern China. Using PCR screening of faecal samples, we detected rosaviruses in Norway rats (Rattus norvegicus) and identified two previously undocumented host species: tanezumi rats (Rattus tanezumi) and Asian house shrews (Suncus murinus). Rosaviruses were particularly common in these animals, with an overall prevalence rate of 32.49% (141/434). For genetic and evolutionary analyses, we selected six representative positive samples to amplify the complete genomes of rosaviruses. Bayesian phylogenetic analysis suggested that our sequences clustered within the genus Rosavirus, where genotype B sequences are the closest relatives. The elevated nonsynonymous-to-synonymous ratios observed in rosavirus B may be attributed to relaxed selection pressures driven by virus spillover events. On the basis of the available data, it is hypothesized that the genus Rosavirus may have originated from Norway rats around the year 1339. In summary, these findings provide valuable insights into the complex evolutionary history of rosaviruses and underscore the urgent need for ongoing surveillance of this virus. [ABSTRACT FROM AUTHOR]

Representation learning algorithms are often used to extract essential features from high-dimensional datasets. These algorithms commonly assume that such features are independent. However, multimodal datasets containing complementary information often have causally related features. Consequently, there is a need to discover features purporting conditional independencies. Bayesian networks (BNs) are probabilistic graphical models that use directed acyclic graphs (DAGs) to encode the conditional independencies of a joint distribution. To discover features and their conditional independence structure, we develop pimaDAG, a variational autoencoder framework that learns features from multimodal datasets, possibly with known physics constraints, and a BN describing the feature distribution. Our algorithm introduces a new DAG parameterization, which we use to learn a BN simultaneously with a latent space of a variational autoencoder in an end-to-end differentiable framework via a single, tractable evidence lower bound loss function. We place a Gaussian mixture prior on the latent space and identify each of the Gaussians with an outcome of the DAG nodes; this identification enables feature discovery with conditional independence relationships obeying the Markov factorization property. Tested against a synthetic and a scientific dataset, our results demonstrate the capability of learning a BN on simultaneously discovered key features in a fully unsupervised setting. [ABSTRACT FROM AUTHOR]

This paper introduces a new probability distribution called the mixture symmetric gamma (MSG) distribution, which is defined as a mixture of two symmetric gamma distributions. Its statistical properties and applications are explored. We first examine its mathematical properties, including the possible shapes of the corresponding probability density function, as well as the moments, and the moment‐generating function. We then look at parameter estimation using various frequentist and Bayesian methods, such as moment estimation, maximum likelihood method, least‐squares method, and Bayesian approaches. In addition, the prediction of future observations under the MSG model is extensively covered, considering both frequentist and Bayesian perspectives, including median prediction, best unbiased prediction, and Bayesian prediction. A comprehensive simulation study is conducted to evaluate the performance of the proposed estimation and prediction techniques. Finally, the practical applicability of the MSG model is demonstrated through the analysis of four real‐world datasets. It is shown to outperform several well‐known competing models in terms of goodness‐of‐fit. The results highlight the inherent simplicity, efficiency, robustness, and intuitive interpretability of the MSG distribution, making it a compelling choice for modeling data with a symmetric pattern, with potential applications in diverse domains. [ABSTRACT FROM AUTHOR]

Mycoplasma pneumoniae pneumonia (MPP) is the predominant community-acquired pneumonia (CAP) in children aged 5 years or older. In recent decades, the annual increase in drug resistance rates of macrolide antibiotics, particularly azithromycin (AZ), has led to complex clinical treatment strategies and substantial healthcare costs associated with MPP. Chinese medicine injections (CMIs), recognized as an effective supplementary therapy, are acknowledged by clinicians in China. It is necessary to explore the efficacy of azithromycin in combination with CMIs. Methods: Randomized controlled trials (RCTs) evaluating azithromycin in combination with seven types of CMIs for MPP in children were identified based on inclusion criteria and assessed using the revised Cochrane risk of bias tool (RoB 2.0). R 4.3.1 and STATA 15.0 were employed to generate ranking probabilities and perform network meta-analysis. Competing interventions were ranked using the surface under the cumulative ranking (SUCRA) probabilities. Results: A comprehensive analysis was performed on 155 RCTs involving 15,014 patients and 8 therapeutic strategies within this Bayesian network meta-analysis (BNMA). The results indicated that AZ combined with seven types of CMIs was more effective than azithromycin alone in overall outcomes. Notably, azithromycin combined with Chuanhuning injection (AZ + CHN) achieved the highest ranking in improving the clinical effectiveness rate (SUCRA, 80.89%); regarding secondary outcome measures, azithromycin combined with Yanhuning injection (AZ + YHN) had the highest probability of improving four different outcomes: disappearance time of cough (SUCRA, 80.01%), disappearance time of pulmonary rale (SUCRA, 87.77%), disappearance time of fever (SUCRA, 95.70%), and disappearance time of pulmonary shadows in X -ray (SUCRA, 97.34%); furthermore, azithromycin combined with Qingkailing injection (AZ + QKL) was more likely to reduce average hospitalization time (SUCRA, 94.60%). Conclusion: This study highlights the potential benefits of seven types of Chinese medicine injections as adjunctive therapy for Mycoplasma pneumoniae pneumonia in children. However, further support and validation of these findings are needed through high-quality randomized controlled trials with larger sample sizes and double-blind designs. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#recordDetails/. [ABSTRACT FROM AUTHOR]

This study presents a novel training technique, visual + electrotactile proprioception training (visual + EP training), which provides additional proprioceptive information via tactile channel during motor training to enhance the training effectiveness. In this study, electrotactile proprioception delivers finger aperture distance information in real-time, by mapping frequency of electrical stimulation to finger aperture distance. To test the effect of visual + EP training, twenty-four healthy subjects participated in the experiment of matching finger aperture distance with distance displayed on screen. Subjects were divided to three groups: the first group received visual training and the other two groups received visual + EP training with or without a post-training test with electrotactile proprioception. Finger aperture control error was measured before and after the training (baseline, 15-min post, 24-h post). Experimental data suggest that both training methods decreased finger aperture control error at 15-min post-training. However, at 24-h post-training, the training effect was fully retained only for the subjects who received visual + EP training, while it washed out for the subjects with visual training. Distribution analyses based on Bayesian inference suggest that the most likely mechanism of this long-term retention is proprioceptive recalibration. Such applications of artificially administered sense have the potential to improve motor control accuracy in a variety of applications. [ABSTRACT FROM AUTHOR]

Utilizing the full potential of rice collections mainly depends on an in‐depth exploration and understanding of the vast diversity in its germplasm. The AfricaRice genebank holds the largest collection of rice germplasm originating from the African continent. In the present study, we comprehensively characterized a collection of 9013 accessions, including Oryza barthii A. Chev., Oryza glaberrima Steud., Oryza longistaminata A. Chev. & Roehr., Oryza sativa L. ssp. indica, and Oryza sativa L. ssp. japonica, for genetic diversity and population structure using genotyping‐by‐sequencing through DArTseq analysis. We identified 27,718 high‐quality single nucleotide polymorphism markers after the genotypic data were filtered. Based on the analyses, the collection has extensive genetic diversity, and the average genetic distance of the entire set was 0.267 (range 0.001–0.469), with 45.1% of pairs of accessions being highly distant and 40.1% moderately distant from each other. Neighbor‐joining tree, principal component, and Bayesian population structure analyses clustered the 9013 accessions into six groups, based roughly on their taxonomic and biological status. The first, second, and third groups consisted of accessions belonging to O. glaberrima, O. barthii, and O. longistaminata, respectively. The fourth, fifth, and sixth groups were improved‐indica, japonica, and traditional‐indica accessions, respectively. The highest value of genetic variance proportion (PhiPT) was found in the species group followed by groups based on cluster analysis and on Bayesian population structure at K = 6. These results allow us to better understand the genetic diversity present in 9013 rice accessions maintained in the AfricaRice genebank and offer a valuable tool for pre breeding, breeding, and further genetic applications. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive risk assessment for a selected hydrogen supply system within a pilot hydrogen power plant. The assessment begins with hazard identification through Hazard and Operability (HAZOP) analysis, followed by quantification using bow-tie analysis and consequence modeling to evaluate the impacts of credible scenarios resulting from random failures. Furthermore, a fuzzy Bayesian network is employed to address uncertainties inherent in the quantitative approaches and to account for event combinations and dependencies. Sensitivity analysis is subsequently conducted to identify critical events. Utilizing three quantitative approaches, fault tree analysis, the Bayesian network, without and with evidence updating, the probability of the top event is determined to be 4.52 × 10−2/year, 4.50 × 10−2/year, and 4.70 × 10−2/year, respectively. Additionally, the estimated firezone resulting from the explosion's 300 mbar overpressure was 12 m. The findings highlight the significant role of pressure regulator valve and pressure transmitter failures, with posterior probabilities of 0.17/year and 1.90 × 10−3/year, in ensuring the system's safety. [Display omitted] • A dynamic approach has been presented to assess the risk via Bayesian network. • The evaluation has been updated by the utilization of field evidence. • The critical nodes in system identified through sensitivity analysis method. • Fuzzy logic has been employed as a means of dealing with prevailing uncertainty. • Consequence modelling was conducted to enable quantitative analysis of scenarios. [ABSTRACT FROM AUTHOR]

Background: Cardiomyocyte senescence plays a crucial role as a pathological mechanism in heart failure (HF). However, the exact triggering factors and underlying causes of HF onset and progression are still not fully understood. Objectives: By integrating multi-omics data, this study aimed to determine the genetic associations between cardiomyocyte and HF using cell senescence-related genes (SRGs). Methods: The study utilized the CellAge database and the SenMayo dataset, combined with high-resolution single-cell RNA sequencing (scRNA-seq) data, to identify SRG and examine differences in cardiac cell expression. To explore the causal relationship with HF using Mendelian Randomization (MR). Genetic variations influencing gene expression, DNA methylation, and protein expression (cis-eQTL, cis-mQTL, and cis-pQTL) were analyzed using the two-sample MR (TSMR) and summary-data-based MR (SMR). Additionally, Bayesian colocalization analysis, germline genetic variation, and bulk RNA data were employed to strengthen the reliability of the results. The application potential of therapeutic targets is ultimately assessed by evaluating their druggability. Results: The expression of 39 SRGs in cardiomyocytes was identified. In the discovery set revealed that CDKN1A (OR = 1.09, 95% confidence interval (CI) 1.02–1.15, FDR = 0.048) could be causally related to HF, and the results are also replicated in the validation set (OR = 1.20, 95% confidence interval (CI) 1.10–1.30, FDR <0.0001). Based on the SMR method, CDKN1A was confirmed as a candidate pathogenic gene for HF, and its methylation (cg03714916, cg08179530) was associated with HF risk loci. The result is validated by Bayesian colocalization analysis, genetic variations, and bulk RNA data. The druggability analysis identified two potential therapeutic drugs. Conclusion: Based on multi-omics data, this study uncovered the reciprocal regulation of cardiomyocyte senescence through CDKN1 A, providing potential targets for HF drug development. [ABSTRACT FROM AUTHOR]

Introduction: Desmoid tumors (DT) are rare, locally invasive tumors originating from connective tissue. Surgical intervention is no longer the standard treatment for DT, as systemic therapy gradually replaces it due to its superior efficacy. Despite the availability of various treatment modalities, there is a need for a first-line systemic treatment regimen that offers both effective disease control and acceptable safety profiles. Methods: To assess the efficacy and safety of different systemic treatment agents for DT, we conducted a systematic review and network meta-analysis. Eligible studies were identified through searches of PubMed, Embase, and the Cochrane Library databases, and data were extracted according to predefined inclusion criteria. Results: Three articles and clinical data from 295 patients with progressive and refractory DT were included in this Bayesian network meta-analysis. When considered by objective response rate (ORR), the efficacy of γ-secretase inhibitor versus placebo (OR 0.12, 95%CI 0.01–1.68) is superior to that of TKI (OR 0.49, 95%CI 0.03–7.62) and chemotherapy (OR 0.90, 95%CI 0.02–40.00). Vascular endothelial growth factor (VEGF)-TKI (OR, 0.09; 95% CI, 0.01–1.79) seemed to have the highest improvement in terms of 1-yr PFS rate, while chemotherapy seemed to have the highest improvement across all therapies in terms of 2-yr PFS rate across all therapies (OR, 0.06; 95 percent CI, 0.01–2.98). In terms of safety, the incidence of AEs is highest for γ-secretase inhibitor versus placebo (OR 0.16, 95%CI 0.02–1.55), while TKI is associated with the least AEs (OR 0.62, 95%CI 0.06–6.97). Conclusion: γ-secretase inhibitor provides superior local control of tumors, while chemotherapy and TKI may offer better long-term survival benefits. Among the three regimens, TKI demonstrated better treatment-related safety. These findings have important implications for guiding clinical practice in systemic treatment of DT. Highlights: The study conducted a systematic review and Bayesian network meta-analysis to evaluate the efficacy and safety of systemic treatment options for progressive and refractory desmoid tumors. The analysis included 3 RCT studies, and the primary clinical endpoints were progression-free survival and objective response rate. The network meta-analysis showed that γ-secretase inhibitors have better tumor response and more severe toxicity, while chemotherapy and TKI may have better long-term survival benefits. [ABSTRACT FROM AUTHOR]

This article presents a Poisson common factor model with an overdispersion factor to predict some multiple populations' mortality rates. We use Bayesian data analysis and an extension of the Hamiltonian Monte Carlo sampler to compute the estimation of the model parameters and mortality rates prediction. We apply the proposed model to the real mortality data of some European countries. Using some model selection measures, we compare the proposed model with a log-linear model and those introduced in Antonio, Bardoutsos, and Ouburg (2015, Bayesian Poisson log-bilinear models for mortality projections with multiple populations. European Actuarial Journal 5 (2): 245–281) and Wong, Forster, and Smith (2018, Bayesian mortality forecasting with overdispersion. Insurance: Mathematics and Economics 83: 206–221). [ABSTRACT FROM AUTHOR]

Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that predict the risk of suffering preeclampsia. Given the properties of our dataset, only the oversampling and undersampling methods that operate with numerical and categorical attributes will be taken into consideration. In particular, synthetic minority oversampling techniques for nominal and continuous data (SMOTE-NC), SMOTE—Encoded Nominal and Continuous (SMOTE-ENC), random oversampling examples (ROSE), random undersampling examples (UNDER), and random oversampling techniques (OVER) are considered. According to the results, when balancing the class in the training dataset, the accuracy percentages do not improve. However, in the test dataset, both positive and negative cases of preeclampsia were accurately classified by the models, which were built on a balanced training dataset. In contrast, models built on the imbalanced training dataset were not good at detecting positive cases of preeclampsia. We can conclude that while imbalanced training datasets can be addressed by using oversampling and undersampling techniques before building prediction models, an improvement in model accuracy is not always guaranteed. Despite this, the sensitivity and specificity percentages improve in binary classification problems in most cases, such as the one we are dealing with in this manuscript. [ABSTRACT FROM AUTHOR]

In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing objectives efficiently. Building upon the foundations laid by existing research, this methodology is expanded to encompass experiments with multiple outcome categories using Dirichlet processes. The core of our approach is centered around weighted entropy measures, particularly in scenarios dictated by Dirichlet distributions, which have not been extensively explored previously. We innovatively adapt the technique initially applied to binary case to Dirichlet distributions/processes. The primary contribution of our work is the formulation of a sequential minimization strategy for the main term of an asymptotic expansion of differential entropy, which scales with sample size, for non-binary outcomes. This paper provides a theoretical grounding, extended empirical applications, and comprehensive proofs, setting a robust framework for further interdisciplinary applications of information-theoretic paradigms in sequential decision-making. [ABSTRACT FROM AUTHOR]

This paper extends the normal-beta prime (NBP) prior to Bayesian quantile regression in linear mixed effects models and conducts Bayesian variable selection for the fixed effects of the model. The choice of hyperparameters in the NBP prior is crucial, and we employed the Variational Bayesian Expectation–Maximization (VBEM) for model estimation and variable selection. The Gibbs sampling algorithm is a commonly used Bayesian method, and it can also be combined with the EM algorithm, denoted as GBEM. The results from our simulation and real data analysis demonstrate that both the VBEM and GBEM algorithms provide robust estimates for the hyperparameters in the NBP prior, reflecting the sparsity level of the true model. The VBEM and GBEM algorithms exhibit comparable accuracy and can effectively select important explanatory variables. The VBEM algorithm stands out in terms of computational efficiency, significantly reducing the time and resource consumption in the Bayesian analysis of high-dimensional, longitudinal data. [ABSTRACT FROM AUTHOR]

Background: The airway microbiome in severe asthma has not been characterised at species‐level by metagenomic sequencing, nor have the relationships between specific species and mucosal immune responses in 'type‐2 low', neutrophilic asthma been defined. We performed an integrated species‐level metagenomic data with inflammatory mediators to characterise prevalence of dominant potentially pathogenic organisms and host immune responses. Methods: Sputum and nasal lavage samples were analysed using long‐read metagenomic sequencing with Nanopore and qPCR in two cross‐sectional adult severe asthma cohorts, Wessex (n = 66) and Oxford (n = 30). We integrated species‐level data with clinical parameters and 39 selected airway proteins measured by immunoassay and O‐link. Results: The sputum microbiome in health and mild asthma displayed comparable microbial diversity. By contrast, 23% (19/81) of severe asthma microbiomes were dominated by a single respiratory pathogen, namely H. influenzae (n = 10), M. catarrhalis (n = 4), S. pneumoniae (n = 4) and P. aeruginosa (n = 1). Neutrophilic asthma was associated with H. influenzae, M. catarrhalis, S. pneumoniae and T. whipplei with elevated type‐1 cytokines and proteases; eosinophilic asthma with higher M. catarrhalis, but lower H. influenzae, and S. pneumoniae abundance. H. influenzae load correlated with Eosinophil Cationic Protein, elastase and IL‐10. R. mucilaginosa associated positively with IL‐6 and negatively with FGF. Bayesian network analysis also revealed close and distinct relationships of H. influenzae and M. catarrhalis with type‐1 airway inflammation. The microbiomes and cytokine milieu were distinct between upper and lower airways. Conclusions: This species‐level integrated analysis reveals central, but distinct associations between potentially pathogenic bacteria and airways inflammation in severe asthma. [ABSTRACT FROM AUTHOR]

To improve the forecasting accuracy of the spread of infectious diseases, a hybrid model was recently introduced where the commonly assumed constant disease transmission rate was actively estimated from enforced mitigating policy data by a machine learning (ML) model and then fed to an extended susceptible-infected-recovered model to forecast the number of infected cases. Testing only one ML model, that is, gradient boosting model (GBM), the work left open whether other ML models would perform better. Here, we compared GBMs, linear regressions, k-nearest neighbors, and Bayesian networks (BNs) in forecasting the number of COVID-19-infected cases in the United States and Canadian provinces based on policy indices of future 35 days. There was no significant difference in the mean absolute percentage errors of these ML models over the combined dataset [ H (3) = 3.10 , p = 0.38 ]. In two provinces, a significant difference was observed [ H (3) = 8.77 , H (3) = 8.07 , p < 0.05 ], yet posthoc tests revealed no significant difference in pairwise comparisons. Nevertheless, BNs significantly outperformed the other models in most of the training datasets. The results put forward that the ML models have equal forecasting power overall, and BNs are best for data-fitting applications. [ABSTRACT FROM AUTHOR]

Reproduction can reduce energy allocation to other life‐history traits such as survival and growth. Resource constraints give rise to (co)variation in life‐history traits and to heterogeneity in energy acquisition and allocation. At each reproductive opportunity, females face a choice between allocation to current reproduction or to maintenance. Many studies compare reproductive trade‐offs between two consecutive years, but few account for the cumulative effects of reproduction over multiple years, a crucial factor in understanding life‐history evolution in long‐lived iteroparous species.We compared short‐ (interannual) and long‐term (cumulative) reproductive trade‐offs with a 14‐year capture–mark–recapture study of eastern grey kangaroos, where females can have substantial skeletal growth for several years after maturation. We used a multivariate approach to compare how interannual and multi‐annual cumulative reproduction affected growth (n = 378 measurements), mass change (n = 376 measurements) and subsequent reproduction (n = 388 measurements), and to quantify (co)variation between these traits among individuals (n = 107) and years (n = 14).Interannually, young females that reproduced experienced decreased skeletal growth compared to young females that did not reproduce. Reproductive females of all ages experienced reduced mass gain and weaning probability in the following year. The cumulative effects of multiple reproductions included decreased skeletal growth, mass gain and weaning probability in the following year. These effects increased with age and reproductive rate. We found positive trait correlations between mass change, leg growth and subsequent reproduction among individuals and years, though weaker at the cumulative than interannual level.Females experience dynamic interannual and cumulative trade‐offs. Our analyses of cumulative costs of reproduction revealed long‐term trade‐offs as well as cumulative costs that were not apparent when estimating interannual costs. Trait correlations suggested heterogeneity in growth and reproduction among females. Years of increased growth were followed by years of increased reproduction, and years of poor growth were followed by years of poor reproduction. Our exploration of both interannual and cumulative costs of reproduction underscores the need to account for long‐term reproductive histories to better understand reproductive trade‐offs in long‐lived iteroparous species. [ABSTRACT FROM AUTHOR]

Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study's findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO, Ambient (outdoor) air pollution (who), 2020), outdoor air pollution is estimated to have caused 4.2 million premature deaths worldwide in 2019. Under this scenario, the development of statistical tools for assessing the impact of a possible reduction of fossil fuels-based transportation systems on air quality is more relevant than ever. This type of instruments can help policy makers to take the right path to tackle the problem of air pollution in urban environments. Thus, this work proposes a method to evaluate the impact of the reduction of vehicle fleets on air quality. The method is based on the construction of counterfactual time series, which represent observed air pollutants under a treatment of interest. In this case, the treatment of interest are the vehicle fleets on the streets. The construction of the counterfactual time series is based on Bayesian dynamic linear models. Several impact assessment measures are implemented, taking advantage of the counterfactual and observed time series (the observed pollutant without the treatment). The flexibility of the Bayesian approach allows easy inference of the impact quantities via high posterior density intervals. Some simulation analyses show that the method works well under different impact scenarios, and one application regarding Medellin's case in Colombia is presented. For the application, it is shown that the drastic reduction in vehicle fleets during the COVID-19 mobility restrictions led to a significant reduction in the presence of different pollutants ( PM 10 , PM 2.5 , NO 2 , NO x ) [ABSTRACT FROM AUTHOR]

Ecosystem services (ESs) refer to the benefits that humans obtain from ecosystems. These services are subject to environmental changes and human interventions, which introduce a significant level of uncertainty. Traditional ES modeling approaches often employ Bayesian networks, but they fall short in capturing spatiotemporal dynamic change processes. To address this limitation, dynamic Bayesian networks (DBNs) have emerged as stochastic models capable of incorporating uncertainty and capturing dynamic changes. Consequently, DBNs have found increasing application in ES modeling. However, the structure and parameter learning of DBNs present complexities within the field of ES modeling. To mitigate the reliance on expert knowledge, this study proposes an algorithm for structure and parameter learning, integrating the InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) model with DBNs to develop a comprehensive understanding of the spatiotemporal dynamics and uncertainty of ESs in the Tarim River Basin, China from 2000 to 2020. The study further evaluates the tradeoffs and synergies among four key ecosystem services: water yield, habitat quality, sediment delivery ratio, and carbon storage and sequestration. The findings show that (1) the proposed structure learning and parameter learning algorithm for DBNs, including the hill-climb algorithm, linear analysis, the Markov blanket, and the EM algorithm, effectively address subjective factors that can influence model learning when dealing with uncertainty; (2) significant spatial heterogeneity is observed in the supply of ESs within the Tarim River Basin, with notable changes in habitat quality, water yield, and sediment delivery ratios occurring between 2000–2005, 2010–2015, and 2015–2020, respectively; (3) tradeoffs exist between water yield and habitat quality, as well as between soil conservation and carbon sequestration, while synergies are found among habitat quality, soil retention, and carbon sequestration. The land-use type emerges as the most influential factor affecting the tradeoffs and synergies of ESs. This study serves to validate the capacity of DBNs in addressing spatiotemporal dynamic changes and establishes an improved research methodology for ES modeling that considers uncertainty. [ABSTRACT FROM AUTHOR]

Background: Persons with dementia in nursing homes often experience cognitive decline (e.g., memory and visuospatial/construction problems), behavioural and psychological symptoms of dementia (BPSD), and impaired activities of daily living. Therefore, comprehensive interventions for this population are needed. We assessed whether a multimodal non‐pharmacological intervention improved cognitive functions, BPSD, and activities of daily living in persons with dementia in nursing homes. Methods: This was a multicentre randomised controlled trial. Participants were 40 persons with dementia (38 women) living in four nursing homes (intervention group = 21; control group = 19). The intervention was conducted for 8 weeks, followed by an 8‐week follow‐up. Outcome measures were scores on the Japanese version of the Neurobehavioral Cognitive Status Examination Five and the ABC Dementia Scale. In the analysis, parameters were estimated using Bayesian statistics and a mixed‐effects linear regression model for the change in each outcome measure. Results: There were significant between‐group differences in changes in ABC Dementia Scale BPSD scores (8‐week: posteriori median = 1.66, 95% Bayesian confidence interval 0.36–3.01; 16‐week: median = 2.37, 95% Bayesian confidence interval 0.05–4.65). There was also a significant between‐group difference in changes in Neurobehavioral Cognitive Status Examination Five constructional ability scores (16‐week: median = 0.93, 95% Bayesian confidence interval 0.35–1.50). Conclusion: This intervention may have a maintenance and improvement effect on BPSD in persons with dementia in nursing homes, and a sustained effect on constructional ability post‐intervention. The intervention may be useful and easy to apply in practice. [ABSTRACT FROM AUTHOR]

Pedestrian Hybrid Beacons (PHBs) facilitate safe pedestrian crossings at marked crosswalks in unsignalized locations. However, few studies have recognized situations in which individuals may cross roads without PHB activation, potentially raising safety concerns. The influential factors contributing to pedestrian and bicycle crashes near PHBs remain insufficiently investigated. This study identifies characteristics of pedestrians and bicyclists prone to crossing without PHB activation. Additionally, this study uncovers differences between crash-prone and non-crash-prone PHB locations. Furthermore, this investigation examines the diverse factors that impact pedestrian and bicycle crashes in proximity to activated PHBs and accessible PHBs in Tucson, Arizona. Descriptive analysis and Bayesian multilevel Poisson-Lognormal regressions are conducted. Results indicate that young individuals (minimum age 13 and median age 29) and males were more likely to cross when PHBs were not activated. Moreover, the odds of pedestrian and bicycle crashes near PHBs increased when approach speeds decreased 5 to 10 minutes before crashes and at night (even with activated PHBs), while they decreased in regions with a greater proportion of non-White individuals and higher household incomes. These findings provide insights for transportation agencies, enabling them to implement targeted education and supplementary traffic control strategies to improve pedestrian and bicycle safety near PHBs. [ABSTRACT FROM AUTHOR]

Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open‐source approaches for cross‐correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation. [ABSTRACT FROM AUTHOR]

Current academic research on fitting the volume of urban theft crimes at a macro size is limited, especially from the urban functionality perspective. Given this gap, this study utilizes a Bayesian model to conduct a fitting analysis of theft crime data from 674 cities in China from 2018 to 2020. This research aims to explore novel pathways for theft crime fitting. Results indicate that the size of urban functionality, particularly points of interest (POIs), exhibits excellent performance in fitting theft crimes, with POIs related to public services and commercial activities demonstrating the most significant fitting effects. This research successfully identifies effective indicators for crime fitting, thereby offering a new perspective and supplement to theft crime research. This study holds significant value for gaining a profound understanding of criminal phenomena and explaining the causes and mechanisms underlying the differences in theft crimes among various cities in China. [ABSTRACT FROM AUTHOR]

Reintubation is associated with higher risk of mortality. There is no clear evidence on the best spontaneous breathing trial (SBT) method to reduce the risk of reintubation. Are different methods of conducting SBTs in critically ill patients associated with different risk of reintubation compared with T-tube? We conducted a systematic review and Bayesian network meta-analysis of randomized controlled trials investigating the effects of different SBT methods on reintubation. We surveyed PubMed, MEDLINE, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to January 26, 2024. The surface under the cumulative ranking curve (SUCRA) was used to determine the likelihood that an intervention was ranked as the best. Pairwise comparisons were also investigated by frequentist meta-analysis. Certainty of the evidence was assessed according to the Grading of Recommendations, Assessment, Development, and Evaluations approach. A total of 22 randomized controlled trials were included, for a total of 6,196 patients. The network included nine nodes, with 13 direct pairwise comparisons. About 71% of the patients were allocated to T-tube and pressure support ventilation without positive end-expiratory pressure, with 2,135 and 2,101 patients, respectively. The only intervention with a significantly lower risk of reintubation compared with T-tube was high-flow oxygen (HFO) (risk ratio, 0.23; 95% credibility interval, 0.09-0.51; moderate quality evidence). HFO was associated with the highest probability of being the best intervention for reducing the risk of reintubation (81.86%; SUCRA, 96.42), followed by CPAP (11.8%; SUCRA, 76.75). In this study, HFO SBT was associated with a lower risk of reintubation compared with other SBT methods. The results of our analysis should be considered with caution due to the low number of studies that investigated HFO SBTs and potential clinical heterogeneity related to cointerventions. Further trials should be performed to confirm the results on larger cohorts of patients and to assess specific subgroups. PROSPERO; No.: CRD42023449264; URL: https://www.crd.york.ac.uk/prospero/ [Display omitted] [ABSTRACT FROM AUTHOR]

Literature on temporal patterns of suicidality among youths during the COVID-19 pandemic is growing. The present work proposes a Bayesian approach to assess temporal patterns of suicide-related behaviours among inpatient adolescents during the COVID-19 pandemic. Data referred to the first hospital discharge record with ICD9-CM codes related to suicide-related behaviour and/or suicidal ideation among adolescents aged 13–19 between 1 January 2017 and 31 March 2021 were collected in the Piedmont region, Italy (n = 334; median age: 15 years, IQR: 14–16; 80% girls). A Poisson Bayesian regression model performed on pre-COVID-19 data (2017–2020), adjusted by seasonality and stratified by sex, was adopted to provide the probability that the predicted counts exceed the observed ones in each pandemic year quarter. A declining trend of suicidality was observed in April–June 2020 among both sex groups. Among females, an increasing pattern of suicidality was registered in early 2021 (January–March) compared to the pre-pandemic period. The present findings contributed to a growing literature on the COVID-19 pandemic's impact on adolescents' suicide-related behaviours from a gender perspective and encouraged wider adoption of Bayesian approaches as valuable tools to explore rare events and deeply enlighten open public health issues. [ABSTRACT FROM AUTHOR]

Bayesian belief network is an effective and practical approach that is widely acceptable for real-time series prediction and decision making. However, its computational efforts and complexity increased exponentially with increased number of states. Hence, this research paper a proposed approach inspired by context-based persuasion analysis of sentiment analysis and its impact on the propagation of false information is designed. As social media text consist of unwanted information and needs to be addressed including effective polarity prediction of a sentimentwise ambiguous word in generic contexts. Therefore, in proposed approach persuasion-based strategy based on social media crowd is considered for analyzing the impact of sentimental contextual polarity in social media including pre-processing. For analyzing the polarity of sentiment, Bayesian belief network is used, whereas Turbo Parser is implemented for visual representation of diverse feature class and spontaneous hold of the relationships between features. Furthermore, to analyze the lexicons dependency on each word in terms of context, a tree-based dependency parser representation is used to count the dependency score. Features associated with sentimental words are extracted using Penn tree bank for sentiment polarity disambiguation. Therefore, a graphical model known as Bayesian network learning is opted to design a proposed approach which take care the dependency among various lexicons. Various predictors, namely, (1) pre-processing and subjectivity normalization, (2) computation of threshold and persuasion factor, and (3) extraction of sentiments from dependency parsing from the retrieved text are introduced. The findings of this study indicate that it is most important to compute the local and global context of various sentimental words to analyze the polarity of text. Furthermore, we have tested our proposed method with a standard data set and a real case study is also implemented based on COVID-19, Olympics-2020 and Russia–Ukraine war for the feasibility analysis of the proposed approach. The findings of this study imply a complex and context-dependent mechanism behind the sentiment analysis which shed lights on the efforts for resolving contextual polarity disambiguation in social media. [ABSTRACT FROM AUTHOR]

Purpose/Objective: Small sample sizes are a common problem in disability research. Here, we show how Bayesian methods can be applied in small sample settings and the advantages that they provide. Method/Design: To illustrate, we provide a Bayesian analysis of employment status (employed vs. unemployed) for those with disability. Specifically, we apply empirically informed priors, based on large-sample (N = 95,593) July 2019 Current Population Survey (CPS) microdata to small subsamples (average n = 26) from July 2021 CPS microdata, defined by six specific difficulties (i.e., hearing, vision, cognitive, ambulatory, independent living, and self-care). We also conduct a sensitivity analysis, to illustrate how various priors (i.e., theory-driven, neutral, noninformative, and skeptical) impact Bayesian results (posterior distributions). Results: Bayesian findings indicate that people with at least one difficulty (especially ambulatory, independent living, and cognitive difficulties) are less likely to be employed than people with no difficulties. Conclusions/Implications: Overall, results suggest that Bayesian analyses allow us to incorporate known information (e.g., previous research and theory) as priors, allowing researchers to learn more from small sample data than when conducting a traditional frequentist analysis. Impact and Implications: We provide a Bayesian tutorial for selecting priors, computing, and interpreting results within the context of disability research. We hope to inspire future research using Bayesian analysis for small sample disability data. [ABSTRACT FROM AUTHOR]

People spontaneously judge others' personality based on their facial appearance and these impressions guide many important decisions. Although the consequences of personality impressions are well documented, studies on the accuracy of personality impressions have yielded mixed results. Moreover, relatively little is known about people's accuracy awareness (i.e., whether they are aware of their judgment accuracy). Even if accuracy is generally low, awareness of accuracy would allow people to rely on their impressions in the right situations. In two studies (one preregistered), we estimated perceivers' accuracy and accuracy awareness when forming personality impressions based on facial photographs. Our studies have three crucial advantages as compared to previous studies (a) by incentivizing accuracy and accuracy awareness, (b) by relying on substantially larger samples of raters (n Study 1 = 223, n Study 2 = 423) and targets (k Study 1 = 140, k Study 2 = 1,260 unique pairs with 280 unique targets), and (c) by conducting Bayesian analyses to also quantify evidence for the null hypothesis. Our findings suggest that face-based personality impressions are not accurate, that perceivers lack insight into their (in)accuracy, and that most people overestimate their accuracy. [ABSTRACT FROM AUTHOR]