Bayesian Network Research Articles

Pharmacological treatment of postoperative recurrence of Crohn's disease: Protocol for systematic review and network meta-analysis.

Crohn's disease (CD) is a chronic inflammatory condition primarily affecting the digestive system. When dealing with complex cases like intestinal blockages or perforations, surgery becomes the primary treatment option. However, surgery doesn't offer a complete cure, and the possibility of recurrence remains. To manage CD recurrence after surgery, various treatment choices are available, including steroids, monoclonal antibodies, immunomodulators, and further surgery. Regrettably, the current body of evidence doesn't definitively establish which of these treatments is the most effective and safe. Thus, our research aims to provide insights into the Validity and security of different treatment approaches for managing CD recurrence after surgery. Search of EMBASE, PubMed, Web of Science Core Collection and the Cochrane Central Register of Controlled Trials will be conducted to include researches that examine the validity of treatments for recurrent CD after surgery. Our analysis will distinguish between two types of studies: randomized controlled trials (RCTs) and non-randomized studies with at least two different treatments, each evaluated separately. We will employ Bayesian network meta-analyses to systematically compare the effectiveness and safety of these treatments. Additionally, subgroup analyses will be performed according to recurrence status and postoperative prophylactic medication. To clarify the variation of studies, sensitivity analyses will be performed. And we may use meta-regression as an additional approach if relevant data are available. We will also rigorously access the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation framework. This analysis will provide a comprehensive assessment of the latest evidence on available treatments for patients with postoperative recurrence of CD, which will provide recommendations for clinical practice. Systematic review registration INPLASY2023110021. (DOI: 10.37766/inplasy2023.11.0021).

Intelligent monitoring and quantitative evaluation of fire risk in subway construction: Integration of multi- source data fusion, FTA, and deep learning

Due to their long, winding passages and poor visibility, subway systems are highly susceptible to rapid smoke spread during fires, risking substantial casualties. This paper examines the causes of fire risks in subways, compiling a comprehensive list of risk factors and using fault tree analysis (FTA) to underscore the importance of fire monitoring systems in the causal chains of subway construction fires. To improve the speed, accuracy, and responsiveness of fire detection in subway construction, this study introduces a novel fire monitoring system incorporating wireless sensor networks, edge computing, multi-source data fusion, and deep neural networks. The system employs a multi-wireless sensor network (M-WSN) for transmitting monitoring data, using Long Short-Term Memory (LSTM) for time series prediction of sensor data, followed by deep belief network (DBN) for fusing multi-source data to identify fire risks. It also includes a gas diffusion model and a weighted windy-centroid localization algorithm (W-CLA) to develop a fire source localization algorithm suitable for windy conditions. Furthermore, an improved framework is established for assessing fire risks in subway construction. Practical applications demonstrate that this system not only extends the range of data collection, achieves 100% accuracy in fire risk identification, and maintains a key fire risk indicator prediction error (RMSE) of 3.34 with fire source localization errors under 0.4 m, but also effectively enhances the system's capability for rapid detection, accurate risk identification, and precise source localization.

Application of DBN-based KRLS method for RUL prediction of lithium-ion batteries

The Remaining Useful Life (RUL) of lithium batteries is vital for maintaining and safely operating the batteries, making precise RUL predictions highly significant. This paper introduces a method for predicting the RUL of lithium-ion batteries, utilizing a kernel adaptive filtering algorithm integrated with Deep Belief Networks (DBN). The method constructs a novel prediction model based on the Fixed-Budget Kernel Recursive Least Squares (FB-KRLS) algorithm. In this approach, the DBN extracts features from the original lithium battery data to reduce data complexity. The Square-root Cubature Kalman Filter (SCKF) is integrated with the FB-KRLS algorithm, employing a dual al-ternating learning strategy to improve the model's nonlinear fitting performance. The model was validated using NASA's lithium battery data, showing that the minimum val-ues for the MAPE, RMSE and MAE were 0.102%, 0.0016 and 0.0014, respectively. Therefore, the proposed method demonstrates potential for application in predicting the RUL of lithium-ion batteries.

A Bayesian network approach to assess the ecosystem integrity of mangroves in Tampamachoco, Veracruz, Mexico

Given the alarming rates of mangrove forest loss, resource managers must count on information regarding the condition of the mangrove forests. We propose a Bayesian network (BN) to assess mangrove forests' ecosystem integrity (EI) to support a mangrove monitoring system in Mexico. This approach allowed us to infer the system's condition based on variables on forest structure and function. We defined the BN structure based on informal interviews with specialists on vegetation and coastal geomorphology. We applied the expectation-maximization learning algorithm to train the model. Data from plots in two mangrove areas of an Avicennia germinans forest, defined based on their undisturbed and disturbed conditions, were used as training datasets. We applied sensitivity analysis to determine the degree of influence of each model variable. We evaluated the prediction capacity of the BN with a k-fold cross-validation (the process is repeated five times, starting from the database in 2 parts). The variables selected for the model were the Holdridge complexity index (HCI, Holdridge et al., 1971), Leaf area index (LAI), litter production (g/month/m2), leaf C, N and P concentration (%), and leaf N:P ratio. The most critical variable to infer mangrove condition was leaf N:P (Variance reduction = 11%), followed by forest structure variables HCI and LAI (Variance reduction > 5%). The cross-validation to test the model resulted in a minimum square error of 0.3, which indicates a reasonable capacity to predict the condition of mangrove integrity. The BN constructed can diagnose the integrity of a monospecific mangrove forest with acceptable precision, considering the environmental factors that define the forest structure and functioning locally. We then asked the experts to review and modify the model to apply to multispecies mangrove ecosystems and environmental contexts.

Sensitivity analysis of human error in the steel industry: exploring the effects of psychosocial and mental health risk factors and burnout using Bayesian networks.

Human error and the high rates of fatalities and other occupational accidents in the steel industry are of significant global relevance. The aim of this study was to investigate the effect of psychosocial, mental health, and burnout risk factors on human error probabilities in an industrial environment using Bayesian networks. This cross-sectional study was conducted in 2023. The participants were 252 employees of a steel company. Error probabilities related to the tasks of participants were estimated using the Human Error Assessment and Reduction Technique (HEART). Other data was collected using a survey that consisted of demographic information, the Maslach Burnout Inventory, Depression Anxiety Stress Scales, and a short version of the Copenhagen Psychosocial Questionnaire. A theoretical model was drawn in GeNIe academic software (version 2.3). The results showed that all the studied variables were able to significantly affect the distribution of human error probabilities. Considering a distribution of 100% for the high state of these variables, the results showed that the greatest increases in error probability were related to two burnout dimensions: emotional exhaustion (29%) and depersonalization (28%). All the variables, with a probability of 100%, increased the probability of high human error probabilities by 46%. The most important variables in terms of their effect on human error probabilities were burnout dimensions, and these variables also had a mediation effect on the psychosocial and mental health variables. Therefore, preventive measures to control human error should first focus on managing the risks of burnout in workers. This, in turn, can also reduce the effect of psychosocial risk factors and mental health problems on human error in the workplace.

Embracing methodological evolution and diversity in logistics and supply chain management research

PurposeThe purpose of this research is to highlight the significance of advancing research methodologies in logistics, operations and supply chain management. It seeks to expand the scope of research questions and explore areas previously constrained by traditional methodological approaches, thereby enhancing the exploration of complex, real-world business issues.Design/methodology/approachThis commentary introduces and discusses the special issue on “Advances in Research Methodologies for Logistics and Supply Chain Management,” exploring methodological innovations, diversity and their potential to address complex business and disciplinary challenges. The commentary assesses a broad spectrum of methodologies, ranging from traditional qualitative and quantitative approaches to overlooked methods such as qualitative comparative analysis, netnography, design science, Bayesian networks, machine learning and repertory grid technique. This diverse methodological approach enables a comprehensive examination of emerging and ongoing challenges in the supply chain. In the final summary section, we highlight additional areas of research method innovation not covered in this special issue, offering a broader perspective on future directions for methodological advancements in SCM research.FindingsThe findings suggest that integrating less explored methodologies from various disciplines encourages a richer, multi-level analysis of the supply chain management landscape. This integration facilitates a deeper understanding of emerging challenges, such as geopolitical issues, global supply chain disruptions and the integration of new technologies. Additionally, the exploration of ‘white space' in research methodologies indicates significant potential for discovering new insights that bridge practical problems with theoretical contributions.Originality/valueThe value of this methodological diversity extends beyond academic enrichment. It catalyzes the generation of innovative insights crucial for business practitioners, policymakers, consultants and academics. By adopting varied research designs and methodologies, the research note can offer a broader spectrum of analytical perspectives, crucial for uncovering nuanced insights into complex, cross-cultural and relationship-based dynamics in supply chain research.

Uncertainty prediction of conventional gas production in Sichuan Basin under multi factor control

The establishment of a natural gas production model under multi factor control provides support for the formulation of planning schemes and exploration deployment decisions, and is of great significance for the rapid development of natural gas. Especially the growth rate and decline rate of production can be regulated in the planning process to increase natural gas production. The exploration and development of conventional gas in the Sichuan Basin has a long history. Firstly, based on the development of conventional gas production, the influencing factors of production are determined and a production model under multi factor control is established. Then, single factor analysis and sensitivity analysis are conducted, and multi factor analysis is conducted based on Bayesian networks. Finally, combining the multivariate Gaussian mixture model and production sensitivity analysis, a production planning model is established to predict production uncertainty under the influence of multiple factors. The results show that: 1) the production is positively correlated with the five influencing factors, and the degree of influence is in descending order: recovery rate, proven rate, growth rate, decline rate, and recovery degree. After being influenced by multiple factors, the fluctuation range of production increases and the probability of realization decreases. 2) The growth rate controls the amplitude of the growth stage, the exploration rate and recovery rate control the amplitude of the stable production stage, the recovery degree controls the amplitude of the transition from the stable production stage to the decreasing stage, and the decreasing rate controls the amplitude of the decreasing stage. 3)The article innovatively combines multiple research methods to further obtain the probability of achieving production under the influence of multiple factors, providing a reference for the formulation of production planning goals.

Modelling and optimization of TPMLMs with slotted stators based on Bayesian DNN

AbstractThe Permanent Magnet Linear Motor (TPMLM) is widely used in different industrial fields. TPMLMs with slots and iron cores have high power density, but their thrust fluctuations and copper losses are significant. Due to the nonlinearity and saturation of magnetic circuits, their electromagnetic models are complex and the accuracy of numerical methods is very inferior. Substantially accurate modelling is crucial for motor optimisation design. In this paper, a data‐driven modelling method based on Bayesian optimisation deep neural network (DNN) is proposed to improve the accuracy of the electromagnetic field. The finite element (FE) modelling under different structural parameters is analysed and provides a training dataset for DNN. Then, a multi‐objective optimisation problem for the slotted TPMLM is carried out based on the multi‐objective black hole algorithm. Compared to the original design, the average thrust of TPMLM increased by 49.37%, the thrust fluctuation percentage decreased by 9.59%, and the coil copper consumption percentage decreased by 2.64%. The results show that the improved DNN model has very high modelling accuracy, providing a new way for motor design and optimisation.

Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

DELM: Deep Ensemble Learning Model for Anomaly Detection in Malicious Network Traffic-based Adaptive Feature Aggregation and Network Optimization

With the rapid advancements in internet technology, the complexity and sophistication of network traffic attacks are increasing, making it challenging for traditional anomaly detection systems to analyze and detect malicious network attacks. The increasing advancedness of cyber threats calls for innovative approaches to identify malicious patterns within network traffic precisely. The primary issue lies in the fact that these approaches do not focus on the essential adaptive features of network traffic. We proposed an effective anomaly detection system for malicious network traffic attacks called the Deep Ensemble Learning Model (DELM). We leverage the structure of the Feedforward Deep Neural Network (FDNN), and Deep Belief Network (DBN), incorporating multiple hidden layers with non-linear activation functions. Integrating Adaptive Feature Aggregation (AFA) with the FDNN algorithm dynamically adjusts the feature aggregation process based on incoming traffic characteristics to improve adaptability. The Conditional Generative Network was employed to enhance DELM for generating data for minority classes. To improve the model’s accuracy, we applied batch normalization and data augmentation techniques for preprocessing, utilized n-gram, one-hot encoding, and feature aggregation methods for effective feature extraction. This study significantly contributes to network security by enhancing systems for detecting malicious network traffic. With its interpretability and adaptability, our proposed model shows promise in addressing the evolving cyber threat and fortifying critical network infrastructure. The experimental results demonstrate that our model performs with higher stability than the existing state-of-the-art detection approaches, as reflected by its higher accuracy, precision, recall, F1-score, and AUC-ROC.

A real-world pharmacovigilance study using disproportionality analysis of United States Food and Drug Administration Adverse Event Reporting System events for vinca alkaloids: comparing vinorelbine and Vincristine

ABSTRACT Background Vinca alkaloids are widely used in cancer treatment for their ability to target microtubule dynamics. While their efficacy in treating certain cancers is well-established, the full spectrum of their adverse event profiles remains an area of ongoing research. Methods We analyzed AEs related to vinorelbine and vincristine using a retrospective case/non-case approach with data from the FDA Adverse Event Reporting System (FAERS). We applied various algorithms to detect AE signals: the reporting odds ratio (ROR) and proportional reporting ratio (PRR) measured disproportionality and association strength; the Bayesian confidence propagation neural network (BCPNN) calculated the Information Component (IC) for associations against background rates; and the multi-item gamma Poisson shrinker (MGPS) yielded empirical Bayes geometric mean (EBGM) values, accounting for reporting variability. Results Both medications significantly involve the blood and lymphatic systems, with vinorelbine reporting 401 cases in this System Organ Class (SOC), exhibiting a ROR of 17.4, PRR of 12.4, IC of 3.63, and EBGM of 12.38. An intersection analysis of Preferred Terms (PTs) has uncovered previously unreported AEs shared by both drugs, including posterior reversible encephalopathy syndrome and inappropriate antidiuretic hormone secretion. Conclusions This analysis highlights the need for ongoing research of the risks associated with vinorelbine and vincristine.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BackgroundA better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.MethodsThe current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).ResultsOf 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.ConclusionThese findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.Trial registrationTrial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Election Voting Trend Prediction System

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

Comparative effectiveness of five Chinese patent medicines for non-alcoholic fatty liver disease: A systematic review and Bayesian network meta-analysis

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a metabolically stressed liver injury closely related to insulin resistance and genetic susceptibility and has become the leading chronic liver disease in China. PurposeTo analyze the effectiveness of five Chinese patent medicines used alone or in combination with western medications (WM) for NAFLD using Bayesian network meta-analysis. MethodsSearches were conducted in Embase, Cochrane Library, PubMed, CNKI, Wanfang Database, VIP, and SinoMed for randomized controlled trials (RCTs) on Danning tablets, Huazhi Rougan granules, Dangfei Liganning capsules, Kezhi capsules, and Qianggan capsules, either alone or in combination with WM for NAFLD, up to January 10, 2024. This study was screened based on pre-designed inclusion and exclusion criteria, and the risk of bias was evaluated using the Cochrane ROB2 tool. The primary outcome was clinical efficacy rate, while secondary outcomes included levels of Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), Triglycerides (TG), and Low-density lipoprotein cholesterol (LDL-C). These data will be analyzed using WinBUGS 1.4.3 and then visualized using Stata 14.0 software. ResultsA total of 77 RCTs involving 7770 patients were included. The results indicated that Huazhi Rougan granules combined with WM (OR = 0.13, 95 % CI 0.05 ∼ 0.26) had a SUCRA probability value of 81.7 %, ranked first in clinical efficacy and significantly improved blood lipids levels including TG, High-density Lipoprotein Cholesterol (HDL-C), and LDL-C, Total cholesterol (TC). For the Chinese patent medicines alone, Danning tablets led with a 75.3 % clinical efficacy rate. Huazhi Rougan granules significantly increased levels of ALT (96.2 %) and AST (MD = -14.48, 95 % CI -23.38 ∼ -5.32). Dangfei Liganning capsules demonstrated significant efficacy in improving TG (73.1 %) and TC (83 %) levels. ConclusionIn the treatment of NAFLD, the combination of Huazhi Rougan granules and WM demonstrated significant clinical effectiveness and improvement in blood lipid profiles. For different outcome indicators, Danning tablets used alone showed the highest clinical efficacy, while significant improvement in liver function indicators was best achieved with Huazhi Rugan granules used alone.

Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Comparative Analysis of Machine Learning Models for Prediction of Autism Spectrum Disorder Using Screening Data

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. In this research paper, an in-depth comparative analysis of various machine learning models applied to the task of classifying autism traits was presented. Our study aimed to assess the performance of these models within the context of identifying individuals with autism based on relevant features and data. The machine learning models investigated in this study encompassed Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes (NB), and Neural Network (NN). The models were evaluated using six essential classification metrics: accuracy, precision, recall, specificity, F1 score, and AUC score. On the training dataset, our results reveal nuanced performance characteristics. SVM and RF excel in precision and recall, showing promise for accurate autism trait classification. KNN exhibits remarkable specificity, suggesting its potential for minimizing false positives. LR and NB demonstrate balanced performance across multiple metrics, while NN exhibits high precision and recall, albeit with higher computational demands. It was concluded that SVM was the best classification model for autism trait classification.

Serum uric acid trajectories and the risk of cardiovascular disease: a longitudinal association and pathway analysis.

Age-specifically longitudinal associations and potential pathways between serum uric acid (SUA) and cardiovascular disease (CVD) remained unclear. This study aimed to explore SUA trajectories in different age populations and to determine their associations and potential pathways with incident CVD. This prospective cohort included 41,367 participants from the Kailuan study, including 30,938 participants aged <55 years and 10,419 participants aged ≥55. The SUA trajectories during year 2006-2012 were identified by latent class growth models. Three SUA trajectories were identified in the overall, aged <55 and aged ≥55 years participants, as "low-stable" (51.9%, 54.4%, and 43.3%), "moderate-stable" (39.0%, 36.9%, and 45.6%), and "high-stable" (9.1%, 9.7%, and 11.1%), respectively. During a median follow-up of 6.75 years, incident CVD occurred in 2302 participants (5.56%). Overall, a high-stable trajectory was independently associated with a higher risk of CVD (hazard ratio [HR], 1.23; 95% [confidence interval], 1.06-1.42). Notably, the associations differed by age, a significant association was only observed in participants aged ≥55 years (HR, 1.29; 95% CI, 1.05-1.58), rather than those aged <55 years (HR, 1.08; 95% CI, 0.89-1.33). The addition of SUA trajectories to a baseline risk model for CVD improved the integrated discrimination improvement value (P < 0.05) and category-free net reclassification improvement value (P < 0.05). Bayesian network showed the conditional probability of high CVD risk associated with aging, elevated SUA trajectories, blood pressure, glucose, and inflammation was 15.5%. High-stable SUA trajectories were independently associated with an elevated risk of CVD, which is mainly induced by hypertension, diabetes, and inflammation, especially in participants aged ≥55 years.

Positive association between constipation and mild cognitive impairment in elders: A cross-sectional study.

This study aimed to examine the association between constipation and mild cognitive impairment (MCI); and further elucidate the possible mechanisms involved. A cross-sectional study was conducted among community-dwelling elders (N = 789) in Nanning, China. Trained research staffs collected detailed information through questionnaires and physical examinations. A Bayesian network model was used to explore the hypothesized causal path. Synergistic effects of constipation with infrequent fruit consumption, inactive physical exercise, or history of stroke were observed in the risks of MCI occurrence. The Bayesian network model analyses showed 3 hypothesized causal-association paths leading to MCI occurrence. Among these, constipation, history of stroke, and years of schooling were directly related to the occurrence of MCI. Years of schooling indirectly affected MCI through infrequent fruit consumption and constipation; or through inactive physical exercises and history of stroke. This study demonstrates a direct association between constipation and increased risks of MCI.

A copula-based approach for multi-modal demand dependence modeling: Temporal correlation between demand of subway and bike-sharing

As a representative mode of shared mobility, bike-sharing serves not only as a convenient way to conduct short-distance trips in urban areas, but also as a feeder mode to public transit, forming the Bike and Ride (BnR) system. Conducting management for such a hybrid multi-modal system faces various challenges, including the complex interactions between bike-sharing and other modes, highly dynamic passenger demand, and the difficulty of accessing direct transfer data. To overcome such difficulties, our study proposes a framework for assessing the dependency between the two usage modes. Firstly, a Dynamic-Time-Warping-based (DTW) method is utilized to determine the catchment area (CA) between the two modes, allowing the BnR-related tendency similarity under a given time scale to be considered. Then, the patterns of probabilistic dependence between travel demand of the two modes are obtained by a copula-based approach, which separates correlations under specific usage levels from single modal demands. A case study on the multi-modal system formed by docked bike-sharing and subway in New York is conducted to validate the proposed framework. The tendency similarity is found to be most pronounced within 500 m on average under a 4-hour interval. For each formed station group (SG), the best-fitted copula type is selected, capturing the strong tail correlations present only at specific usage levels. The results show a variety of different correlation patterns within SGs, despite the close geographic locations they may share. Areas of potential transfer resistance between the two modes are identified, which is more evident in first-mile-related (FMR) activities. In contrast, the two modes display more weak connections in last-mile-related (LMR) activities. The obtained results can be utilized by bike-sharing service providers to analyze demand distributions and conduct efficient station-level rebalancing. Compared to previous methods, our proposed framework is computationally inexpensive since no direct transfer of data or complex inference network is required. It incorporates statistically significant spatial–temporal information, allowing for a more accurate determination of the bi-modal assessment range. Moreover, considering that single-mode influences are mathematically removed, the resulting correlation in principle links to the strength of the connections between the two modes. Therefore, it can be assessed as an indicator of the reliability of the multi-modal system.