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Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future. [ABSTRACT FROM AUTHOR]

Prevention behaviors are important in mitigating the transmission of COVID‐19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID‐19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data‐driven approach is used to train Bayesian belief network (BBN) models using results of a survey of N=7797$N=7797$ participants reporting risk perceptions and prevention behaviors during the COVID‐19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data‐driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID‐19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data‐driven and PMT‐based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies during pandemics. [ABSTRACT FROM AUTHOR]

In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: While an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2085. We built a Bayesian Belief Network (BBN) from long‐term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the conservation LULC scenario (−28% on average between 2020 and 2085), followed by the tourism development scenario (−7%) and the business‐as‐usual scenario (+3%). Climate change under representative concentration pathways (RCP) 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth.Synthesis and applications: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannual variability threaten the current grass‐based protected designation of origin (PDO) production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. Along with decreased total annual production due to decreasing grassland area compounded by more extreme climate change, the seasonal shift in production and increased interannual variability threaten the current grass‐based PDO production system. Further Bayesian modelling co‐developed with local stakeholders and experts could greatly contribute to adaptation planning of the regional production system. [ABSTRACT FROM AUTHOR]

Understanding the underlying mechanism driving ecosystem service relationships is crucial for developing successful urban planning and management policies. There is still a lack of understanding of the driving factors behind ecosystem service relationships, particularly in terms of long-term assessments of their spatial–temporal patterns and researches on the driving effects and thresholds of the driving factors. This study utilized various analytical methods including the InVEST 3.13.0 model, optimal parameter geographic detector, and bayesian belief network to assess ecosystem serviecs and explore their relationships. Moreover, the study identified the crucial driving factors and their effects to the spatiotemporal variation in ecosystem service relationships in the Wuhu city between 1990 and 2020.The results showed that: (1) The distribution of high-value areas for the three ecosystem services in Wuhu City has remained relatively stable over time. High-value areas for food production services are primarily located in traditional polder fields, flood mitigation services are concentrated in the plains in the north of the city, and habitat quality services are situated in the hills in the south. However, with the expansion of the city, there is a risk of further degradation of the three services. (2) Spatial-temporal changes in ecosystem service relationships were regular, and long-term assessments of ecosystem services and their relationships were important. (3) The biophysical factors exerted the most significant influence on the Spatial-temporal patterns of ecosystem service relationships. (4) The combined effect of two driving factors was greater than that of a single factor. (5)There was a threshold of drivers that promotes synergy of ecosystem services. (6) Multiple ecosystem services might exhibit synergy relationships within the threshold of the same driver. The findings from this study could provide urban managers with valuable insights into the ecosystem services relationships.

Vegetation restoration has induced complex spatiotemporal changes in ecosystem service (ES) relationships. However, dynamic analysis on the driving mechanisms underlying ES relationships remains insufficient, constraining the comprehension on sustainable vegetation restoration. In this study, a comprehensive index (ESI) representing the coordination among four ESs (carbon sequestration, water yield, baseflow regulation, and soil conservation) was quantified in Yan’an City, a typical vegetation restoration area. Using the Bayesian belief network, a social-ecological framework was constructed to explore changes in driving factors of ESI during 1990 to 2020. The results revealed that: (1) the coordination of ES relationships showed a significant enhancement over the past 30 years, which primarily attributed to areas with the highest values of water yield. Areas with low values of baseflow regulation posed a risk to the ESI. (2) The combined contribution of FVC and precipitation was consistently close to 70 %, emerging as the dominant driving factors for the ESI. But in the last decade, contributions from human activities have increased significantly. (3) Scenario reasoning highlighted that expanding vegetation cover alone would exhibit threshold effects on the ESI improvement. Additionally, the results revealed a promising prospect of enhancing baseflow and maintaining the ESI through human actions. This study provides an example of using Bayesian belief network to analyze social-ecological interactions, enhancing the understanding of changes in ES relationships under vegetation restoration.

Sharp curves in freeways are known to be unsafe design elements since drivers do not expect them. It is difficult for drivers to estimate the radius of a curve. Therefore, drivers are believed to use other cues to decelerate when approaching a curve. Based on previous successful experiences of driven speeds in curves, drivers are thought to have built expectations of safe speeds given certain cues, minimalising risks. This research employs a Bayesian Belief Network to model driver expectations using measured speeds in 153 curves and data on the characteristics of the curve approaches. This model mimics expectations as the probability of measured speeds given certain cues. Using Bayes theorem, prior beliefs on safe speeds are updated towards a posterior belief when a new cue is observed during curve approach. We refer to this posterior belief as expected safe speed. Drivers are assumed to adjust their operating speed if it does not match their expected safe speed. The model shows that the visible deflection angle has a large influence in setting the expectations of a safe speed for an upcoming curve. In addition, the preceding type of roadway and the number of lanes are both important cues to set a driver’s expectations of a safe speed. Speed and warning signs are shown to be interdependent on the road scene and hence have less influence in setting expectations. This research shows that design and safety assessment of freeway curves should be considered aligned with the road scene upstream of the curve.

Identification of wildlife corridors usually prioritizes ecological data while often overlooking the perspectives of local communities despite their relevance. This oversight may contribute to human-wildlife conflicts in surrounding areas. This study aims to demonstrate how multiple data sources and aspects can be integrated to identify wildlife corridors using the Dong Phayayen-Khao Yai Forest Complex as a case study. Suitable areas serving as additional wildlife corridors for the Asiatic black bear were identified, and appropriate management strategies to enhance the overall suitability of the wildlife corridor were determined. Firstly, road segments having the potential to serve as additional wildlife corridors were selected based on four criteria related to physical barriers. Then a Bayesian Belief Network was developed to assess the corridor suitability for each proposed road segment by including data from various sources and aspects (ecological data, the human dimension and landscape characteristics). Afterward, eight scenarios were tested to evaluate how they could impact the overall suitability. From seven potential road segments, only three segments showed high suitability to be additional wildlife corridors. Within the assessed 58 km2 area, 13 km2 (22%) were identified as highly suitable for wildlife corridors under current situations. Improving human attitudes toward wildlife corridor construction emerged as the scenario that could increase the overall suitability the most (from 13 to 29 km2) while increasing human threats (hunting, wildlife pet trade, and vehicle collisions) was the scenario that reduced overall suitability the most (from 13 to 4 km2). Our framework in this study is practical, adaptable, and flexible for various decision-making processes. It can also be adapted to other areas and focal species by making relevant adjustments to align with specific landscapes and contexts.

The implementation of prefabricated housing (PH) has become prevalent in China recently due to its advantages in enhancing production and energy-saving efficiency within the construction system. However, stakeholders may not always fully realize the benefits of adopting PH due to the emergence of transaction costs (TCs) in the development process of such projects. This study investigated the strategies for developers to make rational choices for minimizing the TCs of the PH project considering their own attributes and external constraints. A Bayesian Belief Network model was applied as the analytical method, based on surveys conducted in China. A single sensitivity analysis indicated that developers influence the TCs of PH through the following three most impactful factors: prefabrication rate, PH experience, and contract payment method. Integrated strategies are recommended for developers in various situations based on a multiple sensitivity analysis. Developers facing challenges due to high prefabrication rates are advised to reduce the risks by procuring highly qualified general contractors and adopting unit-price contracts. For developers with limited PH experience, adopting the Engineering–Procurement–Construction procurement method is the most efficient way to reduce their TCs in the context of China's PH market. This study contributes to the current body of knowledge concerning the effect of traders' attributes and choices on TCs, expanding the application of TC theory and fulfilling the study on the determinants of TCs in construction management. [ABSTRACT FROM AUTHOR]

The continuous increase in population density and the increase in average travel of people by different modes of transportation, whether public or private, play a crucial part in how the city's urban area develops. The latter contribute significantly to the emergence of many problems, including road congestion, loss of time, and pollution in urban areas, noise, and other issues. Machine Learning has extensively beneficial aspects of proposing models in this context. Therefore, we proposed four algorithms of the machine learning techniques that have been implemented to analyze and classify the displacement of an individual's database to support urban decisions, K-Nearest Neighbors (KNN), Artificial Neural Network-multilayer perceptron Neural (net-RBF), Bayesian Belief Network (BNN) and Support Vector Machine (SVM). We will compare their learning metrics using train/test and cross-validation. The obtained results show that net-RBF offers the best accuracy (92.77%), SVM classifier (89.87%), BBN classifier (87.33%), and KNN classifier (86.53%). [ABSTRACT FROM AUTHOR]

This study aims to explore the main factors influencing women's involvement and analyze the interaction between variables to develop policy recommendations that enhance women's involvement in tourism sustainability in Penglipuran Village. This research will use prospective analysis methods, specifically MICMAC Analysis and Bayesian Belief Network (BBN). The study's results identified several potential variables affecting women's involvement, such as awig-awig, gender equality, women empowerment, infrastructure, customary institutions, skills, and culture. Additionally, the study discusses how managing five key nodes with a 100% probability led to a 20% increase in women’s engagement and a 5% increase in tourism sustainability. The findings of this study emphasize the crucial role of handling infrastructure and customary institutions supporting women's tourism involvement. Women are expected to have equal access to resources and opportunities, alongside promoting women’s empowerment.

A systematic understanding of shifts in the temporal and spatial distributions of ecosystem services (ESs) in the Yellow River Basin and the trade-offs and synergies between these services and their driving variables is essential for effective management of the environment and for enhancing human well-being. This study quantitatively assessed the carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, combinded Bayesian Belief Network (BBN) and ESs, determined the crucial elements influencing the supply of ESs via sensitivity analysis, identified synergies and trade-offs between ESs via probabilistic reasoning, and explored the driving processes via scenario analysis. The following are the key findings of this research. (1) Geographically, the Yellow River Basin, SR, CS, and WY exhibited higher levels in southern regions and lower levels in northern regions, whereas HQ showed higher levels in the northwestern region and lower levels in the southeastern region. Both WY and CS showed annual increases in the time series. (2) With respect to trade-offs and synergistic interactions, HQ, WY, and CS had a synergistic connection, whereas SR and HQ, CS, WY had a trade-off connection. (3) According to the BBN sensitivity analysis, the normalized difference vegetation index (NDVI), temperature, precipitation, and land use (LU) were the main variable nodes affecting ESs. (4) The results of the scenario analysis revealed that LU was the primary driver of the four ESs In addition, precipitation and NDVI primarily drove the synergistic and trade-off relationships between HQ, CS, and WY. This study combined BBN and ESs to develop a new method for investigating ES functions and established a solid scientific foundation for ES management and macroeconomic growth planning.

Global climate change and human activities continue to threaten watershed ecosystems. The Jinsha River constitutes the upper reaches of the Yangtze River, so studying its ecosystem services (ES) is of great significance for maintaining ecological security and promoting ecological sustainability in the entire Yangtze River Basin. By using the integrated valuation of ecosystem services and trade-offs (InVEST) models and revised universal soil-loss equation (RUSLE) models, we evaluated five ecosystem services of water yield (WY), habitat quality (HQ), soil retention (SR), food supply (FS), and carbon storage (CS) provided by the Jinsha River Basin ecosystem from 2000 to 2020, as well as their spatial-temporal variations and driving factors. The results show three main features of this system. (1) From 2000 to 2020, each ecosystem service in the Jinsha River Basin exhibited different degrees of fluctuation, except for habitat quality, and each ecosystem service basically showed a spatial distribution pattern of high in the southeast and low in the northwest. (2) There were significant synergistic relationships between CS_SR_HQ and WY_SR_FS, and a significant trade-off between WY_CS. (3) The main driving factors of CS_SR_HQ were net primary productivity (NPP) and land-use type (LU), the main driving factors of WY_SR_FS were annual precipitation (PRE), LU, and rainfall erosivity (R), and the main driving factors of WY_CS varied considerably during the study period. [ABSTRACT FROM AUTHOR]

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or 'fracking'. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study's results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations. [ABSTRACT FROM AUTHOR]

Native aquatic species are in decline, and hatcheries can play an important role in stemming these losses until larger ecological issues are addressed. However, as more federal and state agencies face budget uncertainty and the number of imperiled species increases, it is necessary to develop a tool to prioritize species for conservation propagation. Our objective was to create prioritized lists of aquatic species that may benefit from conservation propagation for five states in the United States. Biologists developed an influence diagram and provided information for multiple attributes affecting prevalence of species. The influence diagram and information for each species was used in a Bayesian belief network to generate a score to prioritize propagation of a species and the feasibility of propagation. When all taxa were ranked together within a state, mussels, amphibians, and a crustacean were included among fishes in the top species that may benefit from propagation. We recognize that propagation is one tool for conservation of imperiled species and that additional factors will need to be addressed to ensure species persistence. Nevertheless, we contend our quantitative approach provides a useful framework for prioritizing conservation propagation. [ABSTRACT FROM AUTHOR]

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the “diabetes capital of the world” owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Abstract Background Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda. Methods In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance. Results Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises). Conclusion Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Quality and safety are intensely related and go hand in hand. Quality of the safety-grade equipment is very important for the safety of a nuclear power plant (NPP) and achieving production goals. During manufacturing of plant components or equipment, deviation from the design might occur at different stages of manufacturing for various reasons, such as a lack of skilled manpower, deviation of materials, human errors, malfunction of equipment, violation of manufacturing procedure, etc. These deviations can be assessed cautiously and taken into consideration in the final safety analysis report (FSAR) before issuing an operating license. In this paper, we propose a Bayesian belief network for quality assessment of safety class equipment of NPPs with a few examples. The proposed procedure is a holistic approach for estimation of equipment failure probability considering manufacturing deviations and errors. Case studies for safety-class dry transformers and reactor pressurizers employing the proposed method are also presented in this article. This study provides insights for probabilistic safety assessment engineers and nuclear plant regulators for improved assessment of NPP safety.

Conserving species inhabiting areas overlapped with human settlements is more challenging than usual as anthropogenic impacts need to be accounted for when management plans are formulated. This study aims to identify conservation priority areas for two otter species and evaluate the impact of land-use changes on conservation management in the coastal wetlands of southern Thailand. We conducted camera-trap surveys across 283 grids of 25 km2 each between 2016 and 2020. A single-species occupancy model was applied to estimate the occupancy probability of two otter species in relation to landscape characteristics and human factors. Occupancy probabilities were then correlated with anthropogenic threat levels obtained from Bayesian Belief Network to identify conservation management hotspots and conservation importance areas under various scenarios. The Smooth-coated Otter shows a higher occupancy probability in the Andaman, where a larger proportion of less disturbed wetlands remains compared to the Gulf. The Small-clawed Otter, in contrast, has higher occupancy in the Gulf. In the Andaman, the Smooth-coated Otter appears to occupy areas associated with lower human impacts and a higher proportion of natural habitats, while in the Gulf, both otter species have higher occupancy in areas where modifications of natural habitats are more intensive. In total, 1350 km2 were identified as very high conservation management hotspots, with an additional 750 km2 identified as very high conservation importance. The majority of these areas were located along the Andaman coast. However, land-use changes expanded the management hotspot areas to 2025 km2 while reducing conservation importance areas to just 175 km2. Our results emphasize the importance of protecting natural habitats and the urgency of implementing management interventions for otters on the Andaman coast, for the long-term survival of these threatened species.

Background: Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria's transmission complexity, control, and integrated modelling, with no available evidence on Uganda's refugee settlements. Using the 2018–2019 Uganda's Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda. Methods: In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance. Results: Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model's spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises). Conclusion: Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements. [ABSTRACT FROM AUTHOR]

The implementation of prefabricated housing (PH) has become prevalent in China recently due to its advantages in enhancing production and energy-saving efficiency within the construction system. However, stakeholders may not always fully realize the benefits of adopting PH due to the emergence of transaction costs (TCs) in the development process of such projects. This study investigated the strategies for developers to make rational choices for minimizing the TCs of the PH project considering their own attributes and external constraints. A Bayesian Belief Network model was applied as the analytical method, based on surveys conducted in China. A single sensitivity analysis indicated that developers influence the TCs of PH through the following three most impactful factors: prefabrication rate, PH experience, and contract payment method. Integrated strategies are recommended for developers in various situations based on a multiple sensitivity analysis. Developers facing challenges due to high prefabrication rates are advised to reduce the risks by procuring highly qualified general contractors and adopting unit-price contracts. For developers with limited PH experience, adopting the Engineering–Procurement–Construction procurement method is the most efficient way to reduce their TCs in the context of China’s PH market. This study contributes to the current body of knowledge concerning the effect of traders’ attributes and choices on TCs, expanding the application of TC theory and fulfilling the study on the determinants of TCs in construction management.

Quality and safety are intensely related and go hand in hand. Quality of the safety-grade equipment is very important for the safety of a nuclear power plant (NPP) and achieving production goals. During manufacturing of plant components or equipment, deviation from the design might occur at different stages of manufacturing for various reasons, such as a lack of skilled manpower, deviation of materials, human errors, malfunction of equipment, violation of manufacturing procedure, etc. These deviations can be assessed cautiously and taken into consideration in the final safety analysis report (FSAR) before issuing an operating license. In this paper, we propose a Bayesian belief network for quality assessment of safety class equipment of NPPs with a few examples. The proposed procedure is a holistic approach for estimation of equipment failure probability considering manufacturing deviations and errors. Case studies for safety-class dry transformers and reactor pressurizers employing the proposed method are also presented in this article. This study provides insights for probabilistic safety assessment engineers and nuclear plant regulators for improved assessment of NPP safety. [ABSTRACT FROM AUTHOR]

This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple Unmanned Aerial Vehicles (UAVs) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The “Tender” is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. The proposed architecture enables operations and analysis supported by the means to detect, assess, and accommodate change and hazards on the spot with effective human observation and coordination. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. The risk assessment model determines risk indicators using an integrated Specific Operation Risk Assessment—Bayesian belief network approach, while its resultant analysis is weighted through the analytic hierarchy process ranking model. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity multi-objective reinforcement learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions. This proposed network architecture enables the UAVs to balance multiple objectives. Estimated MSE measures show that the algorithm produced decreasing errors in the learning process with increasing epoch number.

The use of the required navigation performance (RNP) procedure has been increasing for aircraft navigation, since it allows for better optimisation of the airspace, which is increasingly congested. The present work aims to investigate the application of the functional resonance analysis method (FRAM), combined with the quantitative analysis provided by the Bayesian belief network (BBN), to demonstrate the existing variability in functions that are part of the complex navigation system based on the RNP procedure, specifically when the aircraft approaches the airport (approach phase). As a result, it is possible to analyse the variability that occurs in the studied system and the BBN complemented the study by allowing a quantitative interpretation of the functions considered most important for the execution of an RNP approach procedure. [ABSTRACT FROM AUTHOR]

Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches. [ABSTRACT FROM AUTHOR]

Most maritime accidents are caused by human error. To reduce these errors, increase safety, protect the marine environment, reduce the administrative burden, and optimize maritime trade, the International Maritime Organization (IMO) decided to implement a concept called e-Navigation. The term e-Navigation refers to the integration of the most modern information and communication systems, both on board and ashore. The research scope of this work is the safety of navigation of the ship during the port entrance. Particularly in this case, the elements that affect the safety of the ship during port entrance are analyzed in a case study of New York port. An adaptive model based on the Bayesian Belief Network (BBN) was created, which evaluates the decision-making elements and risk assessment for port entry. The model is adaptable in the context of the different port requirements. With the presented model, all entities (ships, shipping companies, and service providers) have insight into the estimated decision to enter the port based on the given elements. Further research needs to be continued on the issue of comparing the proposed model with similar models, and how much reliance on the model burdens or facilitates the Master's decision. [ABSTRACT FROM AUTHOR]

The Kilombero Valley floodplain in Tanzania is a major agricultural area. Government initiatives and projects supported by international funding have long sought to boost productivity. Due to increasing population pressure, smallholder farmers are forced to increase their output. Nevertheless, the level of intensification is still lower than what is considered necessary to increase production and support smallholder livelihoods significantly. This article aims to better understand farmers' intensification choices and their interdependent determinants. We propose a novel modeling approach for identifying determinants of intensification and their interrelationships by combining a Bayesian belief network (BBN), experimental design, and multivariate regression trees. Our approach complements existing lower‐dimensional statistical models by considering uncertainty and providing an easily updatable model structure. The BBN is constructed and calibrated using data from a survey of 304 farm households. Our findings show how the data‐driven BBN approach can be used to identify variables that influence farmers' decision to choose one technique over another. Furthermore, the most important drivers vary widely, depending on the intensification options being considered. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate value generalisation from a sample of ecosystem assets – municipally managed trees - to all tree assets within an urban ecosystem accounting area. A Bayesian network model is used to machine-learn non-parametric correlation patterns between biophysical site condition variables and output variables of an ecosystem service model – here iTree Eco for modelling the regulating services of urban forests. The paper also demonstrates the use of spatial Bayesian network modelling to quantify the reliability of value generalisation for accounting purposes. Value generalisation entails inferring ecosystem service values for all locations in an ecosystem accounting area, where the accounting practitioner has less information about the asset and its context, than in an available sample of managed sites within the accounting area. The modelling is carried out as a "proof-of-principle" of potential value generalisation and uncertainty analysis methods for ecosystem accounting. It does not cover all regulating ecosystem services of urban forests, nor cultural services. While noting that wide confidence intervals for generalised values pose challenges for using monetary accounts for the accounting purpose of change detection, we find that tree-specific asset valuation is possible in an urban accounting setting. Our findings serve the purpose of raising awareness about asset values of urban green infrastructure, to bring them more on a par with grey infrastructure in urban planning. We also argue that the reliability of the asset value of individual trees is also good enough to be used for non-accounting purposes, such as municipal tree damage assessments. [ABSTRACT FROM AUTHOR]

• Urban expansion degrading ecosystem services. • Enhancing ecosystem services requires consideration of their spatial relationships. • Spatial pattern factors drive changes in ecosystem service relationships. • There is a threshold of drivers that promotes synergy of ecosystem services. Understanding the underlying mechanism driving ecosystem service relationships is crucial for developing successful urban planning and management policies. There is still a lack of understanding of the driving factors behind ecosystem service relationships, particularly in terms of long-term assessments of their spatial–temporal patterns and researches on the driving effects and thresholds of the driving factors. This study utilized various analytical methods including the InVEST 3.13.0 model, optimal parameter geographic detector, and bayesian belief network to assess ecosystem serviecs and explore their relationships. Moreover, the study identified the crucial driving factors and their effects to the spatiotemporal variation in ecosystem service relationships in the Wuhu city between 1990 and 2020.The results showed that: (1) The distribution of high-value areas for the three ecosystem services in Wuhu City has remained relatively stable over time. High-value areas for food production services are primarily located in traditional polder fields, flood mitigation services are concentrated in the plains in the north of the city, and habitat quality services are situated in the hills in the south. However, with the expansion of the city, there is a risk of further degradation of the three services. (2) Spatial-temporal changes in ecosystem service relationships were regular, and long-term assessments of ecosystem services and their relationships were important. (3) The biophysical factors exerted the most significant influence on the Spatial-temporal patterns of ecosystem service relationships. (4) The combined effect of two driving factors was greater than that of a single factor. (5)There was a threshold of drivers that promotes synergy of ecosystem services. (6) Multiple ecosystem services might exhibit synergy relationships within the threshold of the same driver. The findings from this study could provide urban managers with valuable insights into the ecosystem services relationships. [ABSTRACT FROM AUTHOR]

• BBN method is used to explore dependencies between the GII indicators. • Key determinants: R&D, online creativity, and knowledge creation. • High-performing countries prioritize ICT, regulations, and education. • Low-performing countries struggle with innovation linkages, and R&D. • Sensitivity analysis highlights knowledge workers, online creativity, and institutions. This study adopts a Bayesian Belief Network (BBN) methodology to explore relationships among innovation indicators within the Global Innovation Index (GII) framework using 2023 data. The analysis identifies 'research and development', 'online creativity', and 'knowledge creation' as pivotal factors influencing innovation outcomes. The BBN model demonstrates a predictive accuracy of 90.9%. For high-performing countries, 'information and communication technologies', 'regulatory environment', and 'education' are critical, whereas 'investment' shows a low performance in driving innovation excellence. In low-performing countries, 'innovation linkages' and 'research and development' are strongly associated with the low innovation performance of these countries. Sensitivity analysis highlights 'knowledge workers', 'online creativity', and 'institutional environment' as significant drivers, illustrating the complex nature of innovation dynamics. This study enhances our understanding of innovation within the GII framework and offers actionable insights for policymakers and stakeholders. Existing methods often rely on simplistic linear models that fail to capture the complex interdependencies among innovation indicators. This study addresses this limitation by leveraging the BBN approach to provide a better understanding of innovation dynamics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vegetation restoration improved the overall coordination of ES relationships. • The social-ecological framework clearly presented changes in driving factors. • A lower baseflow regulation posed potential risks to the ES relationships. • Current social activities can be encouraged for enhancing ES relationships. Vegetation restoration has induced complex spatiotemporal changes in ecosystem service (ES) relationships. However, dynamic analysis on the driving mechanisms underlying ES relationships remains insufficient, constraining the comprehension on sustainable vegetation restoration. In this study, a comprehensive index (ESI) representing the coordination among four ESs (carbon sequestration, water yield, baseflow regulation, and soil conservation) was quantified in Yan'an City, a typical vegetation restoration area. Using the Bayesian belief network, a social-ecological framework was constructed to explore changes in driving factors of ESI during 1990 to 2020. The results revealed that: (1) the coordination of ES relationships showed a significant enhancement over the past 30 years, which primarily attributed to areas with the highest values of water yield. Areas with low values of baseflow regulation posed a risk to the ESI. (2) The combined contribution of FVC and precipitation was consistently close to 70 %, emerging as the dominant driving factors for the ESI. But in the last decade, contributions from human activities have increased significantly. (3) Scenario reasoning highlighted that expanding vegetation cover alone would exhibit threshold effects on the ESI improvement. Additionally, the results revealed a promising prospect of enhancing baseflow and maintaining the ESI through human actions. This study provides an example of using Bayesian belief network to analyze social-ecological interactions, enhancing the understanding of changes in ES relationships under vegetation restoration. [ABSTRACT FROM AUTHOR]

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

The remarkable explosion of e-commerce has marked the latest years of different industries and put forward a higher requirement for the last mile delivery. The last mile delivery is one of the most complex, costly, and inefficient processes along the entire logistics fulfillment chain in an e-commerce context. Its corresponding risks are major contributors to delivery failure. This work proposes a comprehensive framework on risk identification and analysis in the last mile delivery to support delivery planning. Risks were deduced from available literature, and others were induced through semi-structured interviews with experts in the field. Risks are categorized and the relative probability and severity of individual risks are determined. This study adopts a Bayesian Belief Network (BBN) model to identify the interdependency among risks and rank them, as the conventional ranking methods fail to take interdependency into account. The results indicate that privacy concerns, IT, and natural disasters are the most critical risks. This study will aid logistics service providers to ultimately deciding the solutions of last mile delivery that need to be utilized by prioritizing last mile delivery possible risks to increase their competitiveness and market share and minimize delivery costs.

Many researchers have attempted to identify the factors behind software project failures and their solutions from various perspectives. However, systematic and integrated process definitions of failure as process models for success are lacking. This study aims to build a process definition for software project failure as an anti-pattern by identifying the main phases and their relationships in terms of team behavior. We researched software engineering literature and case studies to gather information about critical incidents and repeating behaviors of teams in failed projects into a novel dataset. Grounded theory was employed to build a theoretical foundation for failure phase definitions from the collected data. The design structure matrix and Bayesian belief network were used for the quantitative assessment of the transitions between phases. The results revealed that common behavioral patterns occurred in approximately 89 percent of the case studies, supporting the decision to consider software project failure as a process. The proposed failure process definition has a simple structure that uses everyday concepts for phase names and reveals the critical behaviors leading a software project to failure Thus, it provides critical insights for software professionals, non-technical stakeholders, and managers to evaluate the progress of their projects and design strategies to avoid failure.

Dhole (Cuon alpinus) is threatened with extinction across its range due to habitat loss and prey depletion. Despite this, no previous study has investigated the distribution and threat of the species at a regional scale. This lack of knowledge continues to impede conservation planning for the species. Here we modeled suitable habitat using presence‐only camera trap data for dhole and dhole prey species in mainland Southeast Asia and assessed the threat level to dhole in this region using an expert‐informed Bayesian Belief Network. We integrated prior information to identify dhole habitat strongholds that could support populations over the next 50 years. Our habitat suitability model identified forest cover and prey availability as the most influential factors affecting dhole occurrence. Similarly, our threat model predicted that forest loss and prey depletion were the greatest threats, followed by local hunting, non‐timber forest product collection, and domestic dog incursion into the forest. These threats require proactive resource management, strong legal protection, and cross‐sector collaboration. We predicted <20% of all remaining forest cover in our study area to be suitable for dhole. We then identified 17 patches of suitable forest area as potential strongholds. Among these patches, Western Forest Complex (Thailand) was identified as the region's only primary stronghold, while Taman Negara (Malaysia), and northeastern landscape (Cambodia) were identified as secondary strongholds. Although all 17 patches met our minimum size criteria (1667 km2), patches smaller than 3333 km2 may require site management either by increasing the ecological carrying capacity (i.e., prey abundance) or maintaining forest extent. Our proposed interventions for dhole would also strengthen the conservation of other co‐occurring species facing similar threats. Our threat assessment technique of species with scarce information is likely replicable with other endangered species. [ABSTRACT FROM AUTHOR]

An approach to recognizing the types of seismic event sources is proposed, based on the combination of several heterogeneous parameters of event records and information about the territory where the seismic event occurred. The following recording parameters are used: the ratio of the amplitudes of body waves, the ratio of parts of the spectra at high and low frequencies, the magnitude, and the spectral constancy parameter. Territorial information includes data on the presence of waterbodies, glaciers, mines, and simplified information on natural seismic activity. Their joint use is done with a special type of Bayesian belief network. The decisions made by the network are probabilistic in nature; the probability is understood in the Bayesian sense, i.e., as the degree of confidence in the truth of the judgment, which consists in attributing an event to one of the types (mine explosion, other explosion on land, underwater explosion, earthquake, icequake). The approach is implemented as a software system, which is included in the program for the interactive analysis of seismic event records LOS. [ABSTRACT FROM AUTHOR]

Peri-urban areas support a broad range of multifunctional demands for public goods. In northwest Europe, peri-urban areas tend to overlap with intensive agricultural land, resulting in conflicts between agricultural use and the public good demands of residents. Sustainable intensification (SI) of agriculture might help reconcile agricultural and well-being goals, but it is unclear how the mix of actors in a peri-urban setting can trigger or restrain SI. In a Dutch case study, we explored how SI of agriculture can contribute to making peri-urban areas more sustainable, and which actors are key enabling factors for implementing SI. We used interviews, surveys, workshops, and empirical analysis to obtain insight into the stakeholder's vision of a sustainable future for the case study area, the farming system and actor network. We integrated these insights in a Bayesian Belief Network, where we linked the actor network to implementation of three SI measures (farm-level efficiency measures, small landscape elements, and direct sales), and used sensitivity analysis to model effects of support for implementation by different groups of actors. The case study has a dense stakeholder network, where, dependent on the SI measure, farmers are triggered by all actors to implement SI, or have a stronger role in uptake themselves. The sensitivity analysis suggested that the future preferred by the stakeholders requires broad support of all actors involved, with local actors without a formal role being essential for uptake. Overall, trade-offs among public goods are almost inevitable when taking up SI measures. [ABSTRACT FROM AUTHOR]

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Liveability can be accepted as an umbrella term covering all the factors that make a place to live. We recognize the versatility of urban liveability and focus on the vibrancy aspect. Regarding the literature, we compile variables affecting urban liveability under the economic, image, and use value of place. This article aims to present a data-driven decision support system to evaluate different dimensions of vibrancy-focused liveability. We adopt a knowledge discovery process to handle the complexity of the liveability concept. This study develops a conditional-based relationship network of vibrancy parameters through the Bayesian Belief Network (BBN). Then, we assess the BBN's correlations with statistics and causal relations with the survey in this study.These results mostly agree with the findings of the relevant literature. The economic value results show that the high density, diversity and accessibility add a premium to the land value of properties. The use value results also demonstrate that the diversity and density of activities, cultural attributes, and high accessibility support place attractiveness. The selected streetscape variables improve image value, except for building enclosure and condition. The study has the potential for urban planners to vitalize neighborhoods by considering urban activities and urban physical attributes. [ABSTRACT FROM AUTHOR]