Search Results (87)

Water management policies in desert agricultural regions critically influence both crop choices and ecosystem dynamics, yet their cascading ecological impacts remain poorly understood. In particular, the complex interactions between water quality, agricultural practices, and wildlife responses require further investigation to inform sustainable management in desert landscapes. Here, we evaluate how water policy, particularly seawater desalination initiatives influencing irrigation and cropping practices, shapes ecological systems in a hyperarid region, the southern Arava Valley of Israel. We integrated community-level questionnaires, agricultural records, animal field observations, and spatially explicit scenario tools into a mixed-methods framework to model social–ecological cascades linking water policy to predator dynamics. Bayesian Belief Networks combined with Generalized Linear Models of predator abundance were used to assess how improved water quality affects cropping patterns and, in turn, regional predator populations. Our findings indicate that desalination is unlikely to alter the predominance of date orchards or the high abundance of range-expanding jackals associated with these systems. However, water quality-driven expansion of field crops corresponds to lower modelled fox abundance and shifts in predicted predator interactions, while jackal populations remain largely influenced by date orchard availability. Under business-as-usual scenarios with lower water quality, farmers are likely to reduce field crop areas, corresponding to further changes in regional predator abundance. These findings suggest that water policy decisions may generate cascading social–ecological responses on both agricultural practices and local desert ecosystems, emphasizing the need for strategies that balance agricultural productivity with ecological sustainability in arid landscapes. Full article

Systematically assessing the supply–demand disparities of urban–rural ecosystem services (ES) is a key pathway to optimizing resource allocation, promoting urban–rural integration and advancing regional sustainable development. Taking Zhengzhou City as a case study, this research evaluates and compares urban–rural differences across four dimensions: potential supply, actual supply, real human needs (RHN), and effective supply. Furthermore, focusing on actual supply, the study integrates a geographical detector and Bayesian belief network to identify key driving factors, delineate optimal optimization zones, and propose differentiated management strategies. The results show that: (1) Urban RHN accounts for 69.70% of the total in Zhengzhou, with a spatial pattern of “higher in the east and core, lower in the west and periphery”, and the internal heterogeneity is significantly greater than that of rural areas. (2) Potential supply is “higher in rural areas and in the west”, whereas actual supply is concentrated in central urban districts, reflecting a net service flow from rural to urban areas. (3) High-level effective supply areas cover 37.28% of urban regions, about 18 percentage points higher than rural regions. Rural deficits are primarily caused by low conversion efficiency of supply rather than insufficient potential. (4) Optimal urban optimization zones are mainly distributed in peripheral urban streets, while rural zones are concentrated in eastern townships. Through multidimensional supply–demand comparison and spatial optimization, this study provides a scientific basis for the coordinated enhancement of urban–rural ES, differentiated governance and regional sustainable development. Full article

The traditional multi-attribute group decision-making (MAGDM) method easily ignores the interference effect among decision-makers (DMs), while quantum theory can effectively portray the uncertainty in the decision-making process and quantify the preference interference among DMs. The asymmetry of evaluation information in social networks can have a significant impact on decision-making. In this paper, a quantum MAGDM algorithm based on probabilistic linguistic term sets (PLTSs) and a quantum-like Bayesian network (QLBN) is proposed (PL-QLBN), utilizing quantum theory and social network concepts and introducing a novel method for calculating interference effects based on belief entropy. Firstly, a complete trust network is constructed based on the probabilistic linguistic trust transfer operator and the minimum path method. A trust aggregation method, considering interference effects, is proposed for the QLBN to determine the DM weights. Next, the attribute weights are calculated based on the entropy weight method. Then, a probabilistic linguistic MAGDM considering interference effects is proposed based on the QLBN. Finally, the feasibility and validity of the provided method are verified through Hefei City’s selection of wastewater reuse alternatives. Full article

Afforestation is a critical nature-based strategy for enhancing ecological resilience and supporting cleaner land-use systems. This study presents a spatially explicit modeling framework to evaluate the long-term impacts of potential afforestation amendments on two key ecosystem services—soil conservation and carbon sequestration—across China’s major shelterbelt program areas under the SSP245 scenario (2020–2070). Using a zonal approach, we integrated Random Forest models, Bayesian belief networks, and Geodetector analysis to identify region-specific afforestation suitability and quantify ecological service gains across eight national shelterbelt program zones. The results reveal pronounced spatial heterogeneity in ecosystem service improvements. (1) High-quality potential afforestation lands, totaling approximately 2.33 × 10⁵ km², are primarily concentrated near the Hu Line (a geographical boundary that divides China into two distinct climatic regions), with the shelterbelt program for upper and middle reaches of Yangtze River accounting for 45.94%. (2) Based on the amended annual afforestation target of 0.47 × 10⁵ km², the adjusted land use projections indicate a significant increase in forest cover. By 2070, the afforestation program for Taihang Mountain exhibits the most significant improvements, with a 47.56% increase in soil conservation and a 10.15% increase in carbon sequestration. (3) Optimization areas differ across zones, with the Taihang mountain area (99.2%) and Pearl river area (70.1%) achieving the highest improvements in soil and carbon services, respectively. These findings provide robust scientific support for data-driven, region-specific afforestation planning under future land-use change scenarios. Full article

Optimizing the spatial pattern of water conservation services (WCSs) is essential for enhancing regional water retention and promoting sustainable water resource management. The Saihanba region, a critical ecological barrier in northern China, has experienced severe degradation due to historical over-logging, leading to weakened WCS functions. This study used remote sensing techniques to interpret land use/land cover change (LULC) and combined it with meteorological and basic ecological data to assess changes in WCS capacity in the Saihanba region, China, under multiple 2035 scenarios using CA-Markov and Bayesian network models. The Bayesian belief network identified priority areas for spatial optimization. Results showed the following: (1) The spatial distribution patterns of WCSs showed a strong dependence on land-use types, with both forest and grassland areas demonstrating superior water conservation capacity compared to other land cover categories; (2) although total WCS capacity varied across scenarios, spatial distribution remained consistent—high-value zones were mainly in the south and central-east, while lower values occurred in the west; and (3) WCS areas were categorized into key optimization, ecological protection, and general management zones. Notably, the Sandaohekou Forest Farm and the western Qiancengban Forest Farm emerged as critical areas requiring urgent optimization. These findings offer practical guidance for spatial planning, ecological protection, and water resource governance, supporting long-term WCS sustainability in the region. The study also contributes to cleaner production strategies by aligning ecosystem service management with sustainable development goals. Full article

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km² Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km²) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Unrealistic cash flow forecasts negatively affect project stakeholders and are a common issue for construction practitioners. This study proposes a new method for predicting the probabilistic cash flow of a project that can automate the calculation process while considering the impact of risks and their inter-related structure. This research integrates a Bayesian Belief Network (BBN) and 5D-BIM to provide a new probabilistic cash flow analysis approach. Here, 5D-BIM is used to facilitate cash flow calculations and automate the process. The BBN has also been implemented to assess the impact of risk factors on project cash flow, considering their complex inter-related structure. In addition, a hybrid approach combining fuzzy set theory, decision-making trial and evaluation laboratory (DEMATEL), and interpretive structural modeling (ISM) is used to form the BBN. The proposed method provides a robust tool for calculating the probabilistic cash flow of the project. The results showed that the project’s cash flow in the last month was IRR 14.4 billion without considering the impact of risks. The probabilistic cash flow of the project indicates that due to the impact of the risks, the project cash flow will be in the range of IRR −142.2 billion and IRR 1.11 billion at the end of the project. This shows the possibility of experiencing between 11 and 130% deviation in the project cash flow due to existing risks. In conclusion, project cash flow is unreliable without considering the impact of risks. This framework supports better financial decisions and allows for the evaluation of cash flow risk management scenarios. Full article

Public-Private Partnerships (PPPs) are essential for accelerating sustainable development as they combine public goals with private sector efficiency, leading to improved service delivery and less financial burden on governments. It is a project delivery model based on long-term contractual arrangements, where the private sector provides services, including engineering, construction, and operation of public infrastructure, taking financial risks. At the project development stage, the private sector carries out a financial risk assessment to ensure economic returns from a PPP investment and secure funding for the project. In this paper, we present a Bayesian Belief Network (BBN)-based model that can be used to assess financial risks, particularly the level of profitability in PPP projects. The proposed model was developed considering PPP projects in the healthcare sector and validated using data on PPP hospital projects in Turkiye. The findings demonstrate that the BBN model is useful for capturing the interdependencies between risks, resulting in different scenarios, and provides effective decision support for investors in PPP projects. This study contributes to the literature by offering a novel application of probabilistic risk assessment to provide a better understanding of interrelated risk factors that may result in different financial scenarios. The model can be used by the private sector to assess risk, estimate profitability, and develop risk mitigation strategies in PPP healthcare projects, which may increase project success, contributing to social, environmental, and economic sustainability. Full article

Climate change will create significant challenges to agriculture. The effects on livestock productivity and crop production are highly dependent on weather conditions with consequences for food security. If agriculture is to remain a viable industry and to maintain future food security, the adaptations and the ideal timeframes for their implementation to mitigate against climate change impacts will be essential knowledge. This study aims to show how farms will be affected and will need to adapt to climate change, based on a holistic examination of the entire farming process. A modified Bayesian belief network (BBN) was used to investigate climate change impacts on livestock, crops, soil, water use, disease, and pesticide use through the use of 48 indicators (comprising climate, agricultural, and environmental). The seasonal impact of climate change on all aspects of farming was investigated for three different climate forcing scenarios (RCPs 2.6, 4.5, and 8.5) for four timeframes (2030, 2050, 2080, and 2099). The results suggest that heat stress and disease in both livestock and crops will require adaptations (e.g., shelter infrastructure being built, new crops, or cultivators grown). Pest intensity is expected to rise, leading to increased pesticide use and greater damage to crops and livestock. Higher temperatures will likely cause increased drought and irrigation needs, while increasing rain intensity might lead to winter flooding. Soil quality maintenance will rely increasingly on fertilisers, with significant decreases in quality if unsustainable. Crop yield will be dependent on new crops or cultivators that can cope with a changing climate being successful and market access; failure to do so could lead to substantial decrease, in food security. Impacts are more significant from 2080 onwards, with the severity of impacts dependent on season. Full article

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects. Full article

The construction industry continues to face challenges such as increased costs, time overruns, and low quality. Off-site construction (OSC) methods are increasingly being adopted as alternatives to traditional construction practices to address these issues, with off-site manufacturing (OSM) representing a key difference in construction methods. However, existing studies have largely neglected the systematic evaluation of OSM risks on quality, cost, and delivery (QCD) outcomes, leaving a significant gap in understanding the complex interdependencies among risk factors. To improve risk management in OSC projects, it is crucial to evaluate the impact of OSM risks on QCD outcomes. This study applies the Bayesian Belief Network (BBN) method to develop an evaluation model that measures the impact of OSM risks on QCD outcomes in OSC projects. The results identify 12 significant risk factors affecting QCD outcomes in OSC projects. Five key risk groups were identified as critical for managing OSM risks. This approach provides a systematic framework for managing OSM risks and optimizing OSC practices in China. Full article

There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a study focusing on the parameters in a companion paper, while this paper focuses on the BN graphical structure. We present the results of several experiments in which we test the impact of errors in the BN structure on its accuracy in the context of medical diagnostic models. We study the deterioration in model accuracy under structural changes that systematically modify the original gold standard model, notably the node and edge removal and edge reversal. Our results confirm the popular belief that the BN structure is important, and we show that structural errors may lead to a serious deterioration in the diagnostic accuracy. At the same time, most BN models are forgiving to single errors. In light of these results and the results of the companion paper, we recommend that knowledge engineers focus their efforts on obtaining a correct model structure and worry less about the overall precision of parameters. Full article

There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this belief to a test in the context of medical diagnostic models. We study the deterioration of accuracy under random symmetric noise but also biased noise that represents overconfidence and underconfidence of human experts.Our results demonstrate consistently, across all models studied, that while noise leads to deterioration of accuracy, small amounts of noise have minimal effect on the diagnostic accuracy of BN models. Overconfidence, common among human experts, appears to be safer than symmetric noise and much safer than underconfidence in terms of the resulting accuracy. Noise in medical laboratory results and disease nodes as well as in nodes forming the Markov blanket of the disease nodes has the largest effect on accuracy. In light of these results, knowledge engineers should moderately worry about the overall quality of the numerical parameters of BNs and direct their effort where it is most needed, as indicated by sensitivity analysis. Full article

The deteriorating state of North America’s bridge infrastructure is a pressing issue, necessitating innovative risk management strategies. This study aims to enhance the seismic resilience of bridge infrastructure using a Bayesian belief network (BBN) model. The research uses literature review, expert opinions, and a Bayesian analysis framework to quantify bridge resilience, despite the scarcity of detailed historical data. The model, supported by conditional probability tables (CPTs), captures the complex interdependencies among parameters and uncertainties in seismic resilience assessment. Preliminary findings show that integrating expert judgment with BBN provides a robust methodology for assessing and enhancing bridge resilience to seismic hazards. This approach contributes to measuring bridge infrastructure resilience and offers practical guidance for policymakers, engineers, and stakeholders in sustainable transportation network development. Full article