Search Results (802)

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (initial support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. Full article

This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article

The increasing complexity of maritime traffic imposes growing demands on the safety and rationality of ship-collision-avoidance decisions. While most existing research focuses on simple encounter scenarios, autonomous collision-avoidance strategies that comply with the International Regulations for Preventing Collisions at Sea (COLREGs) in complex multi-ship environments remain insufficiently investigated. To address this gap, this study proposes a novel collision-avoidance framework that integrates a quantitative COLREGs analysis with a distributed stochastic search mechanism. The framework consists of three core components: encounter identification, safety assessment, and stage classification. A cost function is employed to balance safety, COLREGs compliance, and navigational efficiency, incorporating a distance-based weighting factor to modulate the influence of each target vessel. The use of a distributed stochastic search algorithm enables decentralized decision-making through localized information sharing and probabilistic updates. Extensive simulations conducted across a variety of scenarios demonstrate that the proposed method can rapidly generate effective collision-avoidance strategies that fully comply with COLREGs. Comprehensive evaluations in terms of safety, navigational efficiency, COLREGs adherence, and real-time computational performance further validate the method’s strong adaptability and its promising potential for practical application in complex multi-ship environments. Full article

Chemical, biological, radiological, and nuclear (CBRN) incidents pose significant challenges requiring swift, coordinated responses to safeguard public health. This is especially the case in densely populated urban areas, where the public is not only at risk but can also be of assistance. Public cooperation is critical to the success of mass decontamination efforts, yet prior research has primarily focused on technical and procedural aspects, neglecting the psychological and social factors driving casualty behaviour. This paper addresses this gap through a narrative literature review, chosen for its flexibility in synthesising fragmented and interdisciplinary research across psychology, sociology, and emergency management. The review identified two primary pathways influencing casualty decision making: rational and affective. Rational pathways rely on deliberate decisions supported by clear communication and trust in responders’ competence, while affective pathways are shaped by emotional responses like fear and anxiety, exacerbated by uncertainty. Trust emerged as a critical factor, with effective —i.e., transparent, empathetic, and culturally sensitive— communication being proven to enhance public cooperation. Cultural and societal norms further shape individual and group responses during emergencies. This paper demonstrates the value of narrative reviews in addressing a complex, multifaceted topic such as casualty behaviour, enabling the integration of diverse insights. By emphasising behavioural, psychological, and social dimensions, the results of this paper offer actionable strategies for emergency responders to enhance public cooperation and improve outcomes during CBRN incidents. Full article

Given the evaluation data of all the experts in multi-attribute group decision making, this paper establishes an optimization model for learning and determining expert weights based on minimizing the sum of the differences between the individual evaluation and the overall consistent evaluation results. The paper proves the uniqueness of the solution of the optimization model and rigorously proves that the expert weights obtained by the model have “perfect rationality”, i.e., the weights are inversely proportional to the distance to the “overall consistent scoring point”. Based on the above characteristics, the optimization problem is further transformed into solving a system of nonlinear equations to obtain the expert weights. Finally, numerical experiments are conducted to verify the rationality of the model and the feasibility of transforming the problem into a system of nonlinear equations. Numerical experiments demonstrate that the deviation metric for the expert weights produced by our optimization model is significantly lower than that obtained under equal weighting or the entropy weight method, and it approaches zero. Within numerical tolerance, this confirms the model’s “perfect rationality”. Furthermore, the weights determined by solving the corresponding nonlinear equations coincide exactly with the optimization solution, indicating that a dedicated algorithm grounded in perfect rationality can directly solve the model. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

Carbon emission forecasting is a critical step in addressing climate change and effective environmental management. However, previous studies have concentrated mainly on socioeconomic factors, with less attention directed toward the significant impact of urban form. To address the shortcomings of previous studies, this study introduced three types of landscape indices that can characterize urban form and combined them with conventional socioeconomic factors to create a new carbon emission forecasting method. The enhanced STIRPAT and PLUS models were employed to forecast future changes in various socioeconomic factors and urban form, with the aim of forecasting carbon emissions in 21 cities of Guangdong during 2025–2060. The results confirm that urban form has an obvious influence on carbon emissions. In comparison to the baseline model, which considered only socioeconomic factors, the incorporation of urban form into the carbon emission forecast resulted in a reduction in the mean absolute percentage error from 7.16% to 6.18%. Moreover, carbon emissions were found to be positively correlated with GDP per capita, energy intensity, permanent population, share of secondary sector, LSI, and PLADJ but negatively correlated with PD. Furthermore, Guangdong will not be able to accomplish its “carbon peaking” objective around 2030, except in a low-carbon situation. Our proposed method could enhance the rationality of carbon emission forecasting, thereby providing a reasonable decision-making basis for low-carbon management. Full article

To address the issue of excessive pessimism caused by demand and supply uncertainties in the green hydrogen supply chain, this study develops a two-tier green hydrogen supply chain model comprising upstream hydrogen production stations and downstream hydrogen refueling stations. This research work investigates optimal ordering and production strategies under stochastic demand and supply conditions. Additionally, option contracts are introduced to share the risks associated with the stochastic output of green hydrogen. This study shows the following: (1) Under decentralized decision-making, the optimal ordering quantity when the hydrogen refueling station is excessively pessimistic is not necessarily lower than the optimal ordering quantity when it is in a rational state, and hydrogen production stations will only operate when the degree of excessive pessimism is relatively low. (2) The initial option ordering quantity is always larger than the minimum execution quantity under the option contract; higher first-order option prices and lower second-order option prices can help to increase the initial option ordering quantity. (3) The option contract is effective in circumventing the negative impact of excessive pessimism at hydrogen production stations on planned production quantities. This study addresses the gap in the existing research regarding excessively pessimistic behaviors and the application of option contracts within the green hydrogen supply chain, providing both theoretical insights and practical guidance for decision-making optimization. This advancement further promotes the sustainable development of the green hydrogen industry. Full article

Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subjective experience of clinicians, and existing research on DTI automatic segmentation cannot fully satisfy clinical requirements. Thus, this poses significant challenges for DTI-assisted diagnostic decision-making. This study aimed to deliver AI-driven segmentation for spinal cord DTI. To achieve this goal, a comparison experiment of candidate input features was conducted, with the preliminary results confirming the effectiveness of applying a diffusion-free image (B0 image) for DTI segmentation. Furthermore, a deep-learning-based model, named SCS-Net (Spinal Cord Segmentation Network), was proposed accordingly. The model applies a classical U-shaped architecture with a lightweight feature extraction module, which can effectively alleviate the training data scarcity problem. The proposed method supports eight-region spinal cord segmentation, i.e., the lateral, dorsal, ventral, and gray matter areas on the left and right sides. To evaluate this method, 89 CSM patients from a single center were collected. The model demonstrated satisfactory accuracy for both general segmentation metrics (precision, recall, and Dice coefficient) and a DTI-specific feature index. In particular, the proposed model’s error rate for the DTI-specific feature index was evaluated as 5.32%, 10.14%, 7.37%, and 5.70% on the left side, and 4.60%, 9.60%, 8.74%, and 6.27% on the right side of the spinal cord, respectively, affirming the model’s consistent performance for radiological rationality. In conclusion, the proposed AI-driven segmentation model significantly reduces the dependence on DTI manual interpretation, providing a feasible solution that can improve potential diagnostic outcomes for patients. Full article

Modern challenges in civil engineering require decision-making that supports the development of technologies in line with sustainable development principles, including minimizing environmental impact and improving occupational safety. Open caisson sinking, commonly used in underground construction, is particularly prone to generating complex negative impacts that affect construction quality, material efficiency, and working conditions. This study aims to identify the cause-and-effect relationships and assess the intensity of negative impacts associated with the open caisson sinking process. A comprehensive multi-criteria decision-making approach was developed, based on a novel hybrid method combining fuzzy WINGS and Analytic Network Process (ANP). This approach accounts for uncertainties and difficult-to-measure factors, providing a valuable tool for supporting complex engineering decisions. The proposed method facilitates improvements in process quality, reduces environmental risk, and helps eliminate typical execution errors. Research findings confirm that mitigating adverse impacts during caisson sinking enhances sustainable risk management in construction and supports rational decision-making under uncertainty. The method is universal and applicable in other domains requiring cause–effect analysis and the evaluation of impact intensity, especially in the context of implementing sustainable construction management practices. Full article

Background: The misuse of antibiotics in dental practice significantly contributes to the escalation of antimicrobial resistance. This study aimed to assess the knowledge and attitudes of final-year dental students regarding perioperative antibiotic prophylaxis in oral surgery and to identify potential curricular improvements based on the findings. Methods: A questionnaire was administered to 117 final-year students at Gazi University Faculty of Dentistry in December 2024. The survey presented clinical scenarios related to common oral surgical procedures, evaluating participants’ antibiotic prescribing behaviors. Statistical analyses were performed using descriptive statistics and a One-Sample Chi-Square Test. Results: Students demonstrated a general tendency toward rational antibiotic use in routine clinical scenarios, with statistically significant response patterns favoring the avoidance of unnecessary prescriptions (p < 0.05). However, in complex or borderline cases such as impacted third molar extraction and dental implant placement, response variability was observed. Post hoc analyses revealed no statistically significant differences between closely distributed options, indicating inconsistencies in decision-making in more challenging scenarios. Conclusions: While final-year dental students exhibited a satisfactory level of knowledge regarding appropriate antibiotic use in standard surgical procedures, the variability observed in complex cases underscores the necessity for enhanced educational interventions. Incorporating updated, evidence-based antimicrobial stewardship principles and promoting clinical decision-making through case-based learning are essential to prepare future dental practitioners for responsible antibiotic prescribing, contributing to global efforts to mitigate antimicrobial resistance. Full article

Green building materials play a vital role in mitigating the significant carbon emissions produced by the construction industry. However, the widespread presence of greenwashing, where firms falsely portray their products or practices as environmentally friendly, presents a critical obstacle to the adoption of genuinely sustainable materials. The risk of collusion between building material enterprises and certification institutions further exacerbates this challenge by undermining trust in green certification processes. To investigate these issues, this study develops an evolutionary game model that captures the strategic interactions between building material enterprises and certification institutions. The model incorporates the behavioral assumptions of prospect theory, specifically bounded rationality, loss aversion, and diminishing sensitivity, to reflect the real-world decision-making behavior of the involved actors. The findings reveal three evolutionarily stable strategies (ESS) within the system. First, a higher initial willingness by both enterprises and certifiers to engage in ethical practices increases the likelihood of convergence to an optimal and stable outcome. Second, a greater degree of diminishing sensitivity in the value function promotes the adoption of authentic green behavior by enterprises. In contrast, a lower degree of diminishing sensitivity encourages certification institutions to refrain from collusion. Third, although the loss aversion coefficient does not directly affect strategy selection, higher levels of loss aversion lead to stronger preferences for green behavior among enterprises and noncollusive behavior among certifiers. This research makes a novel theoretical contribution by introducing prospect theory into the analysis of greenwashing behavior in the building materials sector. It also provides actionable insights for improving regulatory frameworks and certification standards to mitigate greenwashing and enhance institutional accountability. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article