Search Results (954)

The Internet of Medical Things (IoMT) has transformed healthcare through real-time patient monitoring, intelligent diagnosis, and seamless exchange of medical data. However, the increasing interconnectivity of IoMT devices has significantly expanded the cybersecurity attack surface, exposing healthcare systems to threats that may compromise patient safety, Data Confidentiality, and service availability. Existing cybersecurity risk assessment methods often face challenges in adequately capturing the ambiguity, incompleteness, and subjectivity inherent in expert-based evaluations of IoMT security risks. To address these limitations, this paper proposes a symmetric hybrid neutrosophic fuzzy-based cybersecurity risk assessment methodology for IoMT environments. The proposed framework integrates Neutrosophic Fuzzy Sets (NFSs), the Analytic Hierarchy Process (AHP), and the Simple Average Method (SAM) to model truth, indeterminacy, and falsity in expert judgments while aggregating multiple expert opinions to produce a comprehensive cybersecurity risk assessment. The framework evaluates cybersecurity risks across seven security dimensions and twenty-eight evaluation sub-factors to identify and prioritize critical IoMT vulnerabilities and risk vectors. The experimental results indicate that Data Protection Assessment is the highest-ranked cybersecurity dimension. At the final evaluation stage, the proposed approach combines normalized criterion weights with expert risk evaluations to produce a cybersecurity risk score of 0.8504, indicating high cybersecurity risk that requires priority mitigation in IoMT situations. The proposed framework is validated by comparing it to traditional multi-criteria decision-making methods and using sensitivity analysis to assess the rankings’ robustness and stability under different decision scenarios and expert preference variations. Results show that the proposed method delivers consistent, accurate, and robust risk prioritization under uncertainty, supporting IoMT cybersecurity decision-making. Healthcare organizations can use the framework to identify cybersecurity threats, prioritize mitigation techniques, and strengthen IoMT systems. Full article

Lakes represent valuable natural resources with significant potential for sustainable tourism development. Their tourism valorization requires a comprehensive assessment framework that integrates environmental, infrastructural, socio-economic, and governance dimensions. This study applies the Fuzzy TOPSIS method to evaluate the sustainable tourism potential of ten selected lakes in Serbia. A multi-criteria framework was developed based on ten indicators, including water quality, tourism pressure and carrying capacity, biodiversity, environmental conservation, tourism infrastructure, accessibility, economic effects, and community involvement. Expert evaluations, supported by available evidence, were transformed into triangular fuzzy numbers in order to account for uncertainty and subjectivity in the assessment process. The Fuzzy TOPSIS model was used to calculate the relative closeness of each lake to the ideal solution for sustainable tourism valorization. The results reveal significant differences among the analyzed lakes. Lake Đerdap achieved the highest ranking (Ci = 0.818), followed by Lake Zaovine (Ci = 0.723), Lake Perućac (Ci = 0.644), and Lake Vlasina (Ci = 0.640), reflecting their favorable overall performance across the selected environmental, infrastructural, accessibility, and tourism-related criteria. Lower-ranked lakes were characterized by infrastructural limitations and less favorable performance in selected tourism-related criteria. The study illustrates the applicability of the Fuzzy TOPSIS approach for sustainable tourism evaluation and provides a practical framework for tourism planning, destination management, and policy-making aimed at supporting sustainable lake tourism development in Serbia. Full article

Prefabricated construction is increasingly adopted worldwide because of its advantages in sustainability and construction efficiency. Nevertheless, the construction phase still faces substantial and multifaceted risks. These risks are characterized by high uncertainty and strong dependence on the subjective judgments of decision-makers, which may limit the wider promotion and application of prefabricated construction. Existing risk assessment methods often struggle to represent neutral and refusal attitudes in expert evaluations with sufficient precision. They also do not fully account for the psychological and behavioral mechanisms of decision-makers under risk. To address these limitations, this study proposes an integrated evaluation framework that combines interval-valued picture fuzzy sets (IVPFSs), cumulative prospect theory (CPT), and TOPSIS. The decision objective is to rank alternative construction-stage risk-control schemes and identify the relatively important risk factors influencing the ranking results. IVPFSs are used to represent support, opposition, neutrality, and refusal degrees in expert evaluations. CPT is incorporated to capture decision-makers’ risk preferences, reference dependence, and loss-aversion behavior. A comprehensive risk indicator system is developed from five dimensions: personnel, management, technology, environment, and materials/equipment. Attribute weights are determined using a coordinated AHP–entropy weighting scheme to balance subjective expert judgment with objective information contained in the evaluation data. The proposed framework is applied to a prefabricated building project in Shanghai. The results indicate that personnel-related factors, especially the technical proficiency of construction workers, are relatively important risk sources in the examined case. The model also provides a structured ranking of alternative risk-control schemes. Overall, this study offers an exploratory case-based decision-support framework for construction-stage risk management in prefabricated building projects and demonstrates the potential value of incorporating behavioral preferences into fuzzy multi-criteria risk assessment. Full article

3D printing has become a revolutionary technology, and one of its major advances is the ability to continuously print objects with carbon-fiber-reinforced composites. However, to meet engineering requirements, material selection requires compromises between conflicting criteria, e.g., thermal stability, cost, and processing parameters. Resolving such conflicts inherently requires a symmetry-oriented trade-off among these competing factors. Selecting suitable composite materials for 3D printing is a complex decision-making problem. This article investigated the mechanical performance of continuous carbon fiber (CCF, as the reinforcement) polyamide composites under various heat treatment conditions. The materials were systematically evaluated by incorporating their mechanical performance, environmental, economic, and social impacts, thereby establishing a comprehensive criteria hierarchy. The mechanical responses at four heat treatment temperatures were incorporated into the decision model. An integrated multi-criteria decision method incorporating the fuzzy full consistency method (FUCOM), indifference threshold-based attribute ratio analysis (ITARA) and the fuzzy multi-attributive border approximation area comparison (MABAC) method was proposed to solve the selection problem of 3D-printed composites. The symmetry-oriented integration of subjective expert judgments and objective information allowed both information sources to jointly contribute to the determination of the composite weights. The model and method were validated using the floor of a high-speed train as a case study, and the results showed that separated-distribution CCF-reinforced composite (S-CCFRC) heat-treated at 50 °C for 4 h was the optimal solution for this engineering context. This research provides a useful tool for selecting composite materials, thereby laying the foundation for optimizing the decision-making process and further advancing research on 3D-printed composite materials. Full article

Water inrush disasters in karst tunnels pose a significant threat to construction safety, project timelines, and the long-term sustainability of infrastructure. Effective risk assessment is crucial for mitigating these hazards and ensuring the resilient development of underground transportation networks. This study proposes a quantitative risk assessment model that integrates the entropy weight method with attribute interval recognition theory to address the uncertainties inherent in complex geological environments. First, a hierarchical evaluation index system is established based on four primary controlling factors: stratigraphy, geological structure, topography, and hydrogeology. Subsequently, the entropy weight method is employed to objectively determine the weight of each index, thereby minimizing human bias. The attribute interval recognition model is applied to calculate the comprehensive attribute measure for each tunnel segment, effectively managing the fuzziness of risk classification boundaries. The risk grade is ultimately determined using the confidence criterion. The proposed model is applied to the Qigan Mountain karst tunnel in Chongqing, China, which is divided into 56 segments for detailed analysis. Results show 41.32% (4088 m) high-risk, 40.14% (3971 m) medium-risk and 18.55% (1835 m) low-risk sections, which are highly consistent with the theoretical water inflow calculation results. The model realizes accurate and quantitative water inrush risk assessment, providing a scientific basis for disaster prevention and control in karst tunnel construction, and further promoting the sustainability and safety of underground engineering in karst areas. Full article

Regulated deficit irrigation (RDI) is an important water-saving strategy in arid regions. To quantify the effects of RDI on foxtail millet yield and water use efficiency and determine an optimal RDI strategy, a three-year field trial was carried out over dry, normal, and wet rainfall years in the Loess Plateau. Full irrigation throughout the whole growth period served as the control, whereas mild, moderate, and severe deficit irrigation treatments were conducted at the jointing–booting stage, heading–flowering stage, and across the whole growing period, respectively. The results indicate that the effects of RDI on foxtail millet yield varied with crop growth stage and deficit severity. During the heading–flowering stage, mild RDI showed statistically similar grain yield and WUE relative to those under full irrigation. In normal and wet years, moderate and severe RDI had no statistically significant effects on grain yield and WUE. Additionally, moderate and severe RDI significantly improved irrigation water use efficiency by 19.94–28.50% and 34.35–47.72%, respectively. The primary reason is that RDI at this stage maintained root development and led to only limited suppression of plant growth. In contrast, moderate and severe RDI at the jointing–booting stage or throughout the whole growth period significantly inhibited root establishment and plant development, reduced dry matter accumulation, and consequently led to substantial yield losses. The inhibitory effect became more pronounced with increasing deficit severity. Specifically, severe RDI at the jointing–booting stage and throughout the entire growth period significantly reduced yield by 19.35–54.98% and 31.47–100%, respectively. Furthermore, to identify the optimal RDI regime adaptable to variable rainfall years, a multi-model comprehensive evaluation system based on yield and WUE was established by integrating three individual evaluation models, including the membership function method, TOPSIS, and grey relational analysis, with the Fuzzy–Borda combined evaluation model. The result showed that the heading–flowering stage is the critical period for implementing RDI in foxtail millet in the Loess Plateau. Mild RDI during this stage is preferred because it maintains stable yield and WUE while substantially reducing irrigation amount over various rainfall years. Additionally, moderate and severe RDI can also maintain stable yield while significantly improving irrigation water use efficiency in normal and wet years. Full article

The alternative fulfillment mechanism for ecological and environmental damage liability represents a pivotal institutional innovation. Its principal function is to enable liable parties—under exceptional circumstances—to flexibly fulfill their ecological and environmental damage liability. This study adopts an empirical research method to systematically investigate the judicial application of the alternative performance mechanism. Findings reveal that under the guidance of the restorative justice concept, courts increasingly subsume diverse alternative performance modalities under the category of “alternative restoration”, leading to the gradual distortion of the alternative restoration centered on the concept of “equivalent restoration” into a “universal solution” for ecological damage cases. This practical tendency significantly weakens the actual effectiveness of ecological restoration. The purpose of this study is to establish a dual mechanism of alternative fulfillment—distinguishing between alternative restoration and alternative compensation—based on ecological principles and within the framework of the dual responsibilities of “restoration—compensation” for ecological environmental damage. The effectiveness of the dual mechanism is quantitatively evaluated using the AHP-fuzzy comprehensive evaluation. The evaluation results show that, after distinguishing between the two types of alternative responsibilities, the comprehensive evaluation scores for ecological damage remedy in cases of ecological destruction, water pollution, and soil pollution increased from 5.155 to 8.935, from 6.406 to 9.116, and from 6.137 to 9.108, respectively, effectively correcting the functional deviation of the current single-dimensional remedy model. This study clarifies the independent application criteria of alternative restoration and alternative compensation, and provides targeted optimization paths for the judicial application of ecological damage remedy. Full article

The geological environment evaluation of abandoned open-pit mines frequently encounters challenges, including the reliance of subjective weighting on judgment matrices, the complexity of weight adjustment, and the inadequate interpretation of systematic evaluation results. Addressing these limitations in existing AHP/FAHP and their combinatory weighting models, this study proposes the IRBMO-G1-EWM-FCE framework. This framework embeds the Improved Red Billed Blue Magpie Optimizer (IRBMO) into the improved G1 method to optimize indicator contribution rates, and subsequently integrates EWM, game theory combinatory weighting, and Fuzzy Comprehensive Evaluation (FCE) to evaluate three abandoned quarries in the Yellow River Basin of Shaanxi Province. The results demonstrate that across 20 independent runs, IRBMO yields a mean fitness value of 1.6496, lower than the 1.7732 of RBMO, with a 63.8% reduction in standard deviation, thereby indicating superior convergence accuracy and stability. The comprehensive membership degrees of the three quarries are A = [0.405,0.143,0.452], B = [0.405,0.450,0.145], and C = [0.742,0.077,0.181], corresponding to evaluation grades of Grade III, Grade II, and Grade I, respectively. While circumventing the construction of complete judgment matrices and consistency modifications, this method incorporates both expert experience and data discreteness into the evaluation, thereby providing an interpretable quantitative tool for geological environment classification, governance priority identification, and restoration decision-making for abandoned open-pit mines. Full article

Ancient timber structures are important carriers of valuable cultural heritage, and their structural safety directly determines whether historic buildings can remain in safe service over time. Cracks represent one of the most widespread and important forms of damage in ancient timber structures. They can directly lead to cross-sectional weakening of structural members, degradation of load-bearing capacity, and the gradual development of overall structural safety risks. To address the limitations of existing crack assessment methods, such as strong subjectivity in weight determination, insufficient accuracy in grade boundary discrimination, and inadequate coupling with mechanical performance, this study proposes a crack safety assessment method for ancient timber structures based on an improved entropy–fuzzy matter-element model. A multi-dimensional evaluation index system is established, incorporating crack geometric characteristics, structural load-bearing capacity, and service time effects. A mechanically driven load-carrying capacity degradation index is introduced to quantitatively characterize the influence mechanism of crack propagation on structural performance deterioration. The entropy weight method is employed to objectively determine the weights of each indicator, and an asymmetric closeness degree is introduced to improve the traditional fuzzy matter-element model, thereby enhancing the stability and accuracy of safety grade classification. A case study of the Bawang Academy, Shenyang Jianzhu University, is conducted. Crack parameters are obtained using image recognition and three-dimensional laser scanning techniques, and a comprehensive structural safety assessment is performed. The results indicate that the proposed method can accurately reflect the actual damage distribution and deterioration level of the structure, providing a reliable theoretical basis and technical support for crack safety evaluation and preventive conservation of ancient timber structures. Full article

To address the lack of standardized safety assessment tools for upper-limb rehabilitation robots, this study developed an integrated testing platform and a quantitative safety assessment framework, demonstrated with FlexoArm1 as a proof-of-concept. A 6-degree-of-freedom bionic arm equipped with multiple sensors was constructed, and a fuzzy PID control algorithm was employed to improve motion trajectory tracking accuracy. A fuzzy multi-criteria safety assessment model was established by combining the Analytic Hierarchy Process (AHP) with the entropy weight method. Experiments were conducted on the rehabilitation robot FlexoArm1. The platform reliably replaced human subjects in range-of-motion testing, interactive torque measurement (peak torque approximately 6.2 N·m in fully active mode), and spasticity simulation, with angular data showing close agreement with Inertial Measurement Unit (IMU) measurements. The assessment model assigned a comprehensive safety score of 70.23 to the tested device, successfully identifying weaknesses in fault detection capability and structural safety design. The proposed bionic-arm-based testing platform and the accompanying safety assessment methodology provide practical tools and a quantitative basis for standardizing safety evaluation and guiding design optimization of upper-limb rehabilitation robots. Full article

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies. Full article

In deep foundation pit risk assessment, expert ratings are often aggregated without preserving the dispersion of individual opinions, yet such dispersion directly reflects the reliability of the assessment. To address this shortcoming, this study integrates dual probabilistic linguistic term sets (DPLTS), entropy theory, and the best–worst method (BWM). In this DPLTS framework, the membership set L(p) encodes the central tendency of expert ratings (the assessed risk level), while the non-membership set U(q) encodes the dispersion of ratings, serving as a proxy for expert disagreement—a source of uncertainty that is as critical as the risk level itself for decision-making. The least common multiple expansion method standardizes information length. Secondary indicator weights are determined using fuzzy entropy and cross-entropy, while primary indicator weights are derived via BWM, forming a combined subjective-objective weighting model. Hierarchical aggregation yields the overall risk expectation value. A case study assesses the project as Level III (moderate) risk, with a low variance of 0.0503 indicating strong expert consensus. The risk expectation varies by less than 4% under different entropy measures, confirming robustness. Comparative analysis with fuzzy comprehensive evaluation and CRITIC–Grey system methods shows consistent results, with all three identifying excavation and support as key risk indicators. The proposed method provides not only a reliable risk level but also a quantitative measure of expert agreement, offering enhanced support for targeted risk management. Full article

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

Regional water systems in rapidly urbanizing hilly basin cities are affected by hydrological variability, population concentration, industrial water demand, and water-use efficiency. This study evaluated the water resources carrying capacity (WRCC) of Jinhua City, southeastern China, from 2011 to 2023 using an integrated 15-indicator system covering water resources support, water-use and population pressure, economic structure and water-use efficiency, and ecological and environmental support. Indicator definitions, units, directions, and data sources were harmonized using official water resources bulletins and statistical records. A combined weighting method integrating the modified Analytic Hierarchy Process and the entropy weight method was coupled with a fuzzy matter-element model and the Hamming closeness measure. WRCC grades were assigned using standard-derived Hamming closeness thresholds based on pooled-reference membership transformation. Obstacle degree, leave-one-indicator-out sensitivity, and redundancy diagnostics were further used for interpretation and robustness assessment. The combined weights were mainly concentrated in water-use and population pressure (35.85%), water resources support (26.77%), and economic structure and water-use efficiency (26.10%). Industrial water use, per capita comprehensive water use, population density, water consumption per 10,000 yuan industrial value added, and water consumption per 10,000 yuan GDP had the highest indicator weights. Annual Hamming closeness ranged from 0.2621 to 0.6391. Jinhua’s WRCC reached Grade II in 2015, 2019, 2020, and 2021, while the remaining years were classified as Grade III. The highest closeness occurred in 2019, whereas 2022 and 2023 declined to Grade III and were close to the II/III threshold. Obstacle diagnosis showed that water-use and population pressure were the dominant subsystem obstacles. Sensitivity analysis showed that the peak year and the lowest year remained unchanged across all leave-one-indicator-out scenarios, whereas the boundary years showed grade sensitivity. The results provide a transparent annual assessment and diagnostic evidence for WRCC management. Full article

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article