Search Results (5,092)

Hesitant fuzzy linguistic sets (HFLSs) combine natural language expressions with membership information in a hesitant manner, thereby breaking the barriers between fuzzy evaluation and natural language-based decision-making. Hence, they have become a crucial tool for constructing linguistic decision-making frameworks. However, HFLSs treat all linguistic expressions in the evaluation system as equally important, which reduces the accuracy of linguistic decision models. In practical situations, decision makers usually assign different weights to different linguistic descriptions. Therefore, in this paper, we propose a generalization of HFLSs called the weighted hesitant fuzzy linguistic term set (WHFLTS) to represent the uncertain subjective preference information of experts, and design a multiple-attribute decision-making procedure. Firstly, the definition of WHFLTS is given. Then, under the Frank operational law, the algebraic and geometric aggregation operators for weighted hesitant fuzzy linguistic information are discussed: namely, the WHFLFWA operator and the WHFLFWG operator. On this basis, a linguistic decision-making strategy is proposed. The example presented in this paper shows that WHFLTS can model uncertain problems in a more adequate way, and the Frank aggregation operators defined in this paper can reflect the decision makers’ attitudes, making them more broadly applicable in decision-making problems. Full article

Background/Objectives: Secondary hypertension (SH) requires complex diagnostic reasoning and guideline-based management, posing substantial challenges for artificial intelligence–driven clinical decision-support systems. This study aimed to comparatively evaluate the performance of three large language models (LLMs) in diagnostic reasoning, clinical management, follow-up planning, and patient-oriented communication in SH. Methods: In this cross-sectional blinded study, three LLMs—GPT-5.2 (OpenAI), Claude Sonnet 4.6 (Anthropic), and Gemini 3.0 Pro (Google)—were evaluated using 10 expert-developed clinical case vignettes representing major etiologies of SH. Model outputs were anonymized and independently assessed by three senior clinicians (two endocrinologists and one cardiologist) using a 7-point Likert scale across five domains: (1) diagnostic accuracy and hallucination control, (2) quality and comprehensiveness, (3) reliability and clinical guidance, (4) efficiency of diagnostic workup, and (5) clinical usability. Group differences were analyzed using Kruskal–Wallis tests with Bonferroni-corrected pairwise comparisons. Inter-rater agreement was assessed using two-way mixed-effects intraclass correlation coefficients with absolute agreement. Results: A total of 90 blinded expert evaluations were analyzed. GPT-5.2 (6.0, Q1–Q3 5.40–6.05) and Gemini 3 Pro (5.2, Q1–Q3 4.55–6.20) (H = 40.055, p < 0.001). The results indicated a clear performance hierarchy, with Claude Sonnet 4.6 receiving the highest overall scores, followed by GPT-5.2 and Gemini 3 Pro. Pairwise analyses showed higher scores for Claude Sonnet 4.6 than the other models in most domains, while efficiency of diagnostic workup showed smaller between-model differences. GPT-5.2 generally showed intermediate performance, with higher ratings than Gemini 3 Pro in reliability and clinical usability. Performance differences were most pronounced in domains requiring complex clinical reasoning, whereas efficiency of diagnostic workup scores was relatively comparable among models. Claude Sonnet 4.6 ranked first in nine of the ten clinical vignettes. Inter-rater agreement analyses demonstrated consistent ranking patterns among evaluators. Conclusions: These exploratory findings suggest heterogeneous and model-dependent performance of LLMs in secondary hypertension–related clinical tasks. A clear clinician-rated performance hierarchy was observed, with differences most apparent in domains requiring complex clinical reasoning. However, given the pilot vignette-based design and limited sample size, these results should be interpreted as hypothesis-generating and require confirmation in larger, multicenter validation studies before routine clinical implementation can be considered. Full article

Evaluating Management Information System (MIS) effectiveness in tertiary education remains conceptually fragmented, with existing models primarily emphasizing technical performance rather than organizational interdependencies. This study develops a structural framework for assessing institutional MIS effectiveness using a sequential mixed-methods design integrating inductive construct development, expert validation, and quantitative dependency modeling. Fifteen themes and fifty-eight subthemes were identified as core dimensions of MIS capability within tertiary education institutions. Dependency and reliance analyses reveal strong systemic interconnections, indicating that institutional MIS functions operate as an integrated organizational system rather than independent domains. The framework is represented as a directed weighted graph to preserve enabling relationships, and a maximum spanning arborescence is derived to provide an interpretable implementation backbone. By modeling structural interdependencies explicitly, the proposed framework supports more informed prioritization of MIS development, strategic alignment across institutional functions, and evidence-based governance decision-making. The framework is intended primarily for evaluating organizational, governance, administrative, and institutional dimensions of MIS effectiveness within tertiary education institutions and does not explicitly assess user-level constructs such as usability, interface design, accessibility, or individual user experience. The study establishes a foundation for future empirical validation and cross-institutional benchmarking. Full article

Background: Equitable allocation of public health budgets across multiple intervention domains remains a major challenge in regional health governance. In Thailand’s Health Region 10, annual healthcare budgets must address diverse health burdens across several provinces, while current planning approaches rely on expert deliberation and historical precedent without systematic exploration of alternative allocation strategies. Public health resource allocation decisions are inherently multi-criteria, integrating health impact, cost-effectiveness, equity, disease severity, clinical and ethical priorities, feasibility, and alignment with national health policy agendas—dimensions that cannot be reduced to a single metric. This study introduces H-RL-MUSYA (Hierarchical Reinforcement Learning for Multi-Domain Unified System of Yielding Adaptive allocations), a decision-support framework designed to assist—not replace—public health practitioners by systematically generating and evaluating a menu of Pareto-efficient allocation strategies across four priority domains: nutrition, mental health, behavioral risk, and accident prevention. The framework explicitly acknowledges that DALYs averted and cost-effectiveness ratios are valuable but partial indicators, and that final resource allocation must integrate additional considerations—including underpinning health policies, priority population needs, feasibility, and contextual judgment—that lie beyond the model’s scope. Results: Applied to Thailand’s Health Region 10 (4.6 million inhabitants), H-RL-MUSYA identified 127 Pareto-efficient policies yielding a representative compromise allocation that averted 847,293 DALYs (34.1% improvement over historical allocations), improved cost-effectiveness by 31.3%, and reduced the health equity Gini coefficient from 0.243 to 0.187. A 12-month prospective pilot confirmed +23.1% composite health improvement with 91% stakeholder acceptance. Conclusions: H-RL-MUSYA demonstrates that AI-assisted policy exploration can meaningfully enrich public health decision-making by surfacing non-intuitive allocation strategies and quantifying equity–efficiency trade-offs, while human expertise, policy context, and democratic deliberation remain essential for final allocation decisions. Full article

Sustainability of urban logistics or last-mile logistics (LML) has been extensively studied from the perspective of experts and professionals whose objectives and interests have been closely linked to their organisations. For this reason, the conclusions drawn from these studies have had an inherent bias that is difficult to overlook. The aim of this study is to identify perceived research priorities in sustainable urban logistics through an innovative approach based on weightings established by a ‘university community’. To carry out this study, a hybrid multi-criteria model is proposed, used to weight twelve criteria employed to compare fourteen categories grouping the lines of research in LML. The main contributions of this work have therefore been: to provide an atypical perspective grounded in the ‘university community’, and to design a hybrid multi-criteria model capable of efficiently forecasting which perceived research priorities will be most relevant in the future in LML. The most significant conclusions of this study highlight the strong value that the ‘university community’ assigns to research areas in LML focused on collaborative workstreams among stakeholders. This is understood as a systemic and strategic approach grounded in the implementation of urban logistics infrastructures that enhance process efficiency through regulatory and collaborative mechanisms aimed at increasing the sustainability of LML. Full article

Background/Objectives: Nursing is characterised by demanding working conditions, including long working hours and staffing shortages. Although Decent Work (DW) has been proposed as a framework for improving labour conditions, existing measures do not adequately capture how these conditions are experienced in practice. This study aimed to develop and validate the Decent Work Scale for Hospital Nurses (DWS-N), conceptualising DW as the experiential realisation of structural labour conditions. Methods: A multi-phase methodological design was employed. In Phase 1, 184 items were generated through concept analysis and refined to 142 items via expert review. In Phase 2, exploratory and confirmatory factor analyses were conducted to examine structural validity and reliability. In Phase 3, cross-validation and criterion-related validity were assessed using an independent sample. Results: A five-factor structure comprising 22 items was identified: Meaningful and Fulfilling Work, Protection of Workers’ Rights, Supportive and Safe Working Environment, Appropriate Working Time and Work–Life Balance, and Adequate Compensation. The scale demonstrated good internal consistency (Cronbach’s α = 0.896) and acceptable fit (CFI = 0.969, RMSEA = 0.037). Cross-validation supported model stability (CFI = 0.917, RMSEA = 0.070). DW was positively associated with work engagement and negatively associated with burnout. Different dimensions of DW showed distinct patterns of association with occupational outcomes. Conclusions: The DWS-N is a valid and reliable instrument for assessing Decent Work in nursing. By capturing the experiential realisation of structural labour conditions, the scale offers a context-specific approach to understanding nurses’ working environments, with potential applications in workforce management and organisational policy. Full article

Output-based indicators in entrepreneurial ecosystem governance systematically misclassify pre-threshold structural progress as policy failure, because feedback dynamics produce no immediate output signal. This study examines how institutional coordination shapes those dynamics. Using system dynamics modelling, we construct a three-stock model (active startups, entrepreneurial capabilities, and institutional support). Calibration is performed via structured expert elicitation using the Repertory Grid Technique (RGT), enabling institutionally grounded parameter estimation where comparable time-series data are unavailable. Three policy scenarios—fragmented support, financial intensification without coordination, and coordinated early intervention—are simulated for Mexico and the United Kingdom. Resource intensification alone yields only temporary gains when feedback structures remain fragmented. Coordinated intervention activates reinforcing feedback among all three stocks, enabling self-sustaining growth beyond a critical coordination threshold. The United Kingdom crosses this threshold earlier due to stronger baseline conditions; Mexico responds later but with larger proportional gains. The model provides a feedback-structural diagnostic that distinguishes pre-threshold structural assembly from genuine stagnation, with direct implications for the design of evaluation frameworks in fragile institutional contexts. RGT demonstrates potential as a calibration strategy for feedback models in data-sparse settings. Full article

Objectives: Our objective was to develop and evaluate a locally trained knowledge-based planning (KBP) model for head and neck (H&N), brain, and central nervous system malignancies using RapidPlan, and to determine whether standard statistical metrics such as the coefficient of determination (R²) and outlier frequency are definitive predictors of clinical utility. Methods: An institutional dataset of 594 plans was retrospectively curated into a 497-plan training set. Performance was evaluated in 370 paired plan comparisons generated with identical beam geometry. Training–validation overlap was explicitly quantified at both plan and patient levels, and a plan-level held-out sensitivity analysis was performed. Additional analyses included monitor units (MUs), subgroup assessment, clinically relevant OAR threshold achievement, and 95% confidence intervals for paired differences. Results: The validation set included 370 plans from 289 patients. At the plan level, 303 validation plans overlapped with the training model, and 67 were held-out cases; at the patient level, no fully patient-independent validation cohort was available. RapidPlan maintained target coverage while reducing OAR doses, including oral cavity Dmean (−7.62%Rx; 95% CI: −8.91 to −6.32; p < 0.001) and larynx Dmean (−7.57%Rx; 95% CI: −9.14 to −6.00; p < 0.001). The same direction of benefit was observed in the plan-level held-out subset. MU did not increase with RapidPlan and decreased from 764.2 ± 275.5 to 695.8 ± 210.3 MU (Delta = −68.4 MU; 95% CI: −89.4 to −47.4; p < 0.001). Conclusions: A high R² was not required for clinically useful optimization objectives in this heterogeneous cohort. However, the retrospective design and patient-level overlap limit claims of full generalizability. The model should therefore be interpreted as a clinically useful standardization and decision-support tool requiring expert review rather than as a replacement for the judgment of physicists and radiation oncologists. Full article

Design management in Industry 5.0 faces a persistent gap due to fragmented conceptualizations that treat AI, human creativity, and machine capabilities as largely separate elements, limiting their effective integration within complex socio-technical systems. Addressing this gap, the present study develops and empirically validates a Design Management Model grounded in human-centered, ethical, and sustainability-oriented principles, framing design management as an adaptive and relational system rather than a linear or technology-driven process. Departing from the automation-oriented logic of Industry 4.0, the study adopts an augmented cognition perspective in which AI functions as a collaborative partner supporting, rather than replacing, human judgment. A sequential mixed-methods approach was employed, integrating systematic literature review, qualitative content analysis, expert evaluation, and Structural Equation Modeling (SEM) based on data from 316 participants, followed by empirical examination in two service-oriented case contexts, namely tourism/hospitality and healthcare services. The findings identify and validate six interrelated domains and demonstrate that Human–AI–Machine Synergy plays a central role in shaping design outcomes. More specifically, the results show that effective design management in Industry 5.0 depends on the coordinated interaction between cognitive processes, technological infrastructures, and organizational strategies, rather than on isolated technological advancement. Empirical applications further illustrate how the model supports ethically guided AI integration, enhances adaptive decision-making, and improves experience-oriented innovation across different contexts. By providing a validated structural framework that connects previously disjointed elements, this study contributes a clearer operational understanding of how human–AI collaboration can be embedded within design management practices in Industry 5.0. Full article

The linear computational complexity embraced by Mamba has demonstrated significant application potential in context modeling for the landslide segmentation tasks from remote sensing images. However, existing methods show deficiencies in terms of discrimination and generalization when applied to extreme remote sensing landslide scenarios, such as low resolution and abnormal lighting. To confront these challenges, we propose a remote sensing image landslide segmentation network (VFM-MoME) jointly guided by a vision foundation model and a mixture of Mamba experts. Specifically, we first design a dual-branch joint encoding architecture that integrates a frequency-aware wavelet block as the main encoding branch with the visual foundation model fusion as the auxiliary branch, thereby mitigating the issue of insufficient generalized features in specific landslide study areas. We also construct a mixture of Mamba expert block to enable the decoder to process both global context and local fine-grained features of landslides, addressing the shortcoming of simple serial Mamba in capturing local details and balancing between global semantic relationships and the edges and textural details of objects. Furthermore, we bring in a binary uncertainty enhancement module to guide the model in exploring challenging samples, thus enhancing the model’s ability to handle ambiguous features. Test results on the publicly available datasets of Landslide4Sense and GVLM demonstrate that our method achieves competitive performance. Full article

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors in a double-blind peer review [...] Full article

Oil–particle aggregate (OPA) formation, fragmentation, and settling govern the fate of oil that strands on sediment-rich shorelines, yet no publicly accessible, size-resolved software tool currently couples these three processes within a single population balance framework. Existing models either resolve OPA formation without breakup or resolve breakup from a prescribed initial distribution, forcing practitioners to chain tools manually. We address this gap by developing OPA-Z, a software tool whose kernel unifies (i) A-DROP coagulation/attachment, (ii) binary fragmentation and shell shredding, and (iii) an analytical advection–diffusion settling solution within a single discretized population balance model. The integrated kernel is wrapped in a graphical user interface (GUI) that enables non-expert scenario testing on a web-hosted application. Model inputs include oil properties (interfacial tension, viscosity, density), particle properties (sand, clay, mixtures, sizes, density), turbulence intensity, and water depth. Outputs include time-resolved OPA size distributions, oil trapping efficiency (OTE), oil-to-sediment ratio (OSR), and cumulative oil settled to the bed. The model operates in a pulse mode of oil release that simulates a slick arriving at the shorelines, representative of nearshore spill response scenarios such as beached oil remobilization or slick stranding events. Model fidelity is demonstrated by reproducing benchmark coagulation data in laboratory systems. The software is designed to integrate with GIS-based particle fields, supporting fast, transparent assessments of nearshore OPA fate to respond to oil spills and for contingency planning. Full article

This study examines the characteristics of Group Multi-Attribute Decision Making (GMADM), including highly ambiguous information, divergent expert opinions, and bounded rationality among decision-makers. From the perspective of symmetry modeling and bias control, we propose an adaptive decision-making framework based on Spherical Fuzzy Sets (SFS). First, a spherical fuzzy quantification system for online reviews is constructed to map multi-source asymmetric information within reviews to Spherical Fuzzy Numbers. Second, an adaptive expert weighting mechanism is developed that integrates individual expert performance with the level of group consensus, dynamically adjusting weights to suppress the asymmetric interference of outlier opinions. Subsequently, we design the Credibility-based Spherical Weighted Arithmetic Mean (CSWAM) to preserve the dominance of expert judgments in a nonlinear manner and construct the Spherical Fuzzy Score function with Adaptive Hesitation Penalty (HP-SC) to ensure robustness and non-negativity in the defuzzification process. Furthermore, we extend DEMATEL and TODIM to the SFS environment, constructing a comprehensive evaluation model that captures causal relationships among attributes and asymmetric information, such as decision-makers’ loss aversion. Finally, empirical results from online movie review usefulness rankings demonstrate that this model can accurately identify and mitigate asymmetric information biases while maintaining decision symmetry equilibrium and exhibiting higher ranking stability. Full article

This exploratory pilot study analyzes the organizational and environmental determinants associated with stated intention to adopt biogas-solar microgrids within a circular bioeconomy framework. A quantitative, applied, cross-sectional design was used with 71 valid individual responses from participants linked to productive, agro-industrial, livestock, energy, and waste management organizations or projects, selected through nonprobabilistic convenience sampling. The analysis does not measure actual investment, implementation, or use; therefore, the results refer only to declared adoption intention and should not be generalized beyond the sample. The questionnaire measured perceived benefits, barriers, institutional conditions, financial feasibility, environmental value, organizational capabilities, and adoption intention. Content validity was supported by expert judgment, and psychometric reliability was assessed using Cronbach’s alpha and McDonald’s omega. Predictive modeling compared supervised classification, regression, and unsupervised segmentation techniques using train-test validation, cross-validation, and interpretability analyses. ExtraTrees achieved the best exploratory classification performance, with a test ROC-AUC of 0.889, while RandomForestRegressor showed the best regression performance; however, these values should be interpreted as sample-specific evidence rather than as a validated predictive tool. Organizational capabilities and environmental criteria emerged as the most influential predictors, and K-Means suggested two tentative readiness profiles with weak separation. The findings suggest that stated adoption intention is associated with a systemic configuration of organizational maturity, environmental legitimacy, financial feasibility, and institutional support, providing preliminary evidence for future larger sample validation and for decision-support discussions in sustainable energy transitions. Full article

This study addresses the limited campus-specific operationalization of attention restoration theory and the insufficient empirical validation of perceptual factors in university settings. It aims to identify key perceptual factors influencing attention restoration on campuses and to develop an evidence-based assessment framework for restorative campus design. An initial set of ten perceptual factors was synthesized from Attention Restoration Theory, Stress Recovery Theory, and Environmental Preference Theory. A two-round e-Delphi process involving 26 multidisciplinary experts from academia and design practice was conducted, followed by combination weighting to derive factor weights and construct a weighted assessment model. The model was validated through a student survey and tested for applicability in a representative campus space. The process achieved consensus on seven primary perceptual factors with the following weights: accessibility (0.187), comfort (0.150), visibility (0.143), sense of belonging (0.143), familiarity (0.142), peacefulness (0.118), and recognition (0.117). A practical assessment tool yielding a 0–10 restorativeness score was developed to quantitatively evaluate campus spaces. Student survey data confirmed the correspondence between model-derived scores and observed issues in the sample space. This study extends restorative environment theories to the hybrid natural-built contexts of university campuses and integrates an online Delphi procedure to develop a decision-support model. The resulting framework and tool enable planners and designers to diagnose deficiencies in campus environments and implement targeted interventions to enhance attention restoration. Full article