Search Results (3,923)

Evaluating street interface morphology is essential for urban design, yet existing approaches often struggle to combine large-scale applicability with higher-level morphological interpretation. This study proposes a scalable framework for assessing street interface morphology using an automated multimodal large language model (MLLM) agent. Using street view imagery (SVI), the framework evaluates four core morphological dimensions—enclosure, continuity, transparency, and roughness–through two complementary analytical streams: objective geometric measurement and subjective morphological assessment. To support reliable evaluation, the framework incorporates a dual-benchmark strategy consisting of manually derived geometric measurements and expert-consensus ratings for calibration and validation. Applied in Shanghai, the framework demonstrated reliable performance across the evaluated dimensions. The optimized agent was further extended to continuous street-segment analysis, demonstrating its applicability to large-scale urban assessment. By integrating objective and subjective evaluation within a scalable and interpretable workflow, the proposed methodology provides a practical tool for street interface morphology analysis and urban design assessment. Full article

Risk assessment of safety-critical software relies heavily on expert reviews prone to high epistemic uncertainty and conflicting judgments. While evidence theory is widely used for information fusion, classical rules often yield counter-intuitive results in high-conflict scenarios. To address this, we propose an uncertainty-driven risk evaluation model based on a dual-dimensional evidence fusion approach. The framework integrates an improved Belief Entropy (BE) and an Evidence Conflict Coefficient (ECC) to quantify reliability from two perspectives: (1) Internal Dimension, using BE to measure inherent uncertainty within individual judgments; and (2) External Dimension, using ECC to measure divergence among multiple sources. By adaptively modifying Basic Probability Assignments (BPAs) with these dual-dimensional weights, the model effectively harmonizes data prior to fusion. Validated through an avionics software airworthiness case study, the methodology significantly enhances fusion stability and accuracy. Results confirm it effectively suppresses extreme deviations and raises the performance floor, providing a robust decision-support tool for safety-critical engineering. Full article

Spam consists of unsolicited messages, and the posting of such irrelevant messages often presents significant challenges in technical forums. Two particular challenges are the dynamic nature of spamming tactics and the inadequacy of adaptable spam databases for automated classifiers. Our work addresses the need for a robust spam classification solution that can be seamlessly integrated with database, SQL, and APEX applications. We developed a labeled spam database by asking experts to categorize 1916 posts as spam or regular posts to ensure accurate classification and then created an SVM-based spam classification model that achieves an average validation accuracy of 90%. Our research enhances the current understanding of spam in technical forums and represents a solution for embedding spam classifiers into widely used platforms with an accuracy of 98.1%. Furthermore, we explore the incorporation of generative topics into our approach by integrating generative topic modeling techniques, such as latent Dirichlet allocation. In our work, the spam classifier is dynamically updated to account for emerging spam patterns and topics based on a generative approach that improves the robustness of the classifier against new spamming tactics and enables nuanced, context-aware filtering of messages. In addition, our experiments highlight the potential of text SVM classifiers for real-time applications through the fine-tuning of text features. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Background/Objectives: Food insecurity affects college students at nearly twice the rate of US households, with documented impacts on student academic performance, physical and mental health, and socialization. While frameworks exist to conceptualize general food insecurity and food insecurity in specific contexts, researchers and practitioners lack resources to guide system-level responses to food insecurity on college and university campuses and assess those responses. In this study, we aimed to develop and validate a simple yet comprehensive framework for assessing food insecurity responses within the context of higher education. Methods: We adapted an eight-phase process for framework development: (1) map selected data sources within the multidisciplinary literature, (2) read and categorize selected sources, (3) identify and name concepts, (4) deconstruct and categorize concepts based on their features, (5) group similar concepts together, (6) synthesize concepts into a framework, (7) validate the framework using expert panel review, and (8) revise as necessary. Results: The developed Campus Food Aid Self-assessment (CFAS) framework consists of six dimensions: Student Services and Supports; Involvement; Advocacy; Awareness and Culture Efforts; Education and Training; and Research, Scholarship, and Creative Works. Expert panelists (n = 7) reviewed the proposed framework and confirmed the clarity, comprehensiveness, and representativeness of the proposed dimensions, conceptual definitions, and operational variables. Conclusions: With a comprehensive yet accessible structure, the CFAS framework supports the development, coordination, and improvement of campus-based strategies to address food insecurity and support positive student outcomes. Full article

Scalable production of interactive 3D assets is a key requirement for XR-based applications, yet the functional integration of GenAI-generated assets into game engines remains challenging for non-expert users. This article proposes and validates a Prompt-to-Trigger workflow that links GenAI-based asset ideation and generation with the implementation of basic interactive behaviors (triggers) in accessible XR platforms. The study adopted a qualitative and exploratory approach, using systematic observation throughout a two-stage development process. This process included an initial phase where 3D assets were generated and refined using tools such as Tripo AI and Meshy, followed by an optimization stage to ensure compatibility with Blender and XR environments like A-Frame and Godot, and subsequently, the creation of AI-powered activation scripts. The results show that GenAI’s current 3D outputs frequently exhibit topological inconsistencies and rigging errors that compromise performance and real-time interoperability, requiring cleanup and optimization before deployment. The Prompt-to-Trigger workflow formalizes this bridge, positioning AI assistance as a functional layer for iterative logic generation. The resulting model provides non-expert creators with structured, actionable framework to prototype complex XR experiences for applied domains like education and multimedia communication. Full article

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

Background: ChatGPT is a large language model (LLM) online chatbot developed by OpenAI and launched in November 2022. Early adoption studies have shown high readiness to use this technology for health-related questions and self-diagnosis. However, the quality and clinical adequacy of health-related responses remain incompletely characterized. This study aimed to explore responses generated by ChatGPT-3.5 and ChatGPT-4.0 to common patient questions regarding scoliosis. Methods: Ten scoliosis-related frequently asked questions (FAQs) were selected from a larger pool of over 250 patient-facing questions compiled from 17 publicly available FAQ webpages and informed by a Google Trends analysis. Questions were harmonized, grouped by theme, and then reduced by rule-based expert review to a final set intended to represent common patient concerns. Results: The median ratings of ChatGPT-3.5 and ChatGPT-4.0 responses ranged from satisfactory, requiring minimal (2) to moderate clarification (3). Across the ten matched questions, no statistically detectable difference was found between models in this study setting (W = 8.0, p = 0.59; Cliff’s δ = −0.12 [95% CI −0.58, 0.40]); however, given the small question set, unblinded rating process, and poor inter-rater reliability, this should not be interpreted as evidence of equivalence, non-inferiority, or comparable model performance. The results apply only to the 10–15 April 2024, online snapshots of ChatGPT-3.5 and ChatGPT-4.0 and should not be generalized to later model iterations. Conclusions: This study should be interpreted as a clinically oriented observational report, intended to inform physician awareness and patient-physician communication rather than validate chatbot accuracy or safety. In this 10–15 April 2024, sample, both model outputs frequently required clinician clarification. Given the small FAQ set, low inter-rater reliability, unblinded design, and single-sample outputs, the findings do not establish equivalence or superiority and apply only to the specific 10–15 April 2024, model snapshots and evaluated questions. Full article

Traditional fuzzy decision-making often relies on manual expert calibration, which is labor-intensive and susceptible to subjective bias. This study addresses these limitations by proposing a novel framework that transforms the intrinsic probabilistic outputs of Large Language Models (LLMs) into Triangular Fuzzy Numbers (TFNs). We introduce a multi-temperature sampling strategy coupled with weighted quantile aggregation and an adaptive interval adjustment mechanism to systematically map model stochasticity to fuzzy possibility distributions. Empirical validation on a structured prototype dataset demonstrates that the proposed method achieves high consistency with expert consensus, with GPT-4.2 exhibiting superior central accuracy and Gemini-2.5 excelling in uncertainty coverage. Furthermore, in complex unstructured scenarios involving business public opinion, the integration of Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) significantly corrects cognitive biases and converges uncertainty boundaries. This research establishes a rigorous pathway from generative AI probabilities to fuzzy decision theory, offering a robust automated solution for quantitative risk assessment and intelligent decision support. Full article

Background: Women with endometriosis frequently experience mobility limitations that affect daily functioning. A specific tool to assess these restrictions would help clinicians to better understand patients’ functional challenges, facilitating more effective communication and shared decision making. Addressing this gap is essential for strengthening patient–professional dialogue and improving individualized care. Objective: To develop the new instrument MobEndo and to perform initial psychometric testing of the tool. Methods: The initial domains and items were generated through semi-structured interviews with patients and based on experts’ advice. Guided by the International Classification of Functioning, Disability, and Health (ICF) framework, exploratory factor analysis was conducted on data from patients diagnosed with endometriosis. Internal consistency was assessed using Cronbach’s alpha, considering values ≥ 0.70 as acceptable. Test–retest reliability was examined using intraclass correlation coefficients (ICCs), and ICC values were judged as excellent if >0.75. Construct validity was evaluated through concurrent, discriminant, and known-groups validity. For the known-groups validity hypothesis, participants were categorized by baseline pain levels. Results: The final questionnaire included 18 items, developed from responses from 301 women (mean age 38.96 ± 6.85). Factor analysis revealed two components—transitioning between body positions and performing movements requiring stabilization and executing load-bearing tasks involving the upper limbs—with the model explaining 71.78% of the total variance. Reliability was excellent, with a Cronbach’s alpha of 0.977. The ICC for the total score was 0.976 (95% CI 0.949–0.988), with similarly high values for each component. Concurrent validity correlations were significant, while discriminant validity showed no relevant associations. Known-groups analyses showed clear differences across pain-level groups. Conclusions: The questionnaire is a valid and reliable tool for capturing women’s perceived mobility limitations in endometriosis. Full article

Background: Occupational exposure to cytotoxic drugs remains a major concern for pharmacy personnel, due to their well-established, carcinogenic, mutagenic and organ-specific effects. Despite the existence of robust international guidelines, evidence suggests substantial variability in compliance, training quality and operational conditions across healthcare settings. Objective: This study aimed to characterise current handling practices, assess working conditions, training, safety procedures, exposure patterns, and perceived risk factors among pharmacy technicians involved in the preparation of cytotoxic drugs in Portugal. Methods: A cross-sectional descriptive study was conducted using a structured questionnaire grounded in international standards (ISOPP, NIOSH, ASHP, USP <800>). The instrument was developed through literature review, expert panel validation (n = 42), and pre-testing. Data were collected electronically between April and May 2025 from pharmacy technicians actively handling cytotoxic drugs in Portugal. Results: A total of 124 valid responses were analysed. Most participants were female (78%) and under 50 years, with nearly one-third having less than one year of experience. Prolonged daily exposure (31.5% participants worked ≥ 5 h/day) extended uninterrupted handling periods (28.2% worked > 120 min), and high preparation workloads were common. While adherence to core protective measures—such as reinforced gowns, double gloves, and Class II B2 biological safety cabinets—was high, important gaps were identified, including incomplete use of closed system transfer devices, inconsistent respiratory and foot protection, limited automation, and insufficient environmental monitoring. Structured competency assessment, periodic training, and formal documentation were frequently absent. Institutional policies on reproductive risk showed strong protection for women but less clarity for male workers. Conclusions: Cytotoxic drug handling practices in Portugal demonstrate satisfactory adherence to fundamental protective measures but reveal significant structural and organisational gaps related to workload management, environmental monitoring, and continuous training. The absence of unified national guidance contributes to variability across institutions. These findings highlight the need for greater standardisation of occupational safety practices. Full article

Raising cybersecurity awareness (CSA) of employees is crucial for all modern organisations. To meet the organisational need for CSA, activities aimed at increasing CSA have been the focus of both industry and research in the past. There are, subsequently, a plethora of CSA activities for organisations to choose from. Nevertheless, research consistently reports that organisations struggle to raise CSA to an appropriate level, and a core issue lies in their ability to select CSA activities and effectively adopt them. This paper used semi-structured interviews with practitioners working on CSA adoption in public-sector organisations to identify what practitioners perceive as success factors. The interviews were analysed through a socio-technical lens and resulted in a taxonomy that groups success factors for CSA adoption in the three socio-technical dimensions: organisational, user-centric, and technical. The taxonomy outlines ten success factors and demonstrates how the participants see success of CSA activities as not only dependent on technical factors but also, and perhaps even more important, user-adaptability and organisational readiness. The results were validated in a workshop with CSA experts across Europe, who highlighted the practical usefulness of the taxonomy as both a map of potential challenges and a teaching tool for educating new CSA practitioners. Full article

Project-based organizations (PBOs) face persistent challenges in managing workload fluctuations that influence performance, competitiveness, and resource sustainability. Although previous research has explored bidding strategies and project inflows and outflows, few studies have systematically modeled workload-capacity dynamics or assessed policy responses to manage them effectively. To address this gap, this study develops a system dynamics (SD) model that integrates both pre-award and post-award project phases with internal and external organizational processes. Data for model development were drawn from the literature, industry reports, and expert interviews, resulting in the identification of 28 variables organized into subsystems covering demand, capacity planning, work execution, competitiveness, and financial performance. The model was validated through dimensional and structural tests, expert review, and further examined using social network analysis (SNA) and sensitivity analysis. The SNA results identified workload, production rate, and organizational capacity as the most influential variables. Sensitivity analysis conducted through Monte Carlo experiments, employing screening, regression, and ANOVA (analysis of variance) methods, revealed that capacity adjustment flexibility, minimum capacity, and demand level are critical factors influencing organizational stability. The validated model was then applied to evaluate policy alternatives under two distinct market conditions. Findings indicate that in lowest-price environments, a competitive, market-share-oriented policy enhances utilization and responsiveness, whereas in average-price markets, a stable capacity policy yields more sustainable outcomes. These results demonstrate how project-based organizations can strategically adjust bidding and capacity policies to stabilize workload dynamics and improve long-term operational resilience under different market conditions. The study contributes theoretically by extending the application of SD modeling to workload-capacity management in PBOs and contributes practically by offering a decision-support tool that helps managers assess capacity strategies, reduce risks, and align organizational policies with long-term sustainability objectives. Full article

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making. Full article

Background/Objectives: This study aimed to develop and evaluate deep learning models for the detection, localization, and classification of impacted maxillary canines, and to compare their performance with cone-beam computed tomography (CBCT) as the reference standard. Methods: This cross-sectional diagnostic accuracy study was conducted at King Abdulaziz University Dental Hospital to develop and validate artificial intelligence (AI) models for detecting and localizing maxillary canine impactions using panoramic and cone-beam computed tomography (CBCT) imaging data. A total of 641 panoramic ra and 158 CBCT scans were collected, of which 158 cases had matched panoramic–CBCT pairs for localization analysis. Images were annotated and validated by expert radiologists and orthodontists, with consensus review ensuring labeling reliability. Data augmentation expanded each panoramic and CBCT category to 500 samples for panoramic and 1000 samples for CBCT, resulting in 1935 panoramic and 5703 CBCT images after preprocessing and normalization. The datasets were divided into (training + validation) (80%) and testing (20%) subsets. MobileNetV2 architectures were used for classification, and whdiographsile, a ResNet-50–based Few-Shot Learning framework, enabled spatial localization of impacted canines. Models were trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Cohen’s kappa and 95% confidence intervals were used to assess agreement between AI predictions and expert annotations. Results: Panoramic classification achieved 94% accuracy, demonstrating the highest performance in normal cases and reduced recall for bilateral impactions. The CBCT classifier achieved 98% accuracy across positional categories. Cross-modality prediction reached 93.5% accuracy, with strong agreement compared to CBCT (Cohen’s kappa = 0.91). Expert review confirmed reliable localization of impacted canines on both imaging modalities. Conclusions: Artificial intelligence applied to panoramic radiographs supports the detection, localization, and characterization of impacted maxillary canines with performance comparable to CBCT. This approach may enable lower-radiation decision support for clinical triage. Full article