Search Results (158)

The accelerating closure of schools in depopulating regions is leaving a growing surplus of public assets whose reuse must be prioritised, yet systematic and transferable tools for supporting such decisions in advance remain scarce. This study proposes an artificial-intelligence-augmented multi-criteria framework that prioritises the adaptive reuse of closed schools using only openly available demographic and spatial data. Four criteria—regional ageing, building floor area, and proximity to administrative and transport infrastructure—were evaluated for 121 closed schools in Chungbuk Province, South Korea, under two weighting schemes, the subjective analytic hierarchy process and the objective entropy method, with a large-language-model agent added as an explanatory layer to interpret context and recommend reuse types. The data-driven weights proved liable to a structural distortion, elevating the single largest building to first place in urgency on the strength of its size alone, a misjudgement the agent corrected through contextual reasoning over the same data. Examining the schools from the opposed standpoints of intervention urgency and reuse potential further revealed a near-perfect inversion between them (Spearman ρ = −0.998), indicating that the most urgent schools are systematically those least able to sustain a market-led conversion. The framework addresses this dilemma not through a single optimal ranking but through spatially differentiated, agent-generated recommendations and is formulated for transfer to other middle-income economies approaching the same demographic transition. Full article

Background: Pre-travel health consultations require individualised risk assessment across itinerary, destination, traveller characteristics, vaccine and medication history, comorbidities, pregnancy and immune status, activities, and access to care. Artificial intelligence (AI), particularly large language models (LLMs), may support pre-consultation education, structured history collection, guideline retrieval, multilingual communication and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated or insufficiently personalised recommendations. Objectives: This scoping review maps the current evidence on AI tools relevant to pre-travel health consultations, characterises implementation gaps, identifies patient-safety risks and proposes a supervised implementation model for travel medicine clinics. Original contribution: Unlike previous reviews of clinical AI, patient-education LLMs or chatbots in chronic illness, this is the first scoping review focused specifically on AI in pre-travel consultations. It uniquely combines a five-tier evidence hierarchy that separates direct travel-medicine AI evidence from indirect clinical-AI safety and equity evidence, and provides a travel-medicine-specific clinical safety risk taxonomy and a supervised implementation framework anchored to authoritative travel-medicine guidance and current AI regulatory regimes. Methods: A scoping review was conducted following PRISMA-ScR reporting, using a Population–Concept–Context eligibility framework and a targeted retrieval in May 2026 covering January 2017 to May 2026. Sources were screened and charted by a single reviewer using a structured eligibility checklist. Quality and applicability were appraised conceptually using MMAT, AMSTAR 2 and JBI text-and-opinion criteria, with GRADE-informed certainty. Results: Of 70 records identified, 11 were included: four direct pre-travel AI sources, one adjacent travel-related decision-support study, four guideline and context sources and two clinical LLM safety sources. The only patient-level implementation involved 26 travellers using a GPT-4 Travel Clinic Assistant in Singapore, where physicians and travellers reported acceptability and workflow benefit but objective effectiveness outcomes were not measured. Broader clinical LLM evidence indicates heterogeneous evaluation methods, vulnerability to hallucinated guidelines, and accuracy that varies widely across model versions and specialties. Conclusions: Current evidence supports supervised AI augmentation of pre-travel consultations but does not support autonomous AI-led vaccine selection, malaria prophylaxis, contraindication screening or individualised travel-risk clearance. Near-term deployment should be restricted to clinician-supervised education, structured intake, source-grounded guideline retrieval, after-visit reinforcement and escalation-triggered workflow support. Priority research includes travel-medicine-specific hallucination audits; equity testing in visiting-friends-and-relatives, migrant, older-adult, First Nations Australian, and Pacific Islander travellers; and prospective trials reported under CONSORT-AI, SPIRIT-AI and TRIPOD + AI. Full article

Post-disaster recovery is a multi-year, multi-dimensional process, yet most remote-sensing assessments rely on single indicators and are hard to apply in data-sparse regions—limiting their value for sustainable, evidence-based reconstruction. We develop a Google Earth Engine (GEE)-based multi-sensor Recovery-Activity Index (RAI), built entirely from free satellite data, and apply it to the 2023 Al Haouz earthquake (Mw 6.8) in the High Atlas, Morocco. The index is framed explicitly as an observed recovery-activity monitoring proxy, not a direct measure of welfare or resilience capacity. Monthly VIIRS nighttime-light (NTL) anomalies, Dynamic World built-up probability, and precipitation-corrected Sentinel-2 NDVI were extracted for a 30 km rural core zone (January 2022–May 2026), deseasonalized, standardized, and integrated. NTL anomalies rose after the earthquake (post-event mean +18%) and appeared to precede built-up anomalies by about two months; because monthly series are short and autocorrelated, we tested this lead with block-bootstrap and block-permutation methods and report it as a reproducible but modest early-activity lead (r = 0.65, p = 0.02; p = 0.14 after correction) that is not an artefact of optical data gaps. NDVI was governed mainly by precipitation (R² = 0.61) with negligible earthquake-attributable change, so vegetation signals do not confound the index. The integrated RAI peaked in December 2024 and proved robust to indicator weighting (pairwise r ≥ 0.97), baseline choice (r = 0.88), and spatial domain (<9% variation), with a genuinely multi-sensor peak (NTL 62%, built-up 43%). Province-level analysis revealed an uneven recovery hierarchy (Chichaoua > Al Haouz > Taroudannt) driven by differences in physical-rebuilding signal rather than baseline luminosity. Running in minutes server-side at no cost, the RAI offers data- and resource-limited administrations a scalable, reproducible tool to flag where reconstruction activity lags and to prioritize targeted ground verification—supporting more equitable, sustainability-oriented recovery governance—rather than serving as a stand-alone, validated recovery measure. Full article

The deployment of three-dimensional Wireless Sensor Networks (3D WSNs) in complex environments demands robust topological control to ensure both reliable and fault-tolerant sensing and communication. In order to simultaneously achieve the two objectives over time, an even distribution of the sensors’ energy consumption is essential. Achieving optimal sensor distribution on non-planar surfaces (3D shapes), such as spheres, while maintaining reliable network routes is a significant algorithmic challenge. While many approaches effectively and efficiently addressed the aforementioned goals in 2D environments, and there exists a significant body of work on coverage, connectivity, or energy efficiency in 3D sensor networks, the solutions for either can not straightforwardly be adapted to the 3D case (e.g., some coverage problems are optimally solved for 2D but are still open problems in the 3D case), or the solutions to the individual problems in the 3D case are not integrated gracefully to solve the entire problem. Moreover, these problems have not been address for the realistic spherical 3D case. This paper presents a novel holistic algorithm designed to generate energy-efficient, optimal sensor topologies over spherical 3D sensor networks that guarantee redundant coverage to deal with sensor failures, connectivity with controlled redundancy support for more efficient communication, and the creation of a hierarchy over the flat network to deal with energy issues, at would be appropriate for real-world tasks. The proposed methodology is executed in three primary phases. First, it approaches the geometric part of the problem to determine the optimal placement of sensor nodes on the surface of a sphere, guaranteeing k-coverage for the target area. Second, it creates a reliable inner-layer backbone network of sensors that establishes k-connectivity ensuring a reliable network for data transmission and distribution of total power in the whole network. Finally, after formulating sensors into clusters, a mathematical formula to change each cluster head is created so that we achieve even distribution of energy consumption across the network. To validate the proposed approach, a 3D WSN software simulator was developed. This tool provides a dynamic visual simulation of the network, enabling the execution, visualization and simulation of the hybrid algorithm and any other 3D WSN. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

Background/Objectives: Exercise is pivotal for glycemic control in type 2 diabetes mellitus (T2DM), yet the relative efficacy of various exercise modalities remains inconclusive. This network meta-analysis aimed to evaluate and provide a preliminary ranking of exercise interventions on HbA1c levels in adults with type 2 diabetes mellitus, to facilitate clinically relevant network comparisons and to generate evidence for future large-scale comparative trials. Methods: A systematic review and network meta-analysis were conducted in accordance with PRISMA guidelines. Electronic databases (PubMed, MEDLINE, Cochrane Library, CINAHL, and ProQuest) were searched from inception to Dec 2024. Randomized controlled trials evaluating exercise interventions in adults with T2DM were included. Risk of bias was assessed independently by two reviewers using the JBI critical appraisal tool. The primary outcome was the change in HbA1c level. Results: Six randomized controlled trials involving a total of 511 participants (256 in the treatment group and 255 in the control group) were included in the final analysis. Both high-intensity interval training (MD = −0.322; 95% CI: −0.559 to −0.084; p = 0.008) and yoga (MD = −0.366; 95% CI: −0.534 to −0.198; p < 0.001) significantly reduced HbA1c compared with the active control. Although the preliminary ranking analysis suggested a higher probability of effectiveness for yoga (SUCRA 1) than for HIIT (SUCRA 0.5), the indirect comparison revealed no statistically significant difference in HbA1c reduction between the two interventions (MD = −0.044; 95% CI: −0.335 to 0.247; p = 0.766). Conclusions: These findings provide preliminary, evidence-generating; however, given the sparse network and absence of head-to-head trials, the treatment hierarchy should be interpreted with extreme caution and selected based on patients’ preferences and tolerance. Registration: PROSPERO [CRD42025650162]. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article

Background/Objectives: Obstructive sleep apnea (OSA) and related sleep disorders affect a substantial proportion of hospitalized patients, with an estimated 48% pooled prevalence of undiagnosed OSA in cardiac inpatients and up to 80% of moderate-to-severe community OSA cases carrying no formal diagnosis at the time of hospital admission. In parallel, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep-disordered breathing through circadian dysregulation, intermittent hypoxia, hypothalamic–pituitary–adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no prior study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis—and OSA in particular—at the point of hospital admission. The present study addresses this gap by developing an admission-time, explainable machine learning framework for the prediction of inpatient sleep disorder diagnoses (ICD-10 G47.x, encompassing OSA G47.3, insomnia G47.0, hypersomnia, and circadian rhythm disorders) and of insomnia specifically (ICD-10 G47.00). Methods: We developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: (i) frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); (ii) physiological and laboratory biomarkers from the first 24 h of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and (iii) sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). Results: XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a hierarchy that recapitulates the canonical clinical phenotype of obstructive sleep apnea in frail inpatients rather than that of primary insomnia, indicating that the model is preferentially capturing the OSA–frailty axis within the broader G47.x outcome. The predicted probability outputs were well-calibrated across all risk deciles. Conclusions: Frailty-derived features, in combination with admission-time clinical and physiological data, can predict inpatient sleep disorder diagnoses—predominantly OSA—with high sensitivity and well-calibrated risk estimates. The deployable, interpretable nature of the XGBoost model makes it directly suitable for integration into clinical decision support systems, offering a screening tool that requires no dedicated instrumentation beyond routine admission data. By flagging high-risk patients at the moment of admission, the framework provides a concrete mechanism for accelerating referral for definitive diagnostic confirmation (overnight oximetry, polysomnography) and earlier initiation of CPAP and related therapies, with direct implications for reducing the persistent diagnostic gap, perioperative risk, and preventable adverse outcomes in frail hospitalized populations. Full article

Understanding how interface layout influences visual search performance is important for optimizing virtual reality (VR) interfaces. This study investigated visual search efficiency and gaze behavior in simulated VR menu interfaces using a screen-based eye-tracking experiment. To enable controlled measurement of gaze behavior and isolate layout-driven perceptual effects, the interfaces were evaluated using a desktop-based VR simulation. The experiment examined two independent variables: menu layout structure and target spatial quadrant. Two representative VR menu layout structures were compared: a grid-based layout arranged as a 4 × 4 matrix and a gallery-based layout consisting of four large and twelve small cards, forming a size-based visual hierarchy. Target locations were distributed across four spatial quadrants: lower-left, lower-right, upper-left, and upper-right. Participants (N = 39) completed visual search tasks while accuracy (ACC), reaction time (RT), and eye-tracking metrics, including total visit duration (TVD) and total fixation count (TFC), were recorded. The results showed that the gallery-based layout supported more efficient visual search than the grid-based layout, as reflected in shorter RTs, reduced overall TVD, and lower overall TFC. Behavioral and eye-tracking analyses also revealed systematic spatial asymmetries, with the upper-right quadrant showing the fastest responses and reduced gaze-based search effort. Importantly, the advantage of the gallery-based layout was most consistent in the upper-right quadrant, indicating that layout structure and target spatial quadrant jointly shaped visual search efficiency. Gaze-distribution heatmaps provided qualitative visual support for these patterns. These findings provide early-stage perceptual evidence for optimizing layout hierarchy in simulated VR menu interfaces and demonstrate the value of screen-based eye-tracking sensors as quantitative tools for evaluating attentional allocation before further validation in immersive HMD-based VR environments. Full article

Sustainable aviation supply chains (SCs) are increasingly exposed to risks arising from environmental regulations, social responsibility pressures, and economic uncertainties. These risks are associated with different SC members and may propagate through operational dependencies among suppliers, maintenance service providers, and airline operators. To support systematic risk assessment, this study proposes a hybrid Analytical Hierarchy Process-Bayesian network (AHP-BN) framework for sustainable aviation SC risk management. The intended contribution is a contextual and structural extension of existing AHP-BN logic to member-level sustainability risk propagation in aviation SCs, rather than a claim that AHP-BN integration itself is fundamentally new. The proposed framework first classifies sustainability risks into environmental, social, and economic dimensions and identifies the risk exposure relationship between SC members and risk factors. For the weighting component, Analytical Hierarchy Process (AHP) is used to derive relative importance weights from specified illustrative pairwise comparison matrices in the numerical experiment. Bayesian network (BN) is employed to model probabilistic dependencies among nodes defined by SC members and risk factors. The two methods are coupled through a weighted expected risk index, which integrates AHP-derived weights, member-specific exposure intensities, probabilities inferred by BN, and losses associated with different risk states. A numerical illustration based on a synthetic aviation SC with suppliers, maintenance service providers, and airline operators is conducted to demonstrate the computational procedure and diagnostic use of the proposed framework rather than to validate an empirical risk profile of the aviation industry. Within this illustrative setting, cost volatility, supplier reliability, emissions regulation, and sustainable aviation fuel availability emerge as the major contributors to the overall risk index under the assumed inputs. The analysis further indicates that the proposed framework can identify critical active pairs of SC members and risk factors, reveal vulnerabilities at the levels of SC members and sustainability dimensions, and provide a transparent decision-support tool for sustainable aviation SC risk assessment, while the resulting rankings should be interpreted as conditional outputs under the assumed input parameters. Full article

To address the inherent limitations of qualitative hygiene inspections, this study establishes a structured MCDM framework to evaluate kitchen hygiene management in Taiwan’s catering industry by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model integrates expert-weighted criteria to facilitate a structured risk-oriented assessment and support sustainable hygiene management through prioritized resource allocation and more systematic hygiene management. The AHP results determined hygiene behavior, cooking and processing, and storage operation management as the most influential criteria, underscoring the critical role of direct food handling practices. The framework was empirically applied to five large-scale catering enterprises and international tourist hotels with multinational operational backgrounds. TOPSIS analysis revealed significant performance variability, with establishment D achieving the highest relative closeness coefficient (0.6125) and establishment E the lowest (0.2358). These findings indicate that operational control measures play a more critical role in food safety and sustainable hygiene governance than supporting infrastructure alone. The proposed model serves as a quantitative decision-support tool for both industry self-assessment and regulatory inspections, facilitating prioritized resource allocation, continuous hygiene improvement, improved food safety governance, and more consistent long-term hygiene management practices. Sensitivity analysis further demonstrated that the overall comparative ranking structure remained generally consistent under alternative normalization conditions, although minor variation was observed between the two highest-performing alternatives. Full article

Against the backdrop of the automotive industry’s transition to low-carbon operations, assessing the sustainability of pure electric vehicle products remains crucial. Existing multi-criteria evaluation methods often follow a compensatory logic, allowing high carbon emissions to be offset by other advantages. This contradicts the core principle that sustainability must be non-negotiable. To address this issue, we propose a two-stage non-compensatory evaluation framework. First, we apply a carbon footprint threshold based on life cycle assessment: any candidate vehicle exceeding this threshold is eliminated. Second, the remaining models are evaluated across ten indicators (economic, social, and technical), and a comprehensive ranking is generated using entropy weighting, fuzzy analytic hierarchy process (FAHP), and the TOPSIS method. This framework has been validated on seven mainstream BEV midsize sedans. The results show that the non-compensatory screening mechanism eliminated two high-carbon-emission models, confirming that environmental criteria must be considered independently. The top-ranked model was not the one with the lowest carbon emissions but rather the one demonstrating balanced performance, indicating that environmental performance and overall competitiveness can be enhanced synergistically. The ranking results remained relatively robust even under a combination of objective and subjective weightings. This study provides a more logically consistent tool for evaluating pure electric vehicles at the product level. Full article

Retrieval-Augmented Generation (RAG) systems depend critically on the quality of document preprocessing, yet no prior study has evaluated PDF processing frameworks by their impact on downstream question-answering accuracy. We address this gap through a systematic comparison of four open-source PDF-to-Markdown conversion frameworks, Docling, MinerU, Marker, and DeepSeek OCR, across 21 pipeline configurations, varying the conversion tool, cleaning transformations, splitting strategy, and metadata enrichment. Evaluation was performed using a 50-question benchmark over a corpus of 36 Portuguese administrative documents (1706 pages, ~492K words), with LLM-as-judge scoring over 50 independent runs per configuration. Statistical significance was assessed via Wilcoxon signed-rank tests with Cohen’s d effect sizes. Two baselines bounded the results: naïve PDFLoader (86.2%) and manually curated Markdown (91.3%). Docling with hierarchical splitting and image descriptions achieved the highest automated accuracy (94.1 ± 1.6%), surpassing even manual curation. A per-question-type analysis revealed that table-dependent questions drive the largest accuracy differences, with a 33-percentage-point gap between basic and hierarchical splitting. Metadata enrichment and hierarchy-aware chunking contributed more to accuracy than the conversion framework alone. An exploratory GraphRAG implementation underperformed basic RAG (82% vs. 94.1%). These findings demonstrate that data preparation quality is the dominant factor in RAG system performance. Full article

Graph-based spatial analysis methods are widely used to evaluate accessibility, visibility, spatial hierarchy, and movement-related properties of architectural floor plans. However, these analyses are often conducted using standalone tools and separate simplified models, which can delay design feedback and introduce additional data preparation steps. This paper presents a BIM-embedded computational workflow for configuring, computing, and visualising spatial graph analyses within Autodesk Revit using Dynamo, Python scripting, and the Accessibility and Visibility Analysis (AVA) package. The contribution is not the development of new graph algorithms, but the documentation of a reproducible workflow that sequences existing tools, graph construction settings, metric configuration, spatial measure computation, and 2D/3D visual feedback within a modelling environment. The workflow is demonstrated through a two-storey residential case study and supports accessibility, visibility, centrality measures, visual step depth, shortest path, isovist, object visibility, and activity-based origin–destination analysis. Particular attention is given to incorporating vertical circulation connections into level-based accessibility graphs for selected cross-level movement analysis. Building on prior AVA–DepthmapX verification by the authors, the paper focuses on workflow transparency, reproducibility, and multi-level accessibility representation. The findings indicate that BIM-embedded spatial graph analysis can support iterative, performance-informed design evaluation. Full article

The evaluation of heritage materials remains a critical challenge within circular economy frameworks, where existing approaches primarily focus on technical and environmental performance while neglecting cultural, historical, and contextual dimensions. This study proposes a Building Heritage Material Passport (BHMP)-based multi-criteria decision-support framework that operates at the material level, integrating structured material data, multi-criteria evaluation, and decision-making within a unified methodology. The approach combines technical indicators (Compatibility and Durability) with heritage-driven indicators (Traceability and Cultural Value) and applies fuzzy scoring together with context-sensitive weighting based on the Analytic Hierarchy Process (AHP), enabling the integration of qualitative and quantitative assessments under conditions of uncertainty. A key feature of the framework is the introduction of a threshold-based decision mechanism that directly translates evaluation outcomes into operational intervention strategies, distinguishing between conservation and reuse pathways. This enables the evaluation process to move beyond descriptive assessment and operate as an explicit decision-support tool. The methodology is validated through its application to two degraded heritage buildings located in the Valle dell’Agri (Basilicata, Italy), characterized by different levels of material traceability and cultural significance. The results demonstrate the ability of the framework to generate consistent, transparent, and context-aware decisions, effectively balancing technical performance with heritage values. The proposed approach contributes to bridging the gap between digital material documentation, multi-criteria evaluation, and decision-making processes, supporting more effective and sustainable management of heritage materials in circular economy contexts. Full article