Search Results (952)

Heritage management has traditionally been shaped by what Laurajane Smith termed the “authorized heritage discourse,” wherein a narrow group of professionals determines values and meanings on behalf of broader communities. This article argues that a more inclusive, socially responsible model of heritage management is both possible and necessary. Drawing on three convergent intellectual traditions—heritage interpretation as originally formulated by Freeman Tilden, eco-museums and the new museology born from the Santiago de Chile Round Table of 1972, and the human-rights-based framework for cultural heritage enshrined in the Council of Europe’s Faro Convention of 2005—the article proposes “heritage literacy” as a conceptual synthesis which can bridge these streams. Heritage literacy denotes a form of socially responsible heritage management that empowers citizens to understand the processes through which heritage is constructed, to participate actively in its interpretation, and to direct their own development through it. The article demonstrates that heritage literacy operates simultaneously as knowledge/wisdom management and as a democratic practice, arguing that it should be recognized as an essential dimension of (cultural/heritage-related) human rights. By tracing the theoretical genealogy of each contributing tradition and synthesizing them into a unified framework, this article offers both a conceptual contribution to heritage studies and a practical orientation for heritage professionals and policymakers seeking to move beyond top–down models of heritage governance. Full article

Background: Obesity is a chronic endocrine–metabolic disorder. The risk of comorbidities increases with a higher body mass index (BMI), particularly when BMI ≥ 35.0 kg/m². Common complications include insulin resistance, type 2 diabetes, dyslipidemia, and chronic low-grade inflammation, which collectively impair DNA stability by promoting the formation of genotoxic species. Methods: This non-randomised, quasi-experimental clinical intervention study included 53 participants (both sexes) with a BMI ≥ 35.0 kg/m², who were assigned to parallel experimental or control streams based on clinical needs and institutional eligibility. During a three-week intervention, the experimental group received a hospital-supervised very-low-calorie diet (VLCD; ~600 kcal/day) under continuous medical monitoring. Conversely, the control group followed a standard reduced-calorie diet (SRD) of 1500 kcal/day in a free-living home environment. Before and after the intervention, primary, oxidative, and permanent DNA damage were measured using alkaline, FPG-modified comet (peripheral blood mononuclear cells), and cytokinesis-block micronucleus cytome assays (phytohaemagglutinin-stimulated binucleated lymphocytes), alongside anthropometric and biochemical tracking. Results: Within-group evaluations revealed that both dietary regimens improved several metabolic health indicators, notably modulating insulin resistance, lipid profiles, and leukocyte counts. However, participants in the VLCD stream experienced significantly greater downward changes in body weight, BMI, and absolute lipid values. Crucially, the VLCD intervention was associated with a highly significant within-group reduction in parameters of permanent chromosomal damage, effectively halving the frequencies of micronuclei and nuclear buds, independent of baseline variations, in adjusted multivariate regression models. Conversely, the home-based SRD regimen demonstrated no measurable impact on permanent genomic damage. Neither diet induced a significant change in repairable primary or oxidative DNA lesions over this short timeframe. Conclusions: These exploratory findings suggest that strict calorie restriction can rapidly stabilise genome stability in advanced clinical settings, warranting future randomised controlled trials with long-term longitudinal follow-up to assess permanent risk reductions. Due to structural baseline variations in age, chronic comorbidities, and compliance environments between the cohorts, direct comparative superiority cannot be definitively established. Full article

Product mismatch, arising from consumers’ inability to physically experience products before purchase, is a major cause of returns in e-commerce, eroding e-tailer profits and intensifying consumers’ concerns about returns. To alleviate these concerns, e-tailers have increasingly adopted return insurance (RI), which reduces consumers’ return freight costs. However, RI may encourage consumers to defer product selection from the pre-purchase search stage to the post-purchase evaluation stage, thereby exacerbating mismatch and increasing return rates. As a countermeasure in live-streaming commerce, augmented reality (AR) provides an immersive product experience that can reduce mismatch and returns. This study develops a game-theoretic model to analyze the strategic interplay between an e-tailer’s RI decision and a live streamer’s AR decision while incorporating consumer search effort. The results show that consumer search effort changes the relationship between the two strategies. When search effort is low, RI and AR function as strategic substitutes; when search effort is high, they function as strategic complements. These findings indicate that the value of a return-management strategy depends on consumer behavior and on the presence of the partner’s AR strategy. The study contributes to the literature on interdependent return-management strategies and provides actionable insights for e-commerce practitioners. Full article

Efficient hydrological-hydraulic analysis requires rapid, reproducible preparation of key GIS inputs. This paper presents two ArcGIS Pro-embedded Python tools that consolidate preprocessing into parameterized, single-run workflows. WATDYN derives hydrologically conditioned flow fields from a DEM and outputs sub-watershed polygons, a vector drainage network, and outlet/junction points. MRET generates a spatial Manning’s roughness coefficient (n) layer by mapping CORINE Land Cover 2018 classes to the literature-based values, producing a model-ready roughness raster with optional tabular export. In the Thessaly water district (EL08), Greece (813.71 km²), WATDYN produced 3249 stream/accumulation polylines and ~3100 sub-watersheds (threshold 5000) in ~2 min, while MRET generated the corresponding n raster in ~1 min. Full article

The rapid growth in the use of carbon fiber-reinforced polymers (CFRPs) and fused-deposition-modeled (FDM) polylactic acid (PLA) has generated substantial non-biodegradable and thermoplastic waste streams, creating urgent needs for scalable recycling and valorization strategies. This study develops and evaluates an integrated route that chemically recovers carbon fibers (CFs) from CFRP waste and converts them into high-performance reinforcements for recycled PLA matrices. CFRP fragments were pre-swollen in acetic acid (120 °C, 1 h), then depolymerized by means of oxidation with 1 M KMnO₄ (100 °C, 2 h), washed, dried (100 °C, 24 h), and size-reduced by means of cryogenic milling. Recycled CFs (treated) and untreated CFRP fragments were blended with 3D-printing PLA waste at 10, 20 and 30 wt.% via melt mixing (175 °C, 5 min, 70 rpm) and molded into ASTM D638 dog-bone specimens. Materials were characterized via XRD, FTIR, Raman, SEM and mechanical testing. XRD and Raman confirmed retention of the graphitic backbone after treatment; FTIR and Raman revealed oxygen-containing surface functionalization consistent with oxidation, while SEM showed effective removal of epoxy and improved fiber surface cleanliness. Compared with neat PLA (tensile strength 45.4 MPa; modulus 2.6 GPa; elongation 6.3%), composites reinforced with chemically recycled CFs exhibited marked mechanical enhancement: at 30 wt.% treated CF, the tensile strength increased to 102.6 MPa (+126%), elastic modulus to 11.7 GPa (+350%), and toughness to 250.3 MPa, while ductility decreased to 2.9%. Equivalent composites with untreated CFRP exhibited smaller gains (30 wt.%: tensile 87.3 MPa; modulus 10.3 GPa), highlighting the benefit of epoxy removal and surface activation for fiber–matrix adhesion. The proposed chemical recycling pathway is operationally simple and cost-effective, produces reusable CFs with preserved graphitic structure and enhanced surface chemistry, and enables the fabrication of high-performance, waste-derived PLA composites suitable for structural and engineering applications. This work demonstrates a viable waste-to-value approach that advances circularity for both CFRP and 3D-printing polymer waste streams. Full article

Reasoning-intensive multimodal retrieval suffers from a counter-intuitive bottleneck: on MM-BRIGHT multimodal-to-text (Query+Image → Documents), the strongest dense multimodal encoder reaches only 27.6 nDCG@10 and the rest of the dense vision–language retrievers cluster between 10.0 and 23.0. The visual signal, encoded as a dense vector, adds noise rather than evidence; even augmenting strong text retrievers with raw image captions degrades performance by up to 12.0 points. We propose VISA, a Visual Symbolic Agent that re-casts multimodal-to-text as text retrieval over three parallel streams. A Vision LLM is dispatched in three roles via separate prompts: a zero-shot router that classifies the query image into up to three parser types from a fixed taxonomy of nine (chart, circuit, equation, screenshot, code, figure, diagram, map, photograph); typed parsers that extract structured text per type; and a holistic captioner. The agent constructs three text streams (raw query, query ⊕ symbolic, query ⊕ caption), scores each with a single frozen 4B-parameter retrieval LLM, and fuses the per-document scores via Reciprocal Rank Fusion or a confidence-weighted linear combination. The whole agent contains no trainable parameters. The key novelty is a change of substrate: rather than projecting the query image into a dense multimodal vector that competes with text, VISA is, to our knowledge, the first retrieval system to convert the image into typed symbolic text and keep retrieval entirely text-side, so that a frozen text retriever can match the literal tokens (axis values, variable names, function signatures) that answering documents actually contain. Across all 29 MM-BRIGHT multimodal-to-text domains, VISA achieves 32.4 nDCG@10, an absolute improvement of +4.8 over the strongest dense multimodal encoder and substantially larger margins over the remaining six dense vision–language baselines. Per-domain analysis shows VISA maintains its margin across STEM and software domains where image content is structure-heavy. In practical terms, VISA is training-free and model-agnostic: it requires no fine-tuning, reuses any off-the-shelf vision LLM and text retriever, caches all per-image parsing so re-runs cost only three query encodes, and can therefore be dropped into an existing text-retrieval stack to add reasoning-intensive multimodal capability without building or training a multimodal encoder. Full article

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup. Full article

High-resolution remote sensing image segmentation is important for urban mapping but remains challenging because of spectral ambiguity, large scale variations, fragmented elongated structures, and background interference. This study aims to improve semantic segmentation in complex aerial scenes by combining local feature extraction, selective multi-scale fusion, and global sequence modeling. We propose DSD-Mamba, an asymmetric dual-stream architecture with a ResNet-18 encoder. The Dense-Sparse Pyramid Fusion Module aligns multi-level features and applies dual Top-k selective value aggregation for cross-scale response filtering and background-response suppression. This Top-k operation is used as a feature-selection mechanism and is not intended to reduce the theoretical memory footprint of dense attention. Scale-Aware Strip Attention refines skip connections through horizontal and vertical dependency modeling, and the Dual-Stream Context Decoder combines a Mamba-based global branch with a CNN-based local branch during upsampling. Experiments were conducted on UAVid, ISPRS Vaihingen, and ISPRS Potsdam under a single-model inference protocol without test-time augmentation. DSD-Mamba achieved mIoU scores of 73.4%, 85.2%, and 87.2%, respectively. Ablation experiments on Vaihingen showed that DSPFM, SASA, and DSCD improved performance over the baseline when evaluated in this setting, with the full model reaching the highest mIoU. The method improves segmentation accuracy under the tested protocols, although its higher FLOPs indicate an accuracy-oriented rather than lightweight design. Full article

Live streaming commerce delivers conversion rates up to ten times higher than conventional online stores, yet research on the psychological mechanisms behind that lift has tested platform features in isolation, through single-mediator pathways. This study examines how three live streaming characteristics (streamer credibility, perceived interactivity, and product demonstration quality) shape consumer purchase intention through consumer trust and perceived value, operating both independently and sequentially. The stimulus–organism–response (S-O-R) framework provides the organising structure, with source credibility and value-based theories anchoring specific paths. A structural equation model was tested on survey data from 478 UK live streaming consumers. All three characteristics predicted consumer trust; only perceived interactivity and product demonstration quality predicted perceived value directly. Streamer credibility reached perceived value entirely through consumer trust. Credibility’s parallel path through value alone was not supported, but its serial path through trust and then value was. Eleven of twelve hypotheses were supported, and bootstrap analysis with 5000 resamples confirmed serial mediation through trust then value for all three antecedents. The asymmetric pattern positions trust as the gateway through which competence-based credibility reaches purchase decisions, with implications for platform-design choices that sequence trust signals before value-oriented promotional cues. Full article

Acid-generating mining waste and polymer waste are two of the most persistent environmental problems facing the mining and manufacturing sectors, respectively. We have investigated the co-recovery of these disparate waste streams for the production of unfired cementitious paving blocks. We established a statistically optimized formulation using response surface methodology (RSM) and a central composite design (CCD). We systematically evaluated three process variables: air-curing time (4–37 days), dosage of the waste mixture (5–68% by weight of dry solids: acid-generating mining waste, hydrated lime, and recycled polymer in a waste-to-polymer mass ratio of 1:1), and type of polymeric aggregate (recycled PET flakes versus granulated rubber). Compressive strength ranged from 4.5 to 42.1 MPa across the 40 experimental conditions. The resulting quadratic model was clearly significant (F = 186.31, p < 0.0001) with solid predictive parameters (R² = 0.9796; R²pred = 0.9627; adequate precision = 42.47). Desirability-based optimization, which limited air curing to industrially feasible timeframes (7–28 days) and maximized waste utilization within a 10–50% by weight, identified PET with 12.4 days of curing and a 50% by weight waste mixture as the optimal configuration, predicting a compressive strength of 37.3 MPa. This value exceeds the 32 MPa threshold for Type I heavy-traffic paving blocks; however, confirmatory tests yielded 34.09 ± 1.08 MPa, indicating that production-scale use should include control of moisture content, compaction, and batch homogeneity. Scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) and X-ray diffraction (XRD) demonstrated that PET inclusions promoted a denser and more continuous interfacial transition zone than shredded rubber, driven by physical entanglement and the pronounced microfilling effect of the fine waste particles. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications—pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protocols, and virtual wards—now generate measurable clinical and economic value. Yet a decade of evaluations and implementation studies suggests that the surrounding ecosystem has matured unevenly: working applications coexist with persistent cross-cutting fragility. In this Perspective we argue that four structural gaps continue to constrain RPM’s promise at scale: (i) economic models that do not credibly compensate the asynchronous clinical work that RPM generates; (ii) ambiguous frameworks for professional liability and accountability for continuous data streams, intensified by artificial-intelligence (AI)-mediated decision support; (iii) privacy, equity, and benefit-sharing arrangements that do not yet make patients unambiguous net beneficiaries—a gap visible across very different health systems internationally; and (iv) engagement and adherence dynamics that determine whether programmes deliver value at all, but are still treated as secondary outcomes. The COVID-19 emergency briefly suspended much of the friction in this ecosystem and produced a useful natural experiment: what scaled rapidly under emergency conditions, and what subsequently atrophied, illuminates which gaps are technical, which are economic, and which are institutional. We close with a six-point research and policy agenda intended to move RPM from localised successes to a trustworthy, generalisable standard of care. Full article

Stream sediment geochemistry is a widely used reconnaissance tool in early-stage mineral exploration, particularly in regions where direct evidence of mineralisation is limited. Because stream sediment anomalies provide indirect geochemical signatures and are typically constrained by limited ground-truth information, labelled datasets are often scarce and spatially biased. This limitation restricts the applicability of supervised learning approaches and highlights the need for robust unsupervised methods. In this study, six unsupervised techniques, Principal Component Analysis (PCA), Non-negative Matrix Factorisation (NMF), Uniform Manifold Approximation and Projection (UMAP), Autoencoder (AE), Deep Embedded Clustering (DEC), and an Averaged Ensemble Index (AVE), were evaluated for integrating multivariate stream sediment geochemical data and delineating gold prospectivity zones. Eight gold-related elements (Au, As, Ag, B, Hg, Mo, Sb, and W) were selected based on regional metallogenic characteristics and previously reported geochemical associations. To facilitate direct comparison, all model outputs were normalised to a fuzzy membership scale ranging from 0 to 1. Model performance was quantitatively assessed using Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) and Matthews Correlation Coefficient (MCC) metrics based on independently verified mineralised and non-mineralised locations. The results indicated that DEC and AE consistently outperformed the other methods investigated, achieving the highest ROC–AUC and MCC values, whereas UMAP exhibited comparatively weaker performance. The findings demonstrated that unsupervised representation learning approaches, particularly DEC and AE, provided a more effective framework for integrating multivariate geochemical data and delineating gold-related anomalies in data-limited exploration environments than conventional dimensionality reduction and heuristic integration methods. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within $[0,1]$ and integrates five sub-indices: energy efficiency ($I_E$), thermal integration ($I_T$), energy self-sufficiency ($I_A$), exergetic quality of outputs ($I_Q$), and co-product valorization ($I_V$). Weights were determined using the Analytic Hierarchy Process ($w_1=0.40$, $w_2=0.24$, $w_3=w_4=0.14$, $w_5=0.08$; $\mathrm{CR}=0.007$). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP ($C1\to C2$) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample ($n=6$) and the reconstruction of $I_A$ and $I_V$ from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting ($w_{I_A}+w_{I_V}=0.22$), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established. Full article