Search Results (6,238)

The increasing availability of digital documentation in cultural heritage has amplified the need for interoperable systems capable of integrating heterogeneous data and supporting risk-informed conservation strategies. In the field of Disaster Risk Management (DRM), the application of structured methodologies—such as the ICCROM-CCI ABC Method—is often hindered by fragmented data sources, inconsistent terminology, and limited interoperability across institutions. This study presents a semantic workflow for the harmonization, enrichment, and integration of cultural heritage risk assessment data within a CIDOC Conceptual Reference Model (CIDOC-CRM)-compliant environment. The proposed system is structured as an Extract–Transform–Load (ETL) pipeline that converts heterogeneous assessment records into interoperable semantic knowledge graphs. The workflow combines controlled vocabularies, project-specific thesauri for risk agents and heritage typologies, and formal ontology mapping implemented through the Mapping Memory Manager (3M) and executed with the X3ML engine. The resulting data are deployed within a ResearchSpace environment, enabling semantic querying, cross-dataset exploration, and integration with external knowledge infrastructures. The workflow was applied to a dataset comprising 295 cultural heritage sites in the municipality of Ravenna (Italy). The transformation process generated a CIDOC-CRM-compliant knowledge graph containing 134,611 RDF triples and 18,954 entities, integrating information on cultural assets, risk scenarios, actors, documentary resources, and quantitative risk assessments. Through the adoption of persistent identifiers and semantic mappings, the workflow also supports interoperability with external cultural heritage resources, including ArCo and GeoNames, facilitating the contextualization and enrichment of local risk assessment data. By transforming fragmented assessment records into structured and interoperable knowledge, the proposed workflow contributes to bridging semantic and information gaps in cultural heritage risk management. The study demonstrates the feasibility of integrating risk assessment data within an ontology-based semantic infrastructure and highlights its potential to support data integration, semantic interoperability, knowledge reuse, and future decision-support applications for preventive conservation and territorial risk management. Full article

Partial differential equations (PDEs) play a central role in scientific and engineering analysis, with applications spanning fluid dynamics, heat and mass transfer, electromagnetism, quantum mechanics, and financial modeling, where they are used to describe diffusion processes, wave propagation, and the evolution of complex systems over space and time. Solving multidimensional partial differential equations (PDEs) is a computationally challenging problem, even for the most advanced classical systems. Over the past decade, quantum computing has attracted significant interest as a potential approach for solving complex computational problems, including multidimensional PDEs. Although a variety of approaches have been proposed for solving PDEs, most of the existing techniques are based on variational quantum algorithms (VQAs). Despite being promising, these VQA-based approaches suffer from low accuracy, long execution times, and limited scalability. In this work, we propose a scalable and efficient quantum algorithm for solving multidimensional PDEs. Our algorithm has two variants. One variant is based on the finite difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, whereas the other is based on FDM, C2Q, and column-by-column decomposition (CCD). We have also evaluated our algorithm using several practical case studies; namely, Poisson, heat, Black–Scholes, and Navier–Stokes equations. The results show that our proposed approach achieves higher accuracy, greater scalability, and faster execution time than the VQA-based approaches. We validated these findings on both noise-free and noisy simulators, as well as on a hardware emulator and real IBM quantum hardware. Full article

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment. Full article

Chronic wounds remain a major global health challenge despite substantial advances in biomaterials, regenerative medicine, and wound-care technologies. Current therapeutic strategies are largely based on the assumption that chronic wounds represent impaired or incomplete healing responses and therefore require augmentation of regenerative processes. This paradigm has driven the development of increasingly sophisticated wound dressings incorporating extracellular matrix analogs, growth factors, stem cells, extracellular vesicles, biosensors, and bioelectronic components. However, the clinical impact of these innovations has often fallen short of expectations. In this review, we propose a conceptual framework intended to generate experimentally testable hypotheses rather than provide a definitive mechanistic model. Persistent alterations in immune, stromal, vascular, extracellular matrix, metabolic, mechanical, and microbial networks create interconnected feedback systems that resist transition toward regeneration. From this perspective, successful therapy requires not only stimulation of repair mechanisms but also disruption of the processes that stabilize chronicity. We discuss how advances in systems biology, immunomodulatory biomaterials, bioelectronics, artificial intelligence, and precision medicine support the emergence of adaptive therapeutic interfaces capable of sensing, interpreting, and reprogramming pathological tissue behavior. Unlike previous reviews that primarily summarize emerging wound dressings or regenerative biomaterials, this Review proposes a systems-level conceptual framework in which chronic wounds are interpreted as stable pathological tissue states maintained by multiscale biological memory. This perspective integrates biomaterials, systems biology, artificial intelligence, and tissue-state dynamics into a unified translational model that has not previously been presented in the wound-healing literature. Previous reviews have predominantly focused on the design, biological activity, or clinical performance of individual biomaterials. In contrast, the present Review proposes a systems-level framework that integrates wound biology, biological memory, tissue-state dynamics, artificial intelligence, and adaptive biomaterials into a unified conceptual model for precision wound medicine. This state-based model reframes advanced wound dressings as tools for biological state engineering and provides a translational framework for the future of chronic wound management. Full article

The global expansion of K-pop has increased demand for AI-driven choreography learning. However, existing motion recognition models often struggle to capture fine-grained rhythm patterns and dynamic motion transitions across consecutive frames, limiting their ability to provide accurate and objective feedback. To address these challenges, this paper proposes a Large Language Model-based Dance Generative Control (L-DGC), an integrated framework for controllable dance generation and evaluation. The framework comprises four stages: a Visual Analysis Phase (VAP) for skeletal extraction; an Audio Analysis Phase (AAP) for rhythmic synchronization; a Multimodal Data Phase (MDP), which employs Long Short-Term Memory (LSTM) and Transformer architectures to evaluate movement accuracy; and a three-dimensional (3D) Transformation Phase (3TP), which converts two-dimensional (2D) skeletal data into 3D character animations within the Unity engine. Guided by an LLM, the framework performs real-time inference and iterative refinement to optimize choreographic data without requiring subjective expert assessment. By quantifying choreographic components and transforming 2D motion data into 3D representations, L-DGC provides an objective evaluation framework for dance learning. The proposed system has significant potential for artificial intelligence (AI)-based dance education, real-time feedback applications, and automated audition platforms in the entertainment industry. Full article

Identifying driving styles is essential for personalizing driving assistance systems and enhancing intelligent transportation services. However, existing approaches predominantly rely on experience-driven feature engineering and annotated data, limiting objectivity and hindering the exploitation of unlabeled naturalistic driving datasets. To address these limitations, this paper proposes an unsupervised framework for driving style identification from naturalistic driving data through context normalization. A Constrained Convolutional Autoencoder (CCAE) integrated with a global self-attention mechanism is developed to map unlabeled driving sequences onto a standardized dynamic reference defined by the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). This process extracts Driving Adaptability Characteristics (DACs) as WLTC-anchored latent representations that characterize normalized driver-specific response patterns across heterogeneous naturalistic contexts. To ensure feature robustness, frequency-domain refinement is applied to eliminate high-frequency noise. The extracted DAC sequences are subsequently partitioned into distinct driving styles using a kernel-mapped clustering algorithm. To evaluate the external relevance and physical interpretability of the identified styles, actual vehicle accident records and raw CAN-bus feature backtracking are introduced as validation evidence. The results show that the identified driving styles exhibit different historical accident probabilities. The proposed CCAE model achieves clearer cluster-level differentiation than traditional feature engineering and unconstrained deep learning models, and the ablation analysis confirms the contribution of the WLTC-based constraint. These findings indicate that the context-normalization framework can extract interpretable and externally relevant driving style representations from unlabeled naturalistic data. Full article

This study investigates the integration of a multi-configuration parametric shading system in buildings with continuous ramp designs to enhance daylight performance and visual comfort. Focusing on the Harbourside Art Museum in Bristol, UK, the research explores how discrete-configuration parametric façade configurations can be optimized to balance daylight access and glare control in complex spatial environments. A parametric simulation workflow was developed using Rhino, Grasshopper, Ladybug, and Honeybee, supported by Radiance and Daysim engines for Climate-Based Daylight Modelling (CBDM). Three performance metrics—Useful Daylight Illuminance (UDI), Annual Sunlight Exposure (ASE), and Daylight Glare Probability (DGP)—were employed to evaluate baseline and optimized models. Optimization was performed using Galapagos (single-objective genetic algorithm, population size = 50 individuals, 100 generations, convergence tolerance = 0.001; the fitness function maximized UDI while penalizing ASE excess above 75 h/year and GFI below 0.75, using a weighted single-objective score: Fitness = UDI − 0.3 × (ASE/250) + 0.3 × GFI) and Colibri 2.0 combined with Design Explorer for exhaustive multi-objective combinatorial analysis. Results from the base model showed high daylight availability but excessive glare, particularly along the ramp. Through systematic optimization, the study identified façade and contextual configurations that achieved a UDI of 0.77, an ASE of 74, and a glare-free index of 0.81. The findings demonstrate that orientation-specific multi-configuration shading, when integrated with contextual design parameters, significantly improves the daylighting performance of architecturally complex spaces. This research offers a replicable methodology for designers aiming to integrate responsive daylighting strategies in public and exhibition buildings. Full article

The dust extraction impeller is a core component of the vehicle engine auxiliary system that filters dust from the intake air to ensure stable engine operation; its reliability directly affects the performance and operational safety of the vehicle. Critically, the dust extraction impeller can exhibit severe erosion wear in extreme environments, but conventional degradation testing methods are costly and require considerable time to complete. Therefore, this study conducted accelerated degradation testing using the change in impeller blade thickness as the degradation indicator and the dust concentration and impeller rotational speed as dual elevated stress factors to obtain time-series degradation data from 48 blade samples. Linear, exponential, power-law, natural logarithmic, and Gompertz models were subsequently fit to the data for a single sample, and then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to select the optimal degradation trajectory model. The accuracy of the selected linear model was verified using the data from all samples, confirming that it can be applied to predict the degradation of the dust extraction impeller over time. The contribution of this study comprises the establishment of a degradation assessment framework combining accelerated degradation testing with TOPSIS-based model selection to provide a practical basis for the reliability design and maintenance planning of vehicle dust extraction impellers operating in extreme environments. Full article

Karpathy’s proposal to replace Retrieval-Augmented Generation (RAG) with plain-text knowledge bases maintained directly by language agents (Agentic Knowledge Curation) has gained traction in industrial applications yet lacks systematic empirical evaluation. To our knowledge, this study presents the first comparative evaluation of this paradigm using blind human assessment by two independent external reviewers. The corpus comprises the technical documentation of a production semantic search system for Spanish legal documents (11 files, ~19,300 tokens) alongside 100 questions verified against source code, distributed across direct retrieval, multi-document synthesis, and reasoning about absence. Claude 3.5 Sonnet was utilized as the reference model to isolate the retrieval architecture’s effect. While overall accuracy was statistically indistinguishable between paradigms, agentic curation showed a significant advantage in direct retrieval over long documents. Conversely, error analysis revealed RAG false negatives associated with the lost-in-the-middle phenomenon, whereas agentic curation exhibited localized degradation in textual fidelity for queries requiring exact reproduction of formulas or sequences. These results characterize the differential error profiles of both paradigms, providing actionable design criteria for engineers managing technical documentation with language models in cloud environments. Full article

Rheumatoid arthritis (RA) is a systemic autoimmune disease in which persistent synovitis drives interconnected cartilage degradation, bone erosion, and functional decline. Conventional synthetic, biologic, and targeted synthetic disease-modifying antirheumatic drugs (DMARDs) remain the foundation of RA management. Hydrogel-based local therapy should therefore be positioned as an adjunct for selected joints rather than as a substitute for systemic disease control. Hydrogels provide a versatile local materials platform because their injectability, tunable crosslinking, tissue retention, stimulus-responsive release, interfacial adhesion, lubrication, and extracellular matrix-mimetic properties can be tailored to the inflamed joint microenvironment. This narrative, evidence-graded review evaluates local hydrogel therapies using two complementary frameworks: the synovium–cartilage–bone pathological axis and a materials-science chain linking composition and crosslinking to structure and properties, release and degradation, and tissue-level outcomes. Evidence is classified as direct RA evidence, transferable evidence from related disease or tissue-engineering models, or conceptual evidence from mechanistic and materials-science studies. Therapeutic outcomes are separately graded as local immunomodulation, structural protection, tissue repair, or functionally validated structural disease modification. Current preclinical evidence supports the use of hydrogels for sustained local delivery and synovial immunomodulation, while selected systems demonstrate cartilage-protective or anti-erosive effects. However, durable multitissue restoration accompanied by functional recovery remains insufficiently demonstrated. Future studies should prioritize RA-relevant long-term models, in vivo intra-articular pharmacokinetics and biodistribution, standardized structural and functional endpoints, repeat-dose safety, and evaluation as add-on therapy to systemic DMARDs. Full article

This article discusses the relevant problem of ensuring transport connectivity under the conditions of temporal restrictions of the road network, which arise during repair, communal and emergency operations. It is established that the existing organizational and intellectual methods of traffic management do not eliminate physical decrease in road capacity, while construction of stationary structures with different levels is limited by high costs and long terms of implementation. The above substantiates the need for the development of mobile overpasses as adaptive engineering solutions ensuring continuity of the traffic flows. The purpose of the research is to develop a scientifically substantiated theoretical and experimental methodology for designing a mobile overpass as an integrated system “structure-moving load”, taking into account its dynamic behavior. The paper proposes an integrated approach based on the use of physical similarity theory and dimensionless analysis. A differential equation of dynamic bending of a beam on an elastic foundation is formulated taking into account inertia, damping, base reaction and the effect of a moving mass, and then its nondimensionalization is performed to obtain a similarity criteria system. The scientific novelty of the research consists in developing a system of dimensionless criteria to describe the relationship between the structural, dynamic and operational parameters of a mobile overpass, as well as in the formation of a criterion base for large-scale modeling and transfer of the results to full-scale structures. The proposed methodology describes the mobile overpass as an integrated transport-engineering system accounting for the coupled interaction between the deformable structure, moving traffic load, elastic foundation, and damping effects. Experimental verification was performed on a specially designed stand in the scale 1:4. The results obtained showed the quasi-static nature of the structure performance with moderate damping and rigid base. It is established that the distribution of engineering stresses along the span length has a regular character and retains its shape when the load level changes, which confirms fulfillment of similarity conditions. Regression analysis revealed a close to linear dependence of stresses on the load mass with a high degree of confidence $(R^2\approx 0.995)$. The practical significance of the research consists in creating an engineering method for express design of mobile overpasses, which allows for assessing their stress–strain state, stability and serviceability without expensive full-scale tests. The proposed approach can be used in designing temporary transportation structures under the conditions of urban area, and in operation in areas of road operations and emergency situations. Full article

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure. Full article

Harmful gas is a major hazard in underground engineering construction, and accurate prediction of its risk level is essential for tunnel safety. Existing prediction methods for harmful-gas risk in non-coal strata tunnels are limited by empirical scoring, subjective indicator assignment, and insufficient quantitative characterization of reservoir performance. To address these limitations, this study proposes an SSA-CatBoost prediction model for harmful-gas risk levels in non-coal strata tunnels. Eight influencing indicators were selected as input variables. Among them, reservoir performance was quantitatively characterized by measured porosity and permeability, while the other six indicators were quantified using engineering-based scoring criteria. A database containing 138 real harmful-gas tunnel cases was constructed, and CatBoost was used as the base classifier. The Sparrow Search Algorithm was introduced to optimize the hyperparameters of CatBoost. The proposed SSA-CatBoost model achieved an average accuracy of 93.63% in five-fold cross-validation and an accuracy of 92.86% on the independent test set. Compared with CatBoost, SSA-SVM, and SSA-XGBoost, the proposed model showed the highest cross-validation accuracy. Engineering validation further showed that all selected validation samples were correctly classified. In addition, replacing empirical reservoir-performance scoring with measured porosity and permeability improved the recognition performance of adjacent risk levels, with the F1-scores of Level III and Level IV increasing from 0.667 and 0.727 to 0.909, respectively. The novelty of this study lies in integrating measured reservoir-performance parameters into a machine-learning-based harmful-gas risk prediction framework, thereby reducing the subjectivity of conventional scoring systems and improving the quantitative characterization of non-coal strata tunnel gas hazards. Full article

Turbofan engine performance is critically sensitive to operating conditions, yet comprehensive frameworks that simultaneously assess exergo-economic, exergo-environmental, and lifecycle cost performance across realistic flight envelopes remain limited, particularly for Gulf-region climates. In this study, we present a comprehensive analysis of the exergo-economic, exergo-environmental, and lifecycle costings of five different configurations of two-spool and triple-spool turbofan engines. The analysis was carried out for a wide range of four operating conditions, namely ambient temperature, flight altitude, Mach number, and % relative humidity, with emphasis on the climate conditions likely to be found in the Gulf region. The computational models developed were validated against published data to confirm their reliability. It was found that fuel consumption was the most significant contributor to total lifecycle ownership cost, between 60 and 75% of hourly operating cost over a 20-year service period. Ambient temperature, Mach number, and Cruise altitude represented the most significant drivers of long-term economic performance, with % relative humidity having little effect. Exergo-economic analysis showed that the major cost mechanisms changed dramatically with operating conditions. Exergy destruction and component inefficiencies determined the costs at Takeoff, with capital investment being the dominant factor when cruising. Increase in both or either ambient temperature and altitude was shown to reduce cost rates but simultaneously reduced thermo-economic efficiency via higher specific exergy costs. However, increase in Mach number enhances both exergy output and cost-effectiveness, confirming that specific exergy cost is a more reliable indicator of true system performance than cost rate alone. The two-spool configurations show superior specific CO₂ emissions, with Case 3 recording the lowest emissions at Takeoff and Case 2 at Cruise. For exergy-based environmental indicators, Case 3 performs best at both Takeoff and Cruise, achieving the lowest environmental destruction coefficient and index, as well as the highest environmental benign index among all five configurations. These findings provide actionable guidance for engine selection, operational optimization, and sustainable propulsion system design. Full article

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article