Search Results (61,745)

The fragmentation of knowledge across multiple sources and players, coupled with limited access to information, is one of the main challenges for the performance-based management of heritage buildings. Despite recent research efforts in the field of Heritage Building Information Modelling (HBIM), this technology alone is insufficient for managing the variety of data related to building performance and is challenging for stakeholders without digital expertise to adopt. These limitations, along with advancements in knowledge technologies, have led to the emergence of federated data modelling approaches as a core strategy for managing the complexity of buildings’ operational information. To address data fragmentation, this research proposes a methodology for linking heterogeneous data on historic building performance. The approach structures heterogeneous data, gathered from multiple sources—HBIM models, sensors, and energy bills—into knowledge graphs that enable semantic integration, cross-domain queries and support interactive visualisation. As part of the BeTwin research project, the methodology is validated through its application to a case study in the Appia Antica Archaeological Park (Rome, Italy). Full article

This paper develops accurate three-dimensional trajectory tracking and anti-sway control strategies for offshore container cranes operating in an open-sea environment. A 5-DOF nonlinear dynamic model is developed that simultaneously accounts for the crane’s structural motion, trolley movement, spreader hoisting with variable rope length, and both lateral and longitudinal payload sway. The model further incorporates external disturbances induced by wave-excited ship motions. To ensure smooth, efficient, and accurate load transportation from the initial to the target position, an effective trajectory-planning scheme is proposed using a quintic polynomial trajectory refined by a ZVD shaper to suppress residual oscillations. A sliding mode control method is then designed to achieve accurate trajectory tracking and load-sway suppression under external disturbances. Numerical simulations demonstrate that the proposed trajectory planning method effectively reduces the residual oscillations and verifies the effectiveness and robustness of the proposed sliding mode control strategy. Full article

Floods rank among the most destructive natural hazards worldwide. In Canada’s capital region—Ottawa and its surrounding areas—flood prediction is crucial, especially in flood-prone zones, to improve flood mitigation strategies, given its historical record-breaking events in 2017 and 2019, which resulted in substantial damage to homes and infrastructure in the region. Previous studies in these regions typically did not use remote sensing techniques or advanced methods to enhance flood susceptibility prediction and extent mapping. This study addressed the gap by incorporating 18 flood conditioning factors and integrating high-performance machine learning algorithms such as Random Forest, Support Vector Machines and XGBoost to develop ensemble flood susceptibility models. The HEC-RAS 2D model was used to simulate hydrodynamic variables based on a 100-year flood scenario. The developed ensemble model for flood susceptibility prediction achieved strong performance (Kappa, F1-score, and AUC all above 0.979) and demonstrated model transferability, maintaining high accuracy (Kappa > 0.850, F1-score > 0.920, AUC > 0.990) when applied to other sub-regions. The hydraulic model reveals that flood velocity and depth differ across sub-regions, reaching maximums of 15 m/s and 15 m, respectively. SHAP analysis indicates Elevation, Handmodel, MNDWI, NDWI, and Aspect are key factors influencing floods. These findings and methods help Natural Resources Canada develop tools and policies for effective flood risk reduction in the Ottawa River watershed and similar regions. Full article

Additive manufacturing, or 3D printing, is a method for creating three-dimensional objects layer-by-layer based on a digital model. This article presents the results of research on selective laser melting (SLM) of 316L stainless steel powder. Its aim is to investigate the relation between the mechanical properties of SLM-fabricated 316L steel samples obtained from uniaxial tensile tests and the SLM process parameters including the build-up strategy. Four different configurations of 3D printing orientation relative to the build platform were considered. The variable parameters of the SLM process were laser power and laser scanning speed. The morphology of the external surfaces and the microstructure of the SLM-processed samples were examined. The results show that samples printed in the longitudinal and transverse configurations had the highest tensile strength. Samples printed in the vertical and diagonal configurations had the greatest dispersion of values of mechanical parameters. The main difference in mechanical properties after doubling the SLM process parameters was a decrease in elongation for samples printed in the longitudinal configuration and an increase in this value for samples printed in the transverse configuration. The use of higher laser powers and laser scanning speeds guarantees a more compact, non-porous microstructure of SLM-processed samples. Full article

The rapid advancement of high-resolution remote sensing technology has significantly expanded observational capabilities in the oil and gas sector, enabling more precise identification of petroleum infrastructure. Remote sensing now plays a critical role in providing real-time, continuous monitoring. Manual interpretation remains the predominant approach, yet is plagued by multiple limitations. To overcome the limitations of manual interpretation in large-scale monitoring of upstream petroleum assets, this study develops an end-to-end, deep learning-driven framework for intelligent extraction of key oilfield targets from high-resolution remote sensing imagery. Specific aims are as follows: (1) To leverage temporal diversity in imagery to construct a representative training dataset. (2) To automate multi-class detection of well sites, production discharge pools, and storage facilities with high precision. This study proposes an intelligent monitoring framework based on deep learning for the automatic extraction of petroleum-related features from high-resolution remote sensing imagery. Leveraging the temporal richness of multi-temporal satellite data, a geolocation-based sampling strategy was adopted to construct a dedicated petroleum remote sensing dataset. The dataset comprises over 8000 images and more than 30,000 annotated targets across three key classes: well pads, production ponds, and storage facilities. Four state-of-the-art object detection models were evaluated—two-stage frameworks (Faster R-CNN, Mask R-CNN) and single-stage algorithms (YOLOv3, YOLOv4)—with the integration of transfer learning to improve accuracy, generalization, and robustness. Experimental results demonstrate that two-stage detectors significantly outperform their single-stage counterparts in terms of mean Average Precision (mAP). Specifically, the Mask R-CNN model, enhanced through transfer learning, achieved an mAP of 89.2% across all classes, exceeding the best-performing single-stage model (YOLOv4) by 11 percentage points. This performance gap highlights the trade-off between speed and accuracy inherent in single-shot detection models, which prioritize real-time inference at the expense of precision. Additionally, comparative analysis among similar architectures confirmed that newer versions (e.g., YOLOv4 over YOLOv3) and the incorporation of transfer learning consistently yield accuracy improvements of 2–4%, underscoring its effectiveness in remote sensing applications. Three oilfield areas were selected for practical application. The results indicate that the constructed model can automatically extract multiple target categories simultaneously, with average detection accuracies of 84% for well sites and 77% for production ponds. For multi-class targets over 100 square kilometers, manual detection previously required one day but now takes only one hour. Full article

Reducing energy demand and carbon emissions in residential buildings requires more than technological upgrades; it demands a nuanced understanding of occupant behaviour. Residential energy use is shaped by both physical design and human actions, yet behavioural factors remain underexplored, contributing to the energy performance gap. This study addresses this issue by developing and validating a behavioural framework grounded in the Theory of Planned Behaviour (TPB) to examine how attitudes, social norms, perceived control, and environmental awareness influence energy-related decisions. Data were collected through an online survey of 310 households in metropolitan Sydney and analysed using Stata v17 software employing principal component analysis and regression modelling. Results reveal that environmental awareness is the most significant predictor of pro-environmental intention, which strongly correlates with actual behavioural outcomes. While attitudes and perceived control were generally positive, subjective norms and awareness remained moderate, limiting behavioural change. The proposed framework demonstrates strong validity and reliability, offering a practical tool for policymakers, designers, and educators to integrate behavioural insights into sustainable building strategies. By prioritising awareness campaigns and normative interventions, stakeholders can complement technical retrofits with behavioural measures, accelerating progress towards low-carbon housing and benefiting both households and the broader community. Full article

As a typical arid region, the change in Xinjiang’s vegetation carbon footprint is crucial for assessing ecological restoration and resource allocation. This study analyzes the changes in the vegetation carbon footprint and its influencing factors in Xinjiang by employing a range of models, including Net Ecosystem Productivity (NEP), carbon emission fitting, carbon footprint analysis, and structural equation modeling (SEM). Furthermore, using the carbon deficit vegetation investment estimation method, we quantify the additional vegetation area and investment required for Xinjiang to achieve carbon neutrality. The results show the following: (1) Net Ecosystem Productivity (NEP) increased slowly, with six regions (Altay, Bortala, Bayingolin, Kizilsu Kirghiz, Tacheng, and Yili) contributing 66.95% of the total NEP, forming the main carbon sink. Meanwhile, carbon emissions rose significantly, coming largely from Urumqi, Changji, Kumul, and Karamay (61.31% of total emissions). (2) The carbon footprint expanded 3.44 times, from 30.41 × 10⁴ km² to 104.49 × 10⁴ km². Human activities were the main positive driver, while vegetation factors negatively influenced the carbon footprint. (3) Based on the 21-year average carbon deficit, achieving carbon neutrality in Xinjiang requires an estimated investment of USD 106.77 × 10⁸ to expand cropland, woodland, and grassland by 8029 km², 1710 km², and 35,016 km², respectively. Implementing vegetation expansion, improving carbon markets, and transforming carbon-source economies are essential to achieving the “double carbon” goal. This study clarifies regional carbon sources/sinks and supports the carbon neutrality strategy in arid ecosystems. Full article

Unmanned aerial vehicle (UAV) tracking faces significant challenges due to small targets and background interference. Traditional anchor-based tracking algorithms require designing numerous proposals to capture such tiny targets, which entails unacceptable computational overhead. On the other hand, anchor-free tracking methods struggle to adapt to target scale variations, resulting in suboptimal tracking accuracy in anti-UAV tracking scenarios. To address these limitations, we pioneer the integration of diffusion models into visual tracking, proposing SiamDiff—a scale-adaptive anti-UAV framework. We reformulate the tracking task as a bounding box prediction problem, where a diffusion model is leveraged to generate scale-adaptive proposals. Furthermore, we propose a Learnable Mask Module (LMM) and a Frequency Channel Fusion Module (FCFM) to enhance discriminative feature extraction for small targets. Additionally, we design a Scale-Aware Diffusion Strategy (SADA) to boost robustness to scale variations. Experimental results on the Anti-UAV and Anti-UAV410 benchmarks demonstrate the effectiveness of our approach, achieving a State Accuracy (SA) of 71.90% and 67.03%, respectively, outperforming the baseline and other trackers. Moreover, our method shows superior adaptability to scale variations, confirming its robustness in complex anti-UAV tracking scenarios. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

This study investigates cross-market dependence and dynamic hedging performance between the U.S. equity market and major commodity assets across distinct crisis regimes. Using daily data for the S&P 500 index and four key commodities (WTI crude oil, gold, wheat, and natural gas), we examine how market linkages evolve during systemic disruptions by applying Multifractal Detrended Cross-Correlation Analysis (MFCCA) and the q-dependent detrended correlation coefficient. Hedging performance is assessed using optimal hedge ratios estimated under two multivariate GARCH frameworks: the Asymmetric Dynamic Conditional Correlation (ADCC-GARCH) and the Generalized Orthogonal GARCH (GO-GARCH) model. The findings reveal strong multiscale and time-varying dependencies that intensify during high-volatility periods, reducing the benefits of conventional portfolio diversification. Hedging effectiveness proves to be regime dependent and strongly influenced by nonlinear cross-market interactions. The GO-GARCH model captures volatility spillovers and asymmetric co-movements more effectively, delivering superior hedging results compared with ADCC, especially during episodes of extreme market stress. Among the analysed commodities, crude oil and gold offer the most reliable hedging properties, whereas wheat and natural gas show unstable performance due to supply side shocks. These results emphasize the need for flexible, dynamically adjusted risk-management strategies during crisis environments. Full article

Evidence increasingly indicates that synaptic vesicle dysfunction emerges early in Parkinson’s disease (PD), preceding overt dopaminergic neuron loss rather than arising solely as a downstream consequence of neurodegeneration. α-Synuclein (αSyn), a presynaptic protein that regulates vesicle clustering, trafficking, and neurotransmitter release under physiological conditions, exhibits dose-, conformation-, and context-dependent actions that distinguish its normal regulatory roles from pathological effects observed in disease models. This narrative review synthesizes findings from a structured search of PubMed and Scopus, with emphasis on α-syn-knockout (αSynKO) and BAC transgenic (αSynBAC) mouse models, which do not recapitulate the full human PD trajectory but provide complementary insights into αSyn physiological function and dosage-sensitive vulnerability. Priority was given to studies integrating ultrastructural approaches—such as cryo-electron tomography, high-pressure freezing/freeze-substitution TEM, and super-resolution microscopy—with proteomic and lipidomic analyses. Across these methodologies, several convergent presynaptic alterations are consistently observed. In vivo and ex vivo studies associate αSyn perturbation with impaired vesicle acidification, consistent with altered expression or composition of vacuolar-type H⁺-ATPase subunits. Lipidomic analyses reveal age- and genotype-dependent remodeling of vesicle membrane lipids, particularly curvature- and charge-sensitive phospholipids, which may destabilize αSyn–membrane interactions. Complementary biochemical and cell-based studies support disruption of SNARE complex assembly and nanoscale release-site organization, while ultrastructural analyses demonstrate reduced vesicle docking, altered active zone geometry, and vesicle pool disorganization, collectively indicating compromised presynaptic efficiency. These findings support a synapse-centered framework in which presynaptic dysfunction represents an early and mechanistically relevant feature of PD. Rather than advocating αSyn elimination, emerging therapeutic concepts emphasize preservation of physiological vesicle function—through modulation of vesicle acidification, SNARE interactions, or membrane lipid homeostasis. Although such strategies remain exploratory, they identify the presynaptic terminal as a potential window for early intervention aimed at maintaining synaptic resilience and delaying functional decline in PD. Full article

Large language models (LLMs) have been increasingly applied in Automatic Speech Recognition (ASR), achieving significant advancements. However, the performance of LLM-based ASR (LLM-ASR) models remains unsatisfactory when applied across domains due to domain shifts between acoustic and linguistic conditions. To address this challenge, we propose a decoupled two-stage domain adaptation framework that separates the adaptation process into text-only and audio-only stages. In the first stage, we leverage abundant text data from the target domain to refine the LLM component, thereby improving its contextual and linguistic alignment with the target domain. In the second stage, we employ a pseudo-labeling method with unlabeled audio data in the target domain and introduce two key enhancements: (1) incorporating decoupled auxiliary Connectionist Temporal Classification (CTC) loss to improve the robustness of the speech encoder under different acoustic conditions; (2) adopting a synchronous LLM tuning strategy, allowing the LLM to continuously learn linguistic alignment from pseudo-labeled transcriptions enriched with domain textual knowledge. The experimental results demonstrate that our proposed methods significantly improve the performance of LLM-ASR in the target domain, achieving a relative word error rate reduction of 19.2%. Full article

Accurate group target association is essential for multi-sensor multi-target tracking, particularly in heterogeneous radar systems where systematic biases, asynchronous observations, and dense formations frequently cause ambiguous or incorrect associations. Existing approaches often rely on strict spatial assumptions or pre-trained models, limiting their robustness when measurement distortions and sensor-specific deviations are present. To address these challenges, this work proposes a robust association framework that integrates deep feature embedding, density-adaptive clustering, and global graph-theoretic matching. The method first applies an autoencoder–HDBSCAN clustering scheme to extract stable latent representations and obtain adaptive group structures under nonlinear distortions and non-uniform target densities. A weighted bipartite graph is then constructed, and a global optimal matching strategy is employed to compensate for heterogeneous systematic errors while preserving inter-group structural consistency. A mutual-support verification mechanism further enhances robustness against random disturbances. Monte Carlo experiments show that the proposed method maintains over 90% association accuracy even in dense scenarios with a target spacing of 1.4 km. Under various systematic bias conditions, it outperforms representative baselines such as Deep Association and JPDA by more than 20%. These results demonstrate the method’s robustness, adaptability, and suitability for practical multi-radar applications. The framework is training-free and easily deployable, offering a reliable solution for group target association in real-world multi-sensor fusion systems. Full article

Objectives: This study aimed to identify the factors influencing person-centered care (PCC) among nurses working at long-term care hospitals for patients with dementia and to propose strategies for strengthening their capacity to provide PCC. Methods: Guided by the ecological model, this descriptive study examined the effects of personal factors (self-compassion and the burden of behavioral and psychological symptoms of dementia [BPSD]), interpersonal factors (communication behavior), and organizational factors (nursing work environment) on PCC. Participants were 152 nurses who had worked for more than two months at four long-term care hospitals in Seoul and Gyeonggi Province, South Korea. Data were collected between 8 January and 4 February 2024, and analyzed using SPSS version 23.0. Results: Hierarchical multiple regression analysis showed that the strongest predictors of PCC were the nursing work environment (β = 0.36, p < 0.001), having received dementia-related education twice (β = 0.26, p = 0.008), self-compassion (β = 0.23, p = 0.017), having received dementia-related education three or more times (β = 0.22, p = 0.036), and communication behavior (β = 0.20, p = 0.026). The final model (Model 3) explained 41.5% of the variance in PCC (adjusted R² = 0.415, F = 5.70, p < 0.001). Conclusions: To strengthen PCC among nurses in long-term care hospitals, comprehensive efforts to improve the nursing work environment are essential. Institutional support should particularly focus on securing sufficient nursing staff and ensuring adequate material resources. In addition, continuous dementia-related education and training programs that foster self-compassion and communication skills among nurses are recommended. Full article

Background: Rehabilitation technologies, including assistive devices, adaptive software, and robotic systems, are increasingly integral to contemporary rehabilitation practice. Yet, ensuring that training in their use is inclusive and accessible remains a critical challenge. Methods: This study reports findings from patient and public involvement (PPI) activities conducted by the National Institute for Health and Care Research (NIHR) HealthTech Research Centre in Rehabilitation. Fifteen contributors participated, comprising rehabilitation professionals and educators, individuals with lived experience of serious illness, injury, or disability requiring rehabilitation, and technology innovators. The purpose of these activities was to identify the factors necessary to ensure that training in rehabilitation technologies is equitable for people with sensory, cognitive, and physical impairments. Findings: Contributors highlighted a series of priority domains that together capture the breadth of challenges and opportunities in this area. These included the need to address physical, sensory, and cognitive accessibility; to foster participation, motivation, and engagement; to strengthen instructional design and delivery; to ensure technological accessibility and integration; to enhance staff training and competence; and to embed participant-centred and policy approaches. Contributions in these domains were synthesised into thematic categories that provide a structured understanding of the training requirements of rehabilitation technology recipients. Evaluation: The PPI process was evaluated using the Guidance for Reporting Involvement of Patients and the Public (GRIPP2) Short Form, supplemented by an evaluation survey. This dual approach ensured that the contributions were systematically documented and critically appraised. Implications: Guided by implementation science, the principal output of this work was a co-created stakeholder engagement strategy, structured using the Implementation STakeholder Engagement Model (I-STEM). This plan will serve as a foundation for future research exploring the education and training needs of diverse stakeholder groups, thereby contributing to the development of more inclusive and effective rehabilitation technology training practices. Full article