Search Results (3,951)

Restless legs syndrome (RLS) is a common sensorimotor disorder with limited treatment options and incompletely understood pathophysiology. Genome-wide association studies have identified numerous risk loci, but translating these findings into causal genes and therapeutic targets remains challenging. We performed a proteome-wide association study (PWAS) integrating RLS genome-wide association study (GWAS) data from FinnGen with two brain pQTL datasets (ROSMAP and Banner). We validated the identified proteins using TWAS, SMR, and colocalization analyses using brain pQTL and eQTL datasets. To further investigate peripheral protein associations, we performed SMR using plasma pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP). We also conducted a phenome-wide association study (PheWAS) to screen for potential off-target effects of the prioritized genes, followed by drug prediction using DSigDB and molecular docking. PWAS identified GLO1, along with GRWD1 and MAP2K5, as significantly associated with RLS. GLO1 was identified by brain-based SMR (p = 0.0001), colocalization (PP.H4 = 0.96), TWAS (p = 0.048), and was confirmed by plasma-based SMR (p = 3.16 × 10⁻⁹) as the only protein associated with RLS. PheWAS analysis, without associations for 783 non-RLS phenotypes, confirmed the specificity of GLO1. Among 27 predicted GLO1-targeting compounds, Gambierol had the strongest binding affinity (−8.3 kcal/mol). This proteogenomic study identifies GLO1 as a prioritized causal gene and promising drug target for RLS, combining brain and plasma data to provide new insights into pathogenesis and candidate drug development. Full article

This study presents pedagogical innovations in the undergraduate course Postharvest Technology and Quality Management at Sultan Qaboos University (SQU), where project-based learning (PBL) is used to integrate academic quality assurance and sustainability education, aligning with the United Nations Sustainable Development Goals (SDGs). This study adopts a descriptive multiple-case approach to analyze five representative student projects and their alignment with the SDGs. The projects address real-world postharvest challenges, including quality preservation, renewable energy use, and food loss reduction. A qualitative cross-case analysis based on SDGs mapping criteria was used to evaluate project alignment and societal outcomes. Representative student projects demonstrate how inquiry-driven learning enhances technical competence and research skills. Quantitative outcomes include a reduction in weight loss from 27.1% to 18.8% in coated tomatoes, increased weight loss up to 46.37% under severe mechanical damage in zucchini, and significant firmness reduction in bruised apples (53.23 N to 21.64 N). Hybrid infrared–hot air drying improved drying efficiency by reducing drying time and enhancing moisture removal, while banana coating experiments showed reduced moisture loss and delayed ripening. The analysis shows that all five projects align with at least two SDGs, with SDG 12 addressed in 100% of the cases. The curriculum is explicitly aligned with SDG 2 (Zero Hunger), 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production), and 13 (Climate Action). The study highlights the societal relevance of course-based projects through their contribution to SDG-related challenges and emphasizes the role of mentorship, teamwork, and experiential learning infrastructure in sustaining effective PBL implementation. Cross-case comparison highlights common sustainability contributions, including a reduction in postharvest losses, adoption of natural preservation methods, and improvements in energy-efficient processing. The findings highlight the potential of course-based PBL as a context-specific approach for integrating sustainability into undergraduate education. Full article

Large-scale engineering of the 3D scenes used in BIM, GIS, digital twins, and geospatial web delivery frequently suffer from significant geometric redundancy after export to mesh-based delivery formats, arising in part from the inconsistent reuse of geometry, where many repetitive components are stored as independent meshes rather than being fully instantiated. This paper proposes an affine–covariant mesh instancing framework designed to achieve a lightweight representation of watertight triangular solids. The core of the method lies in a canonicalization pipeline: each mesh is normalized via volume-centroid translation, principal-axis alignment derived from volume covariance, and anisotropic covariance whitening. This process effectively decouples the influence of translation, rotation, and non-uniform scaling, projecting diverse geometries into a unified canonical space. Within this space, geometric similarity is quantified by evaluating compact descriptors against user-defined tolerances. A greedy clustering strategy is then employed to group affine–similar models based on these descriptors. Finally, the scene is efficiently reconstructed by applying inverse affine transformations to the representative instance of each cluster. The output stores one shared geometry per cluster alongside per-instance $4\times 4$ transform matrices, preserving the original spatial layout while reducing redundant geometry storage. Experiments on four real-world engineering scenes demonstrate varying compression benefits. The results prove particularly effective for scenes containing unlinked repetitive parts and affine–similar parametric components, while also revealing a controllable trade-off between fidelity and compression rate. The method is therefore suitable as a post-export geometry-lightweighting step in mesh-based BIM/GIS integration, infrastructure digital twins, and large-scale 3D mapping workflows. Full article

Accurate recognition and segmentation of seafloor topographic units is of great significance for marine surveying and engineering applications. Efficient segmentation of multibeam bathymetric point clouds typically requires projecting them into two-dimensional images. However, segmentation methods based on single-modality images suffer from incomplete information representation and insufficient model adaptability, which often lead to blurred boundaries, false positives, and missed detections, thereby limiting segmentation accuracy. To address these challenges, this study proposes a seafloor topography recognition and segmentation method based on YOLO11n-seg with bimodal image feature fusion, from the perspectives of image generation and model optimization, aiming to improve segmentation accuracy and robustness. First, an early fusion strategy for bimodal images is adopted. Two types of images generated from point clouds via continuous curvature tension spline interpolation are concatenated at the input level, fusing local texture details with absolute water depth information, thereby enhancing the model’s ability to perceive topographic features. Second, a lightweight Efficient Channel Attention (ECA) module is embedded after the Spatial Pyramid Pooling-Fast (SPPF) module of the backbone network. This module adaptively calibrates channel weights, reinforcing the contribution of the grayscale channel to the final segmentation decision. Finally, a weighted BCE-Dice joint loss function is constructed to mitigate class imbalance between flat seabed and topographic regions, while also optimizing boundary segmentation accuracy. Experimental results on a self-constructed multibeam image dataset demonstrate that the proposed method achieves an mAP@50 of 92.8%, representing an absolute improvement of 7.6 percentage points over the baseline model. Notably, the model has only 2.84 M parameters, maintaining a lightweight profile. Full article

Transmission lines in Southwestern China are highly exposed to compound hazards induced by extreme winds and ice and snow conditions. This study assesses future changes in extreme wind hazards and their spatial overlap with baseline ice susceptibility under the SSP2-4.5 emission scenario, using high-resolution dynamically downscaled climate projections. Compared to the historical period (1995–2014), the results indicate a marked intensification of extreme spring wind events over northwestern Southwestern China and the transitional zone between the Sichuan Basin and the Hengduan Mountains during 2041–2060. The occurrence frequency of wind speeds exceeding historical 50-year return levels is projected to increase by 5–10 times in complex terrain, particularly along the Golmud–Qaidam belt. The Comprehensive Extreme Wind Index (CEWI) identifies the Golmud–Wulanwusu–Qaidam river basin belt as the region of highest wind hazard amplification. Meanwhile, analysis of historical observations reveals that icing-prone conditions occur on more than 25 days each spring in the Nyenchentanglha Mountains and southeastern Tibetan Plateau valleys, establishing a baseline map of ice susceptibility. Due to methodological limitations in projecting future icing, this susceptibility map is used as a static indicator of ice-prone areas. By superimposing projected wind intensification onto the baseline ice susceptibility map, four relative hazard exposure categories are delineated. Regions of highest potential exposure are concentrated in the Bayan Har Mountains and portions of the western Hengduan Mountains, whereas northwestern basins are dominated by high wind risk alone. These results reveal pronounced spatial heterogeneity in the relative amplification of compound hazards under future warming and provide a scenario-informed scientific basis for prioritizing regions in disaster risk reduction and resilient planning of transmission infrastructure in mountainous regions. Full article

This study presents an integrated bibliometric analysis (BA) and systematic literature review (SLR) of construction safety research (CSR) to examine its evolution and emerging technological directions. It aims to move beyond descriptive mapping by linking long-term research trends with recent technological advancements to provide a structured understanding of how construction safety is transitioning toward data-driven and resilient systems. Utilising the PRISMA-guided approach, 1979 publications were analysed, revealing an average annual growth rate of 18%, driven by increasing safety concerns and the rapid implementation of digital technologies. The findings demonstrate that conventional safety research, centred on hazard identification, safety culture, and management commitment, is gradually being complemented by advanced technologies such as artificial intelligence (AI), machine learning (ML), extended reality (XR), and digital twins. These technologies enable predictive risk assessment, real-time monitoring, and immersive training, supporting a shift from reactive to proactive safety management. Despite these advancements, critical gaps remain, including limited real-world validation of AI-based systems, insufficient integration of technologies into cohesive frameworks, and underexplored socio-cultural factors influencing adoption. These challenges were addressed by proposing a research-to-practice framework for integrating emerging technologies into construction safety management. The framework incorporates technological, organisational, and human factors to enhance adaptability, risk management, and overall construction project resilience. Additionally, the research contributes to the body of knowledge by providing a comprehensive and analytically grounded framework that bridges the gap between research and practical implementation, while also identifying future research directions to support the development of intelligent, resilient, and adaptive construction safety systems. Full article

The widespread deployment of unmanned aerial vehicles (UAVs) in remote sensing has highlighted the necessity for robust object detection methods in UAV imagery. However, high-altitude UAV imagery suffers from severe background clutter that obscures target discriminability and extreme scale variations that degrade fine-grained features. To address these challenges, we propose CSSA-YOLO, a clutter-suppressed and scale-aware detection framework built upon YOLOv9. Specifically, we project dense spatial features into a low-rank token space via a Semantic Bottleneck Module (SBM). This projection acts as an information bottleneck, suppressing the background clutter while robustly retaining critical target semantic and structural priors. Furthermore, we develop a Scale-Aware Complete-IoU (SA-CIoU) loss to tackle gradient attenuation for small objects. By analytically integrating a scale-aware modulation factor with a dynamic alignment mechanism into localization optimization, SA-CIoU shifts the optimization priority to the precise localization of small and hard-to-detect instances. Extensive experiments on the VisDrone2019 benchmark demonstrate the superiority of our approach, with CSSA-YOLO achieving an mAP@0.5 of 46.0% and an mAP@0.5:0.95 of 28.4%, yielding an absolute 1.4% improvement over the YOLOv9 baseline. Furthermore, when integrated with a P2-enhanced YOLOv9 architecture, our method achieves a remarkable mAP@0.5 of 49.5%. Notably, evaluations across diverse scenarios, including the infrared (IR) thermal HIT-UAV benchmark and PCB defect detection datasets, further demonstrate the generalizability and robustness of our framework. Full article

Soil total nitrogen (STN) is a key indicator of soil fertility and plays a fundamental role in agricultural productivity and sustainable land management. However, achieving an accurate and spatially continuous estimate of STN at the field scale remains challenging due to inherent soil variability and the constraints of conventional sampling methods. In this study, we employed unmanned aerial vehicle (UAV)-based hyperspectral imagery to estimate STN by integrating spectral preprocessing, feature selection, and machine learning techniques. Multiple feature selection methods, including Pearson correlation analysis, variable importance in projection (VIP), and competitive adaptive reweighted sampling (CARS), were evaluated to identify the most informative spectral bands. Several regression models—support vector regression with radial basis function kernel (SVR-RBF), random forest (RF), Extra Trees, PCA-SVR-RBF, and XGBoost—were compared for STN prediction. Among these, the VIP-PCA-SVR-RBF model yielded the best performance, achieving a test R² of approximately 0.77 and an RMSE of 0.45 g kg⁻¹. The integration of VIP-based feature selection with PCA dimensionality reduction significantly enhanced predictive accuracy and generalization capability compared to the other models tested. Spatial prediction maps derived from the optimal model revealed considerable heterogeneity in STN distribution across the study area. These results underscore the potential of UAV hyperspectral remote sensing for high-resolution mapping of soil nitrogen and offer a promising framework for precision nutrient management in agriculture. Full article

This paper presents a deterministic system that combines textual semantic data from panoramic images with LiDAR point clouds in a mobile mapping setup. Urban scenes often include textual elements, such as signs and business names, that provide key details typically missing from LiDAR-based urban digital twins. The presented method uses deep learning-based OCR to extract text from street panoramas and then categorizes it into urban types using a rule-based classifier. Text regions are geometrically projected into the LiDAR environment by converting image coordinates into viewing rays that intersect LiDAR surfaces, such as facades. Data from multiple panoramas are merged with confidence-weighted spatial clustering to produce consistent semantic markers for urban features. Extracted business names enable text-based searches of the LiDAR point cloud, allowing facility location by category, keyword, or brand. Tests on datasets from European and U.S. cities support plausible facade-level localization and demonstrate the framework’s ability to enhance LiDAR point clouds with searchable semantic information. The main contribution is not a new standalone OCR or LiDAR-processing algorithm, but a deterministic multimodal integration framework that combines deep-learning OCR, geometric back-projection, and cross-view spatial fusion to convert street-level textual cues into reliable, queryable 3D semantic markers within mobile-mapping LiDAR data. Full article

Abiotic stresses caused by climate change pose a significant challenge to global food security, making it necessary to develop stress-resistant crops. Foxtail millet (Setaria italica (L.) P. Beauv.) is a drought-tolerant C₄ cereal and serves as a model crop for elucidating stress adaptation mechanisms and promoting climate-resilient agricultural solutions. This paper reviews the tolerance mechanisms of foxtail millet to abiotic stresses. Physiologically, the species exhibits excellent water-use efficiency, requiring 75% less irrigation than traditional cereals, achieved through enhanced osmotic adjustment via soluble substance accumulation and the maintenance of ion homeostasis. Morphological adaptations include reduced leaf area, adjusted stomatal density, well-developed root systems, and specialized anatomical features that optimize water conservation. At the molecular level, stress tolerance involves complex transcriptional networks mediated by multiple transcription factor family members, including those (NF-Y, DREB, NAC, WRKY, MYB) that coordinate stress-responsive gene expression, antioxidant defense systems, and osmotic adjustment pathways. Furthermore, this review summarizes multi-omics characteristics, including genomics (such as QTL mapping and GWAS), proteomics, transcriptomics, metabolomics, and regulatory networks, for foxtail millet under abiotic stress tolerance. Additionally, reproductive resilience is maintained through efficient mobilization of stem reserves to panicles, phenological plasticity in flowering timing, and preserved gametic viability under thermal stress. Combining advanced molecular breeding with the inherent tolerance of foxtail millet positions this crop as both a solution to climate change and a genetic resource for enhancing the stress resistance of other cereals. These findings establish foxtail millet as a valuable model for developing sustainable agricultural technologies essential for food security under projected climate scenarios. Full article

In smart construction, identifying the competencies required of engineering–procurement–construction (EPC) safety directors is important for improving personnel selection, training, and safety-governance effectiveness. Drawing on dynamic capabilities theory, this study develops an exploratory competency framework for EPC safety directors in smart-construction contexts. A mixed-method design was adopted, combining a structured literature review, bibliometric mapping with CiteSpace, semistructured interviews, expert review, and questionnaire-based item screening. Questionnaire data from 189 valid respondents were analyzed using descriptive statistics, item analysis, Cronbach’s alpha, and KMO/Bartlett tests to preliminarily assess the internal consistency and structural suitability of the proposed indicators. The results indicate that the retained exploratory framework comprises three higher-order dimensions—sensing, seizing, and reconfiguring—covering six competency elements and eighteen indicators after the remaining trend-sensing indicator was integrated into data analytics. Compared with conventional safety-management competency frameworks, the proposed framework places greater emphasis on data analytics, intelligent systems application, and cross-departmental coordination in digitally enabled project environments. The framework can be implemented as a role-profile template for recruitment, training-needs diagnosis, and performance appraisal of EPC safety directors, while further empirical validation is required before it is used as a standardized measurement scale. Full article

This research addresses the latent disconnect between citizens and World Heritage sites, analysing how intensive tourism and declarations focused on monuments (1980s–1990s) have created a distance that makes managing these heritage sites very difficult. The main objective is to propose and validate participatory methodologies that restore social bonds and strengthen urban governance. The identified knowledge gap lies in the lack of operational tools that allow the theory of participation to be put into actual practice, overcoming the current methodological void in assessing social and economic impacts. Under the methodology of the WHATS-UP project, an action-research approach is employed that combines ethnographic work, mapping of key actors, and participatory workshops with shared walking tours in the Alhambra and the Alcázar. This data is integrated into Participatory Geographic Information Systems (PPGIS) to map social perceptions of values and risks. The results show that, although tourism has led to alienation and gentrification, the participatory process succeeds in rescuing “invisible values”, such as memories and traditional trades, that are absent from official narratives. In conclusion, the study proposes a consensus-based co-management model between institutions and the community, transforming heritage into a resource for urban cohesion and resilience. This integration of methodologies, which combines collective mapping with safeguarding plans, enables progress toward protection strategies that are more effective and better reflect contemporary social realities. Full article

A myelination is essential for neural function in the vertebrate central nervous system, yet the molecular details of how the oligodendrocyte differentiation program has evolved remain poorly understood. Here, we performed a cross-species single-cell transcriptomic analysis of oligodendrocyte lineage cells in the spinal cord of five vertebrate species: fugu, mudskipper, chicken, mouse, and human. Pseudotime trajectory analysis revealed a shared oligodendrocyte progenitor cell (OPC) to committed oligodendrocyte precursor (COP) to myelin-forming oligodendrocyte (MOL) differentiation trajectory across all species, and CAME-based cross-species mapping confirmed the homology of OPC and MOL identities, while COP showed reduced mapping in teleosts compared with amniotes. Among stage-specific DEGs, highly shared genes (≥4 species) were organized into four co-expression modules encompassing cell projection organization, myelination, synapse assembly, and ribonucleoprotein biogenesis, with evolutionary core genes (all 5 species) enriched for oligodendrocyte differentiation and Wnt signaling. Strikingly, amniote-exclusive genes were enriched for synaptic vesicle transport, cell projection organization, predominantly at the OPC stage. This asymmetry indicates that amniotes have expanded the oligodendrocyte differentiation program at the progenitor stage, potentially linked to the myelination demands of terrestrial locomotor circuits. Our findings provide insights into how the oligodendrocyte differentiation program has been shaped by both deep evolutionary conservation and lineage-specific adaptation. Full article

We propose a microphysical completion for the scalar sector of dilatonic gravity by identifying the dilaton with the coarse-grained stiffness mode of a constrained complex tension field defined on a discrete relational network. Under a controlled ordered-regime coarse-graining, the real projection of the tension scales as $\Phi \left(\Theta \right)={\Phi }_{0}cos\Theta$, so the Planck mass varies with the phase angle $\Theta$ and the Einstein-frame canonical scalar becomes $\phi \propto ln[\Phi \left(\Theta \right)/{\Phi }_0]$. This logarithmic structure emerges naturally from the Weyl map and provides the correct canonical variable for vacuum models inspired by the Logarithmic Schrödinger Equation (LogSE). We outline how this scalar–tensor interface can satisfy Solar-System constraints through environmental locking and discuss avenues for laboratory and astrophysical tests based on stiffness–coherence coupling. This paper does not introduce a new scalar–tensor EFT class as such; rather, it provides a controlled microphysical origin for a specific scalar stiffness law, $\Phi \left(\Theta \right)\propto cos\Theta$, and for the resulting logarithmic Einstein-frame canonical structure. Full article

The rapid proliferation of unmanned aerial vehicles (UAVs) in civilian sectors has generated diverse research spanning platform engineering, application deployment, and regulatory governance. This scoping review systematically maps the current knowledge landscape of civilian UAVs, their applications, and their regulatory frameworks, and aims to serve as initial practical guidance for researchers and practitioners initiating drone-based projects. Following PRISMA-ScR guidelines, a structured three-stream literature search was conducted using Google Scholar, yielding 109 sources published between 2015 and 2025. This review synthesises findings across three domains: (1) technical specifications, including UAV platform configurations, their common applications, their advantages and limitations, electromechanical systems, flight control architectures, and communication technologies, while also providing key guidance on how to choose the appropriate components for a given application; (2) civil applications across eight sectors—delivery logistics, infrastructure inspection, precision agriculture, environmental monitoring, emergency response, waste management, and commercial uses—to provide inspiration as well as to capture important details on drone projects; and (3) regulatory frameworks and ethical considerations governing UAV operations. Analysis reveals concentrated research attention on autonomy and AI-driven control systems and emerging focus on communication infrastructure. Geographic representation is dominated by US, European, and Chinese contexts, with limited coverage of developing regions. Key knowledge gaps include economic feasibility analyses, standardisation frameworks, developing-world deployment contexts, and environmental lifecycle assessments. Contradictions emerge between optimistic application scalability claims and fundamental constraints in energy storage, swarm communication reliability, and privacy–efficiency trade-offs. This review provides researchers and practitioners with a comprehensive map of current UAV knowledge, identifies critical research gaps, and establishes a foundation for future research in civilian drone technologies. This study aims to systematically consolidate and synthesise fragmented research on civilian UAV technologies, applications, and regulatory frameworks into a unified reference for research and practice. Full article