Search Results (3,739)

The mobile tunnel scanning system (MTSS) enables efficient tunnel inspection; however, motion blur (MB) generated at high travel speeds remains a major factor undermining the reliability of deep-learning-based crack detection. This study focuses on investigating how horizontally oriented MB in MTSS imagery affects the crack-detection performance of convolutional neural networks (CNNs) and proposes a data-centric quality-assurance framework that leverages no-reference image quality assessment (NR-IQA) to optimize model performance. By intentionally applying MB to both public and real-world MTSS datasets, we analyzed performance changes in ResNet-, VGG-, and AlexNet-based models and established the correlations between four NR-IQA metrics (BRISQUE, NIQE, PIQE, and CPBD) and performance (F1 score). As the MB intensity increased, the F1 score of ResNet34 dropped from 89.43% to 4.45%, confirming the decisive influence of image quality. PIQE and CPBD exhibited strong correlations with F1 (−0.87 and +0.82, respectively), emerging as the most suitable indicators for horizontal MB. Using thresholds of PIQE ≤ 20 and CPBD ≥ 0.8 to filter low-quality images improved the AlexNet F1 score by 1.46%, validating the effectiveness of the proposed methodology. The proposed framework objectively assesses MTSS data quality and optimizes deep learning performance, enhancing the reliability of intelligent infrastructure maintenance systems. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

Background/Objectives: The transition from theoretical knowledge to clinical practice poses significant challenges for nursing students globally. This critical period requires comprehensive educational support to build confidence and competence. While short-term preparatory courses have shown promise internationally, their effectiveness within the Saudi Arabian context remains understudied. This study aimed to evaluate nursing students’ satisfaction and self-confidence following participation in short-term preparatory courses conducted before clinical placements at Taibah University, Saudi Arabia. Methods: A descriptive cross-sectional study was conducted from February to April 2025. Data were collected from 117 undergraduate nursing students (response rate: 80.7%) using a validated questionnaire adapted from the National League for Nursing’s Student Satisfaction and Self-Confidence in Learning instrument. The preparatory courses included nursing care plan development, hospital orientation, and infection control procedures delivered over two weeks. Statistical analysis included descriptive statistics and Pearson correlation analysis. Results: Students reported high levels of satisfaction (mean = 4.29 ± 0.92) and self-confidence (mean = 4.31 ± 0.81) scores. The highest satisfaction was with instructor effectiveness (mean = 4.31 ± 1.05) and teaching methods (mean = 4.32 ± 1.01). Students demonstrated strong confidence in personal learning responsibility (mean = 4.44 ± 0.88) and skill development (mean = 4.32 ± 0.95). A strong positive correlation existed between satisfaction and self-confidence (r = 0.79, p < 0.001). Conclusions: Short-term preparatory courses effectively enhanced nursing students’ satisfaction and self-confidence in the Saudi Arabian context. The strong correlation between these constructions suggests that educational interventions improving one dimension is likely to benefit the other. These findings support integrating structured preparatory programs into nursing curricula to facilitate successful clinical transitions. Full article

While the corporate adoption of artificial intelligence (AI) is accelerating, its environmental consequences remain insufficiently understood, particularly in absolute firm-level energy consumption. The main objective of this study is to empirically determine the causal impact of AI adoption on absolute firm-level energy consumption in Chinese publicly listed companies, with a particular focus on the mediating role of green innovation and the moderating role of digital capabilities. This study provides the first large-scale micro-level evidence on how AI adoption shapes corporate energy use, drawing on panel data from Chinese non-financial listed firms during 2011–2022. We construct a novel AI adoption index via Word2Vec-based textual analysis of annual reports and estimate its impact using firm fixed effects, instrumental variables, mediation models, and multiple robustness checks. Results show that AI adoption significantly reduces total energy consumption, with a 1% increase in AI intensity associated with an estimated 0.48% decrease in energy use. Green innovation emerges as a key mediating channel, while the energy-saving benefits are amplified in firms with advanced digital transformation and IT-oriented executive teams. Heterogeneity analyses indicate more substantial effects among large firms, private enterprises, non-energy-intensive sectors, and firms in digitally lagging regions, suggesting capability-driven and context-dependent dynamics. This study advances the literature on digital transformation and corporate sustainability by uncovering the mechanisms and boundary conditions of AI’s environmental impact and offers actionable insights for aligning AI investments with carbon reduction targets and industrial upgrading in emerging economies. Full article

To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article

Background and Objectives: Data on mental health in university students have been increasingly concerning, with high prevalence rates of clinical conditions such as anxiety, stress, and depression. This study aims to evaluate the risk factors associated with mental health status and to develop a predictive model. Materials and Methods: A total of 242 university students were recruited (74.8% women). Participants’ ages ranged from 18 to 56 years (M = 25.81; SD = 7.59). Data collection were conducted through the Depression, Anxiety, and Stress Scale (DASS-21), the Big Five Inventory-10 (BFI-10), and the Coping Orientation to Problems Experienced Inventory (COPE-28). Results: Overall, mean scores across the three clinical dimensions are within the moderate range, but anxiety shows the highest mean value (M = 8.67, SD = 5.69) and is categorized as “extremely severe.” Additionally, identifying as female, living with family or roommates, and having high scores on passive coping styles were significant risk factors for mental health deterioration. In contrast, identifying as male, living with a romantic partner (cohabitation), and having high scores on the Responsibility personality trait were identified as protective factors against mental health impairment. Conclusions: Additional research is warranted to explore additional mediating variables and to develop specific intervention protocols for improving university students’ psychological well-being. Full article

In air–ground cooperative systems, identifying the identities of unmanned ground vehicles (UGVs) from an unmanned aerial vehicle (UAV) perspective is a critical step for downstream tasks. Traditional approaches involving attaching markers, like AprilTags on UGVs, fail under low-resolution or occlusion conditions, and the visually identical UGVs are hard to distinguish through similar visual features. This paper proposes a markerless method that associates UGV onboard sensor data with UAV visual detections to achieve identification. Our approach employs a Dempster–Shafer fused methodology integrating two proposed complementary association techniques: a projection-based method exploiting sequential motion patterns through reprojection error validation, and a topology-based method constructing distinctive topology using positional and orientation data. The association process is further integrated into a multi-object tracking framework to reduce ID switches during occlusions. Experiments demonstrate that under low-noise conditions, the projection-based method and the topology-based method achieves association precision at 89.5% and 87.6% respectively, which is superior to the previous methods. The fused approach enables robust association at 79.9% precision under high noise conditions, nearly 10% higher than original performance. Under false detection scenarios, our method achieves effective false-positive exclusion, and the integrated tracking process effectively mitigates occlusion-induced ID switches. Full article

This paper presents a novel methodology for the optimal sizing of solar photovoltaic (PV) systems in distribution networks by determining the monthly optimum tilt and azimuth angles to maximize solar energy capture. Using one year of solar irradiation data, the Grey Wolf Optimizer (GWO) is employed to optimize the tilt and azimuth angles with the objective of maximizing monthly solar insolation. Unlike existing approaches that assume fixed azimuth angles, the proposed method calculates both tilt and azimuth angles for each month, allowing for a more precise alignment with solar trajectories. The optimized orientation parameters are subsequently utilized to determine the optimal number and placement of PV panels, as well as the optimal location and sizing of shunt capacitor (SC) banks, for the IEEE 69-bus distribution system. This optimization is performed under peak load conditions using the GWO, with the objectives of minimizing active power losses, enhancing voltage profile stability, and maximizing PV system penetration. The long-term impact of this approach is assessed through a 20-year energy and economic savings analysis, demonstrating substantial improvements in energy efficiency and cost-effectiveness. Full article

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin. Full article

Nowadays, integrating Artificial Intelligence (AI) in military communication systems is reshaping current defense strategies by enhancing secure data exchange, situational awareness, and autonomous decision-making. This survey examines advancements of AI in tactical communication networks, including UAV networks, radar-based transmission, and electronic warfare resilience, thereby addressing a key gap in the existing literature. This is the first comprehensive review of AI applied exclusively to current tactical communication systems, synthesizing fragmented literature into a unified defense-oriented framework. A key contribution of this survey is its cross-sectoral perspective, exploring how civilian AI techniques are applied in military contexts to enhance resilient and secure communication networks. We analyze state-of-the-art research, industry initiatives, and real-world implementations. Additionally, we introduce a three-criteria evaluation methodology to systematically assess AI applications based on system objectives, military communication constraints, and tactical environmental factors, enabling a study of AI strategies for multidomain interoperability. Finally, we draft future research directions, emphasizing the need for AI standardization, enhanced adversarial resilience, and AI-powered self-healing networks. This survey provides key insights into the evolving role of AI in modern military communications for researchers, policymakers, and defense professionals. Full article

Skywells are crucial for climate regulation in traditional Chinese dwelling architecture, exhibiting significant variations across climatic regions. This study focuses on humid–hot China, using Huangshan, to explore skywell parameters’ impact on thermal comfort and energy efficiency. Field research on 24 buildings in the World Heritage Site Xidi, Hong Villages, and Chinese Historical Pingshan Village, combined with Grasshopper’s Ladybug tool, established a parametric model. Using orthogonal design, performance simulation, and Python-based machine learning, six morphological parameters were analyzed: width-to-length ratio, height-to-width ratio, orientation, hall depth, wing width, and shading width. After NSGA-II multi-objective optimization, the summer Percentage of Time Comfortable (P_TC) increased by 5.3%, 38.14 h; the Universal Thermal Climate Index (UTCI) relatively improved by 2%; energy consumption decreased by 8.6%, 0.14 kWh/m²; and the useful daylight illuminance increased by 28%, 128.4 h. This confirms the climate adaptability of courtyard-style buildings in humid–hot China and identifies optimized skywell parameters within the study scope. Full article

Background/Objectives: Transfer RNA-derived fragments (tRFs) are small non-coding RNAs increasingly implicated in gene regulation and disease, yet their target specificity and disease relevance remain poorly understood. This is an exploratory study that investigates the phenomenon of identical tRF sequences reported in distinct disease contexts and evaluates the consistency between experimental findings and predictions from both target-based and abundance-based tRF databases. Methods: Five tRFs with identical sequences across at least two peer-reviewed disease studies were selected from a recent systematic review. Their validated targets and disease associations were extracted from the literature. Motifs and predicted targets were cross-referenced using three target-oriented databases: tatDB, tRFTar, and tsRFun. In parallel, the abundance enrichment of cancer-associated tRFs was assessed in OncotRF and MINTbase using TCGA-based abundance data. Results: Among the five tRFs, only LeuAAG-001-N-3p-68-85 showed complete alignment between experimental data and both tatDB and tRFTar predictions. Most of the other four displayed at least partial overlaps in motif/binding regions with some of validated targets. tRF abundance data from MINTbase and OncotRF showed inconsistent enrichment, with only AlaAGC-002-N-3p-58-75 exhibiting concordance with its experimentally validated cancer type. Most functionally relevant tRFs were not strongly represented in abundance-only databases. Conclusions: Given the limited number of tRFs analyzed, this study serves primarily as a pilot analysis designed to generate hypotheses and guide future in-depth research, rather than offering comprehensive conclusions. We did, however, illustrate how the analysis of tRFs can benefit from utilizing currently available databases. Target-based databases more closely reflected experimental evidence for mechanistic details when a tRF or a motif match is found. Yet all database types are incomplete, including the abundance-focused tools, which often fail to capture disease-specific regulatory roles of tRFs. These findings underscore the importance of using integrated data sources for tRF annotation. As a pilot analysis, the study provides insights into how identical tRF sequences might function differently across disease contexts, highlighting areas for further investigation while pointing out the limitations of relying on expression data alone to infer functional relevance. Full article

To address the issue of cracking damage under extreme high-temperature rutting, which is not sufficiently considered in the selection of preventive maintenance programs, the objective of this study was to investigate the preventive maintenance-oriented minor internal damage changes in asphalt concrete with a normal maximum aggregate size of 16 mm (AC-16) under extreme high temperature (70 °C) and load (1.4 MPa) conditions. The changes in void structure within the 0–10 mm rutting depth were tracked through the rutting test and Computer Tomography (CT) image analysis. It was observed that there were notable discrepancies in the three-dimensional (3D) space distribution of void, void volume development, and void morphology between the rut impact zones and the rutted part. The impact zone exhibited a greater prevalence of voids and an earlier onset of cracking. At a rutting depth of only 5 mm, multiple top-down developed cracks (TDCs) of over 6 mm length were observed in the impact zone. At a rutting depth of 10 mm, the TDCs in the impact zone were more numerous, larger, and wider, indicating the necessity for a tailored repair program that includes milling. TDC damage caused by high-temperature rutting is predominantly observed in the upper and middle positions of the height direction, with the bottom position data exhibiting greater inconsistency due to the influence of molding. Furthermore, the combination of void morphology indicators with void volume can effectively track the occurrence and development of microcracks. However, the fine-scale assessment of compaction degree and deformation process using the equivalent void diameter indicator is not sufficiently differentiated. Full article

Background/Objectives: Balance, postural control, and functional independence are essential components for the autonomy of people with neurological conditions. Robotic technologies such as the Lokomat^® have emerged as promising tools in rehabilitation, but their effectiveness when integrated into functional programs requires further evidence. The objective of this study was to evaluate the impact of an intensive robotic intervention on these three functional variables. Methods: A single-group, quasi-experimental pretest–posttest study was conducted with 136 participants who received a robotic rehabilitation intervention using the Lokomat^® device, and focused on functional tasks over several weeks. Balance (using the Berg scale), postural control (using the PASS), and functional independence (using the Barthel index) were assessed, comparing pre- and post-intervention results using parametric and non-parametric tests. Results: The results showed statistically significant improvements in all three variables after the intervention. The mean Berg score increased from 11.76 to 21.91 points (p < 0.001), postural control increased from 15.53 to 21.90 points (p < 0.001), and the Barthel index increased from 24.71 to 41.76 points (p < 0.001). In all cases, the effect sizes were large (d > 0.90). Conclusions: A rehabilitation program including intensive, task-oriented Lokomat^® training was associated with improvements in balance, postural control, and functional independence. Given the single-group design without a control arm, these findings reflect associations and do not establish causality. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article