Search Results (4,624)

Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things’ (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics—including flight trajectory length, altitude profile fidelity, and path smoothness—while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization. Full article

The massive elemental dataset of stream sediments and soils accumulated by the projects of the Regional Geochemistry–National Reconnaissance (RGNR) and the National Multi-Purpose Regional Geochemical Survey (NMPRGS) provide core support for the compilation of geochemical maps and hold irreplaceable significance in the field of mineral exploration and soil environment. Geochemical maps produced by traditional methods are heavily dependent on data volume, which limits the comparisons across different areas and different elements. To facilitate the comparisons, a 19-level fixed-value classification method was proposed for Sn, Li, Mo, Ni, etc. However, the method for Cu is still lacking. Here the method for Cu is proposed to divide Cu content into 19 levels on 18 fixed values which are determined based on many typical values such as the detection limit, quartiles from the RGNR and NMPRGS projects, and cutoff grades. The Jinchuan Cu-Ni deposit is a giant deposit in China which is selected as an illustration on the new method. The elemental data of rocks and stream sediments in the Jinchuan area are used to compile elemental concentration maps and elemental concentration-level maps for the elements of Cu and Ni. The results show that the 19-level fixed-value method can not only effectively evaluate the concentration levels of elements but also enable the comparisons among elements. The Jinchuan Cu-Ni deposit is located in the mineralization areas and high anomaly areas of Cu and Ni. Compared with the previous reported maps of Ni, Sn, and Li in other areas, the new method facilitates the recognition of target metals in geochemical exploration. An element with a concentration level of no less than 15 may be mineralized or is the target metal on the 1:200,000 survey data from sediments. The 19-level fixed-value method may hold significant applications in the fields of mineral exploration and environment assessment. Full article

Background: Adolescence is a developmental window of heightened vulnerability to psychological distress, yet the interplay between pathology and protective factors remains understudied in a low-to-middle-income urban district in North Lima, Peru. This study examined the network structure of resilience, happiness, and mental health indicators in Peruvian adolescents to identify precise intervention targets. Methods: A sample of 559 adolescents (49.9% boys; Mage = 14.72, SD = 1.43) recruited from public secondary schools in Carabayllo, a low-to-middle-income urban district in North Lima, Peru, completed validated measures of resilience (CD-RISC-25), subjective happiness, and mental health (anxiety, depression, and stress). A Gaussian Graphical Model was estimated using non-regularized partial correlations. Node centrality, predictability, and network stability were assessed, and a Network Comparison Test evaluated structural differences by sex. Results: Anxiety, depression, and stress formed a tightly interconnected core, with the strongest edge between stress and anxiety. Among the psychological resources, self-regulation and external resources showed the highest centrality and predictability, followed by personal competence and tenacity. Happiness occupied a peripheral position but maintained a negative association with depression. The network demonstrated strong stability (CS = 0.75). No significant structural or global strength differences emerged between boys and girls. Conclusions: Findings challenge generic well-being approaches, revealing that happiness is a distal factor rather than a central buffer in this population. Instead, the network architecture suggests that interrupting the stress–anxiety loop and fostering self-regulation skills constitute the most effective pathways for school-based mental health protection, regardless of student gender. Full article

An increased interest in decreasing the environmental impact of the construction sector and in vertical urbanization has renewed attention to timber as a primary structural material in multi-story buildings. This study investigated whether an existing 10-story reinforced concrete (RC) residential building can be redesigned as an equivalent mass-timber structure while satisfying the same structural performance requirements. It addressed the lack of like-for-like building-scale comparisons that redesigned an existing multi-story RC residential building into a functionally equivalent mass-timber scheme. A real RC building in Gävle, Sweden, was modeled, analyzed, and designed using StruSoft FEM-Design software in accordance with the Eurocodes and the Swedish National Annex, after which all main structural elements were systematically replaced with timber. Through iterative adjustments of member sizes, support conditions, and added reinforcing elements, both the RC and timber schemes were verified with respect to load-bearing capacity, serviceability, and global stability under identical load combinations. The RC and timber buildings reached maximum utilization ratios of 99% and 98%, respectively; displacements were higher in the timber building but remained within serviceability limits, and both systems were classified as globally stable. The timber alternative reduced the total structural weight to about 19% of the RC building and roughly halved the maximum vertical reaction forces, at the expense of additional beams, columns, and basement wall segments. Moreover, this article developed an equivalent-design methodology for material substitution, a bottom-up reinforcing elements logic that resolved serviceability and stability constraints in tall timber, and a performance trade-off map based on structural performance, offering guidance for future mass-timber design. Full article

This study presents U-Net deep learning of total electron content (TEC) obtained from Global Ionosphere Maps (GIMs) to forecast ionospheric TEC over the African 0–40° N latitude sector during geomagnetic storms which have occurred between 2011 and 2024. Before being utilized in the deep learning procedure, the GIM-TEC data were improved by assimilating ground-based vertical TEC (VTEC) observations from available Global Navigation Satellite System (GNSS) receiver stations. The U-Net one-hour-ahead prediction of TEC was examined during the intense geomagnetic storm of May 2024. Additionally, the model’s accuracy and reliability were evaluated through quantitative comparison with established climatological models, including IRI-2020 and AfriTEC storm time models. The results indicate that the integration of data assimilation with the deep learning framework yields TEC estimates that closely agree with observations, achieving a RMSE of approximately 5 TECU. On the other hand, the IRI-2020 model exhibits substantially larger errors, with RMSE ~10–17 TECU, while the AfriTEC model shows the poorest performance, with RMSE reaching approximately 15–22 TECU. Further, the U-Net was validated using two equatorial and mid-latitude GNSS stations whose data were excluded from the assimilation process, achieving RMSE values of 4.44 and 6.75 TECU and correlation coefficients of 0.93 and 0.97, confirming the model forecasting capability for reproducing ionospheric TEC variability. These results establish the model as a precise, robust tool for TEC prediction in regions with sparse GPS coverage that is crucial for ionospheric monitoring and space weather applications. Full article

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance ($R^2$ = 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article

Sheep wool is a keratin-based natural fiber increasingly explored as a low-impact reinforcement and multifunctional modifier in composites, enabling valorization of coarse or waste wool streams. This systematic review consolidates evidence on raw or minimally processed wool-reinforced composites across polymer matrices and mineral binders. Following a registered protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, Scopus and Web of Science were searched for English-language journal articles (2015–2025), yielding 44 included studies after screening. Evidence mapping shows polymers dominate (33/44; thermosets 19/44), while mineral binders account for 11/44. Wool is mainly used as short fibers (27/44), with woven (9/44) and nonwoven/felt (8/44) architectures appearing in laminates and insulation products. Because heterogeneity limits pooled meta-analysis, outcomes are synthesized using matched-control comparisons where available (27/44) and interpreted with a TRiC appraisal (Transparency, Reproducibility, and Credibility). Mechanical effects are highly conditional: gains in impact/energy absorption and occasional tensile/flexural stress improvements coexist with frequent losses linked to dispersion, wetting/impregnation and void sensitivity. Functional trends are similarly system-dependent, with promising but uneven evidence for acoustic performance, variable thermal conductivity shifts, and formulation-driven fire behavior. Moisture uptake and durability emerge as principal translation bottlenecks, motivating minimum reporting and design practices to improve comparability and application readiness. Full article

Background: The integration of advanced 3D scanning and additive manufacturing technologies in stroke rehabilitation offers promising advancements in the design and production of ankle-foot orthoses. These technological innovations are progressively recognized for their potential to provide more precise and customized orthotic solutions for individuals with stroke-related impairments. Objectives: The primary aim of this study was to biomechanically test and validate the effectiveness of custom ankle-foot orthoses produced through additive manufacturing technology using data captured by a novel photogrammetric scanning system. The customized orthosis was compared with a standard prefabricated orthosis to assess their relative effectiveness in improving gait dynamics and patient satisfaction in stroke rehabilitation. Methods: Participants with equinovarus deformity, a common consequence of stroke, were fitted with custom ankle-foot orthoses, alongside conventional prefabricated orthoses. The study utilized the Qualisys^® motion analysis system for comprehensive biomechanical gait analysis, and the QUEST questionnaire was employed to capture participant feedback on both types of orthoses. Detailed comparisons of gait dynamics were conducted using Statistical Parametric Mapping with each orthosis. Results: The study revealed notable kinematic and kinetic differences between the custom and prefabricated orthoses. The custom orthoses demonstrated superior performance in enhancing gait efficiency, symmetry, and safety. Patient feedback favored the customized orthoses over the prefabricated variants, with higher scores in comfort, fit, and overall effectiveness. Conclusions: This research underscores the effectiveness of custom orthoses produced through additive manufacturing technology for stroke rehabilitation. By offering a comprehensive evaluation of orthotic interventions and establishing a comparative framework, the study serves as a reference point for future research, advocating for a more personalized and evidence-based approach in orthotic design for improving the quality of life of stroke survivors. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

Large-scale terrestrial laser scanning (TLS) point clouds are increasingly used for applications such as digital twins and cultural heritage documentation; however, removing unwanted human points captured during acquisition remains a largely manual and time-consuming process. This study proposes a geometry-aware framework for automatically removing human noise from TLS point clouds by projecting 2D instance segmentation masks (obtained using You Only Look Once (YOLO) v8 with an instance segmentation head) into 3D space and validating candidates through multi-stage geometric filtering. To suppress false positives induced by reprojection misalignment and planar background structures (e.g., walls and ground), we introduce projection-followed geometric validation (or “geometric gating”) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA)-based planarity analysis, followed by cluster-level plausibility checks. Experiments were conducted on two real-world outdoor TLS datasets—(i) Osaka Metropolitan University Sugimoto Campus (OMU) (82 scenes) and (ii) Jinaimachi historic district in Tondabayashi (JM) (68 scenes). The results demonstrate that the proposed method achieves high noise removal accuracy, obtaining precision/recall/intersection over union (IoU) of 0.9502/0.9014/0.8607 on OMU and 0.8912/0.9028/0.8132 on JM. Additional experiments on mobile mapping system (MMS) data from the Waymo Open Dataset demonstrate stable performance without parameter recalibration. Furthermore, quantitative and qualitative comparisons with representative time-series geometric dynamic object removal methods, including DUFOMap and BeautyMap, show that the proposed approach maintains competitive recall under a human-only ground-truth definition while reducing over-removal of static structures in TLS scenes, particularly when humans are observed in only one or a few scans due to limited revisit frequency. The end-to-end processing time with YOLOv8 was 935.62 s for 82 scenes (11.4 s/scene) on OMU and 571.58 s for 68 scenes (8.4 s/scene) on JM, supporting practical efficiency on high-resolution TLS imagery. Ablation studies further clarify the role of each stage and indicate stable performance under the observed reprojection errors. The annotated human point cloud dataset used in this study has been publicly released to facilitate reproducibility and further research on human noise removal in large-scale TLS scenes. Full article

Developments in electric vehicle (EV) technology are pushing on-board chargers (OBCs) toward higher power density and efficiency, making high-frequency transformer loss prediction a critical design bottleneck. However, the accuracy of commonly used analytical winding-loss models varies strongly with frequency, conductor type (Litz/solid), window fill factor, and winding layout (e.g., interleaved), which can render single-model-based optimization unreliable. In this study, six analytical copper-loss models from the literature were independently reimplemented in a unified Python 3.11.5 workflow with a standardized interface to enable fair comparison under identical geometry and operating conditions. The models were benchmarked against 2D finite-element simulations on test scenarios with increasing physical complexity, including high fill-factor Litz windings and interleaved arrangements. The results confirm a regime-dependent behavior: no single model consistently outperforms others across the full design space, and model dispersion increases in geometrically stressed and higher-frequency regions. To manage this uncertainty, variance maps were generated and model disagreement was quantified using the coefficient of variation (CV). Finally, a reliability-oriented multi-objective optimization framework based on NSGA-II was developed, where a SmartTransformerRouter selects a reference loss estimate per candidate and CV is incorporated via constraints/penalties, with optional FEM triggering in high-uncertainty regions. Full article

To address the strong nonstationarity and complex multi-source interference in vibration signals of bogie axle-box bearings, a fault diagnosis method combining Improved Empirical Wavelet Transform (IEWT) and a Convolutional Neural Network (CNN)–Transformer model is proposed. First, the vibration signals are decomposed using the IEWT method, where dynamic frequency-band division adaptively determines the decomposition bands. This yields multiple intrinsic mode functions, and key modes containing fault features are selected based on information entropy. Next, the selected key modes are fused and transformed into polar coordinate projection maps, further enhancing the distinctiveness of fault data features. Finally, CNN is employed to extract local features from the vibration signals, while the Transformer captures long-range dependencies through the self-attention mechanism, significantly improving feature modeling for complex signals. To validate the fault diagnosis performance of the IEWT and CNN–Transformer model, vibration signals from bogie axle-box bearings in urban railways are analyzed. Analysis of the experimental data suggests that the adaptive decomposition of bearing signals using IEWT effectively overcomes the fixed band boundary limitations of traditional EWT, enhancing the precision of signal feature extraction. The integration of polar coordinate projection maps more accurately illustrates frequency variations and amplitude differences in the signals, fully capturing their nonstationary characteristics. Among the five fault categories of bogie axle-box bearings, the proposed method achieves an accuracy of 99.46%, a recall rate of 99.52%, and an F1-score of 0.995, significantly outperforming five classic comparison methods. This demonstrates that the combined strengths of CNN and Transformer yield higher classification accuracy and better robustness in handling complex fault patterns, effectively solving the fault diagnosis challenges for bogie axle-box bearings. Full article

In all-digital transmitters (ADTx), the in-band quantization noise generated by pulse coding provides only limited clean bandwidth (CBW), significantly increasing the difficulty of analog filter design. To address the constrained CBW of RF pulse sequences in ADTx, this paper proposes an optimization strategy for suppressing noise across a broader frequency domain. Distinguished from traditional schemes with limited noise suppression range, the expansion of CBW is innovatively achieved by setting multiple groups of frequency observation points near the carrier frequency, enabling more comprehensive constraints of in-band noise. Meanwhile, aiming at the problems of large look-up table scale and slow query speed, a partitioned look-up strategy is proposed. During a look-up, traversal is confined only to the partition containing the input point, eliminating the need to scan all elements. This strategy substantially reduces the number of error calculations and comparisons, significantly improving the real-time performance of mapping look-up and lowering the computational demands on digital processing devices. Through the collaborative optimization of noise suppression and query efficiency, this study highlights its breakthrough contributions and provides technical support for the optimization of RF pulse sequences in ADTx. Full article

The invasion of Prosopis juliflora poses a growing threat to dryland ecosystems and pastoral livelihoods across East Africa. This study presents an integrative approach that combines satellite remote sensing, machine learning, and citizen science to detect and map the spatial extent and socio-ecological impacts of Prosopis juliflora in Baringo County, Kenya. We evaluated the performance of three satellite platforms, Sentinel-1, Sentinel-2, and PlanetScope, using a Random Forest classifier trained on field collected presence–absence data and vegetation indices. Sentinel-2 outperformed the other sensors, achieving a classification accuracy of 90.65%, with key variables including the Visible Atmospherically Resistant Index (VARI), the Ratio Vegetation Index (RVI), and red-edge bands emerging as the most important predictors. Through Participatory GIS (PGIS), a citizen-science based approach, we engaged gender-disaggregated community groups to capture local perceptions of invasion hotspots and blocked access to grazing routes and water sources, enhancing contextual understanding and validating model outputs. The comparison of satellite-derived maps and PGIS outputs revealed strong spatial congruence, particularly along water bodies, roads, and croplands. Our findings demonstrate the potential of combining Earth observation and citizen science to generate actionable knowledge for managing invasive species in data scarce dryland environments. This hybrid framework supports inclusive and spatially targeted interventions for rangeland restoration and ecosystem resilience. Full article

Precise quantification of fine motor behaviour is essential for understanding neural circuit function and for evaluating therapeutic interventions in neurological disorders. While markerless pose estimation frameworks such as DeepLabCut (DLC) have transformed behavioural phenotyping, the choice of convolutional neural network (CNN) backbone has a critical impact on tracking performance, particularly in tasks involving small distal joints and frequent occlusions. In this study, we present the first systematic comparison of nine CNN architectures implemented in DLC for lateral-view analysis of skilled reaching movements in the Montoya Staircase test, a gold-standard assay for forelimb dexterity in rodent models of stroke and neurodegenerative disease. Using a dataset comprising both control and primary motor cortex (M1)–lesioned mice, we evaluated model performance across six key dimensions: spatial accuracy (RMSE, PCK@5 px), mean average precision (mAP), robustness to occlusions, inference speed, and GPU memory usage. Our results demonstrate that multi-scale DLCRNet architectures substantially outperform conventional backbones. DLCRNet_ms5 achieved the highest overall accuracy, while DLCRNet_stride16_ms5 provided the most favourable balance between precision and computational efficiency. These findings provide practical methodological guidance for neuroscience laboratories and highlight the importance of CNN architecture selection for the reliable quantification of fine motor behaviour in preclinical research. Full article