Search Results (1,613)

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

Background: The consumption of ultra-processed foods (UPFs) represents an important public health challenge, especially among education workers, whose intense routine can negatively impact eating habits. This study aimed to analyze the factors associated with the regular consumption of UPF among employees of the Federal Network of Professional, Scientific and Technological Education (RFEPCT) in Brazil. Methods: This was a cross-sectional study, with a quantitative approach, carried out with 1563 education workers. Validated instruments on eating habits (PeNSE), mental health (DASS-21) and quality of life (WHOQOL-bref) were used. The regular consumption of UPF was defined as intake on ≥5 days in the last seven days. The association between the regular consumption of UPF and sociodemographic, occupational, behavioral, mental health and quality of life variables was assessed by Poisson regression with robust variance, generating adjusted prevalence ratios (PRadj) and respective 95% confidence intervals. Results: The regular consumption of UPF was associated mainly with female gender, a lower age group, Southeast and Midwest regions, dissatisfaction with sleep and the body, physical inactivity and poor sleep quality. In addition, the findings suggested a significant relationship between the worst stress scores and soft drinks (PRadj: 2.11; CI: 1.43–3.13), anxiety and soft drinks (PRadj: 1.83; CI: 1.24–2.70) and depression and industrialized/ultra-processed salty foods (PRadj: 2.43; CI: 1.82–3.26). The same was observed in the scores for the worst perception of quality of life, where there was a prevalence of up to 2.32 in the psychological domain and the consumption of industrialized/ultra-processed salty foods. Conclusions: The findings indicate that multiple interrelated factors—individual, psychosocial and occupational—are associated with the consumption of UPF among education workers. These results reinforce the importance of institutional policies that integrate actions to promote dietary health, mental health care and improved working conditions in the education sector. Full article

Background: Type 2 diabetes (T2D) is a growing public health concern, particularly in low- and middle-income countries. Although prediabetes is a major risk factor for T2D, it remains largely underdiagnosed and untreated. Structured lifestyle interventions have proven effective in preventing diabetes, but their feasibility within the Brazilian public health system remains unclear. Methods: This multicenter pilot randomized controlled trial assessed the feasibility of a culturally adapted lifestyle intervention (PROVEN-DIA) across the five regions of Brazil. A total of 220 adults at high risk for T2D were randomized to an intervention group or a control group (usual care) and followed for three months. Both groups received similar educational content on healthy eating and physical activity, but the intervention group participated in a structured and personalized lifestyle program with regular follow-up sessions. The primary outcome was adherence to dietary recommendations, assessed using the BALANCE Index—a validated dietary score (range: 0–40) based on the Brazilian Cardioprotective Diet that classifies foods into color-coded groups according to nutritional quality—along with engagement in moderate-to-vigorous physical activity (MVPA). Secondary outcomes included diet quality (DQIR), anthropometric and metabolic parameters. Results: Feasibility was demonstrated by a 93.2% retention rate (n = 205). There was no significant difference in the primary outcome (simultaneous improvement in diet and MVPA). However, the PROVEN-DIA group exhibited significantly greater improvements in diet quality, with a 2.8-point increase in the BALANCE Index (vs. 0.5 in the control, p = 0.03), and a significant improvement in the DQIR (p < 0.001). No significant differences between groups were observed in MVPA, HbA1C, glycaemia, or body weight. Conclusions: The PROVEN-DIA intervention proved feasible within the Brazilian public health context, resulting in significant improvements in dietary quality among individuals at high risk for T2D. A larger trial with longer follow-up is warranted to evaluate its effectiveness in preventing the progression to diabetes. However, to enhance physical activity outcomes, specific adaptations and targeted strategies may be required to better support participant engagement in exercise. Full article

Background: A critical gap exists in the current literature regarding pediatric-specific creatinine reference data. This study established age- and sex-stratified reference intervals and a corresponding median (Qcr) model for serum creatinine in children, providing a crucial foundation for improved diagnostic accuracy and clinical decision-making in this vulnerable population. Methods: A total of 9090 children (52.38% males and 47.65% females) who were getting regular check-ups at clinical laboratories in three regions were included in this study to establish Qcr serum and reference ranges for creatinine concentration. Results: The reference values and serum Qcr creatinine were established for children based on age and sex. Both males and females experience an incremental increase in creatinine levels with advancing age. In addition, significant differences were seen across the three areas in other age groups (p < 0.05). Conclusions: These newly established, age- and sex-stratified reference and Qcr values provide a critical resource for clinical laboratories, empowering clinicians to more accurately assess pediatric renal function and enabling more precise, individualized care for children with renal concerns. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

Human Immunodeficiency Virus (HIV) infection remains a major global health issue, with effective antiretroviral therapy (ART) extending life expectancy but also increasing age-related issues like osteopenia and osteoporosis. This cross-sectional study examines bone mineral density (BMD) and related risk factors in Romanian HIV-positive patients, emphasizing regional and therapy influences. The patients varying in HIV infection duration underwent DXA scanning to measure BMD in the lumbar spine, femoral neck, and total femur. A high prevalence of low BMD, especially in the lumbar spine, was identified along with significant associations between reduced BMD and factors such as smoking, alcohol use, vitamin D deficiency and serum phosphorus levels. ART like Protease Inhibitors and Nucleoside Reverse Transcriptase Inhibitors were linked to increased bone loss, emphasizing the multifactorial nature of osteoporosis in HIV-infected individuals and underscore the importance of regular BMD assessments, lifestyle adjustments, and careful management of antiretroviral therapy to minimize fracture risk and enhance overall health and quality of life. Full article

(1) Background: Metabolic-dysfunction-associated steatotic liver disease (MASLD) is now the most prevalent chronic liver disease worldwide, posing a growing public health concern. While dietary improvements are key to prevention, the impact of different vegetable types remains unclear. This study focuses on the association between vegetable consumption and the risk of MASLD in a cohort of Southern Italy. (2) Methods: This research involved 1297 participants from the NUTRIHEP study, examining overall vegetable intake and classifying them into color subgroups to determine optimal quantity and variety for risk reduction. (3) Results: Daily consumption of approximately 325 g (two servings) of total vegetables significantly reduces the risk of MASLD (OR: 0.521; 95% CI: 0.317; 0.858). Among the subgroups, green vegetables were most protective at 35 g/day, while red and orange vegetables offered protection at 130 g/day. A higher intake of the other vegetable category, specifically onions, was associated with a reduced probability of MASLD (OR = 0.995; 95%CI: 0.989; 0.999). (4) Conclusions: These findings suggest a threshold effect, where moderate but regular consumption of specific vegetables offers maximal protection. Consuming excessive amounts may not enhance this benefit within this cohort. Cultural and regional dietary patterns should be considered when designing targeted nutritional interventions. Full article

This study employs a force element analysis to investigate vortex-induced vibrations (VIV) of three side-by-side circular cylinders at Reynolds number Re = 100, mass ratio m* = 10, spacing ratios S/D = 3–6, and reduced velocities Ur = 2–14. The lift and drag forces are decomposed into three physical components: volume vorticity force, surface vorticity force, and surface acceleration force. The present work systematically examines varying S/D and Ur effects on vibration amplitudes, frequencies, phase relationships, and transitions between distinct vortex-shedding patterns. By quantitative force decomposition, underlying physical mechanisms governing VIV in the triple-cylinder system are elucidated, including vortex dynamics, inter-cylinder interference, and flow structures. Results indicate that when S/D < 4, cylinders exhibit “multi-frequency” vibration responses. When S/D > 4, the “lock-in” region broadens, and the wake structure approaches the patterns of an isolated single cylinder; in addition, the trajectories of cylinders become more regularized. The forces acting on the central cylinder present characteristics of stochastic synchronization, significantly different from those observed in two-cylinder systems. The results can advance the understanding of complex interactions between hydrodynamic and structural dynamic forces under different geometric parameters that govern VIV response characteristics of marine structures. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

Weathering steel (WS) is widely used in bridge construction due to its high corrosion resistance, durability, and low maintenance requirements. This paper reviews the performance of WS bridges in Canadian climates, focusing on the formation of protective patina, influencing factors, and long-term maintenance strategies. The protective patina, composed of stable iron oxyhydroxides, develops over time under favorable wet–dry cycles but can be disrupted by environmental aggressors such as chlorides, sulfur dioxide, and prolonged moisture exposure. Key alloying elements like Cu, Cr, Ni, and Nb enhance corrosion resistance, while design considerations—such as drainage optimization and avoidance of crevices—are critical for performance. The study highlights the vulnerability of WS bridges to microenvironments, including de-icing salt exposure, coastal humidity, and debris accumulation. Regular inspections and maintenance, such as debris removal, drainage system upkeep, and targeted cleaning, are essential to mitigate corrosion risks. Climate change exacerbates challenges, with rising temperatures, altered precipitation patterns, and ocean acidification accelerating corrosion in coastal regions. Future research directions include optimizing WS compositions with advanced alloys (e.g., rare earth elements) and integrating climate-resilient design practices. This review highlights the need for a holistic approach combining material science, proactive maintenance, and adaptive design to ensure the longevity of WS bridges in evolving environmental conditions. Full article

We propose a single-phase chiral elastic metamaterial capable of simultaneously exhibiting negative effective mass density and negative bulk modulus in the ultrasonic frequency range. The unit cell consists of a regular hexagonal frame connected to a central circular mass through six obliquely oriented, slender aluminum beams. The design avoids the manufacturing complexity of multi-phase systems by relying solely on geometric topology and chirality to induce dipolar and rotational resonances. Dispersion analysis and effective parameter retrieval confirm a double-negative frequency region from 30.9 kHz to 34 kHz. Finite element simulations further demonstrate negative refraction behavior when the metamaterial is immersed in water and subjected to 32 kHz and 32.7 kHz incident plane wave. Equifrequency curves (EFCs) analysis shows excellent agreement with simulated refraction angles, validating the material’s double-negative performance. This study provides a robust, manufacturable platform for elastic wave manipulation using a single-phase metallic metamaterial design. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article

The rubber plantations in Sankuru province, located in the Democratic Republic of Congo (DRC), have historically been pivotal to the regional economy. However, the absence of suitable silvicultural practices has promoted self-regeneration, resulting in the proliferation of diverse species. This study aims to characterize species richness and plant structure of these plantations. To this end, 80 subplots measuring 0.25 hectares were meticulously established, with a proportionate division between state-owned and farmer plantations. The results obtained from this study indicate that these plantations are home to approximately 105 species, classified into 33 distinct botanical families, with dominant families such as Fabaceae, Meliaceae, Euphorbiaceae, Olacaceae, Clusiaceae, and Moraceae. Despite the similarity between the two types of plantations (Cs = 58%), significant disparities were observed in terms of individuals, 635 ± 84.06 and 828 ± 144.62 (p < 10⁻³); species, 41 ± 7.49 and 28 ± 4.59 (p < 10⁻³); families, 19 ± 3.06 and 16 ± 1.62 (p < 10⁻²); and basal area, 29.88 ± 5.8 and 41.37 ± 7.57 (p < 10⁻²) for state and peasant plantations, respectively. State plantations exhibited greater diversity (H′ = 1.87) and enhanced equity (J’ = 0.43) than peasant plantations. The diametric structure exhibited an inverted J-shaped distribution, indicating constant and regular regeneration of these plantations. The upper canopy dominates the vertical structure in both types of plantations, with a significantly higher proportion in peasant plantations (83.60%) than in state plantations (73.8%), ANOVA (F (2.24 = 21.78), df = 24; p = 4.03 × 10⁻⁶). The findings indicate that the sustainable management of these plantations could incorporate agroecological principles to promote the coexistence of rubber production and biodiversity conservation while contributing to the restoration of degraded ecosystems and the well-being of local communities. Full article