Search Results (265)

Complex landslides have characteristics and parameters that are difficult to analyze. The landslide on 16 June 2024 in the rural community of Quilloturo (Tungurahua, Ecuador) caused severe damage (14 deaths, 24 injuries, and hundreds of affected families) related to the area’s geological, social, and anthropogenic conditions. Its location in the eastern foothills of Ecuador’s Cordillera Real exacerbated the effects of a landslide involving various processes (mud and debris flows, landslides, and rock falls). This event was preceded by intense rainfall lasting more than 10 h, which accumulated and caused natural damming of the streams prior to the event. The lithology of the investigated area includes deformed metamorphic and intrusive rocks overlain by superficial clayey colluvial deposits. The relationship between the geological structures found, such as fractures, joints, schistosity, and shear, favored the formation of blocks within the flow, making mass movement more complex. Geomorphologically, the area features a relief with steep slopes, where ancient landslides or material movements, composed of these colluvial deposits, have already occurred. At the foot of these steep slopes, on plains less than 300 m wide and bordered by the Pastaza River, there are human settlements with less than 60 years of emplacement and a complex history of territorial occupation, characterized by a lack of planning and organization. The memory of the inhabitants identified mass movements that have occurred since the mid-20th century, with the highest frequency of occurrence recorded in the last decade of the present century (2018, 2022, and 2024). Furthermore, it was possible to identify several factors within the knowledge of the inhabitants that can be considered premonitory of a mass movement, specifically a flood, and that must be incorporated as critical elements in decision-making, both individual and collective, for the evacuation of the area. Full article

Recent advances in wearable devices and physiological signal monitoring technologies have motivated research into non-invasive glucose estimation for diabetes management. However, the existing studies are often limited by sample constraints, in terms of relatively small numbers of subjects, and few address personalized differences. Physiological signals vary considerably for different individuals, affecting the reliability of accuracy measurements, and training data and test data are both used from the same subjects, which makes the test result more affirmative than the truth. This study aims to compare the two scenarios mentioned above, regardless of whether the testing/training involves the same individual, in order to determine whether the proposed training method has better generalization ability. The publicly available MIMIC-III dataset, which contains 700,000 data points for 10,000 subjects, is used to create a more generalized model. The model architecture uses a ResNet CNN + Transformer block, and data quality is graded during preprocessing to select signals with less interference for training to increase data quality. This preprocessing method allows the model to extract useful features without being adversely affected by noise and anomalous data that decreases performance; therefore, the model’s training results and generalization capability are increased. This study creates a model to predict blood glucose values from 70 to 250 for 180 classes, using mean absolute relative difference (MARD) as the evaluation metric and a Clarke error grid (CEG) to determine a reasonable error tolerance. For personalized cases (specific individual data during model training), the MARD is 11.69%, and the optimal Zone A (representing no clinical risk) in the Clarke error grid is 82.7%. Non-personalized cases (test subjects not included in the model training samples) using 60,000 unseen data yields MARD = 15.16%, and the optimal Zone A in the Clarke error grid is 75.4%. Across multiple testing runs, the proportion of predictions falling within Clarke error grid zones A and B consistently approached 100%. The small performance difference suggests that the proposed method has the potential to improve subject-independent estimation; however, further validation in broader populations is required. Therefore, the primary objective of this study is to improve subject-independent, non-personalized PPG-based glucose estimation and reduce the performance gap between personalized and non-personalized measurements. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

The microclimate of traditional blocks, a key component of urban fabric, directly affects the overall urban thermal environment. Creating a suitable microclimate is crucial for improving urban living quality. Field measurements, ENVI-met simulations, and the PET index were used to analyze the spatiotemporal variations and core drivers of thermal comfort. Temporally, five open space types showed a unimodal “rise–stabilization–fall” PET curve, with peak heat stress occurring at 11:00–14:00. Courtyards heated fastest, but green spaces had the most stable thermal environment because trees provided shading and transpiration for gentle cooling. Spatially, thermal comfort varied significantly. For example, green spaces rich in trees performed best (PET 5–8 °C lower than pure grassland), while squares and courtyards faced severe midday heat stress (PET mostly moderate or above). Alley comfort depended on aspect ratio and orientation—north–south alleys with an aspect ratio > 2 were 2–3 °C cooler than open spaces, but east–west or narrower alleys (aspect ratio < 1.5) and low-enclosed courtyard control apply to southern Hunan’s hot-humid zone. However, the synergistic principles can be extended to similar southern regions, providing technical reference for traditional block livability and climate-resilient cities. Full article

Background and Clinical Significance: Sacrococcygeal joint dislocation is an extremely rare traumatic condition in the pediatric population and is typically caused by direct trauma to the gluteal region. Most reported cases have been managed conservatively with analgesics or manual reduction, and the application of a caudal epidural block in children with this entity has, to our knowledge, never been previously described. Case Presentation: A 14-year-old girl presented with aggravated coccydynia following a second fall. Six months earlier, she had been diagnosed with sacrococcygeal dislocation after her initial fall, and her symptoms had been well controlled at a Numerical Rating Scale (NRS) score of 3 with acetaminophen and nonsteroidal anti-inflammatory drugs. However, after the recent reinjury, her pain worsened to an NRS score of 6 and did not improve despite continued conservative pharmacologic treatment. Radiographic examination at our institution confirmed anterior angular displacement of the coccyx. Accordingly, an ultrasound-guided caudal epidural block was performed using mepivacaine and dexamethasone. At follow-up evaluations conducted 2 weeks and 2 months after the procedure, her pain had decreased to an NRS score of 2, accompanied by functional improvement. Conclusions: This case suggests that caudal epidural block may serve as a safe and potentially effective therapeutic option for pediatric patients experiencing coccygeal pain following traumatic sacrococcygeal joint dislocation. Full article

The next frontier in postmenopausal osteoporosis management lies not in novel pharmacological agents, but in the systematic integration of mechanism-guided drug selection, fall prevention, and long-term adherence strategies into a unified patient-centered care model. This review is intended for clinicians and clinical researchers involved in the diagnosis, treatment, and long-term management of postmenopausal osteoporosis. We provide a mechanism-to-practice framework that explicitly maps each therapeutic class to the specific molecular pathway it targets: bisphosphonates inhibit osteoclast function downstream of RANKL activation; denosumab blocks RANKL directly at the cytokine level; romosozumab inhibits sclerostin to restore Wnt-mediated bone formation. This mechanistic foundation supports a risk-stratified treatment paradigm in which antiresorptives address accelerated remodeling in moderate-risk patients, while patients at very high fracture risk—characterized by severe bone deficit or recent fragility fractures—benefit from an anabolic-first approach followed by consolidation. Beyond drug selection, we examine the persistent treatment gap in which fewer than 20% of post-fracture patients receive therapy, arguing that fall prevention—responsible for >90% of hip fractures—and medication adherence deserve equal priority in clinical practice. We further analyze key controversies, including T-score- versus FRAX-based intervention thresholds, limitations of the trabecular bone score, cost-effectiveness constraints on anabolic-first sequencing, and evidence gaps in post-denosumab transition strategies. By synthesizing mechanistic insights, guideline recommendations, and critical appraisal of current limitations, this review offers not only an overview of existing knowledge but a coherent decision-support model aimed at improving fracture prevention through comprehensive, individualized care. Full article

Tight sandstone gas constitutes a strategically significant resource in the exploration of unconventional hydrocarbon systems. Current understanding of the geochemical composition and genesis of tight sandstone gas in the Daning–Jixian Block, southeastern Ordos Basin, is insufficient, which hampers a comprehensive assessment of its resource potential. This study is the first to systematically investigate the geochemical characteristics and genetic origin of high-maturity tight sandstone gas in the southeastern Ordos Basin’s Daning–Jixian Block. Gas specimens were systematically acquired from multiple stratigraphic units within the reservoir interval and subjected to compositional and carbon–hydrogen isotope analysis. Compared with other gas fields in the Ordos Basin, the Daning–Jixian Block has higher average methane concentration, and notably lower ethane and propane concentrations; its average δ¹³C₁ and δ²H-CH₄ is heavier, while δ¹³C₂ and δ¹³C₃ are lighter. Based on the δ¹³C₁-δ²H-CH₄ diagram, all gas samples from the block and other basin gas fields fall into the geothermal, hydrothermal and crystalline gas domain, indicating gas genesis associated with over-mature organic matter interacting with external hydrogen. Milkov genetic diagram analysis reveals that the natural gas consists of primarily early-stage kerogen-cracking gas, with a minor contribution from crude oil-derived gas originating from Carboniferous–Permian source rocks. Notably, samples from Daning–Jixian exhibit a unique δ¹³C₁ > δ¹³C₂ reversal, attributed to mixing effects between gas from highly mature kerogen and gas from secondary cracking of crude oil. Consequently, ethane carbon isotopes alone are insufficient for definitive genetic classification. These findings provide a new geochemical interpretation framework for analogous high-maturity tight gas reservoirs. Full article

The tectonic evolution of the Paleo-Asian Ocean during the Early to Middle Permian remains a key issue in understanding the geodynamic history of the Central Asian Orogenic Belt. To address this, we conducted petrological, whole-rock geochemical, zircon U–Pb geochronological, and Hf isotopic analyses of Early Permian biotite granodiorite and Middle Permian porphyritic granite from the south-central Great Xing’an Range. Zircon U–Pb dating yields ages of 273.2 ± 1.4 Ma and 264.4 ± 1.5 Ma, indicating that these intrusions emplaced during Early and Middle Permian. Geochemical analyses show that the rocks are characterized by high SiO₂ and Al₂O₃ contents, and low MgO and CaO contents and belong to the metaluminous to weakly peraluminous series, typical of I-type granites. The rocks are enriched in light rare earth elements and large-ion lithophile elements (e.g., Rb, Ba, K), but depleted in heavy rare earth elements and high field strength elements (e.g., Nb, Ta, P, Ti), with weakly negative Eu anomalies. The Early Permian pluton exhibits low-Sr and high-Yb characteristics and thus fall in the plagioclase stability field. In contrast, Middle Permian pluton was derived from magmas generated by partial melting under high-pressure conditions and that, underwent crystal fractionation during ascent to the mid-upper crust, ultimately forming low-Sr and low-Yb type granites. All zircon ε_Hf(t) values are positive (+4.84 to +14.87), with the corresponding two-stage Hf model ages ranging from 345 Ma to 980 Ma, indicating that the magmas were predominantly derived from juvenile crustal materials accreted during the Neoproterozoic to Phanerozoic. Considering these results, we propose that the Paleo-Asian Oceanic plate continued to subduct beneath the Songliao–Xilinhot block to the north during the Early to Middle Permian, with intense subduction and crustal thickening occurring in the Middle Permian. This suggests that the south-central segment of the Great Xing’an Range was situated in an active continental marginal setting during the Early-Middle Permian. Full article

As a fundamental and indispensable component of modern electronic devices, the printed circuit board (PCB) has a complex structure and highly integrated functions, with its manufacturing quality directly affecting the stability and reliability of electronic products. However, during large-scale automated PCB production, its surfaces are prone to various defects and imperfections due to uncontrollable factors, such as diverse manufacturing processes, stringent machining precision requirements, and complex production environments, which not only compromise product functionality but also pose potential safety hazards. At present, PCB defect detection in industry still predominantly relies on manual visual inspection, the efficiency and accuracy of which fall short of the automation and intelligence demands in modern electronics manufacturing. To address this issue, in this paper, we have made improvements based on the classical Faster-RCNN object detection framework. Firstly, ResNet-101 is employed to replace the conventional VGG-16 backbone, thereby enhancing the ability to perceive small objects and complex texture features. Then, we extract features from images by using deformable convolution in the backbone network to improve the model’s adaptive modeling capability for deformed objects and irregular defect regions. Finally, the Convolutional Block Attention Module is incorporated into the backbone, leveraging joint spatial and channel attention mechanisms to improve the effectiveness and discriminative power of feature representations. The experimental results demonstrate that the improved model achieves a 4.5% increase in mean average precision compared with the original Faster-RCNN. Moreover, the proposed method exhibits superior detection accuracy, robustness, and adaptability compared with mainstream object detection models, indicating strong potential for engineering applications and industrial deployment. Full article

Falls among the elderly represent a leading cause of injury and mortality worldwide, necessitating reliable and real-time monitoring solutions. This study aims to develop a lightweight, accurate, and efficient fall detection framework based on an improved YOLOv5s model. The proposed architecture incorporates a Convolutional Block Attention Module (CBAM) to enhance salient feature extraction, optimizes multi-scale feature fusion in the Neck for better small-object detection, and re-clusters anchor boxes tailored to the horizontal morphology of elderly falls. A multi-scene dataset comprising 11,314 images was constructed to evaluate performance under diverse lighting, occlusion, and spatial conditions. Experimental results demonstrate that the improved YOLOv5s achieves a mean average precision (mAP@0.5) of 94.2%, a recall of 92.5%, and a false alarm rate of 4.2%, outperforming baseline YOLOv5s and YOLOv4 models while maintaining real-time detection speed at 32 FPS. These findings confirm that integrating attention mechanisms, adaptive fusion, and anchor optimization significantly enhances robustness and generalization. Although performance slightly declines under extreme lighting or heavy occlusion, this limitation highlights future opportunities for multimodal fusion and illumination-invariant modeling. Overall, the study contributes a scalable and deployable AI framework that bridges the gap between algorithmic innovation and real-world elderly care applications, advancing intelligent and non-intrusive safety monitoring in aging societies. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

Cytokinin- and Auxin-Based Plant Growth Regulators (PGRs) are commonly employed to increase fruit size due to their ability to modulate cellular structure. This study aimed to evaluate the effects of different PGR application protocols on histological parameters, yield components, and fruit quality in ‘Maxi Gala’ apple. The experiments were carried out under humid subtropical conditions of southern Brazil across two growing seasons (2021/22 and 2022/23), allowing comparison of treatment performance under distinct climatic patterns. Data from common treatments were combined across years for integrated analysis. The PGRs used included 6-benzyladenine (BA) as a cytokinin source; naphthalene acetic acid (NAA) as an auxin source; and tryptophan, a precursor of auxin biosynthesis. PGRs were applied in various combinations and concentrations between 10 days after dormancy break (BBCH 01) and fruit diameters of 25–27 mm (BBCH 74), following a randomized block design with four replicates of twelve trees each. The multivariate analysis of treatments was performed using Principal Component Analysis (PCA). Additionally, an analysis of variance was performed for flesh firmness loss, with means compared using Tukey’s test (p < 0.05). PGRs significantly influenced only the histological parameters of the fruit flesh tissues. BA and tryptophan had the greatest effects on cell size and cell number in the fruit flesh, respectively, both reducing intercellular spaces. Tryptophan was associated with a higher number of smaller cells, whereas NAA promoted larger cell sizes. The combination of BA and NAA, as well as a single application of BA at petal fall, resulted in the highest yield performances and increased the proportion of large fruits. Furthermore, BA enhanced the percentage of red skin coloration and improved flesh firmness during storage. Full article

Rockfalls represent a widespread natural hazard that threatens infrastructures and settlements in mountainous and coastal areas. In Baunei (Sardinia, Italy), steep carbonate cliffs above the SS125 road frequently generate block detachments that endanger traffic and nearby urban areas. The present work adopts a quantitative risk assessment framework, consistent with the Swiss PLANAT guidelines, to evaluate the protective effectiveness of direct-protection forests in combination with engineered barriers. The framework integrates the key components of hazard, exposure, and vulnerability to quantify direct-impact risk and associated economic loss. Using Rockyfor3D simulations, three scenarios were analysed: bare slope, forest only, and forest plus protective works. The results demonstrate that vegetation markedly reduces both runout distance and kinetic energy of falling blocks, halving the direct-impact risk compared to bare-slope conditions. The addition of barriers further decreases residual exposure, with most trajectories intercepted and remaining impacts limited to low-energy classes. Monetised risk estimates confirm an 84% reduction with forest cover alone and near-complete mitigation when complemented by fences, except in short discontinuous segments. The proposed approach offers a replicable and cost-effective tool for rockfall risk management and sustainable protection forest planning in Mediterranean settings. Full article

This study presents MDSCNet, a compact radar image-based deep learning model for multi-action classification in elderly healthcare scenarios. Motivated by the need for real-time deployment on resource-constrained devices, MDSCNet employs a streamlined architecture with a small number of lightweight expansion–depthwise–projection blocks, removing complex attention and squeeze-and-excitation modules to minimize computational overhead. The model is evaluated on a millimeter-wave radar dataset covering five healthcare-related actions: lying, sitting, standing, bed-exit, and falling, performed by 15 participants on an actual electric nursing bed. The experimental results demonstrate that MDSCNet achieves accuracy comparable to state-of-the-art CNN-based methods while maintaining an extremely compact model size of only 0.29 MB, showing its suitability for practical elderly care applications where both accuracy and efficiency are critical. Full article

Urban recreational spaces (URSs) are pivotal for enhancing resident well-being, making the accurate assessment of public perceptions crucial for quality optimization. Compared to traditional surveys, social media data provide a scalable means for multi-dimensional perception assessment. However, existing studies predominantly rely on single-modal data, which limits the comprehensive capturing of complex perceptions and lacks interpretability. To address these gaps, this study employs cutting-edge large vision–language models (LVLMs) and develops an interpretable model, Qwen2.5-VL-7B-SFT, through supervised fine-tuning on a manually annotated dataset. The model integrates visual-linguistic features to assess four perceptual dimensions of URSs: esthetics, attractiveness, cultural significance, and restorativeness. Crucially, we generate textual evidence for our judgments by identifying the key spatial elements and emotional characteristics associated with specific perceptions. By integrating multi-source built environment data with Optuna-optimized machine learning and SHAP analysis, we further decipher the nonlinear relationships between built environment variables and perceptual outcomes. The results are as follows: (1) Interpretable LVLMs are highly effective for urban spatial perception research. (2) URSs within Beijing’s Third Ring Road fall into four typologies, historical heritage, commercial entertainment, ecological-natural, and cultural spaces, with significant correlations observed between physical elements and emotional responses. (3) Historical heritage accessibility and POI density are identified as key predictors of public perception. Positive perception significantly improves when a block’s POI functional density exceeds 4000 units/km² or when its 500 m radius encompasses more than four historical heritage sites. Our methodology enables precise quantification of multidimensional URS perceptions, links built environment elements to perceptual mechanisms, and provides actionable insights for urban planning. Full article