Search Results (17,440)

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

Man-made floods from dams are intentional for different purposes, e.g., spreading sediment and helping deltaic development. Less is known about their effects on slack-water deposits (SWDs) in downstream channels. Since the implementation of the Water and Sediment Regulation Project (WSRP) through a large dam on China’s Yellow River (YR) in 2002, the dynamic sedimentary environment of the river has undergone significant changes. To understand the sedimentary responses of the downstream channels to the man-made floods, this study was conducted following a 24-day man-made flood period in 2021 to investigate SWDs on the floodplains. Sediment samples were collected from four floodplain sites in the lowermost reach of the YR. The study showed that the median grain size (D₅₀) of the man-made flood SWDs on the floodplains ranges from 17 to 131 μm, with an average of 44.14 μm, classifying them as fine-grained deposits. Spatially, D₅₀ of 57.2% of the sampled SWDs exhibited an increasing trend from the riverbank to the main channel. This finding indicates that during the deposition process of floodplain floods, differences may exist in the direction perpendicular to the riverbank. Along the upstream-to-downstream direction, no obvious regularity was observed. Moreover, there is no positive correlation between sediment discharge and the average grain size of suspended sediment. These findings indicate that large man-made floods by a dam will not allow finer particles to settle. Such changes in sediment transport may have a long-term effect on Yellow River deltaic development and stability. Full article

In this study, we investigate how computationally simplified activation functions affect predictive performance, inference latency, and energy usage in long short-term memory-based temperature prediction for wind turbine generator bearings. We tested three different types of long short-term memory (LSTM) cells, along with bidirectional LSTM (biLSTM) networks, to determine their effectiveness in modeling dynamic changes in gearbox bearing temperatures. We compared several activation-function variants, focusing on variants that are either computationally simple or known to give good performance in deep recurrent networks. The results show that the best-performing architectures achieved root mean squared errors (RMSEs) between $0.0798$ and $0.0822$, corresponding to coefficients of determination in the range of $R^2=0.84$–$0.85$. When applied across five turbines, the best-performing architectures (peephole and bidirectional) achieved root mean squared errors of $0.0898$, $0.0882$, and $0.042$, respectively. The best activation function-enhanced variant (the peephole) improved accuracy by approximately $3\%$ while maintaining low model complexity. These findings provide a practical and efficient solution for embedded predictive maintenance systems, providing high accuracy without incurring the computational cost of deeper or bidirectional architectures. Full article

The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a memristor synapse device allows ultra-high-density integration by internetworking with crossbar arrays, which benefits large-scale training and learning using advanced machine-learning algorithms. In this review, we present a statistical analysis of neuromorphic computing device publications from 2018 to 2025, focusing on various memristive systems. Furthermore, we provide a device-level perspective on biomimetic properties in hardware neural networks such as short-term plasticity (STP), long-term plasticity (LTP), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP). Herein, we highlight the utilization of optoelectronic synapses based on 2D materials driven by a sequence of optical stimuli to mimic the plasticity of the human brain, further broadening the scope of memristor controllability by optical stimulation. We also highlight practical applications ranging from MNIST dataset recognition to hardware-based pattern recognition and explore future directions for memristor synapses in healthcare, including artificial cognitive retinal implants, vital organ interfaces, artificial vision systems, and physiological signal anomaly detection. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Transformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information. However, the rigid patch partitioning in vanilla self-attention destroys spatial structures and consistency in observed scenes, leading to limited CD performance. In this paper, we propose a novel dual-scale pixel aggregation transformer (DSPA-Former) to mitigate this issue. The core of DSPA-Former lies in a dynamic superpixel tokenization strategy and bidirectional dual-scale interaction within the learned feature space, which preserves semantic integrity while capturing long-range dependencies. Specifically, we design a hierarchical decoder that integrates multiscale features through specialized mechanisms for pixel superpixel dialogue, guided feature enhancement, and adaptive multiscale fusion. By modeling the homogeneous properties of spatial information via superpixel segmentation, DSPA-Former effectively maintains structural consistency and sharpens change boundaries. Comprehensive experiments on the LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that DSPA-Former achieves superior performance compared to state-of-the-art methods, particularly in preserving the structural integrity of complex change regions. Full article

Knowledge of deep-sea amphipods remains much more limited compared to that of shallow-water or more accessible marine habitats, although there has been an increasing scientific interest in recent decades. Deep-sea amphipods are mainly scavengers and detritivores, playing a role in organic matter recycling; however, their species richness may be underestimated, especially in understudied realms like the deep Mediterranean Sea. Long-term monitoring data are limited, hindering understanding of trends or human impacts. The present work aims to address this gap. In a previous study (1993–1996), twenty-two species of amphipods were identified from samples collected at depths between 734 and 1558 m along the Israeli coast. After twenty years, 16 sites were sampled in 2013 at depths ranging from 198 to 1812 m. Amphipod assemblage and its bathymetric distribution were analyzed to enhance knowledge of the taxon’s occurrence. Full article

Purpose: In this systematic review, we compare the effectiveness of high-intensity laser therapy (HILT) and extracorporeal shockwave therapy (ESWT) in treating lateral elbow tendinopathy (LET). Methods: A comprehensive search of PubMed, the Cochrane Library, and EMBASE was conducted from database inception to 23 June 2025 to identify randomized controlled trials (RCTs) comparing the two interventions. The primary outcome was pain intensity (visual analog scale or numeric rating scale). Secondary outcomes included upper-limb disability (qDASH), grip strength (pain-free or maximal), ultrasound-measured common extensor tendon thickness, and safety (adverse events and withdrawals). Two reviewers independently extracted data and assessed methodological quality using the Physiotherapy Evidence Database (PEDro) scale; the certainty of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Effects were synthesized as SMD (95% CI) using random- or fixed-effects models based on heterogeneity (I²). Significance was set at p < 0.05. Results: Four RCTs met the inclusion criteria and 169 participants were included. Methodological quality was moderate, with moderate-quality evidence indicating a significant improvement in short-term and medium-term upper-limb function in favor of HILT (SMD = −0.42; 95% CI: −0.73 to −0.12 and SMD = −0.50; 95% CI: −0.94 to −0.06, respectively). Evidence ranging from low to moderate quality showed no significant differences between the HILT and ESWT groups in terms of short-term or medium-term resting pain (SMD = −0.50; 95% CI: −1.15 to 0.16 and SMD = −0.42; 95% CI: −1.06 to 0.22, respectively), short-term or medium-term activity pain (SMD = −0.38; 95% CI: −1.05 to 0.29 and SMD = −0.73; 95% CI: −1.65 to 0.19, respectively), short-term or medium-term grip strength (SMD = 0.24; 95% CI: −0.20 to 0.67 and SMD = 0.20; 95% CI: −0.16 to 0.55, respectively), or short-term or medium-term common extensor tendon thickness (SMD = 0.04; 95% CI: −0.50 to 0.59 and SMD = −0.00; 95% CI: −0.55 to 0.55, respectively). Conclusions: HILT appears to offer significant benefits in improving upper-limb function at short-term (<1 month) and medium-term (1–3 months) follow-up. Regarding pain, grip strength, and tendon thickness, the pooled effects did not show clear between-group differences. Evidence certainty ranged from low to moderate, demonstrating that trials with a follow-up period beyond 3 months are needed to evaluate long-term efficacy. Systematic review registration number: PROSPERO: CRD420251026387. Full article

Fibromyalgia is a chronic condition characterized by widespread pain, fatigue, and reduced quality of life. Exercise therapy, including Pilates, is commonly recommended; however, current reviews report inconsistent findings across specific modalities. This PRISMA 2020 systematic review and meta-analysis with a PROSPERO-registered protocol, designed as a focused update of post-2020 RCTs complementing prior comprehensive syntheses, evaluated Pilates-based interventions for pain and fibromyalgia impact (FIQ). HRQoL outcomes were synthesized narratively due to heterogeneity in measurement instruments, and all outcomes were extracted at the first post-intervention assessment (no pooled long-term data were available). Seven RCTs (6–12 weeks; 2–3 sessions/week) met eligibility criteria. Methodological quality was generally moderate (PEDro), and risk of bias was assessed using RoB 2. Certainty of evidence (GRADE) was rated very low for pain and low for FIQ. Among trials reporting adherence (4/7), values ranged from 68% to 92%; adverse event monitoring was inconsistent (systematically reported in 2/7), limiting tolerability conclusions. Between-group effects versus active comparators were small and non-significant for pain (pooled Hedges’ g = −0.10, 95% CI [−0.83, 0.63], p = 0.79; I² = 73%); this wide interval, spanning potential benefit to harm, precludes definitive conclusions. For FIQ, the primary (unadjusted) analysis was non-significant: pooled MD = −5.53 (95% CI [−11.96, 0.89], p = 0.09); sensitivity analysis using ANCOVA-adjusted estimates yielded MD = −6.71 (95% CI [−13.11, −0.30], p = 0.04). Both estimates remained below MCID thresholds and were sensitive to estimator choice. Absence of statistical significance does not demonstrate equivalence; non-inferiority designs with predefined margins would be required. Given very low (pain) to low (FIQ) certainty of evidence, adequately powered trials with standardized protocols and longer follow-up are needed to resolve uncertainty regarding Pilates’ comparative effectiveness within multimodal fibromyalgia management. Full article

Background/Objectives: Postoperative cognitive dysfunction (POCD) represents a significant and potentially preventable complication in elderly patients undergoing colorectal cancer surgery, with reported incidence ranging from 2.8% to 62.2% depending on perioperative management strategies and assessment methods. This narrative review synthesizes current evidence on the epidemiology, pathophysiology, risk factors, and prevention strategies for POCD in this vulnerable population. Methods: A comprehensive narrative review was conducted to examine the current literature on POCD in elderly colorectal cancer patients. Evidence was synthesized from published studies addressing epidemiology, assessment tools, risk factors, pathophysiological mechanisms, and prevention strategies, with a particular focus on Enhanced Recovery After Surgery (ERAS) protocols and multicomponent interventions. Results: Advanced age, pre-existing cognitive impairment, frailty, and surgical complexity emerge as key risk factors for POCD. ERAS protocols demonstrate substantial protective effects, reducing POCD incidence from 35% under conventional care to as low as 2.8% in optimized pathways. The pathophysiology involves multifactorial mechanisms, including neuroinflammation, blood–brain barrier disruption, neurotransmitter dysregulation, and oxidative stress, with surgical trauma triggering systemic inflammatory cascades that activate microglial responses within the central nervous system. Evidence-based prevention strategies include preoperative cognitive and frailty screening, minimally invasive surgical techniques, multimodal opioid-sparing analgesia, regional anesthesia, depth-of-anesthesia monitoring, and structured postoperative care bundles adapted from the Hospital Elder Life Program. Conclusions: The integration of comprehensive perioperative cognitive care protocols represents a critical priority as surgical volumes in elderly populations continue to expand globally. Emerging directions include biomarker development for early detection and risk stratification, precision medicine approaches targeting individual vulnerability profiles, and novel therapeutic interventions addressing neuroinflammatory pathways. Standardized assessment tools, multidisciplinary collaboration, and implementation of evidence-based preventive interventions offer substantial promise for preserving cognitive function and improving long-term quality of life in elderly colorectal cancer patients. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

Al-Cu-Fe quasicrystalline coatings were prepared using detonation spraying, followed by heat treatment at 450 °C for varying durations. Reciprocating sliding wear tests were conducted using an MTF-5000 tribological tester to investigate the tribological behavior of the coatings with varying phase compositions and contents. The results show that heat treatment significantly influences the phase composition and tribological behavior of the quasicrystalline coating. Regarding the phase composition, as the heat treatment duration increased, the phase constitution of the coating evolved from the initial three phases to five phases. The content of the quasicrystalline I phase remained essentially constant with increasing heat treatment time, but exhibited a notable decrease at 241 h mark. For the friction coefficient, shorter heat treatment times resulted in a relatively low range (0.35–0.37), while excessively long heat treatment times led to a significant increase in the friction coefficient (0.44–0.48). Regarding the wear rate, it decreased approximately linearly with increasing heat treatment time, reaching a minimum value after 136 h of treatment. At this point, it is the optimal heat treatment time. In essence, heat treatment modifies the wear mechanism and wear resistance of the coating by altering its phase composition and mechanical properties. Full article

Since the compressor system in underground gas storage (UGS) facilities operates under highly dynamic and complex injection conditions, traditional rule-based operation and mechanism-based modeling approaches prove inadequate for meeting the stringent requirements of high-accuracy prediction under such variable conditions. To address this, a data-driven two-phase prediction framework for compressor energy consumption is proposed. In the first phase, a convolutional neural network with efficient channel attention (CNN-ECA) is developed to accurately forecast key operating condition parameters. Based on these outputs, the second phase employs a compressor performance prediction model to estimate unit energy consumption with improved precision. In addition, a hybrid prediction strategy integrating a Transformer architecture is introduced to capture long-range temporal dependencies, thereby enhancing both single-step and multi-step forecasting performance. The proposed method is evaluated using operational data from eight compressors at the Xiangguosi underground gas storage. Experimental results show that the framework achieves high prediction accuracy, with a MAPE of 4.0779% (single-step) and 4.2449% (multi-step), outperforming advanced benchmark models. Full article

Piezochromic nanomaterials, whose optical responses can be reversibly tuned by mechanical stimuli, have recently gained prominence as versatile platforms for strain-programmable light–matter interactions. Their mechanically responsive band structures, excitonic states, and defect energetics have enabled a wide range of optoelectronic demonstrations—including pressure-tunable emitters, reconfigurable photonic structures, and adaptive modulators—which collectively highlight the unique advantages of mechanical degrees of freedom for controlling optical functionality. These advances naturally suggest new opportunities in photovoltaic technologies, where experimentally validated phase stabilization and defect reorganization under low-strain thin-film conditions could address long-standing limitations in solar absorbers and device stability. Meanwhile, stress-mediated bandgap tuning—largely inferred from high-pressure laboratory studies—presents a conceptual blueprint for future adaptive spectral response and structural self-monitoring. However, the application of these mechanisms faces a major challenge in bridging the magnitude gap between GPa-level high-pressure phenomena and the low-strain regimes of realistic operational environments. Future development requires advances in low-threshold responsive materials, innovative strain-amplifying device architectures, and the pursuit of intelligent, multi-functional system integration. Full article