Search Results (2,288)

Background/Objectives: Type 2 Diabetes Mellitus (T2DM) is a prevalent metabolic disorder associated with major cardiovascular and metabolic complications. Regular physical activity is recommended for glycaemic management, but barriers such as obesity, joint pain, and impaired mobility may limit participation in land-based exercise. Aquatic exercise may provide a feasible alternative as water buoyancy reduces joint loading while allowing aerobic and resistance training. This systematic review and meta-analysis evaluated the effects of aquatic exercise interventions on glycaemic control in adults with T2DM. Methods: The review followed the PRISMA 2020 guidelines. MEDLINE, Cochrane CENTRAL, Scopus, Web of Science, and IEEE Xplore databases were searched. Randomised and non-randomised longitudinal studies involving adults aged ≥35 years with T2DM participating in structured aquatic exercise programmes were eligible. The primary outcome was glycated haemoglobin (HbA1c). Risk of bias was assessed using RoB 2 and RoBANS 2, and certainty of evidence was evaluated using GRADE. Random-effects meta-analysis calculated mean differences (MDs) with 95% confidence intervals. Results: Eleven randomised controlled trials involving 335 participants were included. Aquatic exercise significantly reduced HbA1c compared with passive control conditions (MD = −0.76%; 95% CI −1.21 to −0.32), although heterogeneity was high. No significant differences were observed between aquatic and land-based exercise interventions. Eight studies used wearable heart-rate monitors to regulate exercise intensity. Conclusions: Aquatic exercise may improve glycaemic control compared with sedentary conditions and yields effects comparable to those of land-based exercise in adults with T2DM. Further high-quality trials are needed to clarify optimal exercise dose–response and evaluate more advanced wearable technologies. Full article

Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. Full article

The Stretch-Shortening Cycle (SSC) is a fundamental neuromuscular mechanism that enhances explosive performance by storing and releasing elastic energy, particularly through the Achilles tendon. While tendon stiffness and neuromuscular coordination are known to influence SSC efficiency, the role of ankle joint mobility in this context remains unclear. This study aimed to investigate the relationship between ankle dorsiflexion range of motion and SSC contribution during vertical jump performance in trained adults. Twenty-seven physically active participants (19 males, 8 females) were assessed for ankle dorsiflexion using the Leg Motion system and performed both Squat Jump (SJ) and Countermovement Jump (CMJ) on a force platform. SSC contribution was calculated as the difference between CMJ and SJ heights, expressed in both absolute (centimeters) and relative (percentage) terms. Participants were categorized into high and low mobility groups based on the median dorsiflexion value (13 cm). Statistical analyses, including Pearson correlation (r = −0.262, p = 0.186), linear regression (R² = 0.069), and independent t-tests, showed no significant association between ankle mobility and SSC contribution. No meaningful performance differences were observed between the two groups in CMJ, SJ, or SSC metrics. These findings suggest that ankle dorsiflexion does not independently predict SSC utilization in vertical jumping among trained individuals. Other factors such as tendon stiffness, explosive strength, and neuromuscular coordination may play a more decisive role. Future research should include dynamic mobility assessments and more diverse populations to better understand the interplay of these variables. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

Background: Work-related musculoskeletal disorders (WMSDs) are prevalent among orthopaedic surgeons as a result of prolonged exposure to non-neutral postures and forceful manual tasks during surgery. Although working height is a key determinant of trunk and upper-limb posture, the systematic evaluation of ergonomic working-height recommendations in orthopaedic surgery remains limited. Methods: A simulated left total knee arthroplasty (TKA) was divided into twelve critical surgical steps and analysed across four commonly used surgeon positions (A–D). Two conditions were compared: uncorrected working height (N) and working height corrected according to Canadian Centre for Occupational Health and Safety (CCOHS) recommendations (C). Joint angles were measured from standardized photographs using Kinovea software, and postural load was quantified with the Rapid Entire Body Assessment (REBA) method. Two trained evaluators conducted three independent assessments, yielding 288 REBA scores. Results: Mean REBA scores decreased across all surgeon positions following ergonomic correction, with statistically significant reductions observed in positions A, B, and D. When pooled across all position–step combinations (n = 48), the mean reduction was 0.92 REBA points (95% CI 0.50–1.33; p < 0.001). Notably, 27 of the 48 position–step comparisons exceeded the minimal detectable change threshold. The largest reductions occurred during force-intensive surgical steps, including bone cutting, drilling, and implant impaction. Conclusions: Adjusting working height in accordance with CCOHS ergonomic recommendations reduces surgeons’ postural load during TKA. These findings support the integration of evidence-based ergonomic adjustments into routine orthopaedic surgical practice. Full article

Accurate trajectory tracking of redundant manipulators is difficult because the controller must simultaneously model local couplings between adjacent joints and global dependencies across the whole kinematic chain. Existing reinforcement learning methods typically employ multilayer perceptrons, which do not explicitly exploit manipulator structure and therefore show limited stability and representation ability in high-dimensional continuous control tasks. This paper proposes GT-TD3, a Graph Transformer-enhanced-Twin Delayed Deep Deterministic Policy Gradient framework, for redundant manipulator trajectory tracking. The proposed actor first converts the raw system state into joint-level node features and uses a graph neural network to extract local kinematic coupling information. A Transformer is then employed to capture long-range dependencies among joints. To strengthen the use of structural priors, topology- and distance-related bias terms are incorporated into the attention mechanism, enabling the network to encode manipulator structure during global feature learning. Experiments on a 7-DoF KUKA iiwa manipulator in PyBullet demonstrate that GT-TD3 outperforms MLP, pure GNN, and pure Transformer baselines in tracking performance. The proposed method achieves more stable training, faster convergence, and smoother and more accurate end-effector motion. The results show that the integration of local graph modeling and structure-aware global attention provides an effective solution for high-precision trajectory tracking of redundant manipulators. Full article

Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research. Full article

Traditional cognitive diagnosis models typically rely on expert-annotated Q-matrices to define the relationship between exercises and knowledge concepts. This process is not only highly subjective and costly, but also prone to introducing noise and bias, which directly affects diagnostic accuracy. Meanwhile, most existing deep learning-based methods overlook the rich semantic information contained in concept descriptions, making it difficult to deeply model the intrinsic relationships among knowledge points, resulting in limited interpretability of the models. To address these issues, this paper proposes a cognitive diagnosis model that incorporates key textual information from concept descriptions to refine the Q-matrix (KECQCD). The core innovation of the model lies in leveraging the pre-trained language model RoBERTa to encode concept texts, fusing semantic features with identifier embeddings through a gating mechanism to construct semantically-enhanced concept representations. It designs a concept-exercise heterogeneous information network and employs a graph attention mechanism to adaptively aggregate node features, explicitly modeling high-order knowledge dependencies. Furthermore, a multi-task joint learning framework is established to predict student performance while dynamically correcting association errors in the initial Q-matrix. Experimental results on the public Junyi dataset show that the KECQCD model significantly outperforms mainstream baseline models across multiple metrics, including accuracy (ACC), area under the curve (AUC), and root mean square error (RMSE). Ablation studies confirm the effectiveness of each core module, and diagnostic consistency (DOA) evaluation further demonstrates the enhanced interpretability of the model’s outcomes. This research offers a new solution for building accurate, reliable, and interpretable cognitive diagnosis systems, contributing positively to the advancement of personalized intelligent education. Full article

To enhance flight safety and operational efficiency, extracting key actions, flight parameters, and status information from civil aviation flight crew instructions generated during pre-flight and in-flight procedures is crucial. However, such texts are highly condensed and involve complex multi-role interactions, easily leading to entity boundary drift and category misclassification. To address this, this paper proposes a joint key information extraction framework based on a lightweight pre-trained language model (TinyBERT) and a Role-Aware Fusion mechanism, abbreviated as TR-BiGRU-CRF. This framework introduces the Role-Aware Fusion mechanism to resolve semantic ambiguity caused by multi-party interactions, utilizes TinyBERT for semantic representation that balances accuracy and computational efficiency, and employs BiGRU-CRF for robust sequence feature modeling and decoding. Experiments on a flight crew instruction dataset show that the proposed method achieves 92.2% precision, 91.8% recall, a 92.0% F1 score, and an overall prediction accuracy of 92.6%. Compared to the BiGRU-CRF baseline, it significantly improves accuracy, precision, and F1 score by 11.4, 13.3, and 13.5 percentage points, respectively. These results prove that the proposed method effectively mitigates boundary drift and category confusion, providing strong support for flight crew instruction understanding and safety decision-making. Full article

Background: Spare parts distribution networks (DNs) play a strategic role in retailers’ profitability. Among DN configuration decisions, selecting the optimal stock deployment policy—centralised, decentralised, or hybrid inventory allocation across distribution centres (DCs)—critically affects service levels and logistics costs. This decision becomes more complex with additive manufacturing (AM) as an alternative to conventional manufacturing (CM). While AM enables production with shorter lead times, its higher costs alter stock deployment cost-effectiveness. Given the complexity of joint stock deployment and manufacturing decisions, retailers require decision support systems (DSSs). Methods: To address this need, we develop a DSS through a three-step methodology: (i) a mathematical model evaluates logistics costs across different stock deployment policies and manufacturing technologies; (ii) parametric analysis tests the model across 2000 realistic scenarios; (iii) Random Forest trained on this dataset predicts optimal solutions, with SHapley Additive exPlanations (SHAP) interpreting post hoc recommendations. Results: The DSS achieves 93.4% prediction accuracy—outperforming (+16.4%) the only comparable literature DSS (77%)—while explaining recommendations. SHAP reveals that AM and CM unit costs dominate decision-making, followed by backorder costs. Conclusions: Beyond individual spare parts recommendations, the DSS provides guidelines enabling retailers to maintain cost-effective DNs aligned with evolving customer needs and to plan valuable investments in AM. Full article

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates adverse-weather restoration as a paired-domain completion problem in a shared continuous token space. Specifically, WeatherMAR concatenates degraded and clean token sequences into a joint paired-domain sequence and performs restoration through masked autoregressive modeling, in which self-attention enables direct cross-domain interaction. To strengthen conditional learning while avoiding trivial paired correspondences, we introduce complementary bidirectional masking together with an optional reverse objective used only during training to encourage degradation-aware representations. WeatherMAR further employs a conditional diffusion objective for continuous token prediction and adopts a progress-to-step schedule to improve inference efficiency. Extensive experiments on standard multi-weather benchmarks, including Snow100K, Outdoor-Rain, and RainDrop, show that WeatherMAR achieves the best PSNR/SSIM on Snow100K-S (38.14/0.9684), the best SSIM on Outdoor-Rain (0.9396), and the best PSNR on Snow100K-L (32.58) and RainDrop (33.12). These results demonstrate that paired-domain token completion provides an effective solution for adverse-weather restoration. Full article

In real-world machine learning scenarios, training data are frequently weakly annotated and distributionally misaligned with deployment environments. Specifically, label ambiguity may arise when each instance is associated with a set of candidate labels, and distribution shifts between training and testing are common in practice. Although Partial Label Learning (PLL) and Unsupervised Domain Adaptation (UDA) have been extensively studied individually, they frequently co-occur in practice. For instance, in cross-hospital medical image analysis, datasets may exhibit both inconsistent diagnostic labels due to variations in expert interpretation (label ambiguity) and significant differences in imaging equipment or patient demographics (distribution shift). However, Partial-Label Unsupervised Domain Adaptation (PLUDA) has received limited attention as a unified problem. In this paper, a unified generalization bound is established for Partial-Label Unsupervised Domain Adaptation (PLUDA) and three critical limitations causing existing approaches to fail: ambiguity degree, ideal joint error, and model complexity remain uncontrolled. Motivated by these theoretical insights, we propose Dual-Smoothing over Manifold and Parameter (DSMP) to control all three factors. DSMP employs manifold-based representation smoothing via Laplacian smoothing based on adaptive multi-kernel RKHS similarity and candidate set refinement to address the three critical limitations. Moreover, DSMP leverages sharpness-aware parameter smoothing to ensure stable optimization under weak supervision through loss landscape flattening. Extensive experiments demonstrate that DSMP outperforms existing baselines, achieving superior cross-domain generalization from weakly labeled sources. This work provides theoretical insights and a principled solution to the previously underexplored yet practically important PLUDA problem. Full article

This paper proposes a joint optimization algorithm based on reinforcement learning to address the time slot and power allocation problem in underwater acoustic communication networks (UACNs). By maximizing the total capacity of successful transmissions as the optimization objective, two sub-objectives are formulated corresponding to time-slot scheduling and power allocation. The sub-objective corresponding to time-slot scheduling is addressed by constructing a Markov Decision Process (MDP) model based on Deep Q-Network (DQN) learning. In this model, the agent learns the time slot allocation policy with the goal of increasing the number of successfully transmitted links while reducing the collision. For the sub-objective corresponding to power allocation, another MDP model is developed, solved by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, in which each underwater transmission node acts as an independent agent. The MADDPG approach enables the system to improve channel capacity under energy limitation, which maximizes the total capacity of successfully transmitted links. In terms of model execution, the DQN adopts a centralized training and time slot allocation, while MADDPG uses a centralized training and distributed execution to select the transmission power by each node. Simulation results show that the proposed joint optimization algorithm demonstrates better performance in the number of successfully transmitted links and channel capacity compared to TDMA, Slotted ALOHA, and other algorithms. Full article

With the large-scale integration of renewable energy and the increasing use of power electronics, the issue of wide-band oscillations in power grids has become increasingly prominent. The scarcity and uneven distribution of oscillation samples pose significant challenges for training data-driven models, and traditional generative models struggle to ensure fidelity in both time and frequency domains. To address this, this paper proposes a Time–Frequency Fusion Generative Adversarial Network (TFF-GAN) for generating power grid oscillation risk scenarios. The method constructs a dual-path generation and discrimination framework, where the generator decomposes the signal using Short-Time Fourier Transform (STFT), with time-domain features extracted by a convolutional neural network (CNN) and frequency-domain features extracted from the STFT representation by a dedicated spectral network. These features are then fused using a U-Net structure. The discriminator simultaneously evaluates the authenticity of both the time-domain waveform and the frequency-domain spectrum. A composite loss function, incorporating time-domain loss, frequency-domain loss, and adversarial loss, is used for joint optimization. Experimental results demonstrate that the proposed method generates oscillation scenarios with high fidelity in both time-domain waveforms and frequency-domain spectra, effectively supporting power grid oscillation risk assessment and control strategy validation. Full article

Hammer throw is a complex discipline requiring strength, refined technique, and precise inter-segmental coordination. We developed an IoT-enabled system with inertial measurement units (IMUs) to provide objective, real-time analytics for coaches and athletes. IMUs were mounted on the hip, knee, and ankle to capture tri-axial acceleration and angular velocity during the throwing action. Data were streamed wirelessly and processed to extract rotation rate profiles, joint coordination metrics, and temporal events (winds, turns, and release). Two collegiate athletes performed 10 throws, and the results were compared with video-based analysis. The IMU system captured finer-grained variations in angular velocity and acceleration during rapid rotation phases and achieved an accuracy of 93.5% in classifying higher- and lower-quality throws using cross-validated models. The system developed enables quantitative feedback and continuous progress tracking in training. The feasibility of IMU + IoT integration for hammer throw performance analysis provides a foundation for AI-assisted, on-field decision support. Full article