Search Results (4,515)

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

The growing demand for innovation and the use of diverse materials in cementitious composites necessitate predictive models that account for material variability. Numerical, code-based, and machine learning (ML) models have been developed to predict various concrete properties. However, their accuracy is significantly influenced by factors such as mix design, composition, intrinsic properties, and external conditions. Developing robust models that integrate these variables is essential for improving predictive accuracy and optimizing material performance. This paper presents a comprehensive review of numerical, code-based, and ML modelling techniques for predicting both fresh and long-term concrete properties. Since both numerical and ML models rely on experimental data—either to determine coefficients in numerical approaches or to train ML models—data gathering, preprocessing, and handling are crucial for model performance. Previous studies indicated that data variability significantly impacts accuracy, emphasizing the importance of effective preprocessing. While larger datasets generally improve reliability, some models achieve high accuracy even with very limited data. This review not only demonstrates the superior performance of ML models over traditional numerical approaches but also highlights the relative effectiveness of different ML algorithms based on reported accuracy metrics. ML-based approaches, including both ensemble and non-ensemble models, have exhibited strong predictive capabilities across a wide range of concrete property categories. In contrast, traditional numerical models often yield lower accuracy, although modified versions that incorporate additional parameters have shown improved performance. Furthermore, the integration of optimization algorithms and interpretability tools enhances both predictive reliability and model transparency—critical aspects that are often overlooked. Full article

Pain management in multiple trauma patients presents a complex clinical challenge due to competing priorities such as hemodynamic instability, polypharmacy, coagulopathy, and the urgency of life-saving interventions. In this context, peripheral nerve blocks (PNBs) are increasingly recognized as a valuable asset for their role in managing pain in patients with multiple traumatic injuries. By reducing reliance on systemic opioids, PNBs support effective pain control and facilitate early mobilization, aligning with enhanced recovery principles. This narrative review summarizes current evidence on the use of PNBs in the context of polytrauma, focusing on their analgesic efficacy, integration within multimodal analgesia protocols, and contribution to improved functional outcomes. Despite these advantages, clinical application is limited by specific concerns, including the potential to mask compartment syndrome, the risk of nerve injury or local anesthetic systemic toxicity (LAST), and logistical barriers in acute trauma settings. Emerging directions in the field include the refinement of ultrasound-guided PNB techniques, the expanded use of continuous catheter systems, and the incorporation of fascial plane blocks for anatomically complex or multisite trauma. Parallel efforts are focusing on the development of decision-making algorithms, improved risk stratification tools, and integration into multimodal analgesic pathways. There is also growing emphasis on standardized clinical protocols, simulation-based training, and patient education to enhance safety and consistency in practice. As evidence continues to evolve, the long-term impact of PNBs on functional recovery, quality of life, and healthcare utilization must be further explored. With thoughtful implementation, structured training, and institutional support, PNBs may evolve into a cornerstone of modern trauma analgesia. Full article

Background: This systematic review evaluates the efficacy of rehabilitation-focused exercise interventions for lumbar degenerative disc disease (DDD), a leading cause of chronic low back pain. Methods: Following PRISMA guidelines, a comprehensive search was conducted across international and regional databases (PubMed, Scopus, Web of Science, Magiran, SID, and Noormags) covering the period from January 2010 to January 2025. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251088811. Using keywords such as “lumbar DDD,” “exercise therapy,” and “rehabilitation,” a total of 2495 records were identified. After screening, 20 studies—including clinical trials, quasi-experimental, and experimental designs—met the inclusion criteria and were assessed using the McMaster Critical Review Form for Quantitative Studies. Results: Interventions such as hydrotherapy, core stability training, Pilates, and suspension exercises were found to significantly reduce pain and improve functional outcomes. While multimodal approaches (e.g., aquatic exercise combined with acupuncture) showed positive effects, the comparative studies revealed no significant differences between modalities. Suspension training demonstrated superior efficacy in pain reduction compared to isolated core stability exercises. The methodological quality of included studies ranged from good to excellent, with the majority rated as very good or excellent (McMaster scores: 8 “excellent,” 7 “very good,” and 5 “good”). Common limitations among the studies included methodological heterogeneity, small sample sizes (n = 14–30), and insufficient long-term follow-up. Conclusions: Exercise-based rehabilitation is an effective strategy for managing lumbar DDD. Evidence particularly supports the use of suspension training and aquatic therapy for superior improvements in pain and functional outcomes. Future research should aim to adopt standardized protocols, recruit larger sample sizes, and include extended follow-up periods to produce more robust and generalizable findings. Full article

Background: Plant-based protein supplementation in supporting muscle recovery following resistance exercise remains an area of growing interest, particularly among vegan athletes, as a potential alternative to animal-based proteins. This systematic review aimed to evaluate the effectiveness of plant-based proteins on recovery from resistance exercise-induced muscle damage in healthy young adults. Methods: A systematic and comprehensive search was administered in eight databases up to 1 May 2025, identifying 1407 articles. Following deduplication and screening, 24 studies met the eligibility criteria, including 22 randomized controlled trials and 2 non-randomized studies, with the majority from high income western countries. Results: Interventions primarily involved soy, pea, rice, hemp, potato, and blended plant protein sources, with doses ranging from 15 to 50 g, typically administered post resistance exercise. Outcomes assessed included muscle protein synthesis (MPS), delayed-onset muscle soreness (DOMS), inflammatory biomarkers, muscle function, and fatigue. The review findings reaffirm that single-source plant proteins generally offer limited benefits compared to animal proteins such as whey, particularly in acute recovery settings, a limitation well-documented consistently in the literature. However, our synthesis highlights that well-formulated plant protein blends (e.g., combinations of pea, rice, and canola) can stimulate MPS at levels comparable to whey when consumed at adequate doses (≥30 g with ~2.5 g leucine). Some studies also reported improvements in subjective recovery outcomes and reductions in muscle damage biomarkers with soy or pea protein. However, overall evidence remains limited by small sample sizes, moderate to high risk of bias, and heterogeneity in intervention protocols, protein formulations, and outcome measures. Risk of bias assessments revealed concerns related to detection and reporting bias in nearly half the studies. Due to clinical and methodological variability, a meta-analysis was not conducted. Conclusion: plant-based proteins particularly in the form of protein blends and when dosed appropriately, may support muscle recovery in resistance-trained individuals and offer a viable alternative to animal-based proteins. However, further high-quality, long-term trials in vegan populations are needed to establish definitive recommendations for plant protein use in sports nutrition. Full article

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article

In wind tunnel tests for aerospace and bridge engineering, the accurate prediction of Mach number remains a core challenge to ensure the reliability of airflow dynamics characterization. Pure data-driven models often fail to meet high-precision prediction requirements due to the lack of physical mechanism constraints and insufficient generalization capability. This paper proposes a physical information-based long short-term memory network (P-LSTM), which constructs a physical loss function by embedding isentropic flow equations from gas dynamics, thereby constraining the Mach number prediction solution space within the physically feasible domain. This approach effectively balances the neural network’s ability to capture temporal features with the interpretability of physical mechanisms. Aiming at the scarcity of data in new wind tunnel scenarios, an adaptive weight transfer learning method (AWTL) is further proposed, realizing efficient knowledge transfer across different-scale wind tunnels via cross-domain data calibration, adaptive source-domain weight reweighting, and target-domain fine-tuning. Experimental results show that the P-LSTM method achieves a 50.65–62.54% reduction in RMSE, 48.00–54.05% in MAE, and 47.88–73.68% in MD compared with traditional LSTM for Mach number prediction in the 0.6 m continuous wind tunnel flow field. The AWTL model also outperforms the direct training model significantly in the 2.4 m continuous wind tunnel, with RMSE, MAE, and MD reduced by 85.26%, 95.12%, and 71.14%, respectively. These results validate that the proposed models achieve high-precision Mach number prediction with strong generalization capability. Full article

The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. However, deep learning methods are inefficient when processing long-term series data and are prone to overfitting. To improve the accuracy of cognitive state prediction, we design a Multi-layer Attention Self-supervised Knowledge Tracing Method (MASKT) using self-supervised learning and the Transformer method. In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. The Transformer in MASKT not only solves the problem of long-term dependencies between input and output using an attention mechanism, but also has parallel computing capabilities that can effectively improve the learning efficiency of the prediction model. Finally, a multidimensional attention mechanism is integrated into cross-attention to further optimize prediction performance. The experimental results show that, compared with various knowledge tracing models on multiple datasets, MASKT’s prediction performance remains 2 percentage points higher. Compared with the multidimensional attention mechanism of graph neural networks, MASKT’s time efficiency is shortened by nearly 30%. Due to the improvement in prediction accuracy and performance, this method has broad application prospects in the field of cognitive diagnosis in intelligent education. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Background: Reverse total shoulder arthroplasty (RTSA) is increasingly used for managing cuff tear arthropathy, osteoarthritis, complex fractures, and revision procedures. As the demand for surgical precision and reproducibility grows, immersive technologies such as virtual reality (VR), augmented reality (AR), and metaverse-based platforms are being explored for surgical training, intraoperative guidance, and rehabilitation. While early data suggest potential benefits, a focused synthesis specific to RTSA is lacking. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. A comprehensive search of PubMed, Scopus, and Cochrane Library databases was performed through 30 May 2025. Eligible studies included those evaluating immersive technologies in the context of RTSA for skill acquisition or intraoperative guidance. Only peer-reviewed articles published in English were included. Data were synthesized narratively due to heterogeneity in study design and outcome metrics. Results: Out of 628 records screened, 21 studies met the inclusion criteria. Five studies evaluated immersive VR for surgical training: four randomized controlled trials and one retrospective case series. VR training improved procedural efficiency and showed non-inferiority to cadaveric training. Sixteen studies investigated intraoperative navigation or AR guidance. Clinical and cadaveric studies consistently reported improved accuracy in glenoid baseplate positioning with reduced angular and linear deviations in postoperative controls as compared to preoperative planning. Conclusions: Immersive technologies show promise in enhancing training, intraoperative accuracy, and procedural consistency in RTSA. VR and AR platforms may support standardized surgical education and precision-based practice, but their broad clinical impact remains limited by small sample sizes, heterogeneous methodologies, and limited long-term outcomes. Further multicenter trials with standardized endpoints and cost-effectiveness analyses are warranted. Postoperative rehabilitation using immersive technologies in RTSA remains underexplored and presents an opportunity for future research. Full article

Generative Adversarial Networks (GANs) using Long Short-Term Memory (LSTM) provide a computationally cheaper approach for text generation compared to large language models (LLMs). The low hardware barrier of training GANs poses a threat because it means more bad actors may use them to mass-produce prompt attack messages against LLM systems. Thus, to better understand the threat of GANs being used for prompt attack generation, we train two well-known GAN architectures, SeqGAN and RelGAN, on prompt attack messages. For each architecture, we evaluate generated prompt attack messages, comparing results with each other, with generated attacks from another computationally cheap approach, a 1-billion-parameter Llama 3.2 small language model (SLM), and with messages from the original dataset. This evaluation suggests that GAN architectures like SeqGAN and RelGAN have the potential to be used in conjunction with SLMs to readily generate malicious prompts that impose new threats against LLM-based systems such as chatbots. Analyzing the effectiveness of state-of-the-art defenses against prompt attacks, we also find that GAN-generated attacks can deceive most of these defenses with varying levels of success with the exception of Meta’s PromptGuard. Further, we suggest an improvement of prompt attack defenses based on the analysis of the language quality of the prompts, which we found to be the weakest point of GAN-generated messages. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

E-Learning is an emerging dominant phenomenon in education, making the development of robust models that can accurately represent the dynamic behavior of learners in MOOCs even more critical. In this article, we propose the Temporal Sparse Attention-Gated Recurrent Unit (TSA-GRU), a novel deep learning framework that combines TSA with a sequential encoder based on the GRU. This hybrid model effectively reconstructs student response times and learning trajectories with high fidelity by leveraging tthe emporal embeddings of instructional and feedback activities. By dynamically filtering noise from student interactions, TSA-GRU generates context-aware representations that seamlessly integrate both short-term fluctuations and long-term learning patterns. Empirical evaluation on the 2009–2010 ASSISTments dataset demonstrates that TSA-GRU achieved a test accuracy of 95.60% and a test loss of 0.0209, outperforming Modular Sparse Attention-Gated Recurrent Unit (MSA-GRU), Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and TSA in the same experimental design. TSA-GRU converged in five training epochs; thus, while TSA-GRU is demonstrated to have strong predictive performance for knowledge tracing tasks, these findings are specific to the conducted dataset and should not be implicitly regarded as conclusive for all data. More statistical validation through five-fold cross-validation, confidence intervals, and paired t-tests have confirmed the robustness, consistency, and statistically significant superiority of TSA-GRU over the baseline model MSA-GRU. TSA-GRU’s scalability and capacity to incorporate a temporal dimension of knowledge can make it acceptably well-positioned to analyze complex learner behaviors and plan interventions for adaptive learning in computerized learning systems. Full article