Search Results (3,003)

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

To explore the influence of mine roof on the damage and failure of sandstone surrounding rock under different pressure rates, mechanical experiments with different strain rates were carried out on sandstone rock samples. The strength, deformation, failure, energy and damage characteristics of rock samples with different strain rates were also discussed. The research results show that with the increases in the strain rate, peak stress, and elastic modulus show a monotonically increasing trend, while the peak strain decreases in the reverse direction. At a low strain rate, the proportion of the mass fraction of complete rock blocks in the rock sample is relatively high, and the shape integrity is good, while rock samples with a high strain rate retain more small-sized fragmented rock blocks. This indicates that under high-rate loading, the bifurcation phenomenon of secondary cracks is obvious. The rock samples undergo a failure form dominated by small-sized fragments, with severe damage to the rock samples and significant fractal characteristics of the fragments. At the initial stage of loading, the primary fractures close, and the rock samples mainly dissipate energy in the forms of frictional slip and mineral fragmentation. In the middle stage of loading, the residual fractures are compacted, and the dissipative strain energy keeps increasing continuously. In the later stage of loading, secondary cracks accelerate their expansion, and elastic strain energy is released sharply, eventually leading to brittle failure of the rock sample. Under a low strain rate, secondary cracks slowly expand along the clay–quartz interface and cause intergranular failure of the rock sample. However, a high strain rate inhibits the stress relaxation of the clay, forces the energy to transfer to the quartz crystal, promotes the penetration of secondary cracks through the quartz crystal, and triggers transgranular failure. A constitutive model based on energy damage was further constructed, which can accurately characterize the nonlinear hardening characteristics and strength-deformation laws of rock samples with different strain rates. The evolution process of its energy damage can be divided into the unchanged stage, the slow growth stage, and the accelerated growth stage. The characteristics of this stage reveal the sudden change mechanism from the dissipation of elastic strain energy of rock samples to the unstable propagation of secondary cracks, clarify the cumulative influence of strain rate on damage, and provide a theoretical basis for the dynamic assessment of surrounding rock damage and disaster early warning when the mine roof comes under pressure. Full article

The development and implementation of microbial consortium-based biofertilizers represent a promising direction in sustainable agriculture, particularly in the context of the ongoing global ecological and agricultural crisis. This article examines the agroecological and economic impacts of applying microbial consortiums and explores the mechanisms of technology transfer using the example of two countries with differing levels of scientific and technological advancement–China and Kazakhstan. The analysis of the Chinese experience reveals that the successful integration of microbial biofertilizers into agricultural practice is made possible by a well-established institutional framework that includes strong governmental support for R&D, a robust scientific infrastructure, and effective coordination with the private sector. In contrast, Kazakhstan, despite its favorable agroecological conditions and growing interest among farmers in environmentally friendly technologies, faces several challenges from limited funding to a fragmented technology transfer system. The comparative study demonstrates that adapting Chinese models requires consideration of local specificities and the strengthening of intergovernmental cooperation. The article concludes by emphasizing the need to establish a multi-level innovation ecosystem encompassing the entire cycle of development and deployment of microbial biofertilizers, as a prerequisite for improving agricultural productivity and ensuring food security in countries at different stages of economic development. Full article

To investigate the remediation efficiency of different plant species on cadmium (Cd)-contaminated soil, this study conducted a pot experiment with two woody species (Populu adenopoda and Salix babylonica) and two herbaceous species (Artemisia argyi and Amaranthus hypochondriacus). Soils were collected from an abandoned coal mine and adjacent pristine natural areas within the dam-adjacent section of the Three Gorges Reservoir Area to establish three soil treatment groups: unpolluted soil (T1, 0.18 mg·kg⁻¹ Cd), a 1:1 mixture of contaminated and unpolluted soil (T2, 0.35 mg·kg⁻¹ Cd), and contaminated coal mine soil (T3, 0.54 mg·kg⁻¹ Cd). This study aimed to investigate the growth status of plants, Cd accumulation and translocation characteristics, and the relationship between them and soil environmental factors. Woody plants exhibited significant advantages in aboveground biomass accumulation. Under T3 treatment, the Cd extraction amount of S. babylonica (224.93 mg) increased by about 36 times compared to T1, and the extraction efficiency (6.42%) was significantly higher than other species. Among the herbaceous species, A. argyi showed the maximum Cd extraction amount (66.26 mg) and extraction efficiency (3.11%) during T2 treatment. While A. hypochondriacus exhibited a trend of increasing extraction amount but decreasing extraction efficiency with increasing concentration. With the exception of S. babylonica under T1 treatment (BCF = 0.78), the bioconcentration factor was greater than 1 in both woody (BCF = 1.39–6.42) and herbaceous species (BCF = 1.39–3.11). However, herbaceous plants demonstrated significantly higher translocation factors (TF = 1.58–3.43) compared to woody species (TF = 0.31–0.87). There was a significant negative correlation between aboveground phosphorus (P) content and root Cd (p < 0.05), while underground nitrogen (N) content was positively correlated to aboveground Cd content (p < 0.05). Soil total N and available P were significantly positively correlated with plant Cd absorption, whereas total potassium (K) showed a negative correlation. This study demonstrated that woody plants can achieve long-term remediation through biomass advantages, while herbaceous plants, with their high transfer efficiency, are suitable for short-term rotation. In the future, it is suggested to conduct a mixed planting model of woody and herbaceous plants to remediate Cd-contaminated soils in the tailing areas of reservoir areas. This would synergistically leverage the dual advantages of root retention and aboveground removal, enhancing remediation efficiency. Concurrent optimization of soil nutrient management would further improve the Cd remediation efficiency of plants. Full article

This study investigates the behavior and performance of a newly proposed cone-top pile foundation designed to improve stability in layered, deformable, or strain-sensitive soils. Traditional shallow and uniform conical foundations often suffer from excessive settlement and reduced capacity when subjected to vertical loads and horizontal soil deformations. To address these limitations, a hybrid foundation was developed that integrates an inverted conical base with a central pile shaft and a rolling joint interface between the foundation and the superstructure. Laboratory model tests, full-scale field loading experiments, and axisymmetric numerical simulations using Plaxis 2D (Version 8.2) were conducted to evaluate the foundation’s bearing capacity, settlement behavior, and load transfer mechanisms. Results showed that the cone-top pile foundation exhibited lower settlements and higher load resistance than columnar foundations under similar loading conditions, particularly in the presence of horizontal tensile strains. The load was effectively distributed through the conical base and transferred into deeper soil layers via the pile shaft, while the rolling joint reduced stress transmission to the structure. The findings support the use of cone-top pile foundations in soft soils, seismic areas and areas affected by underground mining, where conventional designs may be inadequate. This study provides a validated and practical design alternative for challenging geotechnical environments. Full article

Cross-line operation and express–local train coordination are both crucial for enhancing the efficiency of multi-level urban rail transit systems. Most studies address suburban railway operations in isolation, overlooking coordination and inducing supply–demand mismatches that weaken system efficiency. This study addresses the joint optimization of cross-line operation and express–local scheduling by proposing a novel train timetable model. The model determines train service plans and departure times to minimize total system cost, including train operating and passenger travel costs. A space–time network represents integrated train–passenger interactions, and an extended adaptive large neighborhood search (E-ALNS) algorithm is developed to solve the model efficiently. Numerical experiments verify the effectiveness of the proposed approach. The E-ALNS achieves near-optimal solutions with less than 4% deviation from Gurobi. Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. Sensitivity analyses further confirm the model’s robustness to variations in transfer walking time, passenger penalties, and waiting thresholds. This study provides a practical and scalable framework for optimizing train timetables in complex cross-line transit systems, offering insights for enhancing system coordination and passenger service quality. Full article

Inverter-equipped heat pumps allow for increased energy efficiency. However, air conditioning (AC) systems often operate at low load ratios below where inverter control is effective, which reduces their energy efficiency. We developed an AC system that increases the apparent load ratio of the heat pump by using a phase-change material (PCM). Cooling and heating experiments were conducted with a PCM heat exchanger, which comprised aluminum plates and fins filled with paraffinic PCM. The result indicated a high heat transfer coefficient of >70 W/(m²·K). A simplified numerical model of the PCM heat exchanger as a lumped constant system was created based on the experiment. The calculations generally reproduced the experimental results, with root mean squared errors of 0.39 K for cooling and 0.84 K for heating, confirming their accuracy. Simulations were then conducted to evaluate the energy performance of the proposed system for the cooling season. While low load operation accounted for 39% of the total AC time for a non-PCM system, it was reduced to 2.7% for the proposed system. The proposed system demonstrated load ratios of 50–60% for most of the season, achieving an energy reduction of 11.4% owing to the improved efficiency at partial load ratios. Full article

Postoperative adhesions are a prevalent complication following abdominal surgeries, often leading to significant clinical challenges. This study introduces an innovative solution utilizing a polyethylene glycol (PEG)-based triblock copolymer to form an injectable, self-healing hydrogel aimed at preventing these adhesions. The hydrogel, formulated with temperature-responsive and self-healing properties through the incorporation of poly (N-isopropyl acrylamide) (PNIPAM) and anion–pi interactions, was synthesized using reversible addition–fragmentation chain transfer (RAFT) polymerization. The hydrogel’s physical properties, biocompatibility, hemostatic effect, and anti-adhesive capabilities were rigorously tested through in vitro and in vivo experiments involving rat models. It demonstrated excellent biocompatibility, effective tissue adhesion, and robust hemostatic properties. Most notably, it exhibited significant anti-adhesive effects in a rat abdominal wall–cecum model, reducing adhesion formation effectively compared to controls. The PEG-based injectable hydrogel presents a promising approach for postoperative adhesion prevention. Its ability to gel in situ triggered by body heat, coupled with its self-healing properties, provides a substantial advantage in clinical settings, indicating its potential utility as a novel anti-adhesion material. Full article

Cross-lingual summarization (XLS) involves generating a summary in one language from an article written in another language. XLS presents substantial hurdles due to the complex linguistic structures across languages and the challenges in transferring knowledge effectively between them. Although Large Language Models (LLMs) have demonstrated capabilities in cross-lingual tasks, the integration of retrieval-based in-context learning remains largely unexplored, despite its potential to overcome these linguistic barriers by providing relevant examples. In this paper, we introduce Multilingual Retrieval-based Cross-lingual Summarization (MuRXLS), a robust framework that dynamically selects the most relevant summarization examples for each article using multilingual retrieval. Our method leverages multilingual embedding models to identify contextually appropriate demonstrations for various LLMs. Experiments across twelve XLS setups (six language pairs in both directions) reveal a notable directional asymmetry: our approach significantly outperforms baselines in many-to-one (X→English) scenarios, while showing comparable performance in one-to-many (English→X) directions. We also observe a strong correlation between article-example semantic similarity and summarization quality, demonstrating that intelligently selecting contextually relevant examples substantially improves XLS performance by providing LLMs with more informative demonstrations. Full article

Federated edge learning (FEEL) is an innovative approach that facilitates collaborative training among numerous distributed edge devices while eliminating the need to transfer sensitive information. However, the practical deployment of FEEL faces significant constraints, owing to the limited and asymmetric computational and communication resources of these devices, along with their energy availability. To this end, we propose a novel asymmetry-tolerant training approach for FEEL, enabled via simultaneous wireless information and power transfer (SWIPT). This framework leverages SWIPT to offer sustainable energy support for devices while enabling them to train models with varying intensities. Given a limited energy budget, we highlight the critical trade-off between heterogeneous local training intensities and the quality of wireless transmission, suggesting that the design of local training and wireless transmission should be closely integrated, rather than treated as separate entities. To elucidate this perspective, we rigorously derive a new explicit upper bound that captures the combined impact of local training accuracy and the mean square error of wireless aggregation on the convergence performance of FEEL. To maximize overall system performance, we formulate two key optimization problems: the first aims to maximize the energy harvesting capability among all devices, while the second addresses the joint learning–communication optimization under the optimal energy harvesting solution. Comprehensive experiments demonstrate that our proposed framework achieves significant performance improvements compared to existing baselines. Full article

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved raccoon optimization algorithm (KYCOA) to resolve the problem of degradation of positioning accuracy due to multi-source error coupling during UAV target positioning. Firstly, a multi-coordinate system transformation model is established to analyze the nonlinear transfer characteristics of the error, and the Taylor expansion is used to linearize the error transfer process and derive the synthetic error model under the geocentric coordinate system. Secondly, the KYCOA is proposed to optimize the error allocation by combining the good point set initialization strategy to enhance the population diversity, and the golden sine algorithm to improve the position updating mechanism in response to the defect of the traditional optimization algorithm, which easily falls into the local optimum. Simulation experiments show that the positioning error distance of the KYCOA is reduced by 66.75%, 41.89%, and 62.06% when compared with that of the original Coati Optimization Algorithm (COA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), respectively. In the real flight test, the target point localization error of the KYCOA is reduced by more than 40% on average when compared with that of other algorithms, which verifies the effectiveness of the proposed method in improving the target localization accuracy and robustness of UAVs. Full article

This study creates and tests a machine learning model that can predict fuel use and emissions (NO_x, CO₂, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R² > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO_x exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. Full article

Automated defect detection for printed fabric manufacturing faces critical challenges in balancing industrial-grade accuracy with real-time deployment efficiency. To address this, we propose RT-DETR-FFD, a knowledge-distilled detector optimized for printed fabric defect inspection. Firstly, the student model integrates a Fourier cross-stage mixer (FCSM). This module disentangles defect features from periodic textile backgrounds through spectral decoupling. Secondly, we introduce FuseFlow-Net to enable dynamic multi-scale interaction, thereby enhancing discriminative feature representation. Additionally, a learnable positional encoding (LPE) module transcends rigid geometric constraints, strengthening contextual awareness. Furthermore, we design a dynamic correlation-guided loss (DCGLoss) for distillation optimization. Our loss leverages masked frequency-channel alignment and cross-domain fusion mechanisms to streamline knowledge transfer. Experiments demonstrate that the distilled model achieves an mAP@0.5 of 82.1%, surpassing the baseline RT-DETR-R18 by 6.3% while reducing parameters by 11.7%. This work establishes an effective paradigm for deploying high-precision defect detectors in resource-constrained industrial scenarios, advancing real-time quality control in textile manufacturing. Full article

Glaucoma is an irreversible neurodegenerative disease that affects the optic nerve, leading to partial or complete vision loss. Early and accurate detection is crucial to prevent vision impairment, which necessitates the development of highly precise diagnostic tools. Deep learning (DL) has emerged as a promising approach for glaucoma diagnosis, where the model is trained on datasets of fundus images. To improve the detection accuracy, we propose a hybrid model for glaucoma detection that combines multiple DL models with two fine-tuning strategies and uses a majority voting scheme to determine the final prediction. In experiments, the hybrid model achieved a detection accuracy of 96.55%, a sensitivity of 98.84%, and a specificity of 94.32%. Integrating datasets was found to improve the performance compared to using them separately even with transfer learning. When compared to individual DL models, the hybrid model achieved a 20.69% improvement in accuracy compared to the best model when applied to a single dataset, a 13.22% improvement when applied with transfer learning across all datasets, and a 1.72% improvement when applied to all datasets. These results demonstrate the potential of hybrid DL models to detect glaucoma more accurately than individual models. Full article