Search Results (5,135)

This study explores the critical role of the flow deflector in suppressing boundary layer separation and enhancing aerodynamic efficiency through systematic geometric parameterization and computational analysis. By defining eight key design variables, this research identifies optimal configurations that significantly delay flow separation at high angles of attack. Computational Fluid Dynamics (CFD) simulations reveal that optimized deflector geometries enhance suction peaks near the airfoil leading edge, redirect separated flow toward the upper surface, and inject momentum into the boundary layer to generate a more positive lift coefficient. The numerical results demonstrate that the optimized design achieves a 58.4% increase in lift coefficient and an 83.3% improvement in the lift–drag ratio by effectively mitigating large-scale vortical structures inherent in baseline configurations. Sensitivity analyses further highlight threshold-dependent “sudden-jump” behaviors in lift coefficients for parameters such as element spacing and deflection angles, while thickness exhibits minimal influence. Additionally, pre-stall optimizations show that strategically aligned deflectors preserve baseline performance with a 0.4% lift gain, whereas misaligned configurations degrade aerodynamic efficiency by up to 9.1%. These findings establish a direct correlation between deflector-induced flow redirection and separation suppression, offering actionable insights for passive flow control in stalled regimes. This research advances fundamental understanding of flow deflector-based separation management and provides practical guidelines for enhancing aerodynamic performance in aerospace applications. Full article

As wind energy development continues to expand toward nearshore and deep-sea regions, enhancing the aerodynamic efficiency of vertical axis wind turbines (VAWTs) in complex marine environments has become a critical challenge. To address this, a composite flow control strategy combining leading-edge suction and trailing-edge gurney flap is proposed. A two-dimensional unsteady numerical simulation framework is established based on CFD and the four-equation Transition SST (TSST) transition model. The key control parameters, including the suction slot position and width as well as the gurney flap height and width, are systematically optimized through orthogonal experimental design. The aerodynamic performance under single (suction or gurney flap) and composite control schemes is comprehensively evaluated. Results show that leading-edge suction effectively delays flow separation, while the gurney flap improves aerodynamic characteristics in the downwind region. Their synergistic effect significantly suppresses blade load fluctuations and enhances the wake structure, thereby improving wind energy capture. Compared to all other configurations, including suction-only and gurney flap-only blades, the composite control blade achieves the most significant increase in power coefficient across the entire tip speed ratio range, with an average improvement of 67.24%, demonstrating superior aerodynamic stability and strong potential for offshore applications. Full article

Background: Soft tissue sarcomas (STSs) histopathological classification system and the clinical and molecular-based tools that are currently employed to estimate its prognosis have several limitations, impacting prognostication and treatment. Clinically driven molecular profiling studies may cover these gaps and offer alternative tools with superior prognostication capability and enhanced precision and personalized treatment approaches identification ability. Materials and Methods: We performed DNA sequencing (DNA-seq) and RNA sequencing (RNA-seq) to portray the molecular profile of 102 samples of high-grade STS, comprising the three most common STS histotypes. Results: The analysis of RNA-seq data using unsupervised machine learning models revealed previously unknown molecular patterns, identifying four transcriptomic subtypes/clusters (TCs). This TC-based classification has a clear prognostic value (in terms of overall survival (OS) and disease-free survival (DFS)), a finding that was externally validated using independent patient cohorts. The prognostic value of this TC-based classification outperforms the prognostic accuracy of clinical-based (SARCULATOR nomograms) and molecular-based (CINSARC) prognostication tools, being one of the first molecular-based classifications capable of predicting OS in STS. The analysis of DNA-seq data from the same cohort revealed numerous and, in some cases, never documented molecular targets for precision treatment across different transcriptomic subtypes. The functional and predictive value of each genomic variant was analyzed using the Molecular Tumor Board Portal. Conclusions: This newly identified TC-based classification offers a superior prognostic value when compared with current gold-standard clinical and molecular-based prognostication tools, and identifies novel molecular targets for precision treatment, representing a cutting-edge tool for predicting prognosis and guiding treatment across different stages of STS. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

Professor Hong Chaosheng, as the founding figure and pioneer of China’s hydrogen and helium cryogenic technology, played a pivotal role in advancing this field from its inception to global competitiveness. This paper systematically reviews the seven-decade-long cryogenic research trajectory of the Technical Institute of Physics and Chemistry, CAS (formerly the Cryogenic Technology Experimental Center), with particular emphasis on milestone scientific achievements and their significant applications. In the 1960s, the Institute’s breakthrough in long-piston-expander-precooled helium liquefaction technology provided critical support for China’s space technology and superconductivity research. Since the 21st century, building upon Professor Hong’s academic legacy, the Institute has successively overcome core technological challenges in developing high-speed helium turbine expanders, high-efficiency oil-flooded screw compressors, and superfluid helium temperature refrigeration systems. These innovations have yielded a complete series of large-scale cryogenic equipment with independent intellectual property rights. These advancements have been successfully applied in national megaprojects such as neutron sources and superconducting magnet testing facilities, with some technical parameters reaching internationally leading standards. Looking ahead, with the rapid development of quantum computing and fusion energy, China’s hydrogen–helium cryogenic technology will continue to optimize equipment performance while expanding application frontiers through enhanced international collaboration, thereby making greater contributions to cutting-edge scientific research and clean energy development. Full article

Intelligent detection of tomato seedling transplant quality represents a core technology for advancing agricultural automation. However, in practical applications, existing algorithms still face numerous technical challenges, particularly with prominent issues of false detections and missed detections during recognition. To address these challenges, we developed the ESG-YOLO object detection model and successfully deployed it on edge devices, enabling real-time assessment of tomato seedling transplanting quality. Our methodology integrates three key innovations: First, an EMA (Efficient Multi-scale Attention) module is embedded within the YOLOv8 neck network to suppress interference from redundant information and enhance morphological focus on seedlings. Second, the feature fusion network is reconstructed using a GSConv-based Slim-neck architecture, achieving a lightweight neck structure compatible with edge deployment. Finally, optimization employs the GIoU (Generalized Intersection over Union) loss function to precisely localize seedling position and morphology, thereby reducing false detection and missed detection. The experimental results demonstrate that our ESG-YOLO model achieves a mean average precision mAP of 97.4%, surpassing lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n in precision, with improvements of 9.3, 7.2, 5.7, and 2.2%, respectively. Notably, for detecting key yield-impacting categories such as “exposed seedlings” and “missed hills”, the average precision (AP) values reach 98.8 and 94.0%, respectively. To validate the model’s effectiveness on edge devices, the ESG-YOLO model was deployed on an NVIDIA Jetson TX2 NX platform, achieving a frame rate of 18.0 FPS for efficient detection of tomato seedling transplanting quality. This model provides technical support for transplanting performance assessment, enabling quality control and enhanced vegetable yield, thus actively contributing to smart agriculture initiatives. Full article

Despite aggressive current therapies against glioblastoma (GB), residual tumor cells may remain at the edge of the surgical cavity after resection. These cells can rapidly proliferate, giving rise to tumor recurrence in more aggressive and drug-resistant forms. As photodynamic therapy (PDT) has advanced, it has emerged as an option to treat this brain tumor. The oncological basis of PDT involves the selective accumulation of a photosensitizer (PS) in the tumor, followed by its activation with electromagnetic radiation to generate reactive oxygen species (ROS), which induce tumor cell death. Given that first- and second-generation PSs present significant limitations, including poor tumor selectivity, suboptimal biodistribution, limited absorption within the therapeutic window, and slow systemic clearance, research has progressed toward the development of third-generation PSs based on nanotechnology to optimize their therapeutic properties. This review addresses the types of tumor cell death induced by PDT, as well as the advancements of PS design, focusing on titanium dioxide (TiO₂) and zinc oxide (ZnO) nanoparticles. These nanomaterials can be designed as carriers, encapsulating or conjugating conventional PSs, or act as PSs themselves, due to their favorable biocompatibility and intrinsic photoreactivity. Additionally, they can be functionalized with targeting ligands to achieve tumor-specific delivery, enhancing therapeutic selectivity while minimizing toxicity to healthy tissue. Overall, these nanotechnology-based PSs represent a versatile and promising therapeutic paradigm that warrants further investigation through basic research, supporting the development and potential clinical translation of a more precise and effective PDT-based intervention for glioblastoma, initially aimed at eliminating intra-surgical post-resection residual tumor cells. Full article

The identification of interdependent edges plays a critical role in improving information propagation efficiency and enhancing network robustness in interdependent networks. However, existing methods exhibit significant limitations when identifying interdependent edges between networks with substantial differences in edge density. This paper proposes a greedy module matching-based method for sparse interdependent edge identification in similar-order heterogeneous networks. The method utilizes degree entropy and betweenness centrality as node characteristic values for sparse and dense networks, respectively. It first leverages structural differences between sparse and dense networks to determine the upper bound of interdependent edges. Then, a clustering algorithm is employed to identify modules in both networks that align with the estimated number of interdependent edges. Finally, a greedy algorithm is applied to infer interdependent edges between sparse and dense networks. The proposed method is validated using synthetic networks and power-communication networks, with network robustness and connection efficiency as evaluation metrics. Additionally, further validation is conducted through applications in problem–answer networks. Experimental results demonstrate that the proposed approach significantly improves the prediction of sparse interdependent relationships in heterogeneous complex networks and has broad applicability across multiple domains. Full article

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they often suffer from large parameter sizes and lack explicit geometric prior constraints for space targets. This paper proposes a novel edge detection method for satellite targets based on knowledge distillation, namely STED-KD. Firstly, a multi-stage distillation strategy is proposed to guide a lightweight, fully convolutional network with fewer parameters to learn key features and decision boundaries from a complex teacher model, achieving model efficiency. Next, a shape prior guidance module is integrated into the student branch, incorporating geometric shape information through shape prior model construction, similarity metric calculation, and feature reconstruction, enhancing adaptability to space targets and improving detection accuracy. Additionally, a curvature-guided edge loss function is designed to ensure continuous and complete edges, minimizing local discontinuities. Experimental results on the UESD space target dataset demonstrate superior performance, with ODS, OIS, and AP scores of 0.659, 0.715, and 0.596, respectively. On the BSDS500, STED-KD achieves ODS, OIS, and AP scores of 0.818, 0.829, and 0.850, respectively, demonstrating strong competitiveness and further confirming its stability. Full article

Multiple sclerosis (MS) is a chronic disease of the central nervous system that significantly impairs gait and mobility, contributing to a high risk of falls, reduced participation in daily activities, and diminished quality of life. Despite existing interventions such as exercise programs and pharmacological treatments, challenges such as fatigue, pain, and limited accessibility underscore the need for alternative therapies. Focal vibration therapy (FVT) has shown promise in improving gait, reducing spasticity, and enhancing mobility in people with MS (PwMS). However, further research is required to evaluate its long-term feasibility and optimize its parameters. This study examined the feasibility and preliminary efficacy of a home-based four-week wearable FVT device on gait and explored how FVT parameters impact gait and mobility outcomes. In this pilot double-blind randomized controlled trial, 22 PwMS were randomized into control and vibration groups (four FVT groups with varying vibration intensities/durations). Participants wore Myovolt^® vibrators on distal quadricep muscles near the rectus femoris insertion (approximately 2 cm from the medial edge of the patella), gastrocnemius/soleus, and tibialis anterior muscles (10 min/muscle, 3 days/week, 4 weeks). Feasibility was evaluated via adherence and satisfaction (QUEST 2.0, interviews). Gait (3D motion analysis) and mobility (T25FW) were assessed at baseline and post-intervention. Data were analyzed using descriptive/inferential statistics and thematic analysis. Of 22 participants, 17 completed post-intervention (16 intervention, 1 control). Wearable FVT showed promising feasibility, with high satisfaction despite minor adjustability issues. Intervention groups improved gait speed (p = 0.014), stride length (p = 0.004), and ankle angle (p = 0.043), but T25FW was unchanged (p > 0.05). High-intensity FVT enhanced knee/hip moments. This study’s results support the feasibility of wearable FVT for home-based management of mobility symptoms in MS with high participant satisfaction and acceptance. Notable gains in gait parameters suggest FVT’s potential to enhance neuromuscular control and proprioception but may be insufficient to lead to mobility improvements. Subgroup analyses highlighted the impact of vibration intensity and duration on knee joint mechanics, emphasizing the need for personalized dosing strategies. Challenges included participant retention in the control group and burdensome biomechanical assessments, which will be addressed in future studies through improved sham devices and a larger sample size. Full article

To tackle the challenges posed by substantial variations in target scale, intricate background interference, and the likelihood of missing small targets in multi-temporal UAV maize tassel imagery, an optimized lightweight detection model derived from YOLOv11 is introduced, named OTB-YOLO. Here, “OTB” is an acronym derived from the initials of the model’s core improved modules: Omni-dimensional dynamic convolution (ODConv), Triplet Attention, and Bi-directional Feature Pyramid Network (BiFPN). This model integrates the PaddlePaddle open-source maize tassel recognition benchmark dataset with the public Multi-Temporal Drone Corn Dataset (MTDC). Traditional convolutional layers are substituted with omni-dimensional dynamic convolution (ODConv) to mitigate computational redundancy. A triplet attention module is incorporated to refine feature extraction within the backbone network, while a bidirectional feature pyramid network (BiFPN) is engineered to enhance accuracy via multi-level feature pyramids and bidirectional information flow. Empirical analysis demonstrates that the enhanced model achieves a precision of 95.6%, recall of 92.1%, and mAP@0.5 of 96.6%, marking improvements of 3.2%, 2.5%, and 3.1%, respectively, over the baseline model. Concurrently, the model’s computational complexity is reduced to 6.0 GFLOPs, rendering it appropriate for deployment on UAV edge computing platforms. Full article

Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change. Full article

This paper proposes a collaborative framework to support task offloading in connected vehicular environments. The approach relies on the dynamic formation of temporary teams of connected vehicles in a mobile edge computing scenario. A novel trust model is introduced, which integrates both quality of service and quality of results into a unified reliability score, and combines this score with distributed reputation to build a comprehensive trust metric. This trust metric is then exploited to guide a decentralized team formation algorithm, ensuring lightweight, interpretable, and scalable decision-making processes. Simulation results demonstrate that the proposed framework improves task execution quality and fairness, especially for low-performing vehicles. These contributions highlight the novelty and strengths of our collaborative model, positioning it as a promising solution for enhancing cooperation in vehicular edge systems. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article