Search Results (4,336)

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

With the accelerated development of urban underground spaces, prefabricated tunnel linings have become a research focus due to their advantages in construction efficiency and cost effectiveness. However, issues such as stress concentration at joints and insufficient overall stability hinder their broader application. This study investigates a cut-and-cover prefabricated tunnel project in the Chongqing High-Tech Zone through scale model tests and numerical simulations to systematically compare the mechanical behaviors of cast-in situ linings and three-segment prefabricated linings under surrounding rock loads. The experimental results show that the ultimate bearing capacity of the prefabricated lining is 15.3% lower than that of the cast-in situ lining, with asymmetric failure modes and cracks concentrated near joint regions. Numerical simulations further reveal the influence of joint stiffness on structural performance: when the joint stiffness is 30 MN·m/rad, the bending moment of the segmented lining decreases by 37.7% compared to the cast-in situ lining, while displacement increments remain controllable. By optimising joint pre-tightening forces and stiffness parameters, prefabricated linings can achieve stability comparable to cast-in situ structures while retaining construction efficiency. This research provides theoretical and technical references for the design and construction of open-cut prefabricated tunnel linings. Full article

Positron emission tomography (PET) and liquid biopsy have independently transformed prostate cancer management. This systematic review explores the complementary roles of PET imaging and liquid biopsy in prostate cancer, focusing on their combined diagnostic, monitoring, and prognostic potential. A systematic search of PubMed, Scopus, and Cochrane Library databases was conducted to identify human studies published in English up to January 2025. Seventeen studies met the inclusion criteria and were analyzed according to PRISMA guidelines. Across the included studies, PET-derived imaging metrics, such as metabolic activity and radiotracer uptake, correlated consistently with liquid biopsy biomarkers, including circulating tumor cells and cell-free DNA. Their joint application demonstrated added value in early detection, treatment monitoring, and outcome prediction, particularly in castration-resistant prostate cancer. Independent and synergistic prognostic value was noted for both modalities, including survival outcomes such as overall survival and progression-free survival. Combining PET imaging and liquid biopsy emerges as a promising, non-invasive strategy for improving prostate cancer diagnosis, monitoring, and therapeutic stratification. While preliminary findings are encouraging, large-scale prospective studies are essential to validate their integrated clinical utility. Full article

Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in the booming Internet of Things (IoT). While content caching seeks the “greatest common denominator” among users to reduce end-to-end delay in content delivery, personalized recommendation, on the contrary, emphasizes users’ differentiation to enhance user satisfaction. Existing studies typically address them separately rather than jointly due to their contradictory objectives. They focus mainly on heuristics and deep reinforcement learning methods without the provision of performance guarantees, which are required in many real-world applications. In this paper, we study the problem of joint optimization of caching and recommendation in which recommendation is performed in the cached contents instead of purely according to users’ preferences, as in the existing work. We show the NP-hardness of this problem and present a greedy solution with a performance guarantee by first performing content caching according to user request probability without considering recommendations to maximize the aggregated request probability on cached contents and then recommendations from cached contents to incorporate user preferences for cache hit rate maximization. We prove that this problem has a monotonically increasing and submodular objective function and develop an efficient algorithm that achieves a $1-{\displaystyle \frac{1}{e}}\approx 0.63$ approximation ratio to the optimal solution. Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. We also show experimental evaluations of hit rate variations by Jensen–Shannon Divergence on different parameter settings of cache capacity and user preference distortion limit, which can be used as a reference for appropriate parameter settings to balance user preferences and cache hit rate for Internet content delivery. Full article

The Tetranychus urticae Koch (Acari: Tetranychidae) is one of the primary pests affecting buckwheat, and its management has become increasingly critical. Entomopathogenic fungi offer a promising way to solve this problem by providing both pest control and disease resistance, as well as promoting plant growth through endophytic colonization. This study investigated the effects of applying Isaria cateniannulata (Liang) Samson & Hywel-Jones and Beauveria bassiana (Bals.-Criv.) Vuill. on different buckwheat varieties, and analyzed the physiological indices of buckwheat, the population of T. urticae and Euseius nicholsi (Ehara & Lee). Results showed that the optimum concentration for fungal colonization on buckwheat was 1 × 10⁷ spores/mL. The combined application of I. cateniannulata and B. bassiana significantly enhanced buckwheat growth, with root length, plant height, main stem diameter, fresh weight, and dry weight reaching 63.3 mm, 24.1 cm, 2.1 mm, 2.0 g, and 0.1 g, respectively. The highest escape rate of T. urticae was 76.33%. Furthermore, the combined application of mixed fungal suspension and E. nicholsi had the best control effect on T. urticae, with pest suppression exceeding 97.83% and an oviposition as low as 0.25 eggs per female. This study is the first to demonstrate that the joint application of I. cateniannulata and B. bassiana can promote buckwheat growth and, when combined with predatory mites, effectively control T. urticae. These findings provide a theoretical basis for the development of integrated biocontrol strategies combining entomopathogenic fungi and predatory mites. Full article

We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T₁), wheat straw return alone (T₂), T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:1) (T₃); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 3:1) (T₄); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:3) (T₅). T₂–T₅ significantly increased dry matter accumulation, soil total N, ammonium N, nitrate N, and organic matter, improving yield by 3.81–26.63%. T₃ exhibited the highest yield increases in two consecutive years. At the jointing and heading stages, Penicillium and Saitozyma dominated under T₃ and positively correlated with dry matter, yield, and nitrogen levels. Straw return combined with Bacillus subtilis and Trichoderma harzianum (20 g m⁻² each) enhanced soil nitrogen availability and dry matter accumulation and translocation. Our findings guide efficient straw utilization, soil microbial regulation, and sustainable high-yield rice production. Full article

This study investigates two novel prefabricated frame joints: prestressed steel sleeve-connected prefabricated reinforced concrete joints (PSFRC) and non-prestressed steel sleeve-connected prefabricated reinforced concrete joints (SSFRC). A total of three PSFRC specimens, four SSFRC specimens, and one cast-in-place joint were designed and fabricated. Seismic performance tests were conducted using different end-plate thicknesses, grout strengths, stiffener configurations, and prestressing tendon configurations. The experimental results showed that all specimens experienced beam end failures, and three failure modes occurred: (1) failure of the end plate of the beam sleeve, (2) failure of the variable cross-section of the prefabricated beam, and (3) failure of prefabricated beams at the connection with the steel sleeves. The load-bearing capacity and initial stiffness of the structure are increased by 35.41% and 32.64%, respectively, by increasing the thickness of the end plate. Specimens utilizing C80 grout exhibited a 39.05% higher load capacity than those with lower-grade materials. Adding stiffening ribs improved the initial stiffness substantially. Specimen XF2 had 219.08% higher initial stiffness than XF1, confirming the efficacy of stiffeners in enhancing joint rigidity. The configuration of the prestressed tendons significantly influenced the load-bearing capacity. Specimen YL2 with symmetrical double tendon bundles demonstrated a 27.27% higher ultimate load capacity than specimen YL1 with single centrally placed tendon bundles. An analytical model to calculate the moment–rotation relationship was established following the evaluation criteria specified in Eurocode 3. The results demonstrated a good agreement, providing empirical references for practical engineering applications. Full article

Background/Objectives: Recent breakthroughs in three-dimensional (3D) printing and high-resolution imaging have opened up new possibilities in personalized medicine, surgical planning, and forensic reconstruction. This study breaks new ground by evaluating the integration of high-resolution peripheral quantitative computed tomography (HR-pQCT) with multimodal imaging and additive manufacturing to assess a chronic, infected gunshot injury in the knee joint of a red deer. This unique approach serves as a translational model for complex skeletal trauma. Methods: Multimodal imaging—including clinical CT, MRI, and HR-pQCT—was used to characterise the extent of osseous and soft tissue damage. Histopathological and molecular analyses were performed to confirm the infectious agent. HR-pQCT datasets were segmented and processed for 3D printing using PolyJet, stereolithography (SLA), and fused deposition modelling (FDM). Printed models were quantitatively benchmarked through 3D surface deviation analysis. Results: Imaging revealed comminuted fractures, cortical and trabecular degradation, and soft tissue involvement, consistent with chronic osteomyelitis. Sphingomonas sp., a bacterium that forms biofilms, was identified as the pathogen. Among the printing methods, PolyJet and SLA demonstrated the highest anatomical accuracy, whereas FDM exhibited greater geometric deviation. Conclusions: HR-pQCT-guided 3D printing provides a powerful tool for the anatomical visualisation and quantitative assessment of complex bone pathology. This approach not only enhances diagnostic precision but also supports applications in surgical rehearsal and forensic analysis. It illustrates the potential of digital imaging and additive manufacturing to advance orthopaedic and trauma care, inspiring future research and applications in the field. Full article

The fixed-time trajectory tracking control problem of the uncertain nonlinear Euler–Lagrange system is studied. To ensure the fast, high-precision trajectory tracking performance of this system, a non-singular terminal sliding-mode controller based on Gaussian process regression is proposed. The control algorithm proposed in this paper is applicable to periodic motion scenarios, such as spacecraft autonomous orbital rendezvous and repetitive motions of robotic manipulators. Gaussian process regression is employed to establish an offline data-driven model, which is utilized for compensating parametric uncertainties and external disturbances. The non-singular terminal sliding-mode control strategy is used to avoid singularity and ensure fast convergence of tracking errors. In addition, under the Lyapunov framework, the fixed-time convergence stability of the closed-loop system is rigorously demonstrated. The effectiveness of the proposed control scheme is verified through simulations on a spacecraft rendezvous mission and periodic joint trajectory tracking for a robotic manipulator. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article

To enhance the generalization of networks and avoid redundant training efforts, cross-image hyperspectral anomaly detection (HAD) based on deep learning has been gradually studied in recent years. Cross-image HAD aims to perform anomaly detection on unknown hyperspectral images after a single training process on the network, thereby improving detection efficiency in practical applications. However, the existing approaches may require additional supervised information or stacking of networks to improve model performance, which may impose high demands on data or hardware in practical applications. In this paper, a simple and lightweight unsupervised cross-image HAD method called Variational Joint Discrimination Network (VJDNet) is proposed. We leverage the reconstruction and distribution representation ability of the variational autoencoder (VAE), learning the global and local discriminability of anomalies jointly. To integrate these representations from the VAE, a probability distribution joint discrimination (PDJD) module is proposed. Through the PDJD module, the VJDNet can directly output the anomaly score mask of pixels. To further facilitate the unsupervised paradigm, a sample pair generation module is proposed, which is able to generate anomaly samples and background representation samples tailored for the cross-image HAD task. The experimental results show that the proposed method is able to maintain the detection accuracy with only a small number of parameters. Full article

Sign language is a complex and dynamic visual language that requires the coordinated movement of various body parts, such as the hands, arms, and limbs—making it an ideal application domain for sensor networks to capture and interpret human gestures accurately. To address the intricate task of precise and expedient SLR from raw videos, this study introduces a novel deep learning approach by devising a multimodal framework for SLR. Specifically, feature extraction models are built based on two modalities: skeleton and RGB images. In this paper, we firstly propose a Multi-Stream Spatio-Temporal Graph Convolutional Network (MSGCN) that relies on three modules: a decoupling graph convolutional network, a self-emphasizing temporal convolutional network, and a spatio-temporal joint attention module. These modules are combined to capture the spatio-temporal information in multi-stream skeleton features. Secondly, we propose a 3D ResNet model based on deformable convolution (D-ResNet) to model complex spatial and temporal sequences in the original raw images. Finally, a gating mechanism-based Multi-Stream Fusion Module (MFM) is employed to merge the results of the two modalities. Extensive experiments are conducted on the public datasets AUTSL and WLASL, achieving competitive results compared to state-of-the-art systems. Full article

With the acceleration of industrialization, deformable mechanisms that can adapt to complex environments have gained widespread applications. Joints serve as carriers for transmitting forces and motions between components, and their stiffness significantly influences the static and dynamic characteristics of deformable mechanisms. A variable stiffness joint is crucial for ensuring the safety and reliability of the system, as well as for enhancing environmental adaptability. However, existing variable stiffness joints fail to meet the requirements for miniaturization, lightweight construction, and fast response. This paper proposes a piezoelectric-actuated variable stiffness miniature rotary joint featuring a compact structure, monitorable loading state, and rapid response. Given that the piezoelectric stack expands and contracts when energized, this paper proposes a transmission principle for stiffness adjustment by varying the pressure and friction between active and passive components. This joint utilizes a flexible hinge mechanism for displacement amplification and incorporates a torque sensor based on strain monitoring. A static model is developed based on piezoelectric equations and displacement amplification characteristics, and simulations confirm the feasibility of the stiffness adjustment scheme. The mechanical characteristics of various flexible hinge structures are analyzed, and the effects of piezoelectric actuation capability and external load on stiffness adjustment are examined. The experimental results demonstrate that the joint can adjust stiffness, and the sensor is calibrated using the least squares algorithm to monitor the stress state of the joint in real time. Full article