Search Results (1,324)

The rapid growth of Internet of Things (IoT) and Industrial IoT applications has intensified the demand for low-latency and reliable computation support for deadline-constrained periodic real-time tasks. While unmanned aerial vehicles (UAVs) enabling mobile edge computing (MEC) can reduce latency by bringing compute closer to data sources, terrestrial MEC deployments often suffer from limited coverage and poor adaptability to spatially heterogeneous demand. In this paper, we study a multiple-UAV-assisted MEC system serving cluster-based IoT networks, where cluster heads generate deadline-constrained periodic tasks for offloading under strict deadlines. To ensure practical feasibility in dense urban environments, we benchmark UAV mobility using a realistic 3D collision avoidance navigation graph with shortest-path execution, rather than assuming unconstrained continuous UAV motion in free space. On top of this benchmark, we systematically compare three multi-agent reinforcement learning (MARL) paradigms for joint navigation and periodic task offloading: (i) continuous 3D control MARL that outputs motion commands directly; (ii) discrete graph-based MARL that selects collision-free shortest paths; and (iii) asynchronous macro-action MARL. Using a high-fidelity 3D digital twin of San Francisco, we evaluate these paradigms under a unified protocol in terms of offloading success, end-to-end latency, and energy consumption. The results reveal clear performance trade-offs induced by realistic 3D collision avoidance constraints and provide actionable insights for designing UAV-assisted MEC systems supporting periodic real-time task offloading. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

At the dawn of the 20th century, seminal studies revealed that muscle fibers produce less heat and generate greater force during elongation than during shortening actions, laying the foundation for contemporary research on eccentric exercise. Today, eccentric exercise is widely used by athletes to enhance strength and by older adults to maintain functional capacity, yet it may cause muscle damage, particularly in unaccustomed muscles. Despite more than a century of investigation, the precise mechanisms of eccentric exercise-induced muscle damage remain incompletely resolved. Nevertheless, eccentric exercise serves as a valuable model for studying muscle injury and repair and adaptation. This review organizes current evidence into nine key themes: (1) eccentric exercise-induced muscle damage and flawed biomarkers, (2) satellite cell-mediated and alternative repair pathways, (3) high-force, low-cost contractions and metabolic impact, (4) repeated bout effect and protective adaptations, (5) architectural remodeling of fascicles, sarcomeres and tendon, (6) distinct neural control, proprioception, and cross-education adaptations, (7) mitochondrial, sarcoplasmic reticulum, and cytoskeletal stress remodeling, (8) connective tissue perturbation, remodeling, and joint stability, and (9) targeted, cautious use of antioxidant supplementation. Rather than offering a comprehensive overview, this review highlights pivotal experiments, concepts, and controversies within these themes to guide readers to the most impactful discoveries in eccentric exercise and muscle damage. Full article

Arch-suspended structures represent a distinctive form of hybrid suspension system. By combining an arch with a suspended floor system, this structural typology leverages the inherent advantages of both components while mitigating the limitations of each when used independently. This synergy effectively reduces peak internal forces and flexural deformations in structural members. Although widely applied in bridge engineering, research on arch-suspended building structures remains scarce. This paper investigates the construction techniques employed for a large-scale arch-suspended building. The stability of temporary support systems during construction is verified, and the mechanical behavior of critical joints—including the composite slab hanging pillar, arch support, and arch roof—is examined through both experimental testing and numerical simulation. The results demonstrate that a partitioned and segmented construction method is feasible for such complex structures. Structural internal forces and deformations can be effectively controlled by installing tubular temporary supports on both sides of the hanging pillars and lattice temporary supports at the base. Step-by-step unloading of these temporary supports ensures their stability throughout the construction process. Furthermore, the welds in the composite slab hanging pillar effectively transfer tensile forces from the middle plate to the side plates, enabling composite action and collaborative load-bearing among the steel plates. When subjected to loads of 2 times and 4.3 times the design load, localized plasticity was observed in the arch support and arch roof, respectively, while the overall structural integrity remained secure. This study provides a valuable reference for the design and construction of innovative long-span building structures, offering insights that can inform the development and practical application of arch-suspended systems in future architectural projects. Full article

In emergency communication networks, service demands and user mobility change dynamically. Low service rates and limited coverage are significant challenges that hinder the effectiveness of emergency services. Due to the flexibility, low deployment cost, and adjustable coverage range of unmanned aerial vehicles (UAVs), UAV-assisted emergency communication networks can serve as a viable method to address these challenges. Given the strong coupling between UAV trajectory optimization and resource allocation, joint optimization is crucial to meet dynamic service demands and user mobility. In this paper, we establish a user mobility model based on the Maxwell–Boltzmann distribution and a service model based on the Poisson process. We formulate an optimization problem to maximize the data transmission rate of emergency services. To address the challenges of high-dimensional continuous action spaces, we propose a shared feature extraction-enhanced PPO (SPOR) algorithm for joint trajectory optimization and resource allocation. Simulation results show that the proposed SPOR algorithm significantly outperforms benchmark methods. Specifically, it achieves at least a 20% improvement in data transmission rate, a 28% improvement in emergency communication service ratio, and a 12% reduction in average service distance. Full article

Surface cracking is a key visible indicator of deterioration in concrete infrastructure and is routinely captured by vision sensors during field inspections. To translate inspection imagery into actionable maintenance information, crack delineation must be accurate at the pixel level and robust to challenging conditions where cracks are slender, discontinuous, low-contrast, and easily confused with joints, stains, texture patterns, and illumination artifacts. This study proposes a lightweight CNN–Mamba hybrid segmentation framework built upon Vm-unet for reliable crack mapping under heterogeneous inspection scenarios and resource-constrained deployment. The framework couples boundary-sensitive convolutional features with long-range state-space representations via a spatially modulated convolution design, refines skip-connection features using reciprocal co-modulation attention to suppress background interference, and enhances cross-scale interactions through a decoder interaction fusion scheme to preserve fine-crack continuity and sharp boundaries. Experiments on a multi-source composite dataset and public benchmarks show consistent improvements over representative CNN-, Transformer-, and Mamba-based baselines. The proposed method achieves 80.11% mIoU and 82.05% Dice on the composite dataset, while maintaining an efficient accuracy–cost trade-off (36.049 GFLOPs, 25.991 M parameters). The resulting crack masks provide a dependable basis for inspection-driven quantitative assessment and maintenance decision support. Full article

The European Green Deal is Europe’s ambitious and multi-layered response to climate change. Translating its objectives into action for a green transition has created a need for new skills and competencies. Vocational and Education Training (VET) systems are uniquely positioned to equip learners with these emerging green and transversal competences through their dual focus on knowledge dissemination and applied practice. However, current VET curricula remain oriented towards traditional occupations and are not adequately aligned with the sustainability and skills needs of the agri-food sector. This study, as part of a joint European-funded project (2023-1-IE01-KA220-VET-00156916: Train to Sustain), aimed to: (1) identify practical strategies for integrating sustainability concepts and innovative pedagogy into VET programs, and (2) gather multi-stakeholder perspectives on how VET agri-food education can be adapted for greater alignment with the green skills required by the sector. Following ethical approval, data were collected through semi-structured focus groups involving key agri-food stakeholder groups across Ireland, Slovenia, Poland and Italy. The data were qualitatively analysed using Reflexive Thematic Analysis (RTA). Five themes were identified: (1) Innovative and Sustainable Practices in Agri-Food systems, (2) Education, Awareness and Consumer Engagement, (3) Institutional and Structural Approaches, (4) Community and Localised Responses, and (5) Barriers, Opportunities and Future Directions. The findings highlight the significant potential VET offers in preparing a workforce with the cross-cutting sustainability competences and sector-specific skills needed to drive the innovation and growth of the agri-food sector. However, achieving this requires institutional change, strengthened collaboration, and a shift from traditional technical training toward curriculum models that embed sustainability principles across diverse local and regional contexts. Full article

This paper presents an interactive inference behavior tree (IIBT) framework, integrating behavior trees (BTs) with interactive inference based on the free energy principle for distributed decision-making in multi-UAV (unmanned aerial vehicle) systems. The proposed IIBT framework enhances conventional BTs by incorporating probabilistic inference, enabling online joint planning and execution among multiple UAVs. The framework maintains full compatibility with standard BT architectures, allowing seamless integration into existing UAV control systems. In this framework, cooperative behavior is modeled as a free-energy minimization process, where each UAV dynamically updates its preference matrix based on perceptual inputs and peer intentions, achieving adaptive coordination in dynamic and partially observable environments. The validation tasks, including cooperative navigation in uncertain environments and task coordination, directly mirror the decision-making and coordination challenges faced in UAV missions. Experimental results demonstrate that the IIBT framework achieves a reduction of over 70% in BT node complexity while maintaining robust, interpretable, and adaptive cooperative behavior in uncertain environments. Full article

To investigate the mechanical performance and failure modes of Prestressed Concrete Cylinder Pipe (PCCP) bell-and-spigot joints under conditions such as differential settlement, this study conducted a full-scale rotation test on a DN1400 PCCP joint and established a three-dimensional non-linear finite element model using ABAQUS. The experimental results indicate that when the relative rotation angle reaches approximately 1.92°, the primary failure mode is the slipping of the rubber gasket from the spigot groove, leading to sealing failure. Meanwhile, the strains in the concrete, mortar coating, and prestressing wires at the joint increase significantly with the rotation angle. The finite element simulation results align well with the experimental data, with an average error of 1.88%. Based on the validated model, a parametric analysis was performed on PCCP joints with diameters ranging from 1400 mm to 4000 mm. The study determined the ultimate relative rotation angle for different diameters based on the concrete visible crack criterion and revealed a significant size effect, characterized by a decrease in the ultimate rotation angle with increasing pipe diameter. These findings provide a theoretical basis for the design, construction, and safety assessment of PCCP pipelines. Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

Rapid and actionable pathogen identification remains a major unmet need in the diagnosis of prosthetic joint infection (PJI). Current diagnostic approaches either provide rapid host response information without pathogen specificity or identify pathogens with delays of days to weeks. Here, we report a chemiresistive nanosensor array combined with machine learning analysis for same-day, pathogen-specific detection based on volatile organic compound (VOC) profiling. A 19-channel nanosensor array was first validated in vitro against a panel of ESKAPEE pathogens, achieving 96% mean classification accuracy using a radial-basis-function support vector machine (SVM) classifier. Data-driven optimization yielded a reduced six-sensor array with high signal-to-noise performance. The optimized platform was evaluated using pooled, uninfected human synovial fluid enriched 1:1 with nutrient media and spiked with Staphylococcus aureus, Staphylococcus epidermidis, or Pseudomonas aeruginosa across a range of 1–10⁶ CFU/mL. All infected samples were detected within 9 h, with distinct VOC signatures enabling accurate pathogen differentiation. Time-to-detection (TTD) demonstrated a strong inverse correlation with initial bacterial concentration, supporting semi-quantitative estimation of bacterial load. Negative controls remained at baseline throughout testing. This chemiresistive VOC-based biosensor platform demonstrates the potential to deliver rapid, integrated detection, identification, and burden estimation of metabolically active PJI pathogens, highlighting its promise for future point-of-care diagnostic applications. Full article

This study investigates the optimal pricing and green investment strategies within a dual-channel seafood supply chain comprising a seafood manufacturer and an e-commerce platform, where the manufacturer faces capital constraints. In response to the emerging model of e-commerce platform financing, we develop a Stackelberg game model to analyze two scenarios: single green investment (by the manufacturer only) and joint green investment (by both the manufacturer and the platform). We derive the equilibrium decisions for both parties under each scenario. The results indicate that enhanced market competitiveness of the direct sales channel increases the manufacturer’s wholesale revenue from the self-operated channel and its direct sales revenue, while also enabling the platform to achieve higher profits through a premium pricing strategy. Furthermore, the choice of green investment mode is contingent upon the intensity of channel competition and the platform’s commission rate. Specifically, when both parameters are low, a single green investment strategy constitutes an equilibrium. Conversely, when direct channel competition is sufficiently intense, a joint green investment strategy emerges as the Pareto-dominant equilibrium, leading to a win–win outcome regardless of the commission rate. These findings offer actionable insights for managers of seafood companies and e-commerce platforms in formulating sustainable channel cooperation and financing strategies. Full article

We classified essential motor actions, dorsal and plantar flexion (lower limb), and arm movement (upper limb) from electroencephalography (EEG)-based brain–computer interface (BCI) signals, using a convolutional neural network (CNN). Different from previous research on upper or lower limb motor imagery in isolation, we integrated both categories in a unified framework to explore a broader range of movements for broader applications. These motor actions are fundamental to daily activities such as walking, running, maintaining balance, lifting, reaching, and exercising. Upper limb EEG data were provided by INTI International University, whereas lower limb data were obtained from a publicly available dataset, recorded using 16-channel Emotiv and OpenBCI systems, respectively, each with distinct sampling rates and signal formats. To improve signal quality and facilitate joint model training, all signals were downsampled to 125 Hz, standardized to 16 channels, segmented using sliding windows, normalized via StandardScaler, and labelled according to action class. The processed data were used to train a CNN model configured with a kernel size of 3 and rectified linear unit activation functions. Training was terminated early at epoch 11 using an early stopping strategy, resulting in approximately 67% accuracy for both training and validation sets. Although this accuracy was moderate for deep learning, a promising outcome for EEG-based multi-class motor imagery classification was obtained, with the challenges posed by limited data availability, low inter-class feature discriminability, and the inherently noisy nature of non-invasive EEG signals. The results of this study underscore the potential of CNN-based models for future real-time BCI applications. By expanding the dataset, deep learning architectures can be refined to improve signal preprocessing techniques. Prosthetic devices need to be integrated to validate the system in practical scenarios. Full article

Gibson’s concept of optic flow established that perception is grounded in lawful structure generated by action. However, no formal mechanical framework has described the invariant structure of action-generated kinesthetic information during skilled manipulation. This study introduces haptic flow as a screw-theoretic invariant defined by the coupled rotational–translational organization of a body–object system. Motion capture data from a two-case comparison (one proficient and one novice golfer) were analyzed using instantaneous screw axes (ISA), pitch evolution, and cylindroid geometry derived from a linear line-complex formulation. The proficient golfer exhibited (1) progressive convergence of ISAs toward a coherent bundle, (2) stabilization of screw pitch through impact, and (3) co-cylindrical alignment of harmonic screws consistent with inertial–restoring conjugacy. In contrast, the novice golfer showed fragmented ISA organization and elevated pitch variability. These differences were descriptive rather than inferential and do not imply population-level generalization. The findings suggest that skilled manipulation is characterized by stabilization of symmetry-bearing screw invariants rather than by independent joint control. Interpreted ecologically, haptic flow is proposed as a mechanically specified candidate invariant generated by lawful body–object coupling. The present study establishes a geometric framework for quantifying such invariants while identifying the need for cross-task and perceptual validation. Full article

Markerless motion capture (MMC) provides a non-invasive alternative for motion analysis; however, its validity at the standard frame rate of 25 Hz commonly used in broadcast and surveillance applications remains to be established. This study evaluated the performance of a 25 Hz multi-camera MMC workflow using consumer-grade cameras for capturing basketball-specific movements. Three highly trained male athletes completed seven tasks, including sprinting and simulated sport-specific skills, while being synchronously recorded by six MMC cameras (DJI Action 5 Pro, 25 fps) and a 10-camera Vicon system (25 Hz). Kinematic data were processed using an RTMDet–RTMPose pipeline and low-pass filtered at 6 Hz. Waveform validity was assessed using Pearson’s correlation coefficient (r) and the root mean square error (RMSE). The displacement magnitudes of 12 joints showed excellent agreement (r = 0.916–0.994; median nRMSE = 0.54–1.32%), indicating robust trajectory reconstruction. In contrast, agreement decreased for derivative variables: velocity (r = 0.583–0.867) and acceleration (r = 0.232–0.677) were highly sensitive to the low sampling rate and numerical differentiation. Although a 25 Hz configuration is insufficient for high-precision impact analysis, it provides acceptable accuracy for macroscopic displacement tracking and external-load quantification in resource-constrained training environments. Future optimization should prioritize temporal synchronization to improve the reliability of derivative variables. Full article