Search Results (3,372)

The massive heterogeneous data streams and stringent low-latency requirements in the Industrial Internet of Things (IIoT) pose new challenges for edge network resource management. This paper addresses the joint optimization problem of Service Function Chain (SFC) orchestration and resource allocation in edge gateway-assisted IIoT networks, formulated as a mixed-integer nonlinear programming (MINLP) model to minimize end-to-end latency and energy consumption while satisfying quality-of-service (QoS) constraints. To tackle this NP-hard problem and the challenges of partial observability in distributed environments, we propose the SFC Orchestration and Resource Allocation-based Multi-Agent Proximal Policy Optimization (SORA-MAPPO) algorithm. The algorithm adopts a centralized training with decentralized execution (CTDE) paradigm with an intelligent agent cooperation mechanism. Simulation results validate the effectiveness of the proposed scheme in complex IIoT scenarios. Full article

This paper investigates an RIS-assisted mobile edge computing (MEC) system without reliable direct links between users and base stations (BSs). Users offload tasks to BSs through reconfigurable intelligent surface (RIS)-reflected links, where offloading decisions, service prices, and RIS-assisted transmission quality are tightly coupled. We formulate a joint design problem that considers task latency, transmission energy consumption, service pricing, BS computing constraints, and RIS phase-shift constraints. The RIS phase shifts are first optimized to improve the effective cascaded channel gain. Then, a distributed price-negotiation-based offloading mechanism is developed to coordinate user association and service pricing under channel-dependent utilities. Analysis and simulations show that the proposed algorithm converges within a finite number of iterations and achieves a balanced tradeoff between user utility and BS revenue. Full article

Background: This scoping review aimed to systematically identify communication strategies used during Joint Media Engagement (JME) and examine their associations with developmental outcomes and contextual factors. Methods: A systematic search of seven databases (up to April 2025) was conducted using Rayyan, following PRISMA-ScR guidelines; 26 studies met inclusion criteria and were synthesized to categorize parent communication strategies and their theoretical underpinnings. Results: Fifteen distinct communication strategies were identified and organized into four theoretical frameworks; Social Learning, Sociopragmatic, Behaviourist, and Theory of Mind along with a fifth category for technical scaffolding. Strategies aligned with Social Learning were most frequently reported and consistently associated with improvements in children’s language, cognitive, and socio-emotional outcomes. Findings also showed that JME strategies vary based on contextual factors, including parent type, geography, device type, media content, and child characteristics. Although most studies did not explicitly focus on JME, those employing mixed methods provided deeper insights. Conclusions: JME is shaped by both interaction quality and context, with Social Learning-based strategies playing a central role in supporting child development. The findings highlight the need for more rigorous, JME-focused research across diverse digital formats to strengthen the evidence-based parent coaching approaches to optimize JME practices in early childhood. Full article

Psoriasis is a chronic immune-mediated inflammatory skin disease often associated with systemic inflammation and multiple comorbidities, resulting in a substantial negative impact on patients’ quality of life and mental health. In addition to cutaneous and joint involvement, patients frequently experience psychological distress, social stigma, and symptoms of anxiety and depression, which may be comparable in clinical relevance to the physical manifestations of the disease. In recent years, biologic therapies have become increasingly established in the treatment of psoriasis due to their targeted action on key inflammatory pathways involved in disease pathogenesis and their high clinical efficacy. Beyond improving disease severity, biologic agents have also been associated with meaningful improvements in health-related quality of life and mental health outcomes. Importantly, currently available evidence suggests that psychological and quality-of-life outcomes in psoriasis are influenced by multiple interacting clinical and psychosocial factors. This narrative review summarizes recent scientific evidence on the relationship between psoriasis, quality of life, and mental health, with particular emphasis on the impact of biologic therapy on these outcomes. The available data suggest that treatment response reflects a multidimensional process, in which clinical improvement, psychological status, and broader psychosocial factors interact to influence patient-reported outcomes. Overall, the reviewed studies indicate that biologic therapies not only reduce disease severity but are also associated with meaningful improvements in health-related quality of life and psychological well-being in patients with psoriasis. Full article

In this paper, we propose and evaluate a feature distillation technique for object detection under poor visibility conditions, and we analyze its impact when deployed on an FPGA platform. We demonstrate via extensive experiments how different detection architectures generalize across scenes, and we infer that a scale-permuted feature extraction is the ideal choice for detection tasks in unconstrained environments with an 11–12% gain. As verified by the experiments, image enhancement often fails to provide significant detection gains. We hence introduce a joint training in a scale-permuted student network that learns dehazed features from a dual teacher network without an explicit dehazing step. The student learns to replicate not only the teacher outputs but also the decision-making process of the teacher by using attention transfer. Although the overall goal is to produce a real-time system capable of providing driving assistance in challenging scenarios, the FPGA implementation of a scale-permuted network is the first of its kind. To achieve effective implementation of the model in FPGA technology, a high-level synthesis approach and model compression techniques are employed to obtain a deployment with a good trade-off between quality and memory footprint metrics. We develop two distilled models using the joint feature distillation technique and show that these perform better in poor visibility scenes when compared to other detectors with similar size or even bigger sizes in some cases. Our 8.5 M model shows an mAP gain of almost 1% compared to YOLOv10-M with 15 M parameters, on the Cityscapes Hazy dataset. On night images from the BDD dataset, our 8.5 M model shows an approximate mAP gain of 4% compared to YOLO26-S with 9.5 M parameters. We further perform cross-domain testing with the DriveIndia dataset to show that our models generalize well beyond the distillation distribution and can be used for generic driving scenarios. Full article

Flavor profile and water dissolvability serve as core evaluation benchmarks for the quality of food and medicinal plant-derived instant granules. Currently, studies integrating flavor and dissolvability analysis to comprehensively characterize the overall performance of such granules remain scarce, and the existing literature lacks systematic comparative research on commercial products across multiple sources and batches. This study investigated 90 batches of four categories of plant extract instant granules and established a dynamic-static joint evaluation system coupled with multiple indicators and the Analytic Hierarchy Process (AHP). The three primary indicators were dissolving extent, dissolving rate, and taste, with equal weights assigned to each; the secondary indicators were classified and integrated based on the results of Principal Component Analysis (PCA) and correlation matrix. Quantitative analysis revealed that the comprehensive evaluation scores of all 90 batches of samples fluctuated between 0.5406 and 0.9503, and obvious disparities existed among different granule varieties. This multi-index evaluation framework effectively avoids the subjective bias inherent in conventional evaluation approaches, and lays a solid scientific foundation for quality supervision, formula optimization research and development, as well as market popularization of plant-based instant granules. Full article

Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score $q_{ij}$, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, $O\left(n^3+nm\right)$ per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work. Full article

Currently, multiple unmanned aerial vehicles (UAVs) can cooperatively work as mobile edge computing (MEC) servers in the sky to provide computation services to ground terminals (GTs). Such an MEC-enabled multi-UAV system will greatly benefit the GTs, each of which can offload its tasks on demand to a nearby UAV. In particular, if a GT has to process computation-intensive deep learning tasks in a catastrophic environment, it can partially offload these tasks to UAVs using a scheme like Partial Program Offloading (PPO). This ensures the quick processing of the deep learning tasks while saving computing resources on both the GT and UAV sides. Nevertheless, UAV–GT offloading links are frequently blocked by ground obstacles in complicated environments, and individual UAVs may have limited computation capacity. Moreover, UAVs lack a constant propulsion energy supply to sustain a long mission time. All these factors lead to a degraded Quality of Service (QoS) for GTs in terms of task latency. To address this issue, we propose to jointly optimize the UAV trajectories, computing resource allocation, and the partial offloading of deep learning tasks. The formulated joint optimization problem is challenging to solve optimally, as it is non-convex and involves multiple coupled constraints. We propose utilizing the Successive Convex Approximation (SCA) method alongside a Block Coordinate Descent (BCD) approach to tackle this joint problem. Numerical results demonstrate that the proposed joint optimization scheme significantly outperforms the benchmark solutions. Full article

To investigate the static behavior of UT-type assembled semi-rigid joints and the effects of bolt pretension loss, two representative joint configurations, UT250 × 150 and UT400 × 200, were studied by combining full-scale tests with refined finite element analysis using ABAQUS. Pure bending, bending-shear, and constant-axial-force-coupled loading conditions were considered, with particular attention paid to the effects of single-bolt and multiple-bolt pretension loss on moment capacity, initial rotational stiffness (K_y), interface slip, and the failure mode of the joints. The results show that the UT-type joint mainly fails through concentrated plastic yielding in the joint zone, and its ultimate moment (M_u) is 12.3–18.7% higher than that of a conventional bolted-welded joint, satisfying the design principle of “strong joint and weak member”. Loss of pretension in a single bolt has only a limited influence on the yield moment (M_y) and ultimate moment (M_u), with a maximum reduction of 8.0% in the ultimate moment (M_u) under negative pure bending; however, it causes clear degradation in the initial rotational stiffness (K_y), and pretension loss in the upper bolt produces a greater stiffness reduction than loss in a single lower bolt, with a maximum reduction of 33.43%. Multiple-bolt pretension loss exhibits a pronounced coupling effect. Simultaneous loss in lower bolts on the same side is the most unfavorable case, leading to a maximum stiffness reduction of 67.78% (coupling coefficient of 1.17), whereas diagonal loss is relatively controllable and generally keeps the stiffness reduction within 7%. When the axial compression ratio does not exceed 0.3, the mechanical response of the joint remains relatively stable, and the adverse effect of pretension loss can be alleviated to a certain extent; further increases in the axial compression ratio accelerate the degradation of both stiffness and load-carrying capacity. The present study provides a useful reference for the design optimization, construction quality control, and in-service maintenance of UT-type semi-rigid joints. Full article

Background: Visual assessment is commonly used in rehabilitation to evaluate movement quality during functional tasks such as sit-to-stand (STS) transfers. However, the extent to which observational ratings align with classifications derived from portable markerless motion capture systems remains unclear. This study examined agreement between novice observational ratings and motion-capture-derived classifications during STS performance. Methods: Fifty healthy adults performed STS transfers across three 18-inch seating conditions (firm, compliant, commode). Two final-year Doctor of Physical Therapy (DPT) students independently rated movement performance using a standardized observational rubric. Simultaneously, a portable markerless motion capture system (Kinotek) recorded joint kinematics, which were converted into ordinal severity classifications to enable a comparison. Inter-rater reliability and agreement were assessed using percent agreement and Krippendorff’s alpha. Results: Exact agreement between novice raters was high across all surfaces (82.3–82.9%), while Krippendorff’s alpha values were low despite high exact agreement (α = 0.250–0.323), consistent with restricted scale use. Agreement between observational ratings and motion-capture-derived classifications was low, with negative alpha values across all conditions (α = −0.224 to −0.561), indicating systematic differences in classification patterns. Observational raters more frequently assigned lower severity categories compared to motion-capture-derived classifications. Conclusions: Findings demonstrate low chance-corrected agreement under conditions of restricted scale use among novice raters and systematic disagreement between observational and motion-capture-derived classifications during STS performance. These findings reflect differences in classification approaches under the operational definitions used in this study. Motion capture was used as an objective comparator rather than a gold standard, and this study does not establish criterion validity. Further research is needed to evaluate agreement patterns in clinical populations and to examine how different measurement approaches influence functional movement classification. Full article

With the increasing demand for video processing in both human perception and machine vision applications, enhancing heavily compressed video has become a critical problem in practical multimedia systems. In many real-world scenarios, video data acquired by image sensors are often compressed for efficient transmission and storage, which introduces compression artifacts and degrades both visual quality and downstream task performance. This issue is especially significant in sensor-based systems such as surveillance cameras and mobile imaging devices. To address these challenges, we propose a novel joint human–machine video enhancement framework for compressed video enhancement that jointly targets human perceptual quality and machine vision performance. The framework integrates four complementary components: a Spatio-Temporal Fusion Module that leverages inter-frame correlations, a High-Frequency Semantic Fusion module for recovering structurally important details relevant to machine tasks, a Texture-Guided Model that enhances low-level visual features, and a Refined Attention Residual Quality Enhancement Module that adaptively emphasizes salient regions. By progressively combining these modules, the framework effectively restores compressed content while preserving task-relevant semantics. The experimental results demonstrate that our method consistently outperforms existing approaches, achieving higher PSNR and SSIM as well as improved object detection and video object segmentation performance. These results highlight the framework’s practical applicability for compressed video enhancement in sensor-based systems, including intelligent surveillance and autonomous imaging platforms. Full article

To address limitations in the modulation-quality analysis of high-order Quadrature Amplitude Modulation (QAM) signals, including insufficient timing synchronization accuracy, challenges in carrier recovery, and coupling between synchronization errors and parameter estimation, a cascaded digital baseband processing framework tailored for measurement scenarios is proposed. The proposed framework is designed to integrate synchronization recovery and parameter measurement. In the timing synchronization stage, a feedforward open-loop structure based on the Oerder–Meyr (OM) algorithm is employed to estimate the optimal sampling instants rapidly. In the carrier synchronization stage, a two-stage recovery structure is constructed, comprising coarse frequency offset estimation based on polarity decision and fine synchronization using an improved frequency–phase detector (FPD), thereby achieving both robust acquisition of large frequency offsets and high-precision compensation of residual errors. On this basis, a unified modulation quality evaluation model is established, enabling the joint estimation of the Error Vector Magnitude (EVM) and the Modulation Error Ratio (MER), as well as amplitude, phase, and frequency errors, within a consistent analytical framework. System-level validation of 256 QAM and 1024 QAM signals is conducted using a MATLAB R2021b-based simulation platform. The results demonstrate that stable synchronization recovery can be achieved under timing, frequency, and phase perturbations, yielding well-defined constellation diagrams. In terms of parameter estimation, the relative errors of all evaluated metrics are maintained within 2%, which is significantly below the conventional 5% measurement criterion. Further analysis indicates that the proposed method maintains strong robustness across varying signal-to-noise ratios (SNRs) and sampling rates. The results confirm that the proposed cascaded processing framework effectively unifies synchronization recovery and modulation quality analysis, significantly improving parameter estimation accuracy while maintaining high synchronization precision. This approach provides a practical and efficient solution for high-order QAM signal testing and measurement systems. Full article

Synthetic aperture radar (SAR) is of great value in intelligent image interpretation. However, the acquisition of real SAR data is costly, and manual annotation heavily relies on expert experience. These factors severely restrict the development of SAR intelligent interpretation algorithms. Meanwhile, the high-frequency details of SAR images contain rich target information. Traditional generation methods cannot effectively capture these key features. To address the above issues, this paper proposes a dual-branch feature decoupling generative adversarial network (GAN) with wavelet constraint designed to achieve high-quality and parameter-controllable SAR image generation. The framework leverages discrete wavelet transform (DWT) to separate spatial structure from high-frequency details, which are independently modeled by a structure branch and a detail branch, respectively. A wavelet consistency loss function is introduced to constrain the distribution of generated and real images in high-frequency subbands, thereby enhancing the model’s capability to model scattering details. To fuse features from the two branches, a cross-attention fusion module is adopted to realize the adaptive compensation of structural features with texture details. Furthermore, to achieve joint control over the semantic attributes and azimuth of generated samples, the framework further integrates auxiliary classification and azimuth regression tasks. A multi-task learning mechanism is constructed to realize precise control over the target’s semantic category and azimuth. For the continuous variable of azimuth, an angle-aware hypernetwork transform module is introduced to perform dynamic convolution modulation on the structure branch at the feature map scale, which improves the model’s fine control capability over continuous azimuth variations. Experimental results on the MSTAR dataset demonstrate that the proposed model can significantly improve the semantic consistency and visual fidelity of the generated samples. The generated samples exhibit high statistical alignment with real data distributions, confirming the model’s effectiveness in characterizing the feature space of SAR imagery and enabling controllable SAR data simulation, thereby augmenting datasets for image interpretation tasks. Full article

The tube-notching process is widely used to manufacture structural joints and ducting systems for fluid transport. In these applications, accurate intersection angles and proper fit-up geometry are essential to ensure reliable assembly and system performance. Consequently, CNC-based automation is increasingly adopted to improve productivity in operations where precision and cycle time are critical. The main problem, however, lies in the complexity of generating accurate cutting trajectories for tube–tube intersections and converting them into machine-executable commands. This study addresses this gap by proposing a simple, novel mathematical model for toolpath generation capable of producing intersection profiles at arbitrary joint angles, including lateral offset (non-coaxial) configurations. A systematic procedure was developed to convert the resulting trajectories into G-code, which was processed in a low-cost CNC plasma cutter designed to experimentally validate the toolpaths. The machine incorporates a fourth axis to enable bevel cutting during tube processing. Experimental results demonstrate stable operation, high dimensional accuracy (error ±0.1°), and consistent cut quality for trajectories generated by the proposed model, confirming the feasibility of the low-cost CNC plasma system and its scalability to diverse fabrication requirements. Full article

This study addresses two key challenges in computational pharmacology: identifying novel therapeutic uses for existing drugs and modeling drug safety-related characteristics. We propose a multi-relational biomedical knowledge graph that integrates gene, drug, and disease associations with adverse effect data, enabling joint modeling of therapeutic and safety-related properties. A Relational Graph Convolutional Network (R-GCN) is employed to learn relationally aware embeddings that capture complex biological interactions across heterogeneous entities. The framework is evaluated on two tasks: (1) drug–disease link prediction for drug repurposing and (2) prediction of drug side-effect burden based on adverse event patterns. The experimental results demonstrate that the R-GCN model outperforms baseline methods, achieving 94.63% accuracy in drug–disease link prediction, while embedding-based classifiers attain up to 97.14% F1-score in side-effect burden classification. Additionally, multi-hop relational reasoning enables the discovery of biologically plausible connections between drugs, genes, and diseases. These findings highlight the effectiveness of knowledge graph-based representation learning in jointly supporting therapeutic discovery and safety-related analysis. While side-effect burden is used as a surrogate measure rather than a direct indicator of drug quality, the proposed framework provides a scalable foundation for integrating real-world pharmacovigilance and regulatory data in future studies. Full article