Search Results (1,214)

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

Background: Periprosthetic joint infection and native hip infections often require staged surgical intervention due to extensive bone and soft tissue destruction. This study evaluates the clinical feasibility and mechanical reliability of a modified functional articulating hip spacer (FAHS) incorporating a cemented dual-mobility-bearing (DMB) metal liner. Methods: We retrospectively reviewed the cases of 20 patients who underwent a DMB-incorporated FAHS between March 2018 and December 2019. The technique involved cementing a DMB metal liner directly into the prepared acetabulum without a standard outer shell. Successful clinical outcome was defined as either transition to second-stage total hip arthroplasty (THA) or stable spacer retention, the latter including cases with definitive eradication or symptom-controlled chronic suppression therapy. Infection eradication required the clinical absence of infection for at least twelve months following the cessation of antimicrobial therapy. Construct-related mechanical complications and radiographic parameters were also analyzed. Results: The mean follow-up was 23.5 months, ranging from 6.0 to 62.6 months. Successful clinical outcome was achieved in 17 patients (85%), with seven (35%) transitioning to second-stage THA and ten (50%) opting for spacer retention. Within the retention group, seven achieved definitive eradication while three were maintained under chronic suppression therapy. Construct integrity was maintained in 80% of the cohort. Mechanical complications included two dislocations (10%) and two implant failures (10%). Radiographic analysis showed higher inclination and anteversion angles of the metal liner in the dislocation cases. Conclusions: The off-label use of DMB-incorporated FAHS represents a feasible option with acceptable mechanical performance in selected cases of PJI and native hip joint infection. However, as mechanical complications cannot be fully prevented, meticulous surgical techniques and careful patient selection remain essential. Full article

The rapid proliferation of mobile Internet of Things (IoT) devices has introduced significant resource scheduling challenges in multi-edge computing networks, where device mobility leads to dynamic network connectivity and load imbalance, complicating task offloading and resource management. To address these issues, this paper presents a mobility-driven hierarchical optimization framework for task offloading and computation resource allocation in multi-region edge computing environments, a functionally coupled hierarchical framework that integrates mobility-aware heuristic offloading with multi-agent deep deterministic policy gradient (MADDPG)-based resource allocation. Devices are first clustered according to their mobility patterns, and offloading decisions are dynamically made based on trajectory and dwell-time characteristics. Each edge server is modeled as an autonomous agent, and an MADDPG framework is adopted to collaboratively optimize resource allocation, with the joint objective of minimizing task processing delay and system energy consumption. Experimental evaluations under diverse mobility and workload conditions show that the proposed approach achieves a 19.0% reduction in task delay compared to the Multi-Objective Gray Wolf Optimization (MOGWO) method at the largest device scale (60 devices) and maintains comparable energy efficiency. Furthermore, it exhibits stronger adaptability and scheduling performance across varying mobility group distributions. These results confirm the effectiveness of the proposed method in enhancing system performance within dynamic mobile edge computing scenarios. Full article

Contemporary physiotherapy requires technological tools to provide effective therapy to the increasing group of patients with neurological conditions, among others. This can be achieved with rehabilitation robots, which can also be exoskeletons—wearable devices that mobilize multiple joints with complex motions representing activities of daily living. To perform kinesiotherapy conveniently in home-like environments, the exoskeletons need to be relatively lightweight. The paper presents the methodology for decreasing the mass of the exoskeleton design with real-life data-driven simulations of motions, followed by multibody dynamics simulations, and finite element method (FEM) multistep optimization. The process includes sequential initial parametric optimization, topology optimization, and final parametric optimization. The steps are used to set initial dimensional and material parameters, extract new geometrical features, and adjust the final geometry dimensions of a new design. The presented case of the SmartEx-Home exoskeleton resulted in a total mass reduction of almost 50% for the main construction elements while meeting the criteria of the minimum safety factor and maximum internal stress and strain for all components. The final design was manufactured and tested with humans, reflecting an almost fully automatic passive and active therapy. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

To enable autonomous route planning for UAV swarms in dynamic air–terrestrial cooperative navigation scenarios within GNSS-denied environments, this paper proposes a lightweight framework based on the Artificial Potential Field (APF) method. In the considered architecture, UAVs act as mobile transit navigation nodes that relay positioning information from sparse ground anchors to terrestrial users. For TOA-based cooperative positioning, the instantaneous geometric configuration of the UAV swarm significantly affects the overall system accuracy. Therefore, the impact of UAV positions on the end-to-end navigation performance is rigorously analyzed, yielding a comprehensive Dilution of Precision (DOP) matrix for the entire air–terrestrial system. By applying the Schur complement, the global performance metric is decomposed, resulting in a scalar evaluation function that directly reflects the geometric quality of the configuration. In practical scenarios involving dynamic and heterogeneous users, real-time trajectory adaptation of the UAV swarm is essential to continuously optimize user positioning accuracy. To this end, an APF-based autonomous joint route planning approach is developed. The potential field is constructed directly from the derived geometric evaluation model, where its negative gradient generates virtual forces that autonomously guide the UAV swarm. This elegantly bridges high-level navigation performance optimization with low-level motion control of the swarm. The simulation results show a 76.1% improvement in the average comprehensive GDOP for users compared to the baseline of hovering UAVs, validating the effectiveness and real-time capability of the proposed lightweight framework. Full article

Chronic osteoarthritis (COA) is a progressive and degenerative condition that causes joint inflammation and pain, often requiring long-term pharmacological management. Conventional treatments may lead to adverse effects, tolerance, and limited analgesic efficacy. This randomized, double-blind clinical trial evaluated the analgesic potential of a full-spectrum Cannabis sativa oil extract administered orally twice daily over six weeks in dogs with COA. Subjects were assigned to three groups: Cannabis, Placebo, and Control. Pain was assessed using the Canine Brief Pain Inventory (CBPI) and the Canine Osteoarthritis Staging Tool (COAST), which ranges from 0 to 4. The Cannabis extract (46.4 mg/mL) total cannabinoids: Cannabidiol (CBD), Cannabidiolic acid (CBDA), Delta-9-Tetrahydrocannabinol (Δ⁹-THC), and Tetrahydrocannabinolic acid (THCA), were administered using a cautious dose escalation protocol. Treatment began at ~0.1 mg/kg every 12 h, increasing by one drop (1.16 mg) every 72 h. This gradual titration continued until reaching the maximum tolerated dose (2 mg/kg every 12 h), which was maintained for the final two weeks. The protocol was designed to minimize adverse effects and allow close monitoring, especially in geriatric or clinically fragile dogs. By day 28, when the DMT was reached, the Cannabis group showed a 39.6% reduction in CBPI scores, compared to 24.7% in the Placebo group and a 1.6% increase in the Control group. COAST scores improved from level 4 to level 3 in 55.5% of dogs in the Cannabis group, with no changes observed in the other groups. We hypothesize that the co-administration of carprofen, meloxicam, or pregabalin with a full-spectrum Cannabis sativa extract—rich in acidic cannabinoids and terpenes—enhances pain relief and mobility in dogs with COA more effectively than conventional therapies alone. This study aimed to assess the efficacy of an oily full-spectrum Cannabis sativa extract as an adjunctive treatment to NSAIDs in twenty-seven dogs diagnosed with COA, and to compare pain intensity across three treatments groups. Full article

Background/Objectives: Joubert syndrome is a rare neurodevelopmental disorder characterized by congenital cerebellar and brainstem malformations affecting networks involved in predictive motor control, sensorimotor integration, and autonomic regulation, resulting in a heterogeneous motor phenotype. Functional impairment is typically described using global gross motor scores, which may not adequately reflect axial control, postural organization, musculoskeletal alignment, or respiratory–postural interactions. The objective of this descriptive pilot case series was to provide a multidimensional functional characterization of children with Joubert syndrome by integrating standardized motor assessments with postural, musculoskeletal, and thoracoabdominal measures. Methods: Six children with genetically and radiologically confirmed Joubert syndrome underwent a single standardized assessment session conducted by the same examiner. This cross-sectional, non-controlled study was based on feasibility sampling, and no a priori power calculation was performed. Gross motor function and postural control were evaluated using the Gross Motor Function Measure-88 and the Balance Assessment Rating Scale. Additional measures included joint range of motion, sacral inclination angle, thoracic configuration, thoracic excursion during quiet breathing, and respiratory rate. Analyses were limited to descriptive statistics. Results: Gross motor performance varied widely across participants, whereas postural control scores did not parallel gross motor performance levels within the cohort. Inter-individual variability was observed in joint mobility, pelvic alignment, and thoracoabdominal configuration, including among children with relatively preserved gross motor scores. Thoracic excursion during quiet breathing demonstrated a relatively narrow and low within-cohort range. Conclusions: In this small exploratory case series, functional characteristics observed in this cohort extended beyond global motor scores. Axial control, postural organization, and thoracoabdominal configuration may represent relevant descriptive domains of functional presentation within a multidimensional framework. Larger, longitudinal, and controlled studies are required to determine their clinical and neurodevelopmental significance. Full article

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

Stroke survivors with spasticity, an involuntary increase in muscle tone, often struggle to access specialized equipment and medical support for their rehabilitation. Rehabilitation exercises are daily routines requiring patients to perform repetitive movements of their spastic joints. To reduce patient mobilization within hospitals, offering orthoses suitable for use in home settings, outside of clinical environments, is required to limit the involvement of healthcare personnel in the treatment of hemiparesis for patients. Such orthoses must be designed to be portable and be able to tolerate the erratic motions of spasms without breaking or injuring patients. This paper presents the use of magnetorheological actuators to design an elbow orthosis, improving weight, reactivity, and transparence necessary for effective rehabilitation of spastic patients. A prototype is designed, built, and characterized experimentally. Results suggest that the technology is lightweight and highly transparent to erratic motion, and thus well-suited for spastic patients. Full article

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

Orthobiologic treatments such as platelet-rich plasma and stem cell therapies are increasingly used to support the healing of tendons, ligaments, and joints. This perspective proposes applying periodization—a structured, progressive model borrowed from athletic training—to rehabilitation following orthobiologic interventions in order to improve functional outcomes. The framework is organized into sequential phases that align with biological stages of healing. Early phases emphasize pain control, inflammation management, and safe, controlled mobility. Rehabilitation then progresses toward gradually increasing load bearing and strength, and toward more specific exercises to promote tissue regeneration while reducing the risk of re-injury. In later phases (mesocycles), the model highlights the importance of neuroplastic adaptations for sustained functional recovery, including neurogenesis, synaptic plasticity, and functional remodeling to safer RTP for athletes. A key advantage of this approach is its adaptability: progression can be individualized according to a patient’s recovery trajectory and response to loading. By aligning rehabilitation progression with intrinsic healing processes and integrating physiological and neuromuscular goals, the proposed model aims to maximize regenerative potential across both athletic and non-athletic populations. Overall, this neuroplastic periodized approach offers a practical, evidence-informed structure to guide clinicians in delivering patient-centered regenerative rehabilitation and may help standardize care after orthobiologic procedures. Full article

In this paper, a trajectory planning method based on an improved beluga whale optimization algorithm is proposed for the trajectory planning of plasma-spraying robot with complex surfaces. Firstly, the system architecture, kinematics model and trajectory planning constraints of the 6-DOF mobile plasma robot are analyzed, including kinematics, dynamics and environmental constraints, and a constrained-objective optimization function with time optimization, energy consumption and smoothness as objectives is established. Secondly, aiming at the shortage of the balance between global search and local development of the original beluga optimization algorithm, the tent chaotic mapping strategy is introduced to enhance the population diversity, and the sine and cosine algorithm is integrated to optimize the search process, so as to improve the convergence accuracy and stability. The experimental part is verified by the standard test function and the special index of trajectory planning. The results show that the IBWO algorithm is significantly better than the original beluga optimization, particle swarm optimization and other comparative algorithms in convergence accuracy, stability and comprehensive performance. In addition, the trajectory planning example shows that the joint trajectory generated by improved beluga whale optimization is smooth and has high constraint satisfaction, which is suitable for complex surface spraying tasks. Full article

Total knee arthroplasty (TKA) is one of the most performed surgical procedures in the United States and is often associated with moderate to severe postoperative pain. Multimodal postoperative analgesia following TKA is essential for optimizing postoperative recovery and enabling early postoperative mobilization. Regional anesthesia using ultrasound-guided peripheral nerve blocks plays an important part in perioperative pain management by targeting the femoral, obturator, and sciatic nerves of the knee joint. A variety of peripheral nerve block techniques have been described, which can be classified as either motor-blocking or motor-sparing techniques. Traditional motor-blocking regional anesthesia techniques, such as femoral and sciatic nerve blocks, provide excellent analgesia but can result in significant quadriceps weakness that delays ambulation after TKA. Motor-sparing regional anesthesia techniques, including the adductor canal block, iPACK block, and genicular nerve block, are becoming more widely used in enhanced postoperative recovery protocols for outpatient and short-stay inpatient TKAs. The peripheral nerve block technique can be selected according to the type of surgical procedure, the planned length of stay, rehabilitation goals, and patient comorbidities. Multiple peripheral nerve blocks provide better analgesia than single-injection blocks, and continuous catheter techniques are used for prolonging analgesia in select patients. An individualized multimodal regional anesthesia approach should be utilized to maximize analgesia after TKA to optimize postoperative outcomes. We present a narrative review of peripheral nerve block techniques and strategies for their use following inpatient or outpatient TKA. Full article

Background/Objectives: Emerging evidence suggests the presence of associations in joint mobility along anatomically defined myofascial continuities, indicating that joint mobility may co-vary across anatomically distant regions. This study aimed to investigate the correlations between the active range of motion (ROM) of joints belonging to the same myofascial chain in healthy, physically active individuals. Methods: Active ROM was measured in 61 adults (21 males and 40 females) at joints contributing to four myofascial chains: the superficial front line (SFL), superficial back line (SBL), functional front line (FFL), and functional back line (FBL), using an inertial measurement unit. Partial Pearson’s correlation coefficients (r), controlling for sex, were calculated to examine the relationships between joint ROM values within lines, with statistical corrections applied when necessary. Results: Significant, yet weak to moderate in most cases, partial correlation coefficients were identified among joints in the upper SFL (0.32–0.44), the lower SBL (0.42–0.44), along the FFL (0.29–0.51), and between the lower segments of the BFL (0.48–0.60). Conclusions: While some joint ROMs within myofascial chains demonstrate weak-to-strong associations, overall interdependence appears mode- and region-specific. These findings suggest that factors beyond fascial continuity, such as neuromuscular control, joint structure, and movement habits, are likely to contribute to ROM variability. Full article