Search Results (2,318)

Cryopreservation of ram semen is an essential tool in assisted reproductive technology; however, oxidative stress generated during the freezing process may compromise sperm quality. This study evaluated the effects of Se and SeNPs on post-thaw sperm quality, PRDX5 expression, and oxidative status in cryopreserved ram semen. In this study, semen samples collected from five mature rams (three collections at 2-week intervals, yielding a total of 15 ejaculates) were frozen in liquid nitrogen using extenders supplemented with selenium (1 μg/mL, S1; 10 μg/mL, S2) or selenium nanoparticles (SeNPs; 1 μg/mL, N1; 2 μg/mL, N2) alongside a nonsupplemented control extender. Post-thaw sperm quality was evaluated using computer-assisted sperm analysis (CASA) for motility, kinematic parameters, viability, membrane integrity (HOST) assays, chromatin condensation assessment, and morphological analysis. Total oxidant status (TOS) measurements and PRDX5 gene expression analysis were performed separately. Low-dose SeNPs (1 µg/mL) significantly improved total motility (55.73 ± 19.01%), progressive motility (25.05 ± 15.34%), viability (57.27 ± 19.30%), HOST-positive spermatozoa (50.87 ± 18.91%), and morphologically normal spermatozoa (88.27 ± 4.10%) compared with the control and high-dose sodium selenite groups (p < 0.05). Chromatin condensation abnormalities were lowest in the SeNP-treated group. S1 and N2 also improved motility and morphology compared with the control; however, the increases were numerically smaller than those observed in the N1 group. In contrast, S2 supplementation showed limited benefit, with values that were similar to those of the control. Morphologically normal spermatozoa were highest in N1, followed by S1 and N2, while S2 and the control exhibited the lowest values (p < 0.05). In contrast, no significant differences were detected in TOS or PRDX5 gene expression among the experimental groups (p > 0.05). These findings indicate that low-dose SeNPs enhance post-thaw sperm functional integrity and cryotolerance without inducing measurable changes in bulk oxidative markers or gene transcription. Full article

Wearable robots can improve human walking economy; however, their effectiveness depends on user adaptation to assistance. This study introduces a framework for real-time estimation of user adaptation that relies only on wearable sensor data during operation. Metabolic measurements were used solely to establish the ground truth adaptation curves for model training and validation but are not required for real-time inference. Five healthy adults completed six days of treadmill walking while wearing a soft hip exosuit that provided hip extension assistance. Thigh-mounted inertial measurement units recorded step timing and hip-angle trajectories, from which three variability-based features (step-frequency variability, maximum hip-flexion variability, and maximum hip-extension variability) were extracted. A Long Short-Term Memory (LSTM) model used these gait-variability inputs to estimate each user’s adaptation level relative to a metabolic cost benchmark obtained from respiratory gas analysis. Across sessions, the metabolic cost decreased by 9.0 ± 5.6% from Day 1 to Day 6 (p < 0.01) with a mean time constant of 202 ± 78 min, In contrast, the variability in step frequency, maximum hip flexion, and maximum hip extension decreased by 66.4 ± 6.8%, 37.9 ± 24.2%, and 42.8 ± 10.6%, respectively, indicating that these reductions were users’ progressive adaptation to the exosuit’s assistance. Under leave-one-subject-out (LOSO) evaluation across five participants, 59.2% of the model predictions fell within ±10 percentage points of the metabolic cost–based adaptation curve. These results suggest that simple kinematic variability measured with wearable sensors can track user adaptation and support practical approaches to real-time monitoring. Such capability can facilitate adaptive control and training protocols that personalize exosuit assistance. Full article

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration procedure, and the performance measurements of the experimental model based on finite element analyses of the 3D model, are also detailed in this paper. Based on these finite element analyses, a region of the robot that introduces clearance during the operation of the experimental model is found. The paper also presents the methodology used for mapping the robot’s workspace with an optical system, which enabled improvements to ensure coverage of the entire pancreas area. The results obtained before and after the mechanical improvements are presented, demonstrating a reduction in clearance by up to 4.1 times following part replacement, as well as a workspace extension that enables the active instrument to reach the entire pancreatic region. Full article

In this study, a PID gain tuning approach using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) algorithm, is proposed for trajectory tracking of delta parallel robots. Owing to their 3-degree-of-freedom (3-DOF) parallel kinematic structure, delta robots offer higher stiffness, precision, and speed capabilities than serial manipulators; they are therefore widely used in high-speed pick-and-place applications due to their low moving mass and the stiffness provided by the closed-chain mechanism. In this study, the proposed DDPG-PID approach is comparatively investigated against the conventional Ziegler–Nichols (ZN) and Cohen–Coon (CC) tuning methods; DDPG is designed to optimize the PID gains (K_p, K_i, K_d) within predefined bounds in a continuous action space. In simulations conducted on four different trajectories—circle, lemniscate, diamond, and star—RMSE, IAE, ISE, ITAE, and maximum error metrics are used for evaluation. According to the results, DDPG-PID achieves the lowest error on all trajectories, reducing RMSE by approximately 35–58% compared to ZN-PID and by approximately 79–82% compared to CC-PID; similarly, improvements are observed in IAE/ISE/ITAE and maximum error values. These findings indicate that DDPG-PID provides more stable and accurate tracking, particularly on complex trajectories involving sharp direction changes, and demonstrate that the proposed method offers a superior automatic PID tuning alternative to classical tuning rules for industrial parallel robot control applications. Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

Cricket fast bowlers rely on the front leg as a mechanical lever during front foot contact, yet the underlying mechanisms that govern front leg behaviour remain unclear. This study examined front leg mechanics in 18 junior fast bowlers (17.2 ± 1.7 years) using a 14-camera 3D motion capture system and force platforms. Joint power and angular impulse analyses were performed to quantify hip and knee extension–flexion mechanics from front foot contact to ball release, enabling the classification of joint function as active (concentric), controlled (eccentric), or negligible. Power and angular impulse profiles revealed that front leg motion was dominated by controlled (eccentric) power at both the hip and knee, indicating that the regulation of knee angle occurred primarily through eccentric braking rather than concentric quadriceps extension. These findings suggest that achieving a “braced leg” position via isolated knee extensor strengthening may be ineffective. To evaluate whether kinematics and anthropometry contributed to performance, a multiple linear regression model was used. Run-up speed at back foot contact emerged as the strongest predictor of ball speed, whereas knee angle at front foot contact showed only a small and non-significant effect. Overall, the results indicate that front leg behaviour reflects coordinated whole-body dynamics, and performance interventions should prioritise momentum generation and timing across the kinetic chain rather than isolated joint actions. Full article

In this paper, we investigate the asymptotic behavior of solutions to a diffusive forest kinematic model, which describes the interactions among young trees, old trees, and airborne seeds. Our study focuses on the stability of the positive equilibrium, the occurrence of Hopf bifurcation yielding spatially homogeneous periodic solutions, and the subsequent Turing instability induced by diffusion in these periodic states. The analysis highlights that the juvenile tree mortality rate, represented by a quadratic function of mature tree density, plays a central dynamical role. Specifically, the parameter corresponding to the mature tree density at which juvenile mortality is minimized serves as a key Hopf bifurcation parameter. This indicates that the system’s transition to periodic solutions and later to diffusion-driven pattern formation can be effectively regulated through this parameter. From an ecological perspective, these results suggest that forest management strategies capable of indirectly influencing factors related to this critical parameter could help control the emergence of spatial patterns, such as forest patches. Furthermore, the functional form of the mortality rate offers a useful foundation for future studies examining how different assumptions regarding tree interaction morphology may influence ecosystem patterning. Full article

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article

(1) Background: We developed a novel technology that regulates human locomotion using transcutaneous electrical spinal cord stimulation to activate spinal locomotor networks and posterior root stimulation to activate leg flexor and extensor motor pools during swing and stance phases, respectively. This technology effectively restores walking in post-stroke individuals while forward propulsion in the stance phase and foot dorsiflexion in the swing phase are insufficient. In this study the effectiveness of regulating the stance and swing phases while healthy volunteers walked on a treadmill with transcutaneous electrical stimulation of the posterior roots, leg muscles, and their combined effects has been examined. (2) Methods: We analyzed the kinematic characteristics of stepping movements in healthy participants with spinal stimulation of the posterior roots and flexor/extensor leg muscles. (3) Results: Our findings clearly show that posterior root stimulation at T12 combined with tibialis anterior muscle stimulation during the swing phase effectively regulates foot dorsiflexion, whereas posterior root stimulation at L2 combined with hamstrings and medial gastrocnemius stimulation during the stance phase effectively regulates forward propulsion. (4) Conclusions: Combined stimulation in the stance and swing phases within the same gait cycle resulted in the most coordinated stepping, and effective control of forward propulsion and foot dorsiflexion. Full article

This paper presents the design of an omnidirectional-mobile paddle boat (OMPB) used in narrow rivers, ponds, and canals. Compared with common propeller boats, the OMPB has advantages such as zero turning radius and shallow draft. Firstly, a prototype is built in which there are four paddles connected with four DC motors, allowing the boat to move like an omnidirectional Mecanum-wheeled vehicle. Subsequently, to develop the OMPB’s autonomous navigation algorithms, a kinematic model is established and dynamic analysis is performed. To improve the ability of resisting disturbances and control precision, a control algorithm based on fuzzy controller is designed for trajectory tracking. Experimental validations cover trajectory tracking performance during both straight-line navigation and turning maneuvers. The results demonstrate that the OMPB is competent to carry out omnidirectional movement, and the actual navigation trajectory is highly consistent with the theoretical trajectory, with a tracking error within 40 mm and a heading angle error within 1.8°. The OMPB platform can be reformed into special-purpose vessels for floating garbage collection and fish feeding in narrow water areas. Full article

Synthesizing anatomically plausible facial expressions for embodied avatars requires bridging the gap between high-level semantic intent and low-level physical constraints. This study presents a unified architecture that establishes a “Semantic-Kinematic Loop,” explicitly coupling FACS-based control with biomechanical regularization. Unlike black-box neural renderers or purely geometric BlendShape systems, our framework employs a multi-stage pipeline: semantic intent is first mapped to Action Units (AUs), which then drive a coarse linear deformation, followed by a fine grained refinement stage using a topology-aware Inverse Kinematics (IK) solver. This solver enforces segment length constraints and inter-region coupling, effectively translating abstract affective signals into physically grounded surface deformations. Furthermore, the framework exploits this kinematic structure to enable controlled perturbation strategies, facilitating the generation of diverse, anatomically valid synthetic training data. The experimental results indicate that this hybrid approach effectively eliminates surface tearing artifacts and achieves superior anatomical fidelity in reproducing complex emotional states. Full article

Parkinson’s disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals were acquired using Xsens inertial sensors from 51 patients with PD and 53 healthy controls. For each participant, subject-specific kinematic networks were constructed by modeling inter-segment similarities through Jensen–Shannon divergence, from which global and local graph-theoretical metrics were extracted. A machine learning pipeline incorporating voting feature selection, and XGBoost classification was evaluated using a nested cross-validation design. The model achieved robust performance (AUC = 0.87), and explainability analyses using SHAP identified a subset of 13 features capturing alterations in velocity, inter-segment connectivity, and network centrality. PD was characterized by increased positional variability, reduced distal limb velocity, and a redistribution of network centrality towards proximal body segments. These features were associated with clinical severity, confirming their physiological relevance. By integrating graph-theoretical modeling, explainable artificial intelligence, and machine learning methodology, this work provides a method of discovering quantitative biomarkers capturing alterations in motor coordination. These findings highlight the potential of ML and kinematic networks to support objective motor assessment in PD. Full article

Conservative management of rotator cuff disorders remains challenging, with no comprehensive, evidence-based framework integrating diagnosis, prognosis, rehabilitation, and biological therapies. Existing recommendations usually address isolated components of care, leading to inconsistent treatment strategies. This article proposes a global, pragmatic protocol for the non-surgical management of rotator cuff lesions, from initial assessment to long-term follow-up. Drawing on clinical expertise supported by recent literature, we outline a stepwise approach that begins with a comprehensive diagnostic process that combines history, clinical examination, and targeted imaging. Based on lesion type, associated shoulder or neurogenic conditions, and patient profile, rotator cuff disorders are stratified into three prognostic categories under conservative care: good, borderline, and poor prognosis, highlighting factors that require treatment adaptation or early surgical consideration. Rehabilitation objectives are structured around four domains: (1) inflammation and pain control, (2) mobility and scapular kinematics, (3) strengthening and motor control with tendon-sparing strategies, and (4) preservation or restoration of anatomy. For each prognostic category, we define a monitoring plan integrating clinical reassessment, ultrasound follow-up, and functional milestones, including return-to-play criteria for athletes. This comprehensive narrative review demonstrates that precise diagnosis and individualized rehabilitation can optimize medical follow-up, active strengthening, and complementary or regenerative therapies. Aligning therapeutic decisions with prognostic and functional goals allows clinicians to optimize patient satisfaction and recovery, providing a clear, evidence-informed roadmap for conservative management of rotator cuff disorders. Full article

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article