Search Results (1,275)

Background: High-performance tennis exposes players to repetitive high-load strokes and abrupt directional changes, which substantially increase musculoskeletal injury risk. This systematic review synthesized evidence on epidemiology, risk factors, and physiotherapy-led preventive strategies in elite adolescent and adult players. Methods: Following a PROSPERO-registered protocol, MEDLINE, Web of Science, and Scopus were searched (2011–2024) for observational studies reporting epidemiological outcomes in high-performance tennis. Methodological quality was appraised with NIH tools, and certainty of evidence was graded with GRADE. Results: Thirty-seven studies met inclusion criteria: 16 in adolescents, 18 in adults, and 3 mixed. Incidence ranged from 2.1 to 3.5 injuries/1000 h in juniors and 1.25 to 56.6/1000 h in adults. Seasonal prevalence was 46–54% in juniors and 30–54% in professionals. Lower-limb trauma (48–56%) predominated, followed by lumbar (12–39%) and shoulder overuse syndromes. Across age groups, abrupt increases in the acute-to-chronic workload ratio (≥1.3 in juniors; ≥1.5 in adults) were the strongest extrinsic predictor of injury. Intrinsic contributors included reduced glenohumeral internal rotation, scapular dyskinesis, and poor core stability. Three prevention clusters emerged: (1) External load control, four-week “ramp-up” strategies reduced injury incidence by up to 21%; (2) Kinetic-chain conditioning, core stability plus eccentric rotator-cuff training decreased overuse by 26% and preserved shoulder mobility; and (3) Technique/equipment adjustments, grip-size personalization halved lateral epicondylalgia, while serve-timing modifications reduced shoulder torque. Conclusions: Injury risk in high-performance tennis is quantifiable and preventable. Progressive load management targeted kinetic-chain conditioning, and tailored technique/equipment modifications represent the most effective evidence-based safeguards for adolescent and adult elite players. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article

Long-range rotating wrap-around-fin rockets may exhibit non-convergent conical motion at high Mach numbers, causing increased drag, reduced range, and potential flight instability. This study employs the implicit dual time-stepping method to solve the unsteady Reynolds-averaged Navier–Stokes (URANS) equations for simulating the flow field around a high aspect ratio wrap-around-fin rotating rocket at supersonic speeds. Validation of the numerical method in predicting aerodynamic characteristics at small angles of attack is achieved by comparing numerically obtained side force and yawing moment coefficients with experimental data. Analyzing the rocket’s angular motion process, along with angular motion equations, reveals the necessary conditions for the yawing moment to ensure stability during angular motion. Shape optimization is performed based on aerodynamic coefficient features and flow field structures at various angles of attack and Mach numbers, using the yawing moment stability condition as a guideline. Adjustments to parameters such as tail fin curvature radius, tail fin aspect ratio, and body aspect ratio diminish the impact of asymmetric flow induced by the wrap-around fin on the lateral moment, effectively resolving issues associated with near misses and off-target impacts resulting from dynamic instability at high Mach numbers. Full article

In this paper, a UAV target tracking method with 6G integrated sensing and communication (ISAC) is proposed to address the surveillance requirements for unmanned aerial vehicle (UAV) targets in the context of the rapid development of low-altitude economy. Firstly, a target tracking system model for UAVs is established based on the ISAC base station transceiver architecture. Then, an unscented Kalman filter (UKF) target tracking framework is designed to tackle the occlusion effect during UAV navigation. Specifically, the measurement position information of the UAV is obtained through a spatial rotation-based parameter estimation method. Subsequently, occlusion is detected by analyzing the Line-of-Sight (LoS) visibility between the UAV and the base station. On this basis, the problem of short-term and long-term trajectory loss under occlusion is solved by integrating cubic interpolation with a constant velocity (CV) model, which enables real-time UAV trajectory tracking. Finally, simulation results demonstrate that: (1) under no occlusion, the average estimation errors of the X/Y/Z axes are 0.82 m, 0.79 m, and 0.68 m, respectively; (2) under short-term occlusion, the average errors of the X/Y/Z axes are 1.25 m, 2.18 m, and 1.05 m, with a convergence time of 1 s after LoS recovery; (3) under long-term occlusion, the average errors of the X/Y/Z axes are 2.87 m, 3.79 m, and 1.85 m, with a convergence time of 5 s after LoS recovery; (4) the velocity estimation error can quickly converge to within 0.2 m/s after re-acquiring observations. The proposed method exhibits small trajectory and velocity estimation errors in different occlusion scenarios, effectively meeting the requirements for UAV target tracking. Full article

Background: It has recently been proposed that the retina plays an important modulatory role in the control of motor function that is usually attributed exclusively to the function of the nigro-striatal dopamine (NSD) system. Indeed, it has been proposed further that Parkinson’s disease (PD) begins in and progresses from the retina and may be effectively treated from there. While previous intraocular work has employed intravitreal (IVIT) administration of toxins to induce experimental PD, the first study series reported here examines the effect of IVIT haloperidol on motor performance while the second study examines the effect of IVIT haloperidol on the unilateral rotation model of PD, both in a circadian context. Methods: Motor tests included open field performance and the latency to perform three motor tests after the IVIT injection of haloperidol with and without amphetamine pretreatment. In a second study, IVIT injections of the melatonin antagonist ML-23 or L-dopa were made after unilateral lesions of the NSD in rats that were placed in a rotometer examining spontaneous ipsilateral and contralateral turning. Results: IVIT haloperidol produced robust changes in several motor parameters during the light and dark phase of the LD cycle which were enhanced by amphetamine pretreatment. In the second study, while IVIT L-dopa had only a minor effect on spontaneous rotation during the light phase, IVIT haloperidol produced a robust effect upon ipsilateral turning. The reduction in spontaneous ipsilateral turning was seen after IVIT injections into the eye ipsilateral or contralateral to the hemisphere in which NSD destruction occurred. Reduced turning was seen during both the light and dark phases of the L/D cycle. Conclusions: These results illustrate that IVIT injections of DA and melatonin receptor antagonists can differentially alter motor function via the retina. This suggests that the retina may be a treatment target not only for PD but also for other DA- and melatonin-mediated disorders such as drug addiction, psychosis and schizophrenia. Full article

Phosphorus inputs play a critical role in modulating microbial dynamics in crop rhizosphere soils, yet their specific effects remain underexplored. This study investigated the impacts of P addition on soil respiration rates, enzyme activities, and microbial communities in maize and soybean rhizosphere soils under a 20-year corn-soybean rotation system. Results demonstrated that P inputs significantly elevated rhizosphere soil respiration rates. In maize, LP treatment yielded the highest initial rate (3.2 times CK on day 0) and maximum rate (1.45 times CK), peaking early (days 0–4). In soybean, HP treatment produced the peak rate, occurring on day 4. Glucosidase activity increased under P treatments, with HP in maize showing values up to 1.5–2 times CK before day 8, and HP in soybean peaking at 1.2 times CK on day 8. Acid and neutral phosphatase activities generally declined initially, reflecting feedback inhibition, while alkaline phosphatase rose early. Microbial community structure shifted markedly. Key taxa like Reyranella and Luteimonas increased with P concentration, while Gp1 decreased. Correlation analysis indicated strong associations; e.g., Proteobacteria positively correlated with acid phosphatase and negatively with neutral phosphatase. These findings underscore the crop-specific responses of rhizosphere microbiomes to P inputs, informing targeted fertilization strategies for enhanced nutrient efficiency and sustainable agriculture. Full article

The lack of deep engagement strategies that include cultural contextualization in the current rehabilitation game design can result in limited user motivation and low adherence in long-term rehabilitation. Integrating cultural semantics into interactive rehabilitation design offers new opportunities to enhance user engagement and emotional resonance in digital rehabilitation therapy, especially in a deeper way rather than visually. This study introduces a framework comprising a “Rehabilitation Mechanism–Interaction Design–Cultural Feature” triadic mapping model and a structured procedure. Following the framework, a hand function rehabilitation game is designed based on Chinese string puppetry, as well body rehabilitation games based on shadow puppetry and Tai Chi. The hand rehabilitation game utilizes Leap Motion for its gesture-based input and Unity3D for real-time visual feedback and task execution. Functional training gestures such as grasping, wrist rotation, and pinching are mapped to culturally meaningful puppet actions within the game. Through task-oriented engagement and narrative immersion, the design improves cognitive accessibility, emotional motivation, and sustained participation. Evaluations are conducted from rehabilitation professionals and target users. The results demonstrate that the system is promising in integrating motor function training with emotional engagement, validating the feasibility of the proposed triadic mapping framework in rehabilitation game design. This study provides a replicable design strategy for human–computer interaction (HCI) researchers working at the intersection of healthcare, cultural heritage, and interactive media. Full article

Background/Objectives: To develop a new Virtual Reality (VR) system software for measuring ocular deviation in strabismus patients. Methods: This prospective study included subjects with basic-type exotropia (XT) and non-refractive accommodative esotropia (ET). Ocular deviation was measured using the alternate prism cover test (APCT) and two VR-based methods: target offset (TO) and a newly developed camera rotation (CR) method. Results: A total of 28 subjects were recruited (5 cases were excluded for preliminary testing and 5 for not meeting inclusion criteria). Among the 18 included patients, 10 (66.7%) had XT and 5 (33.3%) had ET. The median age was 21.5 years (IQR 17 to 25). The mean age was 22.3 years (range: 9–46), with 5 (27.8%) having manifest strabismus and 12 (61.1%) measured while wearing glasses. VR-based methods (TO and CR) showed comparable results to APCT for deviation angle measurements (p = 0.604). Subgroup analysis showed no significant differences in ET patients (all p > 0.05). In XT patients, both TO and CR underestimated deviation angles compared to APCT (p = 0.008 and p = 0.001, respectively), but no significant difference was observed between the two methods (p = 0.811). Linear regression showed CR had a stronger correlation with APCT than TO (R² = 0.934 vs. 0.874). Conclusions: This newly developed VR system software, incorporating the CR method, provides a reliable approach for measuring ocular deviation. By shifting the entire visual scene rather than just the target, it lays a strong foundation for immersive diagnostic and therapeutic VR applications. Full article

Background/Objectives: Cervicogenic headache (CeH) is linked to upper cervical dysfunctions. The obliquus capitis inferior (OCI) muscle may contribute to restricted cervical rotation at the C1–C2 level, altered proprioception and pain. Dry needling (DN) of the OCI is hypothesized to target these dysfunctions. The aim of this study was to investigate whether a single intervention combining DN and manual therapy (MT) compared to sham needling (SN) and MT, improves C1–C2 rotation, functional, headache-related and psychological outcomes in a subgroup of CeH patients with a positive cervical flexion–rotation test (CFRT). Methods: Thirty-four participants were randomly assigned to (1) DN or (2) SN. The primary outcome was C1–C2 rotational mobility. Secondary outcomes included headache-related parameters (frequency, intensity, duration and perceived effect), functional parameters (cervical mobility, pain pressure thresholds, motor control and proprioception) and psychological parameters (central sensitization, pain catastrophizing, coping strategies and kinesiophobia). Outcomes were re-evaluated at one-week follow-up. Results: Linear mixed-effects models showed a significant and clinically relevant increase of C1–C2 rotation in the DN group compared to the SN group post-intervention (mean difference [MD]: 4.51°; 95% confidence interval [CI]: 1.74; 7.28), which was maintained at the 1-week follow-up (MD: 5.44°; 95% CI: 2.55; 8.33). No clinically relevant changes were observed in other secondary outcome measures. Conclusions: Targeting the OCI may be of added value in restoring atlanto-axial dysfunction. While short-term mobility gains were observed, a single intervention appears insufficient as a stand-alone treatment to impact functional or psychological outcomes. Future research involving larger samples should examine DN effects as part of a multimodal approach with long-term follow-up. Full article

Cotton is the most important natural fiber crop worldwide, and its automated harvesting is essential for improving production efficiency and economic benefits. However, cotton boll detection faces challenges such as small target size, fine-grained category differences, and complex background interference. This study proposes RA-CottNet, a high-precision object detection model with both directional awareness and attention-guided capabilities, and develops an open-source dataset containing 4966 annotated images. Based on YOLOv11n, RA-CottNet incorporates ODConv and SPDConv to enhance directional and spatial representation, while integrating CoordAttention, an improved GAM, and LSKA to improve feature extraction. Experimental results showed that RA-CottNet achieves 93.683% $Precision$, 86.040% $Recall$, 93.496% $mAP50$, 72.857% $mAP95$, and 89.692% $F1$-$score$, maintaining stable performance under multi-scale and rotation perturbations. The proposed approach demonstrated high accuracy and real-time capability, making it suitable for deployment on agricultural edge devices and providing effective technical support for automated cotton boll harvesting and yield estimation. Full article

The roller press crushing of ore is a complex process involving the interplay of multiple factors. Roller dimensions, gap settings, and rotational speed all influence this process, which in turn affects the comprehensive crushing performance of the high-pressure grinding rolls (HPGR). Therefore, to simultaneously enhance the HPGR’s size reduction effectiveness (SRE) and throughput while controlling its energy consumption, wear, and edge effect, multi-objective parameter optimization of the HPGR is required. This study utilizes the Discrete Element Method (DEM) to simulate ore comminution within an HPGR. By first dividing the release zone into segments, the particle size distribution of the crushed product at different locations within this zone is investigated. Then, the influence of various factors on the SRE at different locations within HPGR is examined through single-factor experiments. Subsequently, the relative influence of roller diameter, roller width, roller speed, and roll gap on the comprehensive crushing performance of the HPGR is determined through signal-to-noise ratio (SNR) analysis and analysis of variance (ANOVA). Finally, multi-objective parameter optimization of the roller press crushing is conducted based on grey relational analysis (GRA), incorporating the weights assigned to different response target. The results indicate that the proportion of unbroken ore particles is relatively significant, primarily due to the edge effect. Further analysis reveals that along the horizontal diameter of the rollers, regions closer to the roller surface exhibit better SRE. Additionally, roller speed is identified as the most influential factor affecting the uniformity of SRE in the HPGR. The application of GRA to the multi-objective optimization of roller press crushing enables effective balancing of the comprehensive crushing performance in HPGR. Full article

Background: Accurate and stable instrument positioning is critical in dental implant procedures, particularly in anatomically constrained regions. Conventional navigation systems assume a static patient head, limiting adaptability in dynamic surgical conditions. This study proposes and validates a real-time motion compensation framework that integrates optical motion tracking with a collaborative robot to maintain tool alignment despite patient head movement. Methods: A six-camera OptiTrack Prime 13 system tracked rigid markers affixed to a 3D-printed human head model. Real-time head pose data were streamed to a Kuka LBR iiwa robot, which guided the implant handpiece to maintain alignment with a predefined target. Motion compensation was achieved through inverse trajectory computation and second-order Butterworth filtering to approximate realistic robotic response. Controlled experiments were performed using the MAiRA Pro M robot to impose precise motion patterns, including pure rotations (±30° at 10–40°/s), pure translations (±50 mm at 5–30 mm/s), and combined sinusoidal motions. Each motion profile was repeated ten times to evaluate intra-trial repeatability and dynamic response. Results: The system achieved consistent pose tracking errors below 0.2 mm, tool center point (TCP) deviations under 1.5 mm across all motion domains, and an average latency of ~25 ms. Overshoot remained minimal, with effective damping during motion reversal phases. The robot demonstrated stable and repeatable compensation behavior across all experimental conditions. Conclusions: The proposed framework provides reliable real-time motion compensation for dental implant procedures, maintaining high positional accuracy and stability in the presence of head movement. These results support its potential for enhancing surgical safety and precision in dynamic clinical environments. Full article

Current mainstream remote sensing target detection algorithms mostly estimate the rotation angle of targets by designing different bounding box descriptions and loss functions. However, they fail to consider the symmetry–asymmetry duality anisotropy in the distribution of key features required for target localization. Moreover, the equivalent feature extraction mode of shared convolutional kernels may lead to difficulties in accurately predicting parameters with different attributes, thereby reducing the performance of the detector. In this paper, we propose the Feature Equalization and Hierarchical Decoupling Network (FEHD-Net), which comprises three core components: a Symmetry-Enhanced Parallel Interleaved Convolution Module (PICM), a Parameter Decoupling Module (PDM), and a Critical Feature Matching Loss Function (CFM-Loss). PICM captures diverse spatial features over long distances by integrating square convolution and multi-branch continuous orthogonal large kernel strip convolution sequences, thereby enhancing the network’s capability in processing long-distance spatial information. PDM decomposes feature maps with different properties and assigns them to different regression branches to estimate the parameters of the target’s rotating bounding box. Finally, to stabilize the training of anchors with different qualities that have captured the key features required for detection, CFM-Loss utilizes the intersection ratio between anchors and true value labels, as well as the uncertainty of convolutional regression during training, and designs an alignment criterion (symmetry-aware alignment) to evaluate the regression ability of different anchors. This enables the network to fine-tune the processing of templates with different qualities, achieving stable training of the network. A large number of experiments demonstrate that compared with existing methods, FEHD-Net can achieve state-of-the-art performance on DOTA, HRSC2016, and UCAS-AOD datasets. Full article

Background: Dental caries arise from polymicrobial biofilms and require interventions that address both local virulence and systemic burden. Methods: A curated set of 124 neem-derived phytochemicals was screened against Streptococcus mutans glucansucrase (3AIC) and Staphylococcus aureus DNA gyrase B (3U2D) using harmonized AutoDock Vina parameters. Ligand standardization and receptor preparation followed conventional protocols. Results: The most favorable docking scores reached −10.7 kcal·mol⁻¹ for 3AIC and −8.9 kcal·mol⁻¹ for 3U2D. Redocking produced pose RMSD values of 1.52 Å (3AIC) and 0.96 Å (3U2D). Per-receptor ADMET profiles for the six top-ranked compounds indicated median logP values of 4.93 (3AIC) and 4.52 (3U2D), median TPSA values of 80.3 and 62.9 Ų, median rotatable bonds of 2.5 and 1.0, and median QED values of 0.41 and 0.76, respectively. Conclusions: An integrated, dual-target screen prioritized neem constituents with plausible local anti-cariogenic activity and physicochemical features compatible with systemic disposition. These in silico findings motivate targeted experimental validation. Full article

A systematic fuzzy multi-criteria decision making (MCDM) framework is proposed to prioritize near-Earth asteroids (NEAs) for a boulder capture mission, addressing the requirement for rigorous prioritization of asteroid candidates under conditions of data uncertainty. Twenty-eight NEA candidates were first selected through filtering based on physical and orbital properties. Then, objective fuzzy weighting MCDM methods (statistical variance, CRITIC, and MEREC) were applied to determine the importance of criteria such as capture cost, synodic period, rotation rate, orbit determination accuracy, and similarity to other candidates. Subsequent fuzzy ranking MCDM techniques (WASPAS, TOPSIS, MARCOS) generated nine prioritization schemes whose coherence was assessed via correlation analysis. An innovative sensitivity analysis employing Dirichlet-distributed random sampling around reference weights quantified ranking robustness. All methodologies combinations consistently identified the same top four asteroids, with 2013 NJ ranked first in every scenario, and stability metrics confirmed resilience to plausible weight variations. The modular MCDM methodology proposed provides mission planners with a reliable, adaptable decision support tool for asteroid selection, demonstrably narrowing broad candidate pools to robust targets while accommodating future data updates. Full article