Search Results (1,378)

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article

In order to solve the challenges of laboratory safety management in universities, such as insufficient supervision of high-frequency risk behaviors in responsibility traceability, a laboratory safety hazard perception and responsibility traceability system based on deep learning is proposed. Based on the YOLOv5s object detection model, the channel attention mechanism SE and NWD loss functions are introduced, with DeepSORT tracking to realize multi-target tracking and hidden danger perception in laboratory scenarios. Then, the responsibility matching algorithm and visual traceability mechanism are proposed to build a full-chain management system of “risk perception, analysis and tracking, and responsibility traceability”. Experiments show that the mean average precision (mAP) of YOLO-lab in the laboratory scene is 87.8%. Taking the experimenter not wearing a lab coat as an example, through the test of the laboratory scene, the multi-target tracking effect is excellent and the responsibility traceability report is generated, which solves the problem of “visible and uncontrollable behavior, traceable and unproven” in traditional supervision, and provides an intelligent technical path for laboratory safety governance. Full article

The influence of competitive standards on physical performance in professional football remains controversial, particularly when teams from different divisions compete against each other. This study aimed to analyze position-specific differences in physical performance, both in possession and out of possession, between Spanish First and Second Division teams during direct competition. Match data from 25 Copa del Rey fixtures across three seasons were analyzed using a validated multi-camera tracking system. Physical performance variables were examined according to playing position. First Division players accumulated greater total distance during in-possession phases, particularly central defenders (CDs), wide defenders (WDs), central midfielders (CMs), and wide midfielders (WMs), whereas Second Division players covered greater distances during out-of-possession phases, mainly CDs, WDs, and CMs. In addition, First Division CMs exhibited higher high-speed running in possession, while Second Division forwards (FWs) demonstrated greater high-speed running and defensive high-speed running. Differences in maximum velocity were also observed in CDs, with higher values recorded in the First Division. Overall, these findings indicate that physical performance is modulated by the competitive standard and playing position. However, the interpretation of these differences should not be dissociated from the tactical nature of football, as players’ specific positions and the tactical roles derived from them may condition the physical demands imposed during different phases of play, rather than reflecting isolated physical capacity. Full article

Drone localization is essential for various purposes such as navigation, autonomous flight, and object tracking. However, this task is challenging when satellite signals are unavailable. This paper addresses database-free vision-only localization of flying drones using optimal window template matching and velocity fusion. Assuming the ground is flat, multiple optimal windows are derived from a piecewise linear segment (regression) model of the image-to-real world conversion function. The optimal window is used as a fixed region template to estimate the instantaneous velocity of the drone. The multiple velocities obtained from multiple optimal windows are integrated by a hybrid fusion rule: a weighted average for lateral (sideways) velocities, and a winner-take-all decision for longitudinal velocities. In the experiments, a drone performed a total of six medium-range (800 m to 2 km round trip) and high-speed (up to 14 m/s) maneuvering flights in rural and urban areas. The flight maneuvers include forward-backward, zigzags, and banked turns. Performance was evaluated by root mean squared error (RMSE) and drift error of the GNSS-derived ground-truth trajectories and rigid-body rotated vision-only trajectories. Four fusion rules (simple average, weighted average, winner-take-all, hybrid fusion) were evaluated, and the hybrid fusion rule performed the best. The proposed video stream-based method has been shown to achieve flight errors ranging from a few meters to tens of meters, which corresponds to a few percent of the flight length. Full article

The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article

To address the low-speed starting noise in a small electric vehicle, this study proposes and validates a systematic diagnostic and optimization methodology. A novel objective testing method, based on energy tracking and matching, is first employed for precise noise source localization. Combined with electromagnetic force wave analysis, this method identifies the coupling between a 24th-order motor excitation and a powertrain structural mode as the root cause. Subsequently, a low-cost, integrated optimization scheme is presented, which synergistically combines three strategies: motor control refinement, powertrain natural frequency tuning, and mount isolation enhancement. Experimental validation demonstrates that this multi-domain approach reduces the sound pressure level at the driver’s ear by 4–6 dB(A), effectively eliminating the abnormal audible noise during starting and significantly improving the in-cabin sound quality. This paper offers a cost-effective engineering framework for resolving low-speed, low-frequency noise problems in electric vehicles. Full article

Background/Objectives: SLC13A5 encodes a sodium–citrate cotransporter implicated in early-onset epileptic encephalopathy and metabolic brain dysfunction, yet its developmental regulation and molecular context in the human brain remain incompletely defined. Methods: Leveraging human developmental transcriptomes from the Evo-Devo resource, we delineated tissue trajectories and network context for SLC13A5 across the fetal–postnatal life. Results: In the cerebrum, SLC13A5 expression rises from late fetal stages to peak in the first postnatal year and then declines into adulthood, while cerebellar levels increase across the lifespan; liver shows a fetal decrease followed by sustained postnatal upregulation. A transcriptome-wide scan identified extensive positive and negative associations with SLC13A5, and a signed weighted gene co-expression network analysis (WGCNA) built on biweight midcorrelation placed SLC13A5 in a large module. The module eigengene tracked brain maturation (Spearman rho = 0.802, p = 8.62 × 10⁻⁶) and closely matched SLC13A5 abundance (rho = 0.884, p = 2.73 × 10⁻⁶), with a significant partial association after adjusting for developmental rank (rho = 0.672, p = 6.17 × 10⁻⁴). Functional enrichment converged on oxidative phosphorylation and mitochondria. A force-directed subnetwork of the top intramodular members (|bicor| > 0.6) positioned SLC13A5 adjacent to a densely connected nucleus including CYP46A1, ITM2B, NRGN, GABRD, FBXO2, CHCHD10, CYSTM1, and MFSD4A. Conclusions: Together, these results define a developmentally tuned, mitochondria-centered program that co-varies with SLC13A5 in the human brain across the lifespan. It may provide insights to interrogate age-dependent phenotypes and therapeutic avenues for disorders involving citrate metabolism. Full article

Background: Cardiac rehabilitation (CR) and mechanics are individually associated with cardiovascular outcomes in ischemic heart disease (IHD); however, their interaction remains less defined. We hypothesized that a 36-session CR program improves cardiac strain and torsional mechanics in IHD patients. Methods: Ninety IHD patients on guideline-directed medical therapy with complete revascularization were prospectively enrolled, of which 27 electively completed a 36-session standardized exercise CR program. Speckle-tracking echocardiography was utilized to assess left ventricular (LV) global longitudinal strain (GLS) and peak twist, and right ventricular free wall strain (RVFWS) at baseline and after program completion. Participants were propensity-scoring matched 1:1 with 27 patients who declined participation (No-CR). Results: Clinical characteristics were similar between groups (mean age: 63 ± 10 years, 82% male, 31% three-vessel coronary artery disease). When compared with baseline, the CR group experienced a significant improvement in LV GLS (−14.9 ± 2.9 vs. −16.2 ± 3.1%, p = 0.003), with a numerical but non-significant increase in peak LV twist (14.4 ± 7.4 vs. 16.8 ± 5.3°, p = 0.162). The No-CR group showed significant deterioration in RVFWS (−22.9 ± 4.6% vs. −19.3 ± 5.4%, p = 0.009), with no other changes including in GLS (−14.8 ± 3.1 vs. −15 ± 3.3%, p = 0.831). Follow-up comparisons between CR versus No-CR revealed significantly greater peak LV twist (16.8 ± 5.3 vs. 12.1 ± 4.2°, p = 0.001) and a healthier RVFWS (−22.2 ± 4.5 vs. −19.3 ± 5.4, p = 0.044) in CR participants. Conclusions: CR in patients with IHD improved LV GLS and, compared with No-CR, conferred better LV twist and RVFWS. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Multi-object tracking (MOT) in videos captured by Unmanned Aerial Vehicles (UAVs) is critically challenged by significant camera ego-motion, frequent occlusions, and complex object interactions. To address the limitations of conventional trackers that depend on static, rule-based association strategies, this paper introduces STC-SORT, a novel tracking framework whose core is a two-level reasoning architecture for data association. First, a Spatio-Temporal Consistency Graph Network (STC-GN) models inter-object relationships via graph attention to learn adaptive weights for fusing motion, appearance, and geometric cues. Second, these dynamic weights are integrated into a 4D association cost volume, enabling globally optimal matching across a temporal window. When integrated with an enhanced AEE-YOLO detector, STC-SORT achieves significant and statistically robust improvements on major UAV tracking benchmarks. It elevates MOTA by 13.0% on UAVDT and 6.5% on VisDrone, while boosting IDF1 by 9.7% and 9.9%, respectively. The framework also maintains real-time inference speed (75.5 FPS) and demonstrates substantial reductions in identity switches. These results validate STC-SORT as having strong potential for robust multi-object tracking in challenging UAV scenarios. Full article

Background: This study examines how contextual factors influence the match load experienced by U14 athletes. Methods: Ninety-six male players from eight Portuguese regional selection teams were monitored during three official matches each, using WIMU Pro™ inertial devices with ultra-wideband (UWB) tracking systems. Fifteen internal and external load variables were analyzed, including player load/min, high-speed running (HSR), maximum heart rate (HRmax), and high impacts/min. Mixed linear models revealed significant inter-individual variability in all variables, showing sensitivity to match context. Results: Losing teams exhibited higher player load/min. Balanced matches provoked greater cardiovascular and locomotor demands, particularly in HRmax and HSR metrics. Cluster analysis identified three match typologies based on score margin. Team level was strongly associated with final outcomes and quarter performance, reinforcing the predictive value of intra-match consistency. In contrast, match type (score margin) showed limited correlation with team quality or load distribution. Conclusions: These findings demonstrate the multifactorial nature of match load in youth basketball, supporting the implementation of individualized, context-aware training and recovery strategies while guiding long-term athlete development. Full article

Wound healing has been studied extensively in humans and lab animals, but not in dolphins. Severe human wounds require extensive medical intervention to avoid infection. Yet severe wounds on free-ranging dolphins heal without infection in microbial-infested seas, a compelling distinction. An eye-witnessed shark attack on a yearling bottlenose dolphin yielded 8 years of macroscopic markers on a live recuperating dolphin by known days of healing. In total, 106 healing histories were generated from the author’s 20-year ethological study of free-ranging bottlenose dolphins (Tursiops truncatus) in St. Petersburg, FL, USA. Results show that unaided wound healing at sea involves two consecutive macroscopic pigment patterns, wounds form preliminary seals by 4–8 weeks, and most heal to atrophic scars that remodel for years. Macroscopic markers in live recuperating dolphins show strong matches with macroscopic wound patterns in stranded Fraser’s dolphins (Lagenodelphis hosei), demonstrating links between macroscopic markers and immune activities. This is the first study to link macroscopic markers visible as healing-related pigment patterns to immunity. Macroscopic markers are conservation tools for tracking anthropogenic impacts on increased susceptibility to infection at sea and could lead to novel therapies in veterinary and human regenerative medicine. Full article

TIGIT and its ligand CD155 (PVR) are emerging immune checkpoints in colorectal cancer (CRC), but their associations with mutational subtypes and the tumor immune milieu remain unclear. We quantified TIGIT and CD155 proteins by ELISA in paired CRC tumors and matched surgical margins (n = 131) and evaluated associations with clinicopathological features, MSI status, and KRAS/NRAS/BRAF/PIK3CA/AKT1 mutations (n = 104). Both TIGIT and CD155 were significantly elevated in tumor tissue versus margins (p < 0.0001) and showed no association with TNM stage, clinical stage, grade, or tumor location. TIGIT levels were higher in MSI than MSS tumors and in BRAF-mutant compared to BRAF wild-type tumors, while CD155 expression showed no consistent MSI- or mutation-dependent differences. Cytokine profiling identified IFN-g as the only shared positive associate of TIGIT and CD155; CD155 additionally associated with TRAIL, IL-1Ra, M-CSF, and PDGF-bb. In external transcriptomic validation (TCGA-CRC), GSEA indicated enrichment of interferon/inflammatory programs in TIGIT-high tumors, while CD155-high tumors preferentially showed proliferation-related MYC/E2F/G2M signatures. Together, these findings support tumor-wide upregulation of the TIGIT/CD155 axis in CRC and suggest that TIGIT, more than CD155, tracks with MSI/BRAF-associated immune activation, providing a rationale for patient stratification in checkpoint-directed immunotherapy. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article