Search Results (2,527)

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework. Full article

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions. Full article

With advances in electric drive technology, electric tracked vehicles (ETVs) have emerged as a promising solution for high-mobility ground vehicles. However, under high-speed steering conditions, the equivalent motor load inertia varies significantly, introducing strong nonlinear and time-varying characteristics into the ETV that may induce lateral instability and even rollover. To address this issue, a novel augmented deep Koopman operator-based model predictive control (ADK-MPC) method is proposed. First, a high-order sliding-mode (HOSM) observer is designed to estimate the lumped load disturbances associated with the time-varying equivalent motor load inertia. Then, the estimated disturbances are introduced as an augmented state into the DK operator to construct a data-driven augmented model. The proposed model transforms the nonlinear dynamics into a lifted linear time-invariant representation in the augmented-state space while capturing the dominant nonlinear characteristics. Based on the ADK model, an ADK-MPC controller is developed to convert the nonlinear optimization problem into a quadratic programming problem, thereby improving steering stability and reducing computational complexity. Simulation results under steering conditions indicate that the proposed method achieves better yaw rate tracking and lower computational cost than nonlinear MPC. The yaw rate tracking error is reduced by 45.5%, while the average solving time is shortened by 11.7%. Full article

This study seeks to evaluate the extent of risk disclosure procedures within the Jordanian corporate sector and to analyze the impact of key dimensions of audit quality—namely audit opinion, audit fees, and auditor type—on the level of corporate risk disclosure (CRD). The study analyzes 90 annual reports from Jordanian non-financial publicly listed firms spanning 2014 to 2023, yielding 900 firm-year observations. A manual content analysis approach was used to quantitatively evaluate the extent of risk disclosure, augmented by logistic regression to examine the impact of audit quality indicators. The empirical data demonstrate that the number of risk disclosure statements varies across firms, ranging from 2 to 10 words, with an average of 24 phrases. The data demonstrate that factors affecting audit quality—namely unqualified audit opinions, higher audit fees, and Big Four auditors—show a positive and significant link with heightened levels of risk disclosure. This indicates that enhanced audit quality elevates the legitimacy and openness of company reporting, thus reducing information asymmetry between management and stakeholders. Previous research on risk disclosure in Jordan has mostly neglected the influence of audit quality as a factor in transparency. This study is among the few that thoroughly investigate the impact of audit opinion, audit fees, and auditor type on company risk disclosure within the non-financial sector. The results underscore the essential importance of audit quality in improving monitoring and disclosure processes, thereby enriching the existing literature on corporate governance and risk reporting in developing economies. Full article

This study introduces a spatiotemporal framework at the district level in Indonesia to examine and forecast stunting prevalence. The empirical analysis draws on data from 514 districts observed over 2022–2024, with short-term projections extended to 2025–2027 in line with the SDG 2.2 agenda. The modeling methodology is based on a Bayesian spatiotemporal formulation with the SPDE-INLA method. Instead of handling spatial and temporal lags separately, the model simultaneously incorporates them to reflect dependencies that change across both dimensions. This structure facilitates a more flexible representation of underlying risk dynamics. To improve prediction performance, we augment the baseline model with a hybrid component. Specifically, residual variation from the Bayesian specification is further explored using machine learning methods, providing an additional layer of adjustment. Spatial dependence is assessed through three alternative weighting schemes—KNN, Queen contiguity, and distance-based matrices—which are compared prior to selecting the final specification. The empirical specification includes nine key predictors within a semi-parametric framework. Several covariates are allowed to depart from strict linearity by accommodating time-varying effects. Three algorithms were evaluated during the prediction process to determine their abilities to capture the residual structure: XGBoost, Random Forest, and Elastic Net. Spatiotemporal clustering is examined through exceedance probabilities, resulting in the identification of seven unique cluster patterns. The findings consistently indicate that poverty is the main factor influencing stunting dynamics, with evident regional spillovers and temporal variations. Persistent hotspots are primarily located in eastern Indonesia. From a predictive standpoint, the hybrid specification—particularly the variant based on XGBoost—delivers the most stable performance. The forecast results indicate a gradual reduction in stunting prevalence throughout the forecast period. This study establishes persistent geographic inequalities in child nutrition risk and translates them into district-specific intervention priorities, providing decision-support information to further SDG Target 2.2 and its relationships with SDGs 1, 3, 4, and 6. Full article

This paper presents a comprehensive augmented reality (AR)-based rehabilitation system for upper-limb recovery that integrates AR-assisted art therapy, automated markerless goniometry, and the interval mathematical modeling of rehabilitation dynamics. The proposed platform combines four interconnected subsystems: a Python-based markerless video analysis module utilizing three stationary IP cameras, MediaPipe Pose Landmarker, and Kalman filtering; an AR art-therapy application developed for the Magic Leap 2 headset using Unity/OpenXR; a server-side subsystem implemented in NestJS/TypeScript; and (iv) a physiotherapist-oriented web application developed in React. The primary objective of the study is the real-time automated assessment of shoulder joint kinematics during AR-assisted rehabilitation sessions, including flexion (160–180°), extension (50–60°), and abduction (up to 180°). To describe and forecast rehabilitation dynamics, interval mathematical models based on recurrent difference equations were developed, enabling the prediction of subsequent joint angle values using the previous 3–4 observations. Structural and parametric identification of the interval models was performed using the artificial bee colony optimization algorithm. Experimental validation was conducted on rehabilitation data collected from five patients with different clinical diagnoses, including bursitis, epicondylitis, capsulitis, osteoarthritis, and fracture-related impairments. Under the considered experimental conditions, the proposed approach demonstrated promising predictive performance, with an angular prediction error below 5° and a correlation exceeding 95% between predicted and measured rehabilitation trajectories. The developed system implements a unified rehabilitation cycle of “execution–measurement–prediction–adaptation”, enabling the continuous monitoring of recovery dynamics, adaptive adjustment of rehabilitation scenarios, and estimation of the rehabilitation duration required to achieve target motor outcomes. The proposed approach contributes to the development of intelligent AR-based rehabilitation systems by combining markerless motion analysis, predictive interval modeling, and adaptive art-therapy mechanisms within a single clinical framework. Full article

The ionosphere is a major error source in single-frequency GNSS positioning, and Satellite-Based Augmentation Systems (SBAS) mitigate this effect by providing real-time correction information. However, the performance of SBAS under different latitude regions and geomagnetic activity levels still requires further evaluation. Taking EGNOS as an example, this study assesses SBAS-enhanced positioning performance using data from nine IGS stations across Europe. The experiments cover relatively low-, mid-, and high-latitude regions within the EGNOS service area, four representative quarters in 2023, and two disturbed geomagnetic events. Results show: (1) SBAS significantly improves positioning accuracy in all latitude regions, with overall improvement rates ranging from 62.11% to 83.51%. (2) The relatively low-latitude region achieves the largest performance gains, while the mid-latitude region provides the most stable and accurate results. (3) Under disturbed geomagnetic conditions, SBAS still outperforms conventional SPP, but its performance decreases compared with low-Kp periods, with latitude-dependent degradation observed at both MAS1 and SOD3. (4) The integrity analysis further shows that APV II availability reaches 91.85%, whereas CAT I availability decreases to 86.95% under disturbed conditions. Overall, SBAS effectively improves single-frequency positioning accuracy, stability, and integrity, but its performance remains affected by latitude and geomagnetic activity. Full article

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

Post-exercise release of cardiac biomarkers reflects physiological adaptations of the myocardium to exercise; however, data on their kinetics after exhaustive exercise under hypoxia remain scarce. We determined the kinetics of cardiac biomarker changes following a single bout of exercise to volitional exhaustion under normoxia and moderate normobaric hypoxia (2000 m and 3000 m a.s.l.) in trained (n = 12; VO_2max 64.2 ± 2.9 mL·kg⁻¹·min⁻¹) and untrained (n = 12; VO_2max 44.1 ± 7.4 mL·kg⁻¹·min⁻¹) men. Participants performed a graded exercise test (GXT) followed by a constant-workload exercise test (CXT) at the lactate threshold under three conditions (FiO₂ = 20.9%, 16.5%, 14.4%). Venous blood was sampled at rest, immediately post-exercise, and at 2, 6, and 24 h of recovery for determination of cardiac troponin T (cTnT) and I (cTnI), myoglobin (Mb), creatine kinase MB isoform (CK-MB), heart-type fatty acid-binding protein (H-FABP), ischemia-modified albumin (IMA), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) by ELISA. Exhaustive exercise induced significant elevations in all biomarkers, peaking at 2–6 h post-exercise and largely returning to resting values by 24 h. Moderate normobaric hypoxia did not augment the cardiac biomarker response; rather, it attenuated the increases in Mb, NT-proBNP, and IMA, likely due to earlier peripheral fatigue and lower absolute mechanical work. The inhibitory effect of hypoxia on cTnI release was observed exclusively in trained men, suggesting an interaction between training-related cardiac adaptations and the hypoxic stimulus. These findings support the safety of high-intensity exercise at simulated altitudes of 2000–3000 m a.s.l. Full article

Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ($3\times 3\times 3$) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon $p<{10}^{-7}$, Cohen’s $d=4.91$); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ($p=0.470$). Ablation confirmed both PSO and ACO are individually necessary ($\mathsf{\Delta }\ge 2.7$ pp, $p<0.001$). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization. Full article

Quadriceps muscle and tendon injuries are a significant cause of impairment of the knee extensor mechanism, ranging from minor muscle strains to complete tendon ruptures and extensive defects following oncologic resections. This narrative review provides a comprehensive analysis of contemporary concepts in anatomy, biomechanics, diagnosis, surgical management, and rehabilitation, with a particular focus on reconstructive techniques and functional outcomes. While most muscle injuries respond well to conservative management, complete quadriceps tendon ruptures typically require surgical repair to restore extensor continuity. Both transosseous suture techniques and suture anchor fixation demonstrate reliable outcomes, with no clear superiority in clinical results. Chronic ruptures present additional challenges due to tendon retraction and poor tissue quality, often necessitating advanced reconstruction methods such as V–Y tendon lengthening and augmentation with autografts, allografts, or synthetic materials. In cases of large defects, especially following soft-tissue sarcoma resection, free functional muscle transfer (FFMT) has emerged as a key reconstructive strategy. Common donor muscles include the latissimus dorsi, gracilis, rectus abdominis, and vastus lateralis, each offering specific biomechanical advantages. Functional recovery is strongly influenced by the extent of quadriceps preservation, with better outcomes observed when at least two muscle heads remain functional. Rehabilitation protocols vary depending on the surgical approach. Early controlled mobilisation is generally recommended after tendon repair, whereas FFMT requires a more cautious and prolonged rehabilitation process to allow for flap integration and reinnervation. Overall, optimal outcomes depend on a multidisciplinary approach combining appropriate surgical technique, individualized rehabilitation, and careful patient selection. Full article

Objectives: Distraction osteogenesis (DO) is an established technique for bone regeneration but is associated with prolonged consolidation time and extended external fixation. Biophysical and biologic adjuncts have been proposed to accelerate regenerative maturation. This systematic review aimed to comparatively evaluate the available clinical evidence regarding low-intensity pulsed ultrasound (LIPUS) and biologic augmentation strategies in distraction osteogenesis. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251125456). MEDLINE, Embase, Scopus, and Google Scholar were searched from inception to October 2025. Randomized controlled trials and cohort studies evaluating LIPUS, platelet-rich plasma (PRP), bone marrow aspirate concentrate (BMAC), culture-expanded mesenchymal stem cells, or hyperbaric oxygen therapy in distraction osteogenesis were included. Risk of bias was assessed using RoB 2 for randomized trials and structured domain-based criteria for observational studies. Due to substantial clinical and methodological heterogeneity, findings were synthesized narratively. Results: Nine studies involving 304 participants met the inclusion criteria, including randomized controlled trials and cohort studies across multiple anatomical sites and fixation techniques. Randomized trials evaluating LIPUS demonstrated inconsistent reductions in healing index and consolidation time, with no consistent effect on complication rates. Biologic adjuncts such as PRP, BMAC, and cell-based therapies showed signals of improved consolidation parameters in selected studies; however, evidence was limited by small sample sizes and methodological heterogeneity. Hyperbaric oxygen therapy lacked sufficient high-quality evidence to support routine use. Overall, the certainty of evidence was constrained by variability in study design, outcome definitions, and risk of bias. Conclusions: Although both biophysical and biologic adjuncts demonstrate compelling biological rationale, current clinical evidence in distraction osteogenesis remains heterogeneous and inconclusive. Biologic strategies may offer theoretical advantages through direct cellular and growth factor supplementation, whereas LIPUS provides non-invasive mechanotransductive stimulation; however, neither approach can currently be recommended for routine clinical use. High-quality, adequately powered trials with standardized outcome reporting are required to define their true clinical role. Level of Evidence: Level III (Systematic review of Level I–III studies). Full article

High-fidelity ship dynamics models are essential for the reliable operation of maritime autonomous systems. However, existing Markov-based maneuvering models and purely data-driven predictors struggle to capture hydrodynamic memory and degrade under non-ideal sensing. To address these challenges, this paper proposes a novel approach for robust ship motion prediction, the Non-Markovian Memory-Augmented Environment-Perceived and Physics-Informed Network (NMA-EPIN). This method explicitly models long-term hydrodynamic dependencies through a memory-augmented architecture. Within NMA-EPIN, a Control-Physics-Informed Neural Network (CPINN) paradigm enforces velocity–position kinematic consistency and control-logic alignment as soft constraints, suppressing cumulative drift under degraded observations. Experiments on a high-fidelity simulated dataset show that NMA-EPIN attains an average coefficient of determination $R^2=0.977$ under nominal conditions, effectively eliminating the position drift observed in baselines. Under extreme compound perturbations (50% sensor noise, packet loss, and delays), NMA-EPIN retains $R^2\approx 0.91$, which significantly outperforms the baselines. Full article

Background: Evidence on the role of synthetic biomimetic bone substitutes in the surgical management of proximal humerus fractures remains limited. This study aimed to evaluate the clinical, radiographic, and safety outcomes of a porous hydroxyapatite bone substitute used as an adjunct to locking-plate fixation in proximal humerus fractures with metaphyseal bone loss. Methods: We performed a retrospective comparative cohort study including 45 patients treated with locking-plate fixation and porous hydroxyapatite scaffold augmentation and 40 comparable control patients treated with locking-plate fixation without scaffold augmentation. Patients were evaluated clinically and radiographically at 1, 3, 6, and 12 months after surgery. Functional outcome was assessed with the Constant–Murley Score (CMS), and pain was assessed using the Visual Analogue Scale (VAS). Longitudinal changes over time were analyzed using mixed-effects models for repeated measures. Results: CMS improved progressively over follow-up, whereas VAS pain scores decreased significantly over time. No cases of device migration or radiographic resorption were observed during follow-up. Adverse events were recorded, but no complication was considered directly attributable to the implanted biomaterial. Functional recovery and pain reduction followed a similar trajectory in both groups, with no significant group-by-time interaction. Conclusions: In this retrospective series, graft augmentation with a porous hydroxyapatite scaffold during locking-plate fixation of proximal humerus fractures with bone void was associated with progressive functional improvement and pain reduction, without evident device-related safety concerns. Owing to the retrospective, non-randomized design, limited sample size, potential selection bias, and incomplete follow-up in part of the cohort, these findings should be interpreted as supportive of feasibility and short- to mid-term safety rather than as definitive evidence of biomaterial efficacy. Level of Evidence: Level III, retrospective cohort study. Full article

This paper presents the design and development of an adaptive augmented reality (AR) assistance system for complex mechanical assembly tasks. Integrating a wrist-worn optical heart rate sensor to evaluate the user’s cognitive state, the system is intended to run as a standalone application on the Meta Quest 3 headset. The system displays instructions and visual cues directly overlaid on the user’s physical workspace and constantly monitors their heart rate variability through the sensor as an estimate of their cognitive load. When the system detects an overload, it dynamically adjusts the presentation of information—for example, it slows down pacing, simplifies instructions, or switches to a different interaction modality (audio)—as an attempt to reduce the overload. The paper makes three contributions: first, it provides a documented standalone integration of physiological sensing with adaptive interface logic on a mixed reality headset without external compute infrastructure; second, it provides a systematic characterization of platform-specific tracking incompatibilities on the Meta Quest 3, documenting the progression through four spatial registration strategies and the specific failure condition that triggered each transition; third, it reports spatial interface design observations from iterative developer testing in the current prototype configuration, including panel height ranges not previously reported in the AR interface literature at this level of specificity. The paper also discusses the within-subjects evaluation protocol that is planned for final system testing with actual users. The work is intended as an engineering and design contribution that establishes the foundation for subsequent empirical evaluation of adaptive AR guidance in industrial assembly contexts. Full article