Search Results (2,757)

Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical application of co-array domain signal processing for underwater acoustic coprime arrays. We propose a novel array configuration based on coprime minimum disordered pairs, enabling the formation of continuously connected co-arrays without interpolating. To address the challenge of limited snapshots in underwater environments, DOA estimation can be achieved by utilizing traditional multipath matching pursuit (MMP) algorithms under the proposed continuous co-array implementation scheme. In practical applications, physical array element failures are inevitable, and faulty elements can create holes in the originally continuous co-array. While interpolation techniques can mitigate small gaps, their performance deteriorates significantly in the presence of large holes or uneven data distribution. To overcome these limitations, we introduce a sparse signal recovery (SSR) method using a fragment array data validation technique for sparse DOA estimation with an underwater acoustic coprime array. Based on the designed continuous array expansion scheme, the resulting continuous co-array is used to map the positions of element failures, revealing the gaps in the co-array. A validation model is established for partially continuous sub-arrays within the discontinuous co-array, enabling signal direction estimation based on the fragmented array validation. Both simulation and sea trial results confirm that the proposed approach maximizes the utilization of co-array elements without relying on interpolation or prediction, offering a robust solution for scenarios involving sensor failures. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010–2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human–AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care. Full article

Background/Objectives: Intraoperative bleeding remains one of the major challenges in pediatric liver transplantation (PLT), contributing significantly to perioperative morbidity, transfusion-related complications, and prolonged recovery. Although viscoelastic testing has improved intraoperative hemostatic management, there are currently no validated preoperative tools capable of predicting bleeding risk in this vulnerable population. Methods: We conducted a prospective, single-center observational study including 43 consecutive pediatric patients who underwent orthotopic liver transplantation between May 2008 and August 2009. A comprehensive dataset encompassing demographic, clinical, biochemical, and surgical variables was collected. A multivariable linear regression model was developed to predict intraoperative blood loss (IBL). Variable selection was guided by Mallows’ Cp criterion to ensure optimal model fit and clinical interpretability. Model performance was assessed using adjusted R², diagnostic residual analysis, and internal validation to verify regression assumptions. Results: Six independent predictors of IBL were identified: presence of ascites, prior abdominal surgery, operative time, baseline fibrinogen concentration, platelet count, and recipient weight. The final model explained 35.2% of IBL variance (adjusted R² = 0.352; F = 7.68; p < 0.001). Model diagnostics confirmed linearity, normal distribution of residuals, and homoscedasticity, supporting its robustness and reliability. Conclusions: This multivariable model provides an interpretable, clinically applicable framework for individualized preoperative estimation of blood loss in PLT. It may assist in planning perioperative patient blood management strategies and serve as a foundation for future decision-support systems. Limitations include the single-center design and modest sample size; however, internal validation supported the stability and reliability of the model. Full article

Accurate statistical inference for zero-inflated and highly skewed data requires confidence interval procedures with a strong finite-sample performance. The delta–inverse Gaussian distribution provides a flexible framework for modeling such data by combining a point mass at zero with an inverse Gaussian distribution for positive observations, making it suitable for application in various fields such as traffic mortality, insurance, and environmental studies. This paper develops and compares several confidence interval estimation methods for the difference and the ratio of two variances from independent delta–IG distributions. The proposed approaches include adjusted generalized confidence intervals, fiducial confidence intervals, Bayesian credible intervals, the method of variance estimates recovery, and normal approximation methods used as benchmarks. The finite-sample performance of these methods is evaluated through Monte Carlo simulations under various parameter configurations and both balanced and unbalanced sample sizes, with an emphasis on coverage probability and interval width. The simulation results show that AGCI and MOVER generally achieve coverage probabilities close to the nominal level while producing relatively narrow intervals. The MOVER performs particularly well when zero-inflation probabilities are equal, whereas AGCI is more effective when they differ. Illustrative real-data examples are provided to demonstrate practical implementations. Full article

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as an integrated proxy for groundwater-related storage changes, while acknowledging that it also includes contributions from soil moisture and surface water. The approach combines satellite-based observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with key environmental indicators such as rainfall, evapotranspiration, and land use data to track changes in groundwater availability with improved spatial detail. After preprocessing the data through feature selection, normalization, and outlier handling, the model applies six base learners, i.e., Huber regressor, automatic relevance determination regression, kernel ridge, long short-term memory, k-nearest neighbors, and gradient boosting. Their predictions are aggregated using a random forest meta-learner to improve accuracy and stability. The ensemble achieved strong results, with a root mean square error of 0.13, a mean absolute error of 0.108, and a determination coefficient of 0.97—far better than single-model baselines—based on a temporally independent train-test split. Spatial analysis highlighted clear patterns of groundwater depletion linked to land cover and usage. These results can guide targeted aquifer recharge efforts, drought response planning, and smarter irrigation management. The model also aligns with national goals under Morocco’s water sustainability initiatives and can be adapted for use in other regions with similar environmental challenges. Full article

Multi-object tracking (MOT) in vineyard environments remains challenging due to frequent and long-term occlusions caused by dense foliage, overlapping grape clusters, and complex plant structures. These characteristics often result in identity switches and fragmented trajectories when using conventional tracking methods. This paper proposes OATSAM-Track, an occlusion-aware multi-object tracking framework designed for vineyard fruit monitoring. The framework integrates lightweight MobileSAM-assisted instance segmentation to estimate target visibility and occlusion severity. Occlusion-state reasoning is further incorporated into temporal association, appearance memory updating, and identity recovery. An adaptive temporal memory mechanism selectively updates appearance features according to predicted occlusion states, reducing identity drift under partial and severe occlusions. To facilitate occlusion-aware evaluation, an extended vineyard multi-object tracking dataset (GrapeOcclusionMOTS) with SAM-refined instance masks and fine-grained occlusion annotations is constructed. The experimental results demonstrate that OATSAM-Track improves identity consistency and tracking robustness compared to representative baseline trackers, particularly under medium and severe occlusion scenarios. These results indicate that explicit occlusion modeling is beneficial for reliable fruit monitoring in precision agriculture. Full article

Significant earthquakes can cause widespread infrastructure damage, social implications, and substantial economic losses. To mitigate these impacts, earthquake forecasting models have been developed to estimate earthquake occurrences and improve recovery efforts, with the Epidemic-Type Aftershock Sequence (ETAS) model being the most informative statistical framework for characterizing earthquake sequences. In this study, the ETAS model is used to estimate the model parameters for seismicity in Canada using the historical earthquake catalogue and to forecast long-term seismicity for seven different regions in Canada. Furthermore, the model is used to generate synthetic earthquake catalogues in order to assess its ability to replicate observed seismic patterns. The study identifies the southwestern region of Canada, associated with the coastal area of British Columbia, as being at the highest seismic risk, with a 66% exceedance probability for M7.5 events or above to occur in 30 years. In contrast, Alberta features the least seismic risk, with a 4% exceedance probability for events above 6.5 magnitude. For southeastern Canada, associated with Eastern Ontario and Southern Quebec, an exceedance probability of 74% for events above 6.0 magnitude poses the potential for significant damage due to the larger exposed population. Moreover, the resulting seismicity maps show the model’s capability for real-events analysis, but improvements are needed for further applications. Full article

When analyzing the long-term viability of small, declining populations, it is essential to recognize that inbreeding and the erosion of genetic diversity are primarily driven by the effective population size, which is often a fraction of the total census count. The globally endangered Lear’s macaw (Anodorhynchus leari) is a restricted-range species endemic to the Caatinga ecoregion in NE Brazil. This species was only known in captivity due to wildlife illegal trade, until 1978, when a small population close to extinction was discovered in the wild, estimated at ca. 60 individuals in 1983. Conservation efforts have allowed for population recovery in recent decades, reaching a population of ca. 2273 individuals in 2022. Given these drastic population changes, a genetic assessment is important to empower conservation strategies with knowledge about the level of genetic variability, population genetic structure, inbreeding levels, and demographic history. We used a set of eight species-specific microsatellites to provide the first genetic assessment of the wild population of this species by genotyping non-invasive samples (molted feathers) collected in the known breeding and roosting sites of the species. Our results revealed a low effective population size (N_e = 49–80), which represents the main conservation concern. We also observed evidence of past bottlenecks. However, moderate levels of genetic diversity, no evidence of inbreeding, and a wide connectivity across the study area confirm a single population and set the ground for the potential natural recovery of this species and the recolonization of breeding sites across its former range. Full article

Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the network structure of the Korean won exchange rate against 36 major trading-partner currencies. The model combines the generalised Yule–Walker equations with structured penalisation to jointly estimate instantaneous and lagged interactions in a data-driven manner. This approach allows for the recovery of economically meaningful spillover networks while maintaining tractability in high dimensions. Using daily data from 2019 to 2023, the results reveal pronounced contemporaneous spillovers among currencies closely tied to Korea’s trade and financial networks, notably the U.S. dollar, Chinese yuan, Japanese yen, and key ASEAN currencies. Monte Carlo simulations confirm the estimator’s consistency and convergence properties, while empirical forecasting exercises demonstrate systematic improvements in both mean-squared and robust error metrics relative to benchmark VAR and spatial autoregressive models. The evidence highlights that modelling sparse, high-dimensional time series structures enhances predictive accuracy and interpretability, particularly under nonstationary and heterogeneous conditions. The proposed framework provides a flexible tool for exploring interconnected time series in economics and finance, offering new insights into exchange-rate linkages and risk transmission in globally integrated markets. Full article

This paper presents a conceptual and thermodynamic assessment of an innovative cogeneration system based on the aluminium–water reaction, designed to simultaneously produce hydrogen and electricity. The proposed layout integrates a liquid aluminium combustion chamber with a dual-stage heat recovery section and a steam turbine cycle, enabling the valorisation of industrial aluminium scraps within a circular-economy framework. A steady-state thermodynamic model was developed in Aspen Plus to evaluate system performance under different operating conditions, with a sensitivity analysis on key parameters such as the aluminium-to-water ratio (2.4–4), combustion efficiency, and steam generation cycle parameters. The system performance is investigated in terms of useful output (i.e., hydrogen and electricity production), including a simplified economic evaluation for the assessment of sustainability. Results indicate that, for equivalence ratios ensuring acceptable peak temperatures (≤1700 °C), the system can deliver 2–3 MW of electric power per kg/s of aluminium and achieve cogeneration efficiencies up to 83–87%, assuming a high conversion rate of water into hydrogen (roughly 0.106 kg of produced H₂ per kg of inlet Al, if 95% of mole conversion is considered). The minimum break-even levelized cost of hydrogen is estimated to be 15.7 EUR/kg under current economic conditions. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Reconfigurable intelligent surface (RIS) demonstrates significant potential in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) wireless communication systems. However, the introduction of RIS leads to a substantial number of parameters in the channel matrix, making channel estimation highly challenging. By exploiting the sparsity of mmWave channels, compressed sensing algorithms, such as the orthogonal matching pursuit (OMP) algorithm, can significantly reduce the pilot overhead. Nevertheless, traditional OMP algorithms typically require extensive prior knowledge about the number of effective paths, which is often difficult to obtain. To address this problem, we propose a novel multi-user joint correlation allocation (MUJCA) algorithm, which requires only minimal and easily measurable prior information. Our key idea is to divide the RIS coverage area into multiple sub-regions, each associated with a known number of scatterers, which is a pre-measured quantity, with users distributed within these sub-regions. Then, the MUJCA algorithm exploits joint correlation of multiple users to facilitate sparse channel recovery and transforms it back into the spatial channel. Simulation results show that the proposed MUJCA achieves higher channel estimation accuracy than existing benchmark algorithms. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

This article introduces an integrated control scheme combining an Adaptive Extended Kalman Filter (AEKF) with a Passivity-Based Control (PBC) approach to stabilize a DC-DC boost converter feeding both constant voltage and constant power loads (CPLs) in DC microgrids. Unlike conventional observers, the AEKF adapts its covariance matrices in real time to accurately estimate both system states and the unknown load dynamics introduced by CPLs, thereby eliminating the need for additional sensors and enhancing estimation convergence. Coupled with the PBC, the estimated disturbances are compensated via a feedforward path, significantly improving the system’s resilience to input voltage fluctuations and load variations. Through a Lyapunov-based stability analysis, the combined strategy is proven to ensure large-signal stability while maintaining a rapid transient recovery profile, even under significant parametric uncertainties. The simulation of this algorithm was implemented using PLECS, thoroughly validating the effectiveness and robustness of the proposed method. Full article

Background and Objective: Postoperative hospital length of stay (LOS) reflects surgical recovery and resource demand but remains difficult to predict due to heterogeneous perioperative trajectories. We aimed to develop and validate an interpretable machine learning framework that integrates multimodal perioperative data to accurately predict LOS and uncover clinically meaningful drivers of prolonged hospitalization. Methods: We studied 97,937 adult surgical cases from a large perioperative registry. Routinely collected perioperative data included patient demographics, comorbid conditions, preoperative laboratory values, intraoperative physiologic summaries, and procedural characteristics. Length of stay was modeled using a supervised regression approach with internal cross-validation and independent holdout evaluation. Model performance was assessed at both the cohort and individual levels, and explanatory analyses were performed to quantify the contribution of clinically defined perioperative domains. Results: The model achieved R² = 0.61 and MAE ≈ 1.34 days on the holdout set, with nearly identical cross-validation performance (R² = 0.60, MAE ≈ 1.34 days). Operative duration, diagnostic complexity, intraoperative hemodynamic variability, and preoperative laboratory indices—particularly albumin and hematocrit—emerged as the strongest determinants of postoperative stay. Patients with shorter recoveries typically had brief operations, stable physiology, and normal laboratory profiles, whereas prolonged hospitalization was linked to complex procedures, malignant or respiratory diagnoses, and lower albumin levels. Conclusions: Interpretable machine learning enables accurate and generalizable estimation of postoperative LOS while revealing clinically actionable perioperative domains. Such frameworks may facilitate more efficient perioperative planning, improved allocation of hospital resources, and personalized recovery strategies. Full article