Search Results (2,515)

Underwater acoustic communication (UAC) is of great strategic importance for marine resource exploration and security collaboration. However, its open physical nature exposes communication links to severe eavesdropping and localization threats, while limited bandwidth and severe attenuation further exacerbate the difficulty of secure transmission. To address this, this study introduces the underwater acoustic reconfigurable intelligent surface (UARIS) to reconfigure acoustic propagation paths, leveraging its programmable reflection capability to enhance link quality and provide additional spatial degrees of freedom for location privacy protection. Accounting for the partial observability caused by the coarse observations of a mobile eavesdropping user (EU), noisy channel state information (CSI), and the practical constraint of UARIS discrete phase quantization, a utility maximization problem is formulated to jointly optimize the secrecy rate and location privacy. To tackle the strong non-convexity and coupled constraints in dynamic environments, a Gated Recurrent and Conformal-calibrated Soft Actor–Critic (GC-SAC) algorithm is proposed. Specifically, GC-SAC employs a gated recurrent unit (GRU) to capture the temporal statistical features of channel evolution. By integrating a risk prediction network with a conformal calibration mechanism, conservative estimation and robust regulation of multidimensional constraint risks are enhanced. Simulation results demonstrate that the GC-SAC algorithm achieves faster convergence and superior stability in dynamic underwater environments. Compared with representative baselines, the proposed algorithm exhibits significant advantages in secrecy rate and location privacy protection, validating its effectiveness for UARIS-assisted secure resource optimization in underwater scenarios. Full article

As a distributed approach to Artificial Intelligence (AI) model construction over wireless networks, federated learning (FL) based on multi-device collaborative training can protect data privacy, as well as increase the computing load of local model updates. In contrast, split learning (SL) with proper model splitting can adapt to the computation and transmission capabilities among devices. In this paper, while taking advantage of FL and SL, we concentrate on a semi-decentralized hybrid federated split learning (SD-HFSL) framework, in which we surpass the limitations of a single central server and allow the shared split models to be aggregated among multiple edge servers. To verify the importance of latency optimization for training efficiency, we analyze the convergence performance of SD-HFSL while jointly considering the limited computation and communication resources. Then, aiming at maximizing the long-term training efficiency, we propose an online optimization problem that includes local model splitting and device association. Considering that the training latency is unknown to the system a priori, a context-aware online training algorithm with sublinear regret is proposed based on the framework of contextual multi-armed bandit (CMAB), where the edge servers can observe the context information of device sites for latency estimation, followed by the iterative optimization based on the evaluated information in different contexts. Experiments on several neural network models show that the proposed algorithm reduces training latency and improves test accuracy compared with the selected benchmarks. Full article

We examine the estimation of the Kullback–Leibler (KL) divergence and the use of the goodness-of-fit test for multivariate normality. Our starting point is the maximum entropy principle for Shannon entropy: among all distributions with a fixed mean vector and covariance matrix, the multivariate Gaussian distributions uniquely maximize entropy. As a result, the KL divergence from a moment-matched Gaussian distribution to an unknown density can then be written as the entropy difference, which is a suitable information-theoretic measure of divergence from the Gaussian distribution. To estimate, we use k-nearest neighbor (kNN) estimators based on Shannon entropy and KL divergence derived from the Kozachenko–Leonenko approach and subsequent improvements, along with the consistency and $L^2$-convergence results established for these estimators. Motivated by previous entropy-based goodness-of-fit ideas developed for Rényi-type functionals for generalized Gaussian and Student-type models, we describe a KL-based test statistic as being the difference between the entropy of a Gaussian model fitted to the sample mean and covariance and the KL divergence between the unknown entropy and the kNN estimate. The statistic converges to zero for multivariate normality and converges to a strictly positive bound with non-Gaussian alternatives. The results of Monte Carlo simulations conducted across various dimensions and sample sizes indicate that the proposed method provides accurate Type I error control among the alternatives considered and demonstrates promising empirical power. Full article

Understanding the impact of neighborhood-level factors on stroke prevalence is crucial for addressing existing disparities. However, there is a distinct lack of ecological studies at the census tract level that investigate the social determinants of health (SDOH) influencing stroke prevalence within the U.S. Health and Human Services Region 3 (HHS Region 3: Delaware, Maryland, Pennsylvania, Virginia, West Virginia, and the District of Columbia). This study adopted a multivariate modeling approach to investigate the association between the 13 indicators of the Health Opportunity Index (HOI) and stroke prevalence at the census tract level in HHS Region 3 using four HOI indicator profiles and to highlight the specific SDOHs that are most associated with stroke prevalence. The four HOI indicator profiles include: (a) neighborhood and built environment profile, (b) social and community context profile, (c) resource profile, and (d) economic profile. The methodological approach was quantitative, using secondary data. The sample size was 8021 census tracts. The HOI was estimated for each census tract in the study area. Ordinary least squares regression (OLS) analysis and spatial lag model (SLM) were run to examine whether the 13 indicators of the HOI (categorized into four profiles) reliably predict stroke prevalence and to determine the most appropriate model that best identifies the strongest predictors of stroke prevalence. The results show that affordability, education, spatial segregation, and income inequality indicators were the strongest predictors of stroke prevalence in HHS Region 3. This granular research identifies the neighborhood-level SDOH most strongly linked to stroke prevalence, which can be leveraged to guide the development of targeted public health programs, quality improvement initiatives, resource allocation, and policy creation to combat stroke-related morbidity and mortality across census tracts in HHS Region 3. For example, the built environment, encompassing factors like employment access, affordable housing, and walkability, profoundly influences stroke prevalence and provides urban planners with practical insights for developing healthier, more equitable communities, such as creating neighborhood parks to encourage physical activity, a key factor in stroke prevention. This study also provides neighborhood organizations with the evidence needed to pursue grant funding and raise awareness about the socio-structural influences on stroke outcomes in their respective neighborhoods. Lastly, the insights generated from our study can facilitate collaborative decision-making processes with communities in HHS Region 3 regarding the prioritization of neighborhood-level SDOH for targeted public health interventions. This prioritization should focus on addressing predictors of stroke prevalence that are congruent with the community’s established priorities, thereby maximizing cost savings. Full article

Background/Objectives: The objective was to describe the one-year outcomes of salvage trabeculectomy (TLE) in eyes with persistent elevation of intraocular pressure (IOP) requiring early surgical intervention after failed minimally invasive glaucoma surgery (MIGS). Methods: This retrospective observational study included 38 eyes of 38 consecutive Japanese patients who underwent TLE within 100 days after Trabectome (TOM) or microhook ab interno trabeculotomy (μTLO) because of uncontrolled IOP despite maximally tolerated medical therapy. Surgical success was defined as (1) IOP reduction ≥30% from baseline, (2) 5 < IOP < 18 mmHg, (3) no additional glaucoma surgery, and (4) no loss of light perception. The Kaplan–Meier method was used to estimate the one-year success rate. Changes in IOP, medication use, best-corrected visual acuity (BCVA), and mean deviation (MD) were analyzed using the Wilcoxon matched-pairs signed-rank test and a linear mixed-effects model. Results: The median interval between MIGS and TLE was 41.5 days (interquartile range, 28–70 days). The one-year surgical success rate was 86.8% (Kaplan–Meier estimate). IOP and medication use were significantly reduced after TLE (p < 0.0001) and remained stable throughout the 12-month follow-up. BCVA did not differ significantly between baseline and 12 months after TLE, whereas a small but statistically significant difference in MD was observed. No serious vision-threatening complications were encountered. Conclusions: TLE performed shortly after failed MIGS achieved substantial IOP reduction with acceptable safety over a one-year follow-up period. TLE may be considered as one of the surgical options in cases where sufficient IOP reduction cannot be achieved after failed MIGS, and no effective alternative treatments are available. Full article

This study aimed to optimize vacuum frying parameters, frying temperature (80–120 °C) and frying time (10–20 min), using response surface methodology (RSM) to maximize the quality of rice crackers from black glutinous rice. Vacuum frying temperature and time had no significant (p > 0.05) effect on protein, fiber, total anthocyanin content, and total flavonoid content. An increase in frying temperature increased the expansion ratio and total phenolic content (TPC), while decreasing bulk density and DPPH. Extending frying time significantly (p ≤ 0.05) increased fat content. Increasing both frying temperature and time reduced hardness, moisture, and water activity, and significantly changed color. These trends were evaluated using regression models with R² values ranging from 0.858 to 0.999. Based on the developed models, the optimal condition was estimated at approximately 110 °C for 10 min, graphically predicting rice crackers with 23.32%db fat, hardness of 4.83 N, and TPC of 2.63 mg GAE/g. Compared with atmospheric frying (160 °C, 10 min), the optimal vacuum frying condition (110 °C, 10 min) reduced fat by 36.16%, decreased hardness by 68.65%, and increased TPC by 95.49%, suggesting that vacuum frying can produce black glutinous rice crackers with lower fat, higher antioxidant compounds, and greater crispiness under these specific parameters. Full article

Traditional global localization systems frequently struggle with perceptual ambiguities in dynamic environments and structurally similar scenes, which severely compromises navigation robustness. Concurrently, conventional path planning methodologies rarely integrate proactive safety considerations regarding high-risk environmental features. To resolve these critical limitations, this paper introduces a comprehensive semantic-assisted framework for mobile robots to enhance both global localization and navigation. First, we develop a semantic-aware place representation derived from LiDAR point clouds. By explicitly filtering dynamic objects and assigning category-specific weights, this approach mitigates perceptual aliasing and ensures robust scene recognition. Furthermore, we implement a Hyper-Semantic Point Histogram (HyperSPH) to embed semantic encoding directly into local geometric features. A Semantic Geometric Consistency Filter is subsequently applied to eliminate matching outliers and maximize registration accuracy. For secure navigation, we propose the Semantic-guided Twin Delayed Deep Deterministic Policy Gradient with Long Short-Term Memory (S-TD3-LSTM) algorithm within a deep reinforcement learning architecture. This strategy extracts temporal correlations via Long Short-Term Memory networks and integrates a dedicated semantic cost function to optimize obstacle avoidance policies. Extensive experiments demonstrate that the proposed localization module achieves superior retrieval and pose estimation precision over conventional methods. In complex path planning scenarios, the S-TD3-LSTM algorithm ensures stable convergence and generates highly adaptive trajectories. By proactively identifying and bypassing semantic hazards, the integrated system drastically minimizes exposure to dangerous zones, successfully establishing a rigorous balance between path efficiency and execution safety. Full article

Youth soccer performance is influenced by multiple factors, including age, body size, and physical capacities, but the relative contribution of these variables to aerobic and explosive performance remains unclear. Understanding these relationships can improve the interpretation of field tests and support individualized training prescription. This study was designed to examine the association of age category, body mass, and height with physical performance in youth soccer players by jointly considering aerobic and explosive capacities, in order to support the interpretation of field tests within training prescription. Forty-five male players (15 U16, 15 U17, 15 U19) from the same club were assessed across two standardised on-field testing sessions, including the 45–15 test (estimated maximal aerobic speed, MAS) and vertical jump tests (squat jump, SJ; countermovement jump, CMJ; countermovement jump with free arms, CMJ_FH). Performance variables (SJ, CMJ, CMJ_FH, MAS) were treated as outcomes, while category, body mass, and height were included as predictors. A multivariate analysis was performed, followed by univariate analyses for each indicator. Results showed a significant multivariate effect of age category on overall performance (p < 0.001; η²p = 0.482), whereas height and body mass were not significant (p > 0.05). In univariate analyses, age category was associated with all variables: SJ (p = 0.005; adj. R² = 0.160), CMJ (p < 0.001; adj. R² = 0.287), CMJ_FH (p = 0.004; adj. R² = 0.173), and MAS (p < 0.001; adj. R² = 0.352). Performance increased progressively from U16 to U17 to U19, with larger between-category differences in aerobic capacity. In conclusion, age category was more strongly associated with the performance profile than height and body mass when considered jointly; these findings should be interpreted in light of the observational design and the lack of biological maturation measures. Full article

Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instability, while most existing approaches are evaluated primarily through predictive accuracy rather than the predictability limits of the underlying system. This paper develops a predictability-aware framework that combines nonlinear dynamical diagnostics with a Lévy-driven neural stochastic differential equation model. Drift and diffusion are parameterized by neural networks and driven by $\alpha$-stable Lévy motion, enabling the representation of non-Gaussian fluctuations, abrupt shocks, and regime changes. To learn under discontinuous dynamics, we introduce a structurally constrained training objective based on a strong-form discretization of the underlying SDE. To characterise intrinsic predictability, we employ phase-space reconstruction and maximal Lyapunov exponent estimation. These diagnostics are interpreted as finite-sample measures of trajectory divergence and effective instability in a stochastic system, rather than evidence of low-dimensional deterministic chaos—a distinction motivated by well-documented limitations of chaos testing in financial data. Experiments on multiple West African currency pairs demonstrate competitive short-horizon forecasting performance relative to econometric and neural baselines while providing a principled framework for analysing predictability degradation under heavy-tailed stochastic dynamics. Across currencies and model classes, forecasting accuracy deteriorates beyond horizons comparable to the estimated Lyapunov time, suggesting that forecast degradation reflects intrinsic dynamical instability rather than model-specific limitations. The results support the view that reliable exchange-rate prediction is fundamentally a short-horizon problem and illustrate how stochastic dynamical modelling and predictability diagnostics can be combined to characterise forecasting limits in heavy-tailed financial systems. Full article

Background and Objectives: Premature ventricular contractions are among the most common arrhythmias encountered in clinical practice. However, this disorder can be associated with arrhythmia-induced cardiomyopathy or be the first sign of primary myocardial diseases. Certain morphologies of premature ventricular contractions are associated with a higher risk for sudden arrhythmia and cardiac dysfunction in the adult population. There is data on the clinical value and significance of the contraction morphology in adults, but there is a lack of such data for children. Materials and Methods: This observational prospective study of pediatric outpatients with premature ventricular contractions was conducted at Vilnius University Hospital Santaros Clinics. Inclusion criteria comprised children aged 3–17 years with more than 5% premature ventricular contractions over 24 h. Exclusion criteria included previously diagnosed congenital heart defects and cardiomyopathies, channelopathies, or the presence of any acute condition. The electrocardiographic morphology and measurements were assessed, analyzed, and described in this study. Results: The electrocardiograms of 80 patients were analyzed according to the ECG-estimated morphology of the arrhythmia complex, arrhythmic QRS complex duration, ratio with the normal QRS complex, and maximum deflection index in V5–V6 derivations. Cardiac MRI abnormalities (8 of 30 MRI studies) was reliably associated with a PVC duration of >150 ms and the maximal amount of extrasystoles per 24 h, with a median amount of 29.6%. A long postcoupling interval (>0.9 s) was associated with PVC progression. Conclusions: In this exploratory pediatric cohort, wider PVC QRS duration and higher maximal PVC burden were associated with ventricular MRI abnormalities, while longer postcoupling interval was associated with PVC progression. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

We investigate dynamic weighted fractional information-theoretic measures for linear stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H\in (1/2,1)$. Motivated by recent constructions of fractional Deng entropy and building upon explicit Gaussian solutions and closed-form fractional moments derived in previous work, we establish fully analytical expressions for the Shannon entropy, Rényi entropy, Tsallis entropy, extropy, and a continuous weighted fractional entropy $\mathbb{E}\left[X_t^p(-\log p_{X_t}\left(X_t\right))\right]$ for $p\ge 0$, expressed directly in terms of known fractional moments without density estimation. All derived measures share a universal asymptotic scaling law growing as $H\log t$, establishing a precise quantitative link between long-memory effects and information dynamics. The weighted fractional entropy further reveals remarkable structural properties as a function of the weighting order p, exposing a dual role of long memory on the system’s informational content. As a concrete application, we characterize anomalous diffusion in aging soft materials through an explicit critical time linking maximal uncertainty to the memory exponent H and the macroscopic aging rate. All results are validated through extensive Monte-Carlo simulations, demonstrating excellent agreement with the closed-form expressions across a wide range of Hurst exponents H and weighting orders p. Full article

Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with next-window service-level agreement (SLA) feasibility. The local branch uses MobileNetV3-Small for fast onboard color recognition, while the edge branch uses ResNet-18 for stronger remote inference. Low-confidence samples are offloaded only when the SLA predictor estimates that the wireless link is feasible; otherwise, the system enters fallback, meaning that the current prediction is not treated as immediately actionable. The evaluation follows a hard cross-illumination split: indoor and fluorescent light samples are used for training and validation, and indoor night and sunlight samples are reserved for testing. Under this setting, the local model achieves 76.89% accuracy and 73.25% macro-F1, while the edge model achieves 81.26% accuracy and 77.58% macro-F1. The SLA predictor, trained on enhanced telemetry features while preserving the original target label, achieves 85.74% accuracy, 85.57% macro-F1, 0.9420 ROC-AUC, and 0.9585 PR-AUC on temporally held-out records. The joint policy achieves 93.23% coverage and 79.90% success over active decisions, using local inference for 82.76% of the samples, edge offloading for 10.47%, and fallback for 6.77%. These results indicate that the framework is best understood as a tunable risk management layer for UAV perception rather than a pure accuracy maximization classifier. It avoids blind offloading and reduces forced decisions when both visual confidence and communication feasibility are weak. Full article

The two-year research involving laboratory and field studies supported by Geant4 computer simulation is aimed at determining the optimal parameters of 1 MeV accelerated electrons and 80 keV X-ray pre-planting irradiation of wheat seeds in order to find the optimal dose range which increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. During the laboratory studies we measured the germination rate and biometric properties of plants, as well as the type, number, and average diameter of fungi found in the irradiated and non-irradiated seeds after irradiation with electrons and X-rays with the dose range 2–1000 Gy. Following the laboratory studies showing that the doses exceeding 30 $\mathrm{Gy}$ decreased the germination rate of wheat, field studies evaluated the impact of pre-planting irradiation with the doses in the range of 5–30 Gy on the wheat productivity and the rate of fungal diseases in wheat plants grown from irradiated and non-irradiated seeds. It has been found that the dose range 5–15 Gy is more preferable for pre-planting wheat irradiation, both for e-beam and X-rays, since it increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. The X-ray dose of 15 $\mathrm{Gy}$ is found to be the most effective since it increased the yield up to 40% and also suppressed the Septoria glume blotch up to 40%. Since seed irradiation requires a particularly delicate approach given that the goal of irradiation is not only to reduce the rate of fungal diseases in the plants but also to increase the crop yield without detriment to the soil and the plant itself, consistency of dose uniformity across the seeds during pre-planting irradiation ensures the high reliability and repeatability of the irradiation effect. Our approach to irradiation planning with the use of Geant4 computer simulation allows us to precisely estimate the dose distribution in individual seeds and the distribution of radiation-chemical yield of radicals occurring as result of radiolysis in order to predict the effect of pre-planting irradiation and select the optimal irradiation parameters for maximizing the yield and crop quality. Full article

We prove a complete moment convergence criterion for weighted maximal partial sums of extended negatively dependent (END) random variables under slowly varying weights. For every $r>1$, and for triangular weight arrays that are uniformly bounded, quadratically non-degenerate, and uniformly non-degenerate on their active coefficients, we show that the summability of $n^{r-1}l\left(n\right)E\left[{(S_n^{*}/n-\epsilon )}_{+}\right]$ for all $\epsilon >0$ is equivalent to the weighted moment condition ${E\left[\right|X|}^{r+1}l\left(\right|X\left|\right)]<\infty$. The slowly varying factor l gives a refined borderline scale: it weakens the pure $(r+1)$-moment condition when $l\left(t\right)\to 0$, strengthens it when $l\left(t\right)\to \infty$, and recovers the classical scale when l is bounded away from zero and infinity. The proof uses weight-dependent monotone clipping, a Rosenthal-type maximal inequality for END sequences, Potter bounds and Karamata-type estimates for slowly varying functions, and a Bonferroni lower-bound argument based on a linear set of significant coefficients. Particular attention is paid to the preservation of the END structure under clipping, centering, and signed weights. Several corollaries and borderline heavy-tail examples are included, and possible modeling interpretations are briefly discussed without claiming finite-sample risk bounds beyond the theorem. Full article