Search Results (23,461)

This paper presents a hybrid control strategy for robust voltage regulation of a DC–DC boost converter used in a renewable-rich DC microgrid. The DC microgrid may comprise batteries, photovoltaic, and wind energy sources connected to a common DC bus, where voltage fluctuations arise due to variable generation and dynamic load profiles. To ensure optimal and efficient output voltage regulation under these conditions, a novel Linear Quadratic Regulator (LQR) driven self-regulating Sliding Mode Control (SMC) approach is developed. The proposed scheme is realized by combining the optimal performance of an LQR voltage-reference tracking controller with the robustness of a tangent-hyperbolic-based-sliding-mode reaching law defined over an LQR-driven sliding surface. To reduce chattering and improve adaptability to bounded disturbances, the waveform of the hyperbolic switching function in the reaching law is adaptively modulated via an online indirect supervised learning law. The control parameters are tuned offline using numerical optimization. Simulation results under different scenarios, including input voltage disturbances, load variations, and model uncertainties, show that the proposed method achieves superior voltage regulation, reduced chattering, and enhanced dynamic response compared to conventional controllers. The framework ensures reliable EV integration into intelligent DC microgrids. Full article

This review paper comprehensively examines the influence of spatial torsion on quantum fluctuations from the perspectives of metric-affine gravity (MAG) and the stochastic variational method (SVM). We first outline the fundamental framework of MAG, a generalized theory that includes both torsion and non-metricity, and discuss the geometrical significance of torsion within this context. Subsequently, we summarize SVM, a powerful technique that facilitates quantization while effectively incorporating geometrical effects. By integrating these frameworks, we evaluate how the geometrical structures originating from torsion affect quantum fluctuations, demonstrating that they induce non-linearity in quantum mechanics. Notably, torsion, traditionally believed to influence only spin degrees of freedom, can also affect spinless degrees of freedom via quantum fluctuations. Furthermore, extending beyond the results of previous work [Koide and van de Venn, Phys. Rev. A112, 052217 (2025)], we investigate the competitive interplay between the Levi-Civita curvature and torsion within the non-linearity of the Schrödinger equation. Finally, we discuss the structural parallelism between SVM and information geometry, highlighting that the splitting of time derivatives in stochastic processes corresponds to the dual connections in statistical manifolds. These insights pave the way for future extensions to gravity theories involving non-metricity and are expected to deepen our understanding of unresolved cosmological problems. Full article

Understory shrub–grass coverage is a key indicator of forest ecosystem structure and function, and its accurate retrieval via remote sensing is essential for regional ecological assessments. To address the critical limitation in existing multi-angle remote sensing inversion methods: high-resolution images lack angular information while multi-angle datasets suffer from low spatial resolution, making it difficult to achieve large-scale and fine-grained inversion of understory shrub–grass coverage. Here, we developed an inversion method for estimating understory shrub–grass coverage by integrating multi-angle Moderate Resolution Imaging Spectroradiometer data with high-resolution Sentinel-2 imagery to produce 10 m resolution coverage maps; we then used this method to analyze spatiotemporal changes in Changting County from 2016 to 2025. The results demonstrated that the method achieved high accuracy (R² = 0.8418, RMSE = 0.07), meeting the requirements for quantitative understory shrub–grass coverage estimation. Understory shrub–grass coverage exhibited a concentric decreasing pattern from the surrounding mountainous areas toward the central plain, with high-coverage zones concentrated primarily in the west, southwest, and south. Over the 2016–2025 period, understory shrub–grass coverage displayed a fluctuating upward trend: approximately 60% of the study area showed improvement, while 37.73% experienced slight degradation. The change persistence was dominated by positive trends, with an area proportion of 54.14%, which was close to that of the anti-persistent type (44.87%). This study provides technical support for the high-resolution inversion of understory vegetation structure. Full article

Type 1 diabetes (T1D) constitutes a chronic metabolic disorder attributed to the autoimmune destruction of insulin-producing pancreatic β cells, which most frequently occurs in childhood. Long-term complications of T1D are expected to occur mainly in adult life, whereas cognitive dysfunction can also occur in children and adolescents with T1D. Most studies demonstrate mild cognitive impairment, especially in the domains of memory, attention and executive functions, all of which affect academic performance, which may also negatively influence adherence to appropriate glucose monitoring and insulin treatment in children and adolescents with T1D. As a result, mild cognitive dysfunction can be an obstacle to both optimal glycemic control during childhood and adolescence and academic achievements for young individuals with T1D. The major metabolic changes occurring around the onset of diabetes, such as severe hyperglycemia and diabetic ketoacidosis, may have a negative impact on brain plasticity during this vulnerable period of neurodevelopment, especially in children diagnosed at a younger age. The pathophysiological mechanisms involved are closely related to increased oxidative stress and the accumulation of advanced glycation end products in the brain, thus leading to neuron cell damage and apoptosis. On the other hand, hypoglycemic episodes and glucose fluctuations may also impair neuronal integrity. The aim of the current narrative review is therefore to present the existing literature data on the clinical aspects, risk factors and molecular mechanisms associated with cognitive dysfunction in children and adolescents with T1D. Full article

Aims: Elevated uric acid (UA) levels correlate with worse heart failure (HF) outcomes, but past studies used single UA measurements. The effect of intra-individual UA fluctuations, beyond mean levels, is unclear. This study assesses the relationship between serum UA variability and adverse clinical outcomes in HF patients. Methods: We analyzed 18,115 HF patients from the SHEBAHEART registry (2009–2025) with at least three UA measurements within three years of diagnosis. UA variability was quantified as the mean deviation (MD) from each patient’s average UA level and divided into quartiles: Q1 (≤−0.69 mg/dL), Q2–Q3 (>−0.69 and <1.53 mg/dL, reference), and Q4 (≥1.53 mg/dL). All-cause mortality was the primary outcome and HF hospitalization was secondary. Cox regression, propensity score matching, and subgroup analyses were used. Results: Over a median follow-up of 4.3 years (IQR 1.6–7.7), 36% of patients were hospitalized for HF and 65.5% died. UA variability showed a graded association with outcomes. Low variability (Q1) was linked to reduced mortality (HR 0.79, 95% CI 0.75–0.83) and HF hospitalization (HR 0.84, 95% CI 0.79–0.90), while high variability (Q4) increased mortality (HR 1.58, 95% CI 1.51–1.69) and hospitalization risk (HR 1.17, 95% CI 1.10–1.25) (all p < 0.001). These associations remained after propensity score matching and across HF subgroups. Conclusions: UA variability is a robust, independent predictor of mortality and HF hospitalization. Serial UA monitoring may enhance risk stratification in HF management. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article

Monoenoic fatty acids (MUFAs), defined by the presence of a single carbon–carbon double bond within a long aliphatic chain, constitute a structurally diverse and ecologically significant class of lipids widely distributed in aquatic organisms. In marine and freshwater environments, MUFAs are fundamental components of membrane phospholipids and storage lipids, where mono-unsaturation modulates melting point, lipid packing, and bilayer dynamics, enabling homeoviscous adaptation to fluctuations in temperature, pressure, salinity, and oxygen availability. Positional and geometric isomerism (e.g., cis-Δ5, Δ7, Δ9, Δ11, Δ13, and trans forms) further enhances biochemical diversity, providing sensitive chemotaxonomic markers and indicators of trophic transfer across food webs. In addition to common straight-chain monoenes, rare methyl-branched, cyclopropane-containing, and acetylenic derivatives occur in specialized aquatic taxa, reflecting evolutionary adaptation and ecological niche differentiation. Computational QSAR analyses suggest that monoenoic fatty acids and their unusual analogues occupy bioactivity spaces associated with lipid metabolism regulation, vascular and inflammatory modulation, antimicrobial defense, and membrane stabilization. This review integrates structural chemistry, biosynthesis, ecological distribution, trophic dynamics, and predicted biological activity of monoenoic fatty acids in aquatic systems, highlighting their dual role as adaptive membrane constituents and as biologically active mediators linking molecular lipid architecture to hydrobiological function and environmental change. Full article

As emerging nonvolatile memory devices, ferroelectric field-effect transistors (FeFETs) have attracted significant attention for memory applications. However, due to the stochastic nature of fabrication processes and material properties, FeFETs exhibit pronounced device-to-device (DTD) variations, leading to threshold voltage dispersion and inconsistency in memory window (MW), which severely constrain array-level performance and reliability. In this study, a compact model-based variability analysis methodology for FeFETs has been proposed. Specifically, the Preisach ferroelectric (FE) hysteresis model was combined with the MIT Virtual Source (MVS) physical compact model to establish a macro-model for FeFETs, and statistical simulations were performed to evaluate device-level variations. Using the proposed framework, how fluctuations in two key FE parameters, film thickness (t_FE) and coercive field (E_C), affect FeFET transfer characteristics, threshold voltage (V_TH), and MW was systematically investigated. Monte Carlo (MC) simulations were further conducted to quantify the distribution width and statistical features of V_TH under different variability scenarios. The results indicate that random fluctuations in process-related parameters broaden the FeFET I_d-V_g characteristics, induce shifts in high/low threshold voltages, and cause MW variations. Moreover, when t_FE and E_C fluctuate simultaneously, the dispersions of V_TH and MW become significantly larger than those induced by a single-parameter fluctuation. The proposed compact-modeling framework and variability analysis approach enables the efficient evaluation of parameter tolerance and performance margin in FeFET arrays, providing guidance for storage-array design. Full article

The acceleration of climate change poses a growing threat to human health, particularly by exacerbating non-communicable diseases (NCDs) such as cardiovascular and respiratory conditions. Rising global temperatures amplify air pollution and environmental toxins, disproportionately affecting vulnerable populations. This narrative review explores the complex pathways linking climate-related environmental stressors to adverse health outcomes, focusing on the intermediary roles of epigenetic modifications and alterations in the microbiota. Epigenetic processes, including DNA methylation and histone modifications, may mediate how environmental exposures influence gene expression and disease susceptibility. Concurrently, changes in microbiota composition induced by pollutants and temperature fluctuations can promote inflammatory responses and immune dysfunction. Elucidating these molecular mechanisms is essential for developing targeted interventions and adaptive strategies to mitigate the health impacts of climate change. This review underscores the importance of identifying epigenetic and microbiota-based biomarkers for early risk stratification and for informing public health prevention and adaptation policies. A transdisciplinary approach, grounded in the One Health framework, is critical to addressing the growing burden of climate-sensitive diseases and reducing health inequalities. Full article

This study used machine learning methods and spatial analysis to examine groundwater quality in Winkler County, Texas, focusing on nitrate pollution. By analyzing 85 years of groundwater data from six aquifers, the study uses advanced machine learning models Random Forest, Decision Tree, Linear Regression, and XGBoost to predict contamination levels and explore spatial and temporal trends. These models were chosen because of their ability to handle larger and more complex datasets and their ability to capture nonlinear relationships between water quality parameters and environmental variables. These machine learning algorithms are particularly effective at identifying patterns and interactions that may not be obvious with traditional analytical methods, and get more reliable and accurate results. Our decadal analysis specifically identified systematic fluctuations in nitrate levels, with a notable increase since the early 2000s, driven by the synergistic effects of rising temperatures and intensified agricultural land use. Climate change, pressured by rising temperatures and lessened precipitation, along with natural factors such as the formation of sinkholes, has been identified as a key driver of groundwater quality fluctuations. Elevated nitrate levels were mostly related to agricultural irrigation and excessive use of synthetic fertilizers. The machine learning model also highlights how land cover changes and human activities are contributing to groundwater quality deterioration. This research reinforces the value of integrating machine learning and spatial analysis for groundwater management. This is especially true in areas affected by sinkholes. It provides important information to reduce man-made impacts to water quality in West Texas. Full article

This population-based study aimed to analyze the annual incidence and case fatality trends, and the clinical-epidemiological profile of gastroschisis in the state of Paraná, Brazil, between 2013 and 2024. Specifically, temporal trends in annual incidence and mortality rates related to gastroschisis were examined. Maternal, gestational, and neonatal characteristics were analyzed. Data from the Live Birth Information System and the Mortality Information System were analyzed using polynomial regression modeling. During the study period, 1,798,727 live births were recorded, including 491 cases of gastroschisis and 179 related deaths. The mean incidence was 2.73 per 10,000 live births. A significant 39.5% decrease over the study period was observed (p < 0.001). The case fatality rate was 36.5%. The mothers of children with gastroschisis were: young mothers (<25 years old; 77%), with low education (87.7%) and no partner (59.1%). High frequencies of cesarean deliveries (84.3%), prematurity (57.3%), low birth weight (63.7%), and low Apgar scores were also observed. The profiles of the mothers and children at birth were unfavorable when compared to the population of live births. Gastroschisis incidence in Paraná declined significantly from 2013 to 2024. While the annual incidence showed a decreasing trend, mortality fluctuated. The persistently high case fatality rate underscores the need for public policies focused on prenatal care and specialized neonatal management. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

Interval-censored survival data arise frequently in biomedical and epidemiological studies where event times are observed only within observation intervals. Classical non-parametric estimators, such as the Kaplan–Meier (KM) estimator under imputation and the Turnbull estimator, often suffer from instability, irregular fluctuations, and overfitting when sample sizes are small or when the prevalence rate is low. Recent methodological developments, which include smoothed and penalized approaches, have been proposed to improve stability and reduce estimation error in such settings. This study evaluates and benchmarks the finite-sample performance of a nonparametric penalized likelihood KM estimator under interval-censored data. The method is compared with the classical KM estimator using four imputation strategies, that is, midpoint, regression, uniform, and multiple imputation. From a symmetry perspective, midpoint and uniform imputation preserve interval symmetry through deterministic and probabilistic mechanisms, respectively, whereas regression and multiple imputation intentionally introduce structural asymmetry to reflect data-driven risk heterogeneity and distributional uncertainty. To assess and benchmark the performance of the penalized KM estimator, an extensive Monte Carlo (MC) simulation study was conducted across varying sample sizes and prevalence rates using error-based metrics. The MC simulation results revealed that the nonparametric penalized KM estimator consistently outperforms the classical KM estimator in small samples across all prevalence rates. The gains are more pronounced under low prevalence rates where the penalized KM estimator is superior for small to relatively moderate samples of $n\approx$ 40–100. Among the imputation techniques, regression and multiple imputation generally exhibited superior performance. Real data application further confirms these findings, demonstrating that the nonparametric penalized KM estimator yields more stable and accurate survival curves than the classical KM estimator in small samples. Full article

Reservoirs are critical infrastructure for water and energy security, and require accurate and timely monitoring of reservoir water extent to make informed decisions. Optical remote sensing provides frequent, large-area observations; however, automated water extraction is often complicated by dam operation and surface heterogeneity, which increase spectral variability. Supervised methods, though widely used, generally require manual labels and often perform poorly when transferred across sensors and regions, limiting operational deployment. In this paper, we develop a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework, an automated algorithm designed for multi-source optical imagery. SWD consists of two stages: pixel-level classification and object-level refinement. Initially, SWD integrates spatial priors with spectral features to automatically derive high-confidence samples, which are then utilized to parameterize Gaussian mixture model to represent multimodal spectral distribution throughout the image. Furthermore, superpixel-constrained region growing is applied to refine shoreline and ensure object-level consistency. We validated SWD across 36 test cases comprising three sensors, six reservoirs, and two hydrological conditions. Compared with Random Forest and U-Net, SWD achieved the best performance. Specifically, (1) in cross-scale tests, SWD achieved high consistency with IoU ≥ 0.774; (2) in cross-region transfers, SWD maintained stable generalization (SD: 0.010); and (3) in hydrological response assessments, SWD captured water-level fluctuations with minimal bias variation (ΔRE < 1%). In addition, SWD framework is computationally efficient, with processing times of 0.49–1.29 s/Mpx on a standard CPU. This study demonstrates that SWD effectively addresses spectral variability and surface complexity in reservoir water area detection across multi-source optical imagery. It operates without manual labels or model training, enabling automated, large-scale and multi-temporal reservoir water monitoring. Full article

Sleep disorders are prevalent in the world, and they lead to severe health issues such as cardiovascular disease and cognitive disabilities. Conventional polysomnography-based diagnosis is based on manual EEG analysis under the supervision of trained specialists, which is time-consuming and may have inter-rater variability. Although the predictions of deep learning (DL) models on the task of sleep classification of EEG have been promising, they, in many cases, do not explain the multiscale, temporal dynamics that physiological signals are characterized by. In this work, a hybrid model that is a combination of CNN and multifractal detrended fluctuation analysis (MF-DFA) was proposed to detect localized temporal features and long-term fractal-based dynamics of single-channel EEG recordings. The performance of the suggested model was tested using two separate polysomnographic datasets: the CAP Sleep Dataset of five-class sleep disorder classification (Healthy, Insomnia, Narcolepsy, PLM, and RBD) and the ISRUC Sleep Dataset on the three-class subject-independent validation. In the CAP dataset, the framework had an accuracy of 86.38%. Cross-dataset transfer to the ISRUC Sleep Dataset, where only the classification head was fine-tuned on a small labeled subset while all feature-extraction layers remained frozen from CAP training, achieved 87.50% accuracy, demonstrating that the learned representations generalize across differing recording protocols, sampling rates, and diagnostic label spaces. The experiments of ablation proved the paramount importance of the MF-DFA features, and the lack of them led to low classification rates. The findings demonstrate the clinical feasibility of applying fractal analysis in conjunction with DL to detect sleep disorders in an automated, generalizable manner, suitable for use in large-scale monitoring and resource-starved clinical environments. Full article