Search Results (427)

This study proposes a reinforcement learning (RL)-based optimization framework for the environmental control system of battery rooms in Energy Storage Systems (ESS). Conventional rule-based air-conditioning strategies are unable to adapt to real-time temperature and humidity fluctuations, often leading to excessive energy consumption or insufficient thermal protection. To overcome these limitations, both value-based (DQN, Double DQN, Dueling DQN) and policy-based (Policy Gradient, PPO, TRPO) RL algorithms are implemented and systematically compared. The algorithms are trained and evaluated using one year of real ESS operational data and corresponding meteorological data sampled at 15-min intervals. Performance is assessed in terms of convergence speed, learning stability, and cooling-energy consumption. The experimental results show that the DQN algorithm reduces time-averaged cooling power consumption by 46.5% compared to conventional rule-based control, while maintaining temperature, humidity, and dew-point constraint violation rates below 1% throughout the testing period. Among the policy-based methods, the Policy Gradient algorithm demonstrates competitive energy-saving performance but requires longer training time and exhibits higher reward variance. These findings confirm that RL-based control can effectively adapt to dynamic environmental conditions, thereby improving both energy efficiency and operational safety in ESS battery rooms. The proposed framework offers a practical and scalable solution for intelligent thermal management in ESS facilities. Full article

Copepods are key components of marine food webs, linking primary producers such as microalgae to higher trophic levels, including many fish species. This study investigates long-term changes in the composition, density, and biomass of copepod communities along the Romanian coast of the Black Sea over six decades (1956–2015), based on historical records and recent monitoring from 18 sampling stations. Mean copepod density declined markedly over the study period, particularly during the cold season, decreasing from values exceeding 1000 ind/m³ in the 1960s to <300 ind/m³ after 2000, while biomass showed weaker but comparable long-term fluctuations. Seasonal variability was pronounced, with significantly higher densities and biomass during the warm season. Generalised Additive Models (GAMs) explained up to 40–55% of the variance in copepod density and biomass, depending on the season. During the warm season, phosphate exerted a positive effect on copepod abundance, consistent with bottom-up control via phytoplankton productivity, whereas during the cold season, temperature showed a positive effect and salinity a negative effect, indicating stronger physical control of copepod persistence. Species composition shifted over time, with a reduction in constant species and an increase in rare or accidental taxa in later decades. These results indicate that climate variability and anthropogenic pressures have reshaped copepod communities, with potential consequences for food-web efficiency and ecosystem resilience in the Black Sea. Full article

Predicting the transverse tensile properties of unidirectional CFRP plies is often based on micromechanical representative volume elements (RVEs) with circular fiber cross-sections, whereas microscopic observations show pronounced ellipticity and size variability in actual fibers. A two-dimensional plane-strain micromechanical framework with elliptical fiber cross-sections is developed as a virtual testing tool to quantify how fiber volume fraction, cross-sectional aspect ratio and statistical fluctuations in the semi-minor axis influence the transverse tensile response. Random RVEs are generated by a hard-core random sequential adsorption procedure under periodic boundary conditions and a minimum edge-to-edge gap constraint, and the fiber arrangements are validated against complete spatial randomness using nearest-neighbor statistics, Ripley’s K function and the radial distribution function. The matrix is described by a damage–plasticity model and fiber–matrix interfaces are represented by cohesive elements, so that high equivalent-stress bands in matrix ligaments and the associated crack paths can be resolved explicitly. Parametric analyses show that increasing fiber volume fraction raises the transverse elastic modulus and peak stress by thinning matrix ligaments and promoting longer, more continuous high-stress bands, while the cross-sectional aspect ratio redistributes high stress among ligaments and adjusts the balance between peak strength and the degree of failure localization. The observed size variability is represented by modeling the semi-minor axis as a normal random variable; a larger variance mainly leads to a reduction in transverse peak stress through stronger stress localization near very thin ligaments, whereas the elastic slope and the strain at peak stress remain almost unchanged. The proposed framework thus provides a statistically validated and computationally efficient micromechanical basis for microstructure-sensitive assessment of the transverse behavior of UD-CFRP plies with non-circular fiber cross-sections. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

The operation of the superheated steam temperature system significantly impacts the safety and economy of thermal power units. To ensure its stable operation under large-scale variable load conditions, a modified active disturbance rejection control strategy based on parameter adaptation is proposed. Firstly, a typical superheated steam temperature system model is introduced, and the cascade control structure is applied to the model. Then, on this basis, a modified active disturbance rejection control strategy based on parameter adaptation is proposed, and the parameter tuning method of the modified active disturbance rejection control is introduced. Finally, the control performance of the proposed control strategy under a wide range of variable loads is verified through comparative simulations under nominal working conditions and uncertain working conditions. To further illustrate the effectiveness of the proposed strategy, the method is applied to a certain 660 MW unit in the field. After implementing the method, the fluctuation range of superheated steam temperature on the A and B sides decreased to only 34.0% and 53.0% of the original, respectively, and the fluctuation variance on the A and B sides decreased to only 28.5% and 43.3% of the original, respectively. The above field application results fully demonstrate that the control strategy proposed does not merely remain at the theoretical simulation level, but is a key technical means that can be effectively implemented and effectively solve the problem of superheated steam temperature control in thermal power units. Full article

This study was focused on the assessment of fungal occurrence, mycotoxin dynamics, aflatoxin carry-over, and associated biochemical responses in dairy cattle. Moisture emerged as the dominant factor for fungal communities, promoting the co-proliferation of fungal genera adapted to high water activity conditions (a_w > 0.90) and antagonism against xerotolerant and xerophilic species. Aspergillus spp. dominated dry substrates (a_w < 0.75), Fusarium spp. showed strong positive associations with high-moisture matrices (a_w > 0.90), and Penicillium spp. exhibited intermediate, substrate-dependent behavior. Mycotoxin levels fluctuated non-linearly, independently of fungal counts: ochratoxin A (OTA) concentrations in corn silage increased from approximately 12 μg/kg at the onset of the ensiling period to >240 μg/kg at silo opening, indicating dynamic mycotoxin accumulation during storage, while zearalenone (ZEA) oscillated from 40 to 170 µg/kg. Despite the variation in total aflatoxins (AFLA-T) across feed matrices, aflatoxin M1 (AFM1) in milk remained low (0.0020–0.0093 μg/kg), confirming limited carry-over. Serum biochemical parameters—alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total bilirubin (BIL-T), total protein (PROT-T)—remained within physiological limits, yet multivariate analyses revealed metabolic modulation linked to aflatoxin exposure. AFM1 explained >7% of the variance in serum biochemical profiles according to PERMANOVA (p = 0.002), showed significant MANOVA effect (Pillai = 0.198), and displayed a significant canonical association (p < 10⁻¹³). Linear discriminant analysis further separated Normal vs. Borderline hepatic profiles, indicating subclinical physiological adaptation to chronic low-dose exposure. Full article

Background: This study explores an innovative integration of big data analytics techniques aimed at enhancing predictive modeling in financial markets. It investigates how combining sentiment analysis with latent profile analysis (LPA) can accurately forecast stock prices. This research aligns with big data methodologies by leveraging automated content analysis and segmentation algorithms to address real-world challenges in data-driven decision-making. This study leverages advanced computational methods to process and segment large-scale unstructured data, demonstrating scalability in data-rich environments. Methods: We compiled a corpus of 3843 financial news articles on Teva Pharmaceuticals from Bloomberg and Reuters. Sentiment scores were generated using the VADER tool, and LPA was applied to identify eight distinct sentiment profiles. These profiles were then used in segmented regression models and Structural Equation Modeling (SEM) to assess their predictive value for stock price fluctuations. Results: Six of the eight latent profiles demonstrated significantly higher predictive accuracy compared to traditional sentiment-based models. The combined profile-based regression model explained 47% of the stock price variance (R² = 0.47), compared to 10% (R² = 0.10) in the baseline model using sentiment analysis alone. Conclusion: This study pioneers the use of latent profile analysis (LPA) in sentiment analysis for stock price prediction, offering a novel integration of clustering and financial forecasting. By uncovering complex, non-linear links between market sentiment and stock movements, it addresses a key gap in the literature and establishes a powerful foundation for advancing sentiment-based financial models. Full article

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

Understanding the spatiotemporal evolution of soil salinization is essential for elucidating its driving mechanisms and supporting sustainable land and water management in arid regions. In this study, the Alar Reclamation Area in Xinjiang, a typical desert–oasis transition zone, was selected to investigate the drivers of spatiotemporal variation in soil salinization. GRACE gravity satellite observations for the period 2002–2022 were used to estimate groundwater storage (GWS) fluctuations. Contemporaneous Landsat multispectral imagery was employed to derive the normalized difference vegetation index (NDVI) and a salinity index (SI), which were further integrated to construct the salinization detection index (SDI). Pearson correlation analysis, variance inflation factor analysis, and a stepwise regression framework were employed to identify the dominant factors controlling the occurrence and evolution of soil salinization. The results showed that severe salinization was concentrated along the Tarim River and in low-lying downstream zones, while salinity levels in the middle and upper parts of the reclamation area had generally declined or shifted to non-salinized conditions. SDI exhibited a strong negative correlation with NDVI (p ≤ 0.01) and a significant positive correlation with both irrigation quota and GWS (p ≤ 0.01). A pronounced collinearity was observed between GWS and irrigation quota. NDVI and GWS were identified as the principal drivers governing spatial–temporal variations in SDI. The resulting regression model (SDI = 0.946 − 0.959 × NDVI + 0.318 × GWS) established a robust quantitative relationship between SDI, NDVI and GWS, characterized by a high coefficient of determination (R² = 0.998). These statistics indicated the absence of multicollinearity (variance inflation factor, VIF < 5) and autocorrelation (Durbin–Watson ≈ 1.876). These findings provide a theoretical basis for the management of saline–alkali lands in the upper Tarim River region and offer scientific support for regional ecological sustainability. Full article

Geopolitical risks, particularly in energy-producing regions, significantly impact national economic development. This study uses the Generalized Variance Decomposition Spectrum Representation method to analyze the relationship between international energy prices (coal, oil, and natural gas) and geopolitical risks. The findings show that geopolitical risk serves as a net transmitter of risk, with a short-term effect on energy prices that diminishes over time. Oil prices are most sensitive to geopolitical risks, while coal prices are least affected. The study also identifies distinct spillover effects between geopolitical behavioral risks and threat risks, with the former contributing more to price fluctuations. The results highlight the complex interplay between energy prices and geopolitical risks, with implications for global economic stability. Full article

Background/Objectives: Longitudinal metabolomics analysis offers valuable insights into how metabolic pathways change according to age and health status. However, metabolite levels can fluctuate due to biological factors (e.g., age, diet, and health status) and technical factors (e.g., sample handling, storage times, and instrument performance), with some metabolites exhibiting greater sensitivity to these sources of variability than others. This study aimed to characterize the longitudinal and technical stability of untargeted plasma and cerebrospinal fluid (CSF) metabolites and to identify a subset that remains reliable over the extended time scales required for epidemiological research. Methods: Untargeted ultrahigh-performance liquid chromatography–mass spectrometry (LC-MS) metabolomic profiles were available from multiple visits in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and Wisconsin Alzheimer’s Disease Research Center (ADRC) studies. For this analysis, we constructed a subset of generally healthy participants with samples drawn at four time points (~2.5 years apart): two visits analyzed in 2017 and two visits analyzed in 2023, corresponding to two distinct analytical waves. We computed Rothery’s intraclass correlation coefficients (ICCs) to quantify intra-wave and inter-wave stability, evaluated pooled quality-control (QC) variation, classified metabolite stability by established thresholds, and developed a composite score integrating longitudinal stability and susceptibility to technical variance. Results: Across all metabolites, median stability was classified as ‘fair’ (Rothery’s ρ > 0.40 to ≤0.75) for both plasma and CSF. Although analytical batches were bridged using pooled QC samples, inter-wave stability was significantly lower than intra-wave stability, reflecting increased technical variability across waves. Using the composite score, we identified subsets of metabolites with ‘excellent’ stability and low susceptibility to batch effects in plasma and CSF. Stability patterns varied across biochemical super pathways. Conclusions: This work highlights metabolites suitable for long-term epidemiological studies and informs experimental design and analytical strategies for combining data across cohorts and analytical batches. Full article

WiFi sensing relies on capturing channel state information (CSI) fluctuations induced by human activities. Accurate motion segmentation is crucial for applications ranging from intrusion detection to activity recognition. However, prevailing methods based on variance, correlation coefficients, or deep learning are often constrained by complex threshold-setting procedures and dependence on high-quality sample data. To address these limitations, this paper proposes a training-free and environment-independent motion segmentation system using commercial WiFi devices from an image-processing perspective. The system employs a novel quasi-envelope to characterize CSI fluctuations and an iterative segmentation algorithm based on an improved Otsu thresholding method. Furthermore, a dedicated motion detection algorithm, leveraging the grayscale distribution of variance images, provides a precise termination criterion for the iterative process. Real-world experiments demonstrate that our system achieves an E-FPR of 0.33% and an E-FNR of 0.20% in counting motion events, with average temporal errors of 0.26 s and 0.29 s in locating the start and end points of human activity, respectively, confirming its effectiveness and robustness. Full article

Chemical contaminants in drinking water represent a widespread threat to human health, making water quality monitoring an essential mitigation measure. This study aimed to assess the quality of drinking water by conducting comprehensive multi-year seasonal monitoring at seven distribution points in central Romania, determining the spatial and temporal trends of relevant physical parameters (pH and electrical conductivity) and chemical contaminants (NO₂, NO₃, NH₄, Cl, and SO₄). The pollution degree was evaluated using the pollution index and the overall pollution assessment index. The principal component analysis attributed over 60% of water quality variance to NO₂, NO₃, and NH₄ pollution, linked to incomplete nitrification or external loading, such as agricultural practices. Additionally, a human health risk assessment was performed according to U.S. EPA guidelines, calculating the chronic daily intake, hazard quotient, and hazard index for nitrogen compounds via oral and dermal exposure pathways for both adults and children. The results showed significant seasonal fluctuations in nitrogen compounds and electrical conductivity. The pollution indices classified the water bodies across a spectrum from “light” to “significant” pollution degrees. The health risk assessment revealed that NO₃ was the primary risk driver, with hazard index values exceeding the threshold of one in specific locations and seasons, indicating potential adverse health effects, particularly for children. Full article

Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods—variance thresholding, chi-square test, mutual information, ANOVA F test, and ReliefF—are first applied in parallel to score features, and Robust Rank Aggregation (RRA) is then used to obtain a consensus feature ranking that is less sensitive to the bias of any single scoring criterion. An L1-regularized logistic regression model is subsequently constructed on the candidate feature subset defined by the RRA ranking to achieve task-coupled sparse selection, thereby linking feature selection stability, feature compression, and classification performance. Results: FRL-TSFS was evaluated on six representative metabolomics and gene expression datasets under a mildly perturbed scenario induced by 10-fold cross-validation, and its performance was compared with multiple baselines using the Extended Kuncheva Index (EKI), Accuracy, and F1-score. The results show that RRA substantially improves ranking stability compared with conventional aggregation strategies without degrading classification performance, while the full FRL-TSFS framework consistently attains higher EKI values than the other feature selection schemes, markedly reduces the number of selected features to several tens of metabolites or genes, and maintains competitive classification performance. Conclusions: These findings indicate that FRL-TSFS can generate compact, reproducible, and interpretable biomarker panels, providing a practical analysis framework for stability-oriented feature selection and biomarker discovery in untargeted metabolomics. Full article

Background: A comprehensive temporal analysis of mtDNA haplogroup variation across Lithuanian history remains limited. This study investigates the mtDNA variation landscape during the Iron Age by comparing newly reported Iron Age individual mtDNA data with the new data from present-day Lithuanians. Methods: Remains of individuals from the Iron Age Lithuania (n = 101) were processed using standard protocols for ancient DNA processing. For the present-day Lithuanians (n = 279), whole mitogenomes were sequenced. Thirty-six polymorphic sites within the Hypervariable Region I were used for haplogroup assignment, phylogenetic and population genetic analyses. Results: Fifteen distinct haplogroups in the Iron Age and the present-day Lithuanians were identified. Haplogroup R0/H remained the most frequent across time. Haplogroups U, T, and N were prominent in the Iron Age. Haplogroups M and D were introduced after the Iron Age. Phylogenetic and population genetic analyses revealed greater mtDNA diversity in the present-day Lithuanians. Significant difference in molecular variance was observed during the Iron Age. Barring the Viking period, the Iron Age mtDNA variation matched the present-day Lithuanian and European populations. Conclusions: Our study showed that mtDNA variation over time remained stable with some random fluctuations and gained more diversity in the present-day Lithuanians. Full article