Search Results (5,502)

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data. Full article

Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are employed to leverage diverse viewpoints for enhancing security threat detection. This paper introduces a novel intrusion detection approach using multi-view fusion within a federated learning framework, proposing an integrated AE Neural SVM (AE-NSVM) model that combines auto-encoder (AE) multi-view feature extraction and Support Vector Machine (SVM) classification. This approach simultaneously learns representative features from multiple views and classifies network samples into normal or seven attack categories while employing federated learning across clients to ensure adaptability and robustness in diverse network environments. The experimental results obtained from two benchmark datasets validate its superiority: on TON_IoT, the CAE-NSVM model achieves a highest F1-measure of 0.792 (1.4% higher than traditional pipeline systems); on UNSW-NB15, it delivers an F1-score of 0.829 with a 73% reduced training time and an 89% faster inference compared to baseline models. These results demonstrate the advantages of multi-view fusion in federated learning for balancing accuracy and efficiency in distributed intrusion detection systems. Full article

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization (FEMCO) algorithm for distribution network optimization. The model-driven module employs spectral clustering to decompose the network into multiple autonomous subsystems and performs distributed reconstruction through gradient descent. The data-driven module, built upon Long Short-Term Memory (LSTM) networks, learns temporal dependencies between load curves and operational parameters to enhance predictive accuracy. These two modules are fused via a Random Forest ensemble, while FEMCO jointly leverages Monte Carlo global sampling, Federated Learning-based distributed training, and Genetic Algorithm-driven evolutionary optimization. Simulation studies on the IEEE 33 bus distribution system demonstrate that the proposed framework reduces power losses by 25–45% and voltage deviations by 75–85% compared with conventional Genetic Algorithm and Monte Carlo approaches. The results confirm that the proposed hybrid architecture effectively improves convergence stability, optimization precision, and adaptability, providing a scalable solution for the intelligent operation and distributed control of modern power distribution systems. Full article

Intrusion Detection Systems (IDS) are vital to cybersecurity but suffer from severe class imbalance in benchmark datasets such as NSL-KDD and UNSW-NB15. Conventional oversampling methods (e.g., SMOTE, ADASYN) are efficient yet fail to preserve the latent semantics of rare attack behaviors. This study introduces the Minority-class Intrusion Detection Synthesizer GAN (MIDS-GAN), a divergence-minimization framework for minority data augmentation under structured feature constraints. MIDS-GAN integrates (i) correlation-based structured feature selection (SFS) to reduce redundancy, (ii) trainable ACON activations to enhance generator expressiveness, and (iii) KL-divergence-guided alignment to ensure distributional fidelity. Experiments on NSL-KDD and UNSW-NB15 demonstrate significant improvement on detection, with recall increasing from 2% to 27% for R2L and 1% to 17% for U2R in NSL-KDD, and from 18% to 44% for Worms and 69% to 75% for Shellcode in UNSW-NB15. Weighted F1-scores also improved to 78%, highlighting MIDS-GAN’s effectiveness in enhancing minority-class detection through a principled, divergence-aware approach. Full article

Behavior is not a mere sequence of responses to stimuli but the dynamic expression of internal processes such as planning, prediction, valuation, and inference. These functions arise from distributed and metabolically costly neural systems and are best understood by considering behavior and neural activity together. This article presents a narrative and conceptual review of the neuroscience of behavior, integrating biological, environmental, and computational perspectives. We synthesize evidence from motor control, neural population dynamics, predictive processing, and spontaneous behavior, showing that behavior cannot be explained without the neural systems that generate it, and that neural activity gains meaning only through detailed behavioral models. Neural dynamics correlate with latent variables, such as intention and prediction error, that structure adaptive action across timescales. Recent advances in behavioral analysis, dimensionality reduction, and computational modeling enable the analysis of neural and behavioral data with comparable complexity, revealing shared computational architectures that link population activity with the organization of action. Our methodology involved a targeted literature search in PubMed and Web of Science (1919–2025), supplemented by seminal earlier works. By combining mechanistic and functional analysis, we outline a unified framework that explains how brains, bodies, and environments together generate flexible, adaptive behavior. Full article

Objective: This study aims to determine how intestinal obstruction influences the tumor immune microenvironment (TIME) and its impact on prognosis in colorectal cancer (CRC). Methods: Immune cell densities (CD4⁺, CD8⁺, CD20⁺, CD68⁺) within central tumor (CT) and invasive margin (IM) compartments were quantitatively analyzed using immunohistochemistry (IHC) and QuPath digital pathology in surgical resection samples from 328 patients (164 obstructed colon cancer [OCRC] vs. 164 non-obstructed [NOCRC], cohorts matched by propensity scoring). Findings on tumor-infiltrating immune cell spatial distribution were integrated with peripheral blood immune cell counts and clinicopathological characteristics to characterize the obstructed colon cancer immune microenvironment. Associations with disease-free survival (DFS) and overall survival (OS) were evaluated. Results: OCRC exhibited higher lymphocytic infiltration, particularly in the CT compartment, compared to NOCRC, with significantly elevated CT-CD8⁺ T cell density in T4-stage OCRC (p < 0.005). Obstruction enhanced immune cell correlations across compartments, especially in T4 tumors, and the CD68⁺/CD8⁺ ratio effectively discriminated obstruction status (CT area under the curve (AUC): T4 = 0.879). Peripheral lymphocytopenia was pronounced in obstructive cases (p = 0.003). Critically, T4 OCRC showed a complete loss of all correlations between tumor-infiltrating immune cells and peripheral parameters. Despite increased infiltration, high CD8⁺ density in OCRC correlated with worse prognosis, indicating a paradoxical role influenced by obstruction context. CD68⁺ macrophages in the invasive margin consistently predicted improved survival (Disease-free survival [DFS]: Hazard ratio [HR] = 0.59, p = 0.008). Conclusions: Intestinal obstruction in CRC, particularly in T4-stage tumors, may represent an immunologically active state that alters local immune infiltration and systemic–local immune crosstalk. These findings suggest that obstruction status could refine prognostic stratification and inform therapeutic strategies, although validation in larger cohorts is warranted. Full article

Mancozeb is a broad-spectrum fungicide used extensively in agriculture to protect crops against a wide range of plant diseases. Although its capacity to induce oxidative stress is well documented, the cytotoxic effects of mancozeb on red blood cells (RBCs) remain poorly characterized. The present study aimed to investigate the cytotoxic effects of mancozeb on isolated RBCs, with particular focus on oxidative stress-induced cellular and molecular alterations. Human RBCs were exposed to mancozeb (0.5–100 µM) for 24 h. No hemolytic activity was observed across the tested concentrations. However, 10 and 100 µM mancozeb induced a significant increase in intracellular reactive oxygen species (ROS), leading to lipid and protein oxidation and impaired Na⁺/K⁺-ATPase and anion exchanger 1 (AE1) function. These changes resulted in altered RBC morphology, reduced deformability, and increased methemoglobin levels. Alterations in glycophorin A distribution, anion exchanger 1 (AE1) clustering and phosphorylation, and α/β-spectrin and band 4.1 re-arrangement indicated disrupted membrane–cytoskeleton interactions. A release of extracellular vesicles (EVs) positive for glycophorin A and annexin-V was also observed, consistent with plasma membrane remodeling. Despite increased intracellular calcium, eryptosis remained minimal, possibly due to activation of protective estrogen receptor (ER)-mediated pathways involving ERK1/2 and AKT signaling. Activation of the cellular antioxidant system and the glutathione redox system (GSH/GSSG) occurred, with catalase (CAT) playing a predominant role, while superoxide dismutase (SOD) activity remained largely unchanged. These findings offer mechanistic insights regarding the potential health impact of oxidative stress induced by pesticide exposure. Full article

The largest and endemic bivalve of the Mediterranean Sea, Pinna nobilis, is on the brink of extinction after a mass mortality event (MME) that has affected its populations since autumn 2016. Since then, different actions have been performed to improve the conservation status of P. nobilis. The monitoring of survivors in open coastal systems along the Spanish Mediterranean coast showed, after an 8-year period since the start of the MME (2017–2024), that the geographical distribution of the survivors in open sea is currently concentrated in a few regions, with focal points of specimen density in Cap de Creus (Catalonia) and Menorca (Balearic Islands). During the exhaustive monitoring of individuals of P. nobilis, the active participation of citizen science became decisive, locating almost half of the survivors. Most individuals were found in marine protected areas, mainly in Posidonia oceanica meadows in the upper 15 m. As a safety measure, several survivors were translocated to safer areas, while evaluation of the impact of the translocation showed no demonstrable effects. The knowledge acquired during these years has highlighted the necessity for collaborative monitoring, specifically to understand the current critical situation of P. nobilis and to implement effective conservation measures for this emblematic species. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

The railway architectural heritage in Jilin Province, as a significant component of Northeast China’s modern railway network, demonstrates how construction techniques, cultural integration, and social transformation have evolved throughout different historical periods. In this study, we conducted a systematic survey of 474 railway heritage buildings along the province’s main line. In order to quantitatively classify the spatiotemporal distribution characteristics of the heritage sites, we used five key Geographic Information System (GIS) methods—kernel density estimation, nearest neighbour index, spatial autocorrelation, standard deviational ellipses, and mean centre analysis—along with information entropy, relative richness, and the Bray–Curtis dissimilarity index. We continued our binary logistic regression using four prerequisite parameters—location, structure, architecture, and function—which contribute to the prerequisite, fundamental, and driving factors of architectural heritage. We concluded that local culture shapes geopolitics, population migration triggers economic conservation, and design trends carry ideology. These three factors intertwine to influence architecture and spatial patterns. Compared with previous studies, this research fills the gap concerning the architectural characteristics of towns at various lower-and mid-level stations, as well as the construction activities during the affiliated land period. This study provides a systematic framework for analysing railway heritage corridors and supports their sustainable conservation and reuse. Full article

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline “novelty detection → active labelling → incremental supervised update” and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of ≈92–94% and resistance to distribution drift are noted. Full article

Background/Objectives: In the present study, ergosterol, a novel natural and animal-free alternative sterol, was investigated, and its effects on liposomal properties were assessed. Importantly, ergosterol’s fungal origin offers a sustainable substitute for cholesterol, aligning with current trends in natural and vegan-friendly formulations. Methods: This study explored the effect of ergosterol content (10 mol% vs. 20 mol%) on the encapsulation efficiency (EE), physical properties, morphology, antioxidant activity, lipid peroxidation, and storage stability of Serpylli herba extract-loaded liposomes. Results: Liposomes with 20 mol% ergosterol exhibited significantly higher EE (~81.0%) than those with 10 mol% (~75.6%), along with improved resistance to UV- and freeze-drying-induced reduction in EE. Extract loading resulted in a reduced particle size, indicating favorable bilayer interactions, whereas lyophilization increased size and polydispersity, reflecting structural destabilization. However, 20 mol% ergosterol improved vesicle uniformity and surface charge stability, suggesting enhanced bilayer rigidity. Zeta potential and mobility trends supported improved colloidal stability in ergosterol-enriched systems under all tested conditions. Over 28 days at 4 °C, non-treated extract-loaded liposomes with a higher ergosterol content demonstrated enhanced vesicle integrity. During storage, UV-treated and lyophilized liposomes with 20 mol% ergosterol maintained more consistent size and charge profiles, indicating better membrane reorganization and stability. Nanoparticle tracking analysis demonstrated that ergosterol content modulates vesicle concentration in a dose-dependent manner, highlighting the role of membrane composition in liposome formation and potential dose uniformity. Transmission electron microscopy analysis of extract-loaded liposomes demonstrated well-defined vesicles with intact structural features. A study in a Franz diffusion cell revealed that ergosterol-enriched liposomes significantly delayed polyphenol release compared to free extract, confirming their potential for controlled delivery. Antioxidant activity was preserved in all liposomal systems, with higher ergosterol content supporting improved ABTS radical scavenging potential after stress treatments. FRAP assay results remained stable across formulations, with no major differences between sterol levels. TBARS analysis demonstrated that Serpylli herba extract significantly reduced UV-induced lipid peroxidation in ergosterol-enriched liposomes, underscoring its protective antioxidant role. Conclusions: Higher ergosterol content enhanced liposomal performance in terms of encapsulation, structural resilience, and antioxidant retention, particularly under UV and lyophilization stress. Ergosterol-containing liposomes exhibited improved stability, favorable particle size distribution, and high encapsulation efficiency, while maintaining the antioxidant functionality of the incorporated Serpylli herba polyphenol-rich extract. These findings highlight the potential of ergosterol-based liposomes as robust carriers for bioactive compounds in pharmaceutical and nutraceutical applications that align with current trends in green and vegan-friendly formulations. Full article

This study presents a strut-and-tie modeling (STM) framework for reinforced concrete (RC) deep beams strengthened with intraply hybrid composites (IRCs), integrating comprehensive experimental data from beams with three different span lengths (1.0 m, 1.5 m, and 2.0 m). Although the use of fiber-reinforced polymers (FRPs) for shear strengthening of RC members is well established, limited attention has been given to the development of STM formulations specifically adapted for hybrid composite systems. In this research, three distinct IRC configurations—Aramid–Carbon (AC), Glass–Aramid (GA), and Carbon–Glass (CG)—were applied as U-shaped jackets to RC beams without internal transverse reinforcement and tested under four-point bending. All experimental data were derived from the authors’ previous studies, ensuring methodological consistency and providing a robust empirical basis for model calibration. The proposed modified STM incorporates both the axial stiffness and effective strain capacity of IRCs into the tension tie formulation, while also accounting for the enhanced diagonal strut performance arising from composite confinement effects. Parametric evaluations were conducted to investigate the influence of the span-to-depth ratio (a/d), composite configuration, and failure mode on the internal force distribution and STM topology. Comparisons between the STM-predicted shear capacities and experimental results revealed excellent correlation, particularly for deep beams (a/d = 1.0), where IRCs substantially contributed to the shear transfer mechanism through active tensile engagement and confinement. To the best of the authors’ knowledge, this is the first study to formulate and validate a comprehensive STM specifically designed for RC deep beams strengthened with IRCs. The proposed approach provides a unified analytical framework for predicting shear strength and optimizing the design of composite-strengthened RC structures. Full article

Patients with systemic lupus erythematosus (SLE) and lupus nephritis (LN) are at increased risk of severe infections, making effective vaccination strategies essential. While antibody responses to SARS-CoV-2 vaccination have been studied in SLE, less is known about innate immune correlates. Therefore, we evaluated cytokines with a particular emphasis on interferon and chemokine profiles. To fulfill the immunological picture, we also assessed neutralizing antibodies against SARS-CoV-2 variants, lymphocyte subpopulations, and selected gene expression signatures in 33 patients stratified by vaccination status: fully vaccinated (FV, n = 23) and partially vaccinated (PV, n = 10). Serum analyses showed that FV patients exhibited increased type I (IFN-α2, IFN-β) and type III (IFN-λ1, IFN-λ2/3) interferons, as well as elevated pro-inflammatory cytokines (IL-1β, IL-6, TNF-α, and IL-12p70) and IL-10, whereas neutralizing antibody (Neut. Ab.) titers against wild-type and variant strains, including Omicron, were comparable between groups. Immunophenotyping demonstrated preserved T- and B-cell subset distributions, except for reduced CD8⁺CD197⁺CD45RA⁻ (central memory) T cells in FV patients. ISG15 gene expression was upregulated in the T cells of FV patients. Correlation analyses linked IL-6 with disease activity and IL-8, GM-CSF, IFN-β, IL-10, and Alpha Neut. Ab. with organ damage. Complement C3 correlated inversely with IFN-α2 and IFN-γ, while C4 correlated positively with Alpha and Omicron Neut. Ab. These findings highlight that vaccination in SLE induces distinct interferon and cytokine signatures without consistent enhancement of neutralizing antibodies against SARS-CoV-2, underscoring the importance of integrated immune correlates in assessing vaccine responses in this population. Full article

This paper presents a comprehensive analysis of the thermal stability and decomposition mechanisms of IPN hydrogels based on polyvinyl alcohol (PVA) and a copolymer network of poly(ethylene glycol) diacrylate–poly(ethylene glycol) methacrylate (PEGDA–PEGMA). Using thermogravimetric analysis (TGA/DTG) and multi-approach kinetic analysis (Friedman and Ozawa–Flynn–Wall isoconversion methods, nonparametric kinetics, Shestaka-Berggren model), the influence of composition on the processes of dehydration, thermal destruction, and the distribution of activation energy by degrees of conversion was investigated. The constructed three-dimensional kinetic “landscapes” made it possible to identify characteristic features of the behavior of various samples, including differences in the rate and mechanisms of destruction. It was found that an increase in the content of PVA enhances moisture binding and shifts the T_max of dehydration to higher temperatures, while an increase in the concentration of PEGDA forms a denser network that limits moisture retention and accelerates thermal decomposition. Calculation of diffusion coefficients using the Fick model showed a decrease in D with an increase in network density, which reflects an increase in resistance to moisture mass transfer. The combination of the data obtained demonstrates the multistage nature of thermal destruction and allows for the targeted selection of hydrogel compositions for biomedical, environmental, and materials science applications, including drug delivery systems, sorbents and heat-resistant coatings. Full article