Search Results (25,180)

The active constituents of essential plant oils (EOAIs), monoterpenoid carvacrol and monoterpene p-cymene, are widely distributed in many aromatic plants and their products. They differ in that carvacrol has a phenolic functional group. The numerous pharmacological effects of these two EOAIs are well known. In different doses/concentrations, they exhibit analgesic, neuroprotective, vasorelaxant, anti-inflammatory, antiviral, antibacterial and antiparasitic effects. The acute toxicity of carvacrol and p-cymene in rats and the free-living nematode Caenorhabditis elegans was investigated. Furthermore, the impact of subacute administration of these two terpenes on general health, CNS integration, i.e., motor coordination and balance of rats, as well as their effects on the movement of adult C. elegans, was also examined. The aim was to compare the effects and describe in more detail the selective toxicity of carvacrol and p-cymene. The calculated LD₅₀ value of carvacrol was 790.15 ± 1.15 mg/kg, while the LD₅₀ value of p-cymene is above 3000 mg/kg. Tested doses of carvacrol and p-cymene administered for 28 days (50, 100, and 200 mg/kg) did not exert any effect on the CNS of rats or cause any clinical disorders. LC₅₀ value of carvacrol for adult C. elegans was 184.13 ± 1.51 μM and for p-cymene 1268 ± 1.65 μM. In subacute testing, carvacrol showed negative effects on C. elegans reproduction, distance traveled, movement speed and rotational index at lower concentrations than p-cymene, indicating higher toxicity, which may be due to its phenolic structure. On the other hand, although less toxic to C. elegans, p-cymene exhibited a specific effect on worm motility, with more rolling which should be further investigated, and can be a consequence of cuticle damage or loss of orientation. Full article

Polarized side-scattering techniques are widely used in aerosol detection, oceanographic optics, and biomedical sensing due to their high sensitivity to weak optical signals in low-scattering coefficient media. Conventional polarized Monte Carlo methods face significant challenges in such regimes due to geometric mismatch, where photon exit positions deviate substantially from the detector plane. This study addresses the geometric mismatch issue in polarized Monte Carlo simulations for side scattering in low-scattering media (scattering coefficient ${\mu }_s=$ 1 cm⁻¹), where photon exit positions often deviate from the detector plane. We propose a novel algorithm incorporating backward ray tracing with geometric projection correction to enhance simulation accuracy. Experimental validation was conducted using 532 nm laser illumination on both 500 nm polystyrene microspheres (${\mu }_s=$ 0.21 cm⁻¹) and 5 nm TiO₂ nanoparticles (${\mu }_s=$ 1.06 × 10⁻⁶–1.06 × 10⁻⁵ cm⁻¹). The results demonstrate excellent agreement between simulations and experiments, confirming the algorithm’s capability to accurately capture the polarization characteristics of side-scattered light. This work provides a high-fidelity simulation tool for designing optical sensors in low-scattering media and holds direct applicability in nanoparticle concentration sensing and aerosol monitoring. Full article

The kickout has become a key source of possession in Ladies Gaelic Football (LGF), with successful teams winning 80% of their own and 29% of the opposition kickouts (KO). Several investigators have alluded to the role of strategy in Gaelic games; however, none have successfully identified, defined, or measured the impact of these strategies on the game. This novel study aims to identify and define the strategies used by Senior Inter-County LGF teams to win their own and opposition KOs, developing a valid and reliable analysis system to analyse KO performance in LGF. Eighty-nine games (2172 kickouts) from the 2019–2023 TG4 All-Ireland Senior LGF Championship were analysed using NacSport Scout. Four offensive and three defensive strategies have been newly identified, defined and analysed. A generalised linear mixed model was employed to model the key predictors of successful possession from the kickout. Numbers committed and both offensive and defensive strategies used were found to have significant influences on KO outcome, with a zonal press with 11+ players and the Flat 4 strategy being the most effective for winning the kickout. The findings of the current study provide a novel insight for coaches, players, pundits and analysts for kickout strategies employed by LGF teams. Full article

The demand for high-energy-density and fast-charging solid-state lithium metal batteries (SSLMBs) often subjects practical devices to internal thermal loads, making high-temperature operation a common operational condition rather than an isolated scenario. To address the interfacial degradation and dendrite growth accelerated by such thermomechanical stresses, we developed a composite gel electrolyte (CGE) by incorporating an optimal concentration of active Li_6.4La₃Zr_1.4Ta_0.6O₁₂ (LLZTO) into a fluoropolymer network. The abundant Lewis acidic sites on the LLZTO surfaces promote competitive solvation decoupling by interacting with anions, thereby modulating the primary solvation sheath of Li⁺. This localized modulation lowers the lithium-ion migration activation energy to 0.248 eV and facilitates a dual-interfacial passivation mechanism. Specifically, a rigid, inorganic-rich solid electrolyte interphase (SEI) forms to suppress morphological instability at the lithium anode, while an organic-dominated cathode electrolyte interphase (CEI) enhances the oxidative stability up to 4.3 V. As a result, symmetric cells demonstrate stable electrodeposition for over 450 h at 80 °C and 0.5 mA cm⁻². Furthermore, NCM811/Li full cells utilizing this CGEs exhibit significantly improved thermal resilience and cycling stability. Full article

Background/Objectives: Edible bird’s nest (EBN) benefits skin, but its transgenerational effects are unknown. This study investigated whether maternal EBN or its key component, sialic acid (SA), could program offspring skin pigmentation and antioxidant capacity. Methods: Pregnant Sprague-Dawley rats were supplemented with EBN or equi-sialic acid SA. Offspring skin brightness (L*, ITA°), melanin content, and key molecular targets (e.g., MITF, TYR, TRP1/2, PMEL, RAB27A, p-CREB, p-ERK, CAT, GCS, MDA) were assessed at postnatal days 0–21. Results: Maternal EBN induced a dose-dependent skin-brightening effect in offspring. High-dose EBN increased skin L* by 10.46% and ITA° by 14.28%, while reducing total melanin by 26.77%. This was mediated by downregulation of the MITF-TYR/TRP axis and its upstream CREB/ERK signaling, suppression of melanosome transport proteins (PMEL, RAB27A), and enhancement of antioxidant defenses (increased CAT/GCS, decreased MDA). SA alone showed similar but weaker effects. Conclusions: This study demonstrates that maternal EBN intake programs offspring skin towards a lighter phenotype and enhanced antioxidant status through multi-faceted regulation of melanogenesis. The superior efficacy of whole EBN over pure SA highlights the value of the intact food matrix, suggesting EBN as a promising functional food for maternal nutrition. Full article

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

This study investigates the factors affecting surface integrity during the machining of near-β Ti-5553, a critical material in the aerospace and defense industries. Considering this alloy as a difficult-to-machine material, the turning process was examined by analyzing the effects of cutting speed, feed rate, and cooling strategy (dry, conventional, and 30 MPa/High-Pressure cooling) on cutting force, temperature, surface roughness, and residual stress. The primary novelty of this research lies in its integrated approach: rather than evaluating surface integrity metrics in isolation, it simultaneously models interrelated responses to residual stress, cutting temperature, cutting force, and surface roughness under high-pressure coolant (HPC) conditions. Furthermore, it introduces a robust machine learning framework that uniquely applies data augmentation (Gaussian jittering and interpolation) to overcome the conventional constraints of limited experimental machining data, providing a highly accurate predictive tool. The experimental data were expanded using data augmentation methods (Gaussian jittering and interpolation) and modeled using five different machine learning algorithms (Extra Trees, Random Forest, Gradient Boosting, KNN, and AdaBoost). The results revealed that cooling pressure plays a dominant role, particularly in residual stress (importance score: 0.926) and cutting temperature (0.657). It was observed that high-pressure cooling (HPC) reduces thermal gradients, thereby lowering tensile stresses and improving surface integrity. When algorithm performances were compared, the Extra Trees and Random Forest models achieved the most accurate predictions after hyperparameter optimization. Specifically, the optimized Extra Trees regressor demonstrated exceptional predictive capability for residual stress, achieving an accuracy of 98.47%, a remarkably high coefficient of determination (R² = 0.9997), and a minimal Mean Squared Error (MSE = 6.8289). These quantitative results confirm that the proposed machine learning framework provides a highly reliable and precise tool for controlling surface quality in HPC- assisted machining. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

Background/Objectives: The valorization of bioactive compounds from food industry by-products aligns with sustainable development goals and represents a strategy for obtaining functional ingredients. Hazelnut (Corylus avellana L.) skins are a phenolic-rich residue with high antioxidant potential, but their extraction conditions and cellular mechanisms of action remain insufficiently explored. Methods: Ultrasound-assisted extraction was optimized using a 3³ Full Factorial Design (FFD) by investigating temperature (30–50–70 °C), extraction time (1–2–3 h), and solvent composition (water/ethanol). Antioxidant activity was evaluated using multiple in vitro assays, including Total Phenolic Content (TPC), DPPH, ABTS, FRAP, and β-carotene bleaching (BCB) assays. The optimized extract (OE) was chemically characterized by UHPLC–MS/MS and its activity was evaluated in HepG2 cells for biocompatibility, modulation of intracellular ROS levels, and antioxidant pathway activation. Results: Optimal extraction conditions were identified as 30 °C, 70.86 min (1.181 h), and 21.13% ethanol (v/v), yielding an extract with enhanced antioxidant capacity. UHPLC–MS/MS analysis revealed 25 bioactive compounds, mainly flavonoids and phenolic acids, relevant for oxidative stress modulation. The extract significantly reduced tert-butyl hydroperoxide (TBH)-induced intracellular ROS levels, restoring antioxidant proteins involved in the Nuclear Factor erythroid 2-related factor 2 (NRF-2)-mediated defense pathway. Conclusions: The optimized hazelnut skin extract combines strong antioxidant efficacy with cellular compatibility, supporting its potential application as a functional ingredient for nutraceutical and pharmaceutical strategies targeting oxidative stress-related conditions. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

Listeria monocytogenes (L. monocytogenes) is a significant zoonotic pathogen responsible for listeriosis, a foodborne infection with high mortality. The inflammasome, an innate immune complex, plays a critical role in controlling pathogenic infections through its rapid inflammatory output. During L. monocytogenes infection, pore-forming toxins such as listeriolysin-O and flagellin are quickly recognized by pattern recognition receptors (PRRs), triggering inflammatory responses and activating the host’s anti-infection immunity. However, excessive or chronic inflammasome activation and subsequent interleukin-1β (IL-1β) release are implicated in the pathogenesis of L. monocytogenes. Although inflammasome activation is an effective defense against L. monocytogenes, the bacterium has evolved multiple mechanisms to inhibit this immune pathway. Hence, research on inflammasomes activation is crucial for better understanding the pathogenic mechanism of L. monocytogenes. In this review, we highlight recent advances in the understanding of the molecular mechanisms of inflammasome activation by L. monocytogenes infection. We then discuss advances in the role of the inflammasome pathway in the pathogenesis of L. monocytogenes, along with an overview of the applications of inflammasome inhibitors. Extensive studies into the mechanisms by which L. monocytogenes activates the inflammasome could lead to the discovery of novel therapeutic targets and strategies to fight L. monocytogenes infections. Full article

Diabetic retinopathy (DR) is a leading cause of vision loss worldwide and represents a complex neurovascular complication of diabetes mellitus driven by chronic hyperglycemia. Increasing evidence identifies oxidative stress—defined as an imbalance between reactive oxygen species (ROS) production and antioxidant defenses—as a central pathogenic mechanism linking metabolic dysregulation to retinal injury. The retina is particularly vulnerable to oxidative damage due to its high metabolic demand, elevated oxygen consumption, and abundance of polyunsaturated fatty acids. Hyperglycemia activates multiple interconnected biochemical pathways, including the polyol and hexosamine pathways, protein kinase C signaling, advanced glycation end-product formation, and lipid peroxidation, all of which converge on excessive ROS production and mitochondrial dysfunction. Growing attention has focused on oxidative stress biomarkers as tools to characterize DR severity and progression. Elevated systemic markers of lipid, protein, and DNA oxidation, together with impaired antioxidant capacity, correlate with disease stage, while oxidative biomarkers detected in aqueous and vitreous humor reflect localized retinal injury. Importantly, oxidative stress biomarkers are also associated with functional outcomes, including best-corrected visual acuity and diabetic macular edema. Integration of systemic and ocular oxidative biomarkers with clinical staging may improve risk stratification and support personalized therapeutic strategies in DR. Full article

Drought stress severely limits plant growth and productivity, yet the molecular basis of drought tolerance and post-drought recovery remains incompletely understood in many forage legumes. Medicago ruthenica is a perennial legume native to arid and cold regions and exhibits strong drought resilience. Results: We integrated key physiological traits related to stomatal regulation, photosynthesis, osmotic adjustment and antioxidant defense with RNA-seq across four stages (well-watered control, CK; drought for 9 days, D9; drought for 12 days, D12; and rewatering for 4 days, RW). Drought triggered stage-dependent physiological shifts, and transcriptome profiling identified >3000 drought- and rewatering-responsive genes enriched in primary metabolism, redox homeostasis and hormone signaling. WGCNA highlighted two drought-associated modules (MEcyan and MEcoral1) and prioritized three hub transcription factors for functional validation: 861 (AP2/ERF), 22 (WRKY) and 89 (bZIP). Overexpression of each gene in tobacco improved drought tolerance, as indicated by enhanced growth/root traits, increased osmolyte accumulation and antioxidant enzyme activities, and reduced membrane damage. Conclusions: Together, these results provide an integrated view of drought stress response and recovery in M. ruthenica and identify 861, 22 and 89 as candidate regulatory genes for engineering drought resilience in legumes. Full article