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Kinmen peanut (Arachis hypogaea L. cultivar Kinmen No. 1) is a unique crop used to produce local specialty “peanut candy”; however, the peanut skins (PSs) are treated as waste owing to the bitter taste. To support the valorization of this agro-industrial by-product, peanut skin ethanolic extract (PSE) was prepared and evaluated for its hypolipidemic potential in a cholesterol/fat-fed hamster model, together with its antioxidant capacity and chemical composition. Hamsters were fed a cholesterol/fat-enriched diet supplemented with PSE at 0.1%, 0.2%, or 0.4% (w/w) for 8 weeks. Serum lipid profiles were determined, and derived atherogenic indices were calculated. In parallel, antioxidant activity was assessed using 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and reducing power assays, while chemical characterization included total phenolics, crude phytosterols, and HPLC profiling of representative phenolic compounds. PSE significantly reduced serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) compared with the cholesterol/fat-enriched control, whereas triglycerides were not significantly altered. The LDL-C/HDL-C ratio was also reduced in PSE-treated groups, with the greatest reduction observed in the 0.1% PSE group (0.33 ± 0.04 vs. 0.56 ± 0.12 in the negative control). In addition, PSE exhibited marked antioxidant activity, with IC₅₀ values of 141.3 and 76.2 μg/mL in the DPPH and ABTS assays, respectively. Chemical analyses showed that PS contained 1098 ± 189 µg β-sitosterol equivalents/g PS and 199.3 ± 4.6 mg gallic acid equivalent (GAE)/g PS, and HPLC identified p-coumaric acid, ferulic acid, gallic acid, chlorogenic acid, daidzein, catechin, and resveratrol as representative phenolic constituents. Collectively, these findings support Kinmen peanut skin as a promising value-added source of bioactives for functional ingredient development targeting cholesterol dysregulation and oxidative processes. Full article

In this study, the active components in the seed of Mangifera indica L. were isolated, the main chemical components were identified, and then their antioxidant activities and their effects on liver injury and intestinal microbiota were evaluated. The results showed that all the components of mango column chromatography exhibited antioxidant activity. F2 had the lowest IC₅₀ value of 93.61 μg/mL and exhibited the strongest DPPH radical scavenging activity. Given its superior overall antioxidant activity, F2 was selected for further compositional analysis and activity evaluation. UPLC-MS/MS analysis showed that the isolated components of mango F2 contained 11 active ingredients, including mangiferin, gallic acid and quercetin. The results showed that specific mango fractions significantly reduced serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, and showed a protective effect on liver injury induced by alcohol. rRNA sequencing analysis showed that high alcohol intake could reduce the species diversity of intestinal microbiota in mice, and mango fractions could effectively alleviate this phenomenon. High alcohol intake decreases the relative abundance of Bacteroidota and increases the abundance of Bacillota and Thermodesulfobacteriota phyla. The high-dose mango group alleviated the above changes, which was manifested by an increase in the relative abundance of Bacteroidota and Thermodesulfobacteriota bacteria. The relative abundance of families such as Muribaculaceae in the high-dose mango group decreased compared to the model group. This study provides a scientific basis for the analysis and high-value utilization of mango components, and provides a new alternative for protecting against alcoholic liver injury and regulating intestinal microbiota. Full article

The ice–rock interface shear mechanism is fundamental to understanding ice–rock avalanche hazards. This study conducts a series of direct shear tests under various normal stresses to analyze the mechanical response and acoustic emission (AE) evolution of the interface, establishing a shear strength prediction model. Results indicate that the roughness significantly affects mechanical properties and AE responses: as the roughness increases, the shear strength, cohesion, and internal friction angle improve significantly, while peak AE ringing counts and energy exhibit an increasing trend. During failure, the proportion of shear cracks decreases while tensile cracks increase, reflecting a shift in crack development modes driven by the roughness. Based on AE characteristics and stress–displacement relations, the shear failure process is categorized into five stages: initial, crack development, crack propagation, crack coalescence, and residual stages. Incorporating the effects of the roughness and cementation force, a shear mechanical model was established. Experimental data verify the model’s rationality; however, its applicability may be limited when the roughness is excessively high. Full article

Future contributions to sea level rise from the Antarctic Ice Sheet due to climate change remain one of the largest uncertainties for future sea level. Improving predictions of ice mass loss is a major goal of numerical ice sheet models, but a major difficulty is that ice sheet models assume an empirical fit to modern-day observed speeds to infer sliding parameters. While this results in accurate modern-day comparisons, predictions for future or past climates that have substantially different conditions will be inaccurate if the empirical sliding law used is not appropriate. To help constrain which basal physics is most appropriate and therefore which basal parameterizations should be used in ice sheet models, here, we pursue an understanding of which physical mechanisms are most likely to explain the spatial variability in flowline speeds throughout the Antarctic Ice Sheet. Specifically, we compare observed flowline surface speeds with predictions of speeds from internal ice deformation and Weertman sliding using a conservative range of physical parameters. Despite large uncertainties, we find a number of flowlines where the predictions can be distinguished from each other and one can infer that one of the two mechanisms, or a third mechanism, Coulomb frictional failure, may likely be principally responsible. Geographic patterns in the dominant mechanism are observed. Weertman sliding appears dominant in several flowline clusters in East Antarctica, and there are regional consistencies in the estimated nearness to flotation at locations of inferred initiation of Coulomb failure. Weertman sliding at faster rates is also observed within regions of inferred Coulomb failure, consistent with theoretical expectations. The key finding that the dominant deformation mechanism varies along and between Antarctic flowlines may complicate how ice sheet models need to be parameterized if accurate predictions of future ice loss and sea level rise are to be accurate. Full article

Ice accretion poses a threat to fixed-wing aerial vehicles as it alters the wings’ shape and thus degrades the aerodynamic performance. In manned aircraft, the icing detection system assists the pilot and utilises dedicated sensors. However, in unmanned aerial vehicles (UAVs), onboard icing detection can generally only be achieved using standard sensors in conjunction with dynamical models, because dedicated sensors are rarely available. In this paper, we propose two approaches based on the particle filter for both icing detection and accurate state and aerodynamic parameter estimation in the presence of icing, with different levels of severity. The first approach uses the observation likelihood for icing hypothesis testing with a complement of the Gaussian kernel to compute icing probability. The second approach uses a discrete jump approach based on a Bernoulli process and a subset of particles to test the icing hypothesis for faster icing detection by estimating changes in icing-related aerodynamic parameters. Using both approaches, the simulation results demonstrate improved estimation accuracy compared to an extended Kalman filter (EKF), under both moderate and severe icing conditions. With adequate tuning, the proposed approaches show potential for indirect icing detection in UAVs. They also enable the computation of icing severity and provide a more accurate and reliable estimate of the icing probability compared to the EKF. Full article

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments. Full article

Innovation is a central driver of competitiveness, resilience, and sustainability in the agri-food sector, particularly among small and medium-sized enterprises (SMEs). However, traditional science- and technology-based models may not fully grasp the innovation dynamics in this domain, and research explicitly addressing agri-food SMEs remains limited. This study adapts, integrates, and extends existing Innovation Capability (IC) and related constructs into a unified WI–PI–IA framework (Willingness to innovate–Propensity to innovate–Innovation Ability) for agri-food SMEs. The framework is empirically tested through a sectoral quantitative case-study based on structured questionnaires administered to twenty SMEs operating in the Sardinian sheep dairy industry. The findings confirm the framework’s validity, highlighting the role of contextual factors and revealing distinct innovation patterns between cooperatives and private firms. This study is, to our knowledge, the first to conceptualise IC in agri-food SMEs as the outcome of the three above constructs and offers a comprehensive and context-sensitive approach that contributes to academic research and directs policymakers towards factors that affect agri-food SME innovation outcomes, considering their unique structures and specific challenges they face. Full article

Stock-ranking prediction is an important task in quantitative finance because it directly influences portfolio construction and alpha generation. Recent Graph Neural Network (GNN) models provide a promising way to describe inter-stock dependencies, but many existing methods still have difficulty balancing rapidly changing market interactions with relatively stable structural relationships. They are also easily affected by financial micro-structure noise. To address these issues, this paper proposes USTGCN, a Unified Spatio-Temporal Graph Convolutional Network for stock-ranking prediction. USTGCN adopts a dual-stream temporal encoder based on ALSTM and GRU to capture short-term dynamic patterns and longer-horizon structural information, respectively. We further introduce a rolling-window correlation smoothing strategy to build a more stable dynamic graph, and then integrate the dynamic and structural graph views through a shared fusion layer. Skip connections are used to preserve original temporal information during spatial aggregation. Experiments on the CSI100 and CSI300 benchmark datasets show that USTGCN achieves IC values of 0.141 and 0.154, respectively, and exhibits improved drawdown control during stressed market periods, indicating its practical value for quantitative trading. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (${\mathcal{L}}_{align}$), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces ${\mathrm{AE}}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, ${\mathrm{AE}}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. ${\mathrm{AE}}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

Sea ice segmentation based on Synthetic Aperture Radar (SAR) images has become an important technical means for polar climate change monitoring and navigation safety guarantee. However, the existing methods have limitations in the utilization of SAR polarization information and the modeling of local diversity details of sea ice, which leads to insufficient segmentation, especially in complex ice-water boundary regions. To address these issues, this paper proposes a novel Polarization-Fused Edge-Enhanced UNet (PFEE-UNet) designed specifically for sea ice segmentation from high-resolution SAR images. Specifically, we design the Cross-Polarization Channel Interaction (CPCI) module, which employs a dual interaction strategy of hierarchical inter-group cascading and symmetric cross-fusion. This approach effectively leverages the complementary features of the HH and HV polarization channels, significantly enhancing the distinction between sea ice and open water. Additionally, we present the Dense–Sparse Diversity Enhancement (DSDE) module, which combines a spatial-channel joint attention mechanism to strengthen the model’s ability to capture spatial relationships within complex ice–water structures, effectively alleviating misclassifications caused by abrupt local texture changes. Finally, we design the Selective Edge Fusion (SEF) module, which dynamically selects and integrates multi-level edge features, improving the continuity of sea ice boundaries and preserving its morphological integrity. The experimental results show that the proposed PFEE-UNet model outperforms mainstream segmentation methods on the AI4Arctic/ASIP sea ice dataset, achieving an average Intersection over Union (IoU) of 84.48%, which surpasses existing methods such as HRNet (82.52%) and DeepLabv3+ (82.40%). Additionally, PFEE-UNet was applied for end-to-end ice–water segmentation on real-world Sentinel-1 SAR scenes, demonstrating its effectiveness and robustness for practical sea ice monitoring. Full article

The progression of prostate cancer to castration-resistant disease (CRPC) remains a clinical challenge in which oxidative stress intersects with the PI3K/AKT–androgen receptor (AR) axis. Quercetin (QRC) is a redox-active dietary flavonol, yet its mechanistic impact on CRPC is incompletely defined. Here, we tested whether QRC suppresses AR output by directly modulating AKT. C4-2B and 22Rv1 CRPC cell lines were treated with increasing QRC concentrations, with or without enzalutamide (Enz). Proliferation and viability were monitored by IncuCyte imaging and SYTOX Green incorporation. AKT phosphorylation (S473), AR phosphorylation (S210/213), AR abundance and localization, and prostate-specific antigen (PSA) secretion were assessed by immunoblotting, immunofluorescence, and dot blot, respectively. Docking and molecular dynamic simulations were performed to identify and evaluate a putative QRC-binding site on AKT. QRC produced a dose-dependent cytostatic effect (IC₅₀ 24.37 μM in C4-2B; 21.54 μM in 22Rv1) without marked cell death, reduced pAKT(S473) by up to 80%, decreased pAR(S210/213), and diminished nuclear AR and PSA secretion. Simulations suggested a putative druggable allosteric pocket in the AKT1 N-lobe, with G159 emerging as a potential anchor residue. Enz cotreatment with QRC did not produce additive effects, consistent with a model in which QRC acts upstream of ligand-driven AR activation and thereby limits the incremental benefit of AR antagonism under these conditions. These data support QRC as an AKT–AR axis modulator in CRPC and provide a target engagement framework beyond simple ROS scavenging. Full article

This study presents a numerical investigation of the effects of atmospheric icing on the aerodynamic performance and power output of the NREL 5 MW reference wind turbine. In cold climate regions, ice accretion on wind turbine blades significantly alters the airfoil geometry, leading to aerodynamic degradation characterized by increased drag, reduced lift, and substantial power losses. Understanding these effects is therefore essential for reliable performance prediction and efficient turbine operation under icing conditions. To address this problem, numerical simulations were conducted on six representative blade sections using the FENSAP-ICE framework, which integrates flow field calculations, droplet transport, and ice accretion modeling within a unified computational environment. The analyses were performed under different atmospheric icing conditions, considering liquid water content values of 0.22 g/m³ and 0.50 g/m³ and ambient temperatures of −2.5 °C and −10 °C. The median volumetric diameter was fixed at 20 µm, and the icing duration was set to one hour for all cases, allowing for both glaze and rime ice formations to be systematically examined. The results reveal that ice accretion becomes increasingly pronounced toward the blade tip, mainly due to higher relative velocities and increased collection efficiency in the outer sections. Glaze icing conditions produce irregular horn-shaped ice formations and lead to severe aerodynamic degradation, whereas rime ice forms more compact structures near the leading edge and results in comparatively lower performance losses. The degraded aerodynamic coefficients obtained from the iced airfoils were subsequently incorporated into BEM-based power calculations, indicating that total power losses can reach up to 40% under severe icing conditions, with the outer blade sections contributing most significantly to this reduction. Furthermore, an economic assessment based on annual energy losses highlights the substantial impact of atmospheric icing on wind turbine performance and operational costs. Full article

Height anomaly, defined as the separation between the quasi-geoid and the reference ellipsoid, is fundamental to quasi-geoid refinement. While the Goddard Mars Model-3 (GMM-3) developed by NASA’s Goddard Space Flight Center (GSFC) and the JPL Mars gravity field MRO120D (JGMRO_120D) model developed by NASA’s Jet Propulsion Laboratory (JPL) stand as two representative Martian gravity field models, the systematic differences between them and their associated physical implications remain insufficiently quantified. This study establishes a validated computational framework for Martian height anomaly determination using updated physical parameters and spherical harmonic expansions. Validation against terrestrial datasets confirms high reliability (standard deviation: 0.0695 m relative to International Centre for Global Earth Models (ICGEM)), ensuring confidence in subsequent analysis. Our analysis reveals three critical findings: (1) Systematic latitudinal biases between GMM-3 and JGMRO_120D exhibit a monotonic gradient from −1.3 m near the equator to +3.9 m at the North Pole, suggesting differential parameterization of polar mass loading or tidal models between the two centers. (2) Polar clustering of uncertainties and outliers exceeding the 95th percentile (>7 m) concentrate non-randomly at latitudes >60°, which is attributed to sparse satellite tracking and seasonal ice cap modeling limitations. (3) There is error amplification in lowland terrains, where relative errors exceed 60% in flat regions (near-zero anomalies), posing critical risks for precision landing missions. While global consistency between models is high (R² = 0.9999), the identified discrepancies provide new constraints on Mars’s geophysical models and essential guidance for future gravity field improvements and mission planning. Full article

Background: Chronic critical illness (CCI) affects ~7.6% of ICU patients worldwide and is associated with poor outcomes, including 25% in-hospital and 50% one-year mortality. A proposed key mechanism is the inflammation-immunosuppression-catabolism (ICS) triad, which contributes to multiple organ failure and independently increases mortality. Although early identification of ICS could improve risk stratification, no clinically applicable predictive tool currently exists. This study aimed to develop and validate a prognostic nomogram to predict ICS development in ICU (Intensive Care Unit) patients. Methods: This real-world analysis used electronic health records from the Russian Intensive Care Dataset (RICD). ICS was defined as C-reactive protein > 20 mg/L, albumin < 30 g/L, and lymphocyte count < 0.8 × 10⁹/L. Variables with >30% missing data were excluded, and remaining missing values were handled by multiple imputation. A Cox proportional hazards model was used to construct the nomogram. Internal validation was performed using an 8:2 training–validation split. Results: Among 1963 eligible patients, 540 (27.5%) developed ICS. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified nine significant predictors: age, body mass index, SOFA (Sequential Organ Failure Assessment) and FOUR (Full Outline of UnResponsiveness) scores at admission, pneumonia and anemia at admission, platelet count, total protein, and creatinine. The nomogram showed good discrimination, with C-indices of 0.763 (95% CI: 0.741–0.783) in the training set and 0.735 (95% CI: 0.689–0.784) in the validation set. At the optimal cutoff, sensitivity was 0.75, specificity was 0.63, positive predictive value was 0.43, and negative predictive value was 0.87. Conclusions: This study presents the first nomogram for predicting ICS in ICU patients, using nine admission variables to reliably identify low-risk individuals. Further external validation is required. Full article