Search Results (2,438)

Several studies have demonstrated that methionine supplementation in fish diets enhances immune status, inflammatory response, and resistance to bacterial infections by modulating for DNA methylation, aminopropylation, and transsulfuration pathways. However, the immunomodulatory effects of methionine in viral infections remain unexplored. This study aimed to evaluate the effect of methionine supplementation on immune modulation and resistance to the viral haemorrhagic septicaemia virus (VHSV) in rainbow trout (Oncorhynchus mykiss). Two diets were formulated and fed to juvenile rainbow trout for four weeks: a control diet (CTRL) with all nutritional requirements, including the amino acid profile required for the species, and a methionine-supplemented diet (MET), containing twice the normal requirement of DL-methionine. After feeding, fish were bath-infected with VHSV, while control fish were exposed to a virus-free bath. Samples were collected at 0 (after feeding trial), 24, 72, and 120 h post-infection for the haematological profile, humoral immune response, oxidative stress, viral load, RNAseq, and gene expression analysis. In both diets, results showed a peak in viral activity at 72 h, followed by a reduction in viral load at 120 h, indicating immune recovery. During the peak of infection, leukocytes, thrombocytes, and monocytes migrated to the infection site, while oxidative stress biomarkers (superoxide dismutase glutathione S-transferase, and glutathione redox ratio) suggested a compromised ability to manage cellular imbalance due to intense viral activity. At 120 h, immune recovery and homeostasis were observed due to an increase in the amount of nitric oxide, GSH/GSSG levels, leukocyte replacement, monocyte influx, and a reduction in the viral load. When focusing on the infection peak, gene ontology (GO) analysis showed several exclusively enriched pathways in the skin and gills of MET-fed fish, driven by the upregulation of several key genes. Genes involved in recognition/signalling, inflammatory response, and other genes with direct antiviral activity, such as TLR3, MYD88, TRAF2, NF-κB, STING, IRF3, -7, VIG1, caspases, cathepsins, and TNF, were observed. Notably, VIG1 (viperin), a key antiviral protein, was significantly upregulated in gills, confirming the modulatory role of methionine in inducing its transcription. Viperin, which harbours an S-adenosyl-L-methionine (SAM) radical domain, is directly related to methionine biosynthesis and plays a critical role in the innate immune response to VHSV infection in rainbow trout. In summary, this study suggests that dietary methionine supplementation can enhance a more robust fish immune response to viral infections, with viperin as a crucial mediator. The improved antiviral readiness observed in MET-fed fish underscores the potential of targeted nutritional adjustments to sustain fish health and welfare in aquaculture. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

The pressure of climate change on water resources has made the development of reliable hydrological models increasingly important, especially for data-scarce regions. However, due to the limited availability of ground-based observations, it considerably affects the accuracy of models developed using these inputs. This also limits the ability to investigate future hydrological behavior. Satellite-based data sources have emerged as an alternative to address this challenge and have received significant attention. However, the transferability of these datasets across different model classes has not been widely explored. This paper evaluates the transferability of satellite-derived inputs to eleven types of models, including process-based (SWAT), data-driven methods (XGBoost and WGAN), and hybrid model structures that utilize SWAT outputs with AI models. SHAP has been applied to overcome the black-box limitations of AI models and gain insights into fundamental hydrometeorological processes. In addition, uncertainty analysis was performed for all models, enabling a more comprehensive evaluation of performance. The results indicate that hybrid models using SWAT combined with WGAN can achieve better predictive accuracy than the SWAT model based on ground observation. While the baseline SWAT model achieved satisfactory performance during the validation period (NSE ≈ 0.86, KGE ≈ 0.80), the hybrid SWAT + WGAN framework improved simulation skill, reaching NSE ≈ 0.90 and KGE ≈ 0.89 during validation. Models forced with satellite-derived meteorological inputs additionally performed as well as those forced using station-based observations, validating the feasibility of using satellite products as alternative data sources. The future hydrological status of the basin was assessed based on the best-performing hybrid model and CMIP6 climate projections, showing a clear drought signal in the flows and long-term reductions in average flows reaching up to 58%. Overall, the findings indicate that the proposed framework provides a consistent approach for data-scarce basins. Future applications may benefit from integrating spatio-temporal learning frameworks and ensemble-based uncertainty quantification to enhance robustness under changing climate conditions. Full article

The debate over CEO compensation persists despite extensive efforts by academics and technocrats to understand its determinants. Most research has focused on how firm-specific characteristics and CEO-specific traits influence CEO compensation. However, the results have been contradictory, indicating that other factors may also play a role. This study examines the impact of macroeconomic factors on the compensation of CEOs. It examines how price variables such as interest rates, inflation, and exchange rates affect the fixed salaries and total compensation of CEOs at six South African banks listed on the Johannesburg Stock Exchange. Conducted over a 15-year period, this quantitative longitudinal study utilized secondary data from annual reports and the IRESS database. Panel data regression analysis was employed to interpret the data. The findings reveal a positive relationship between interest rates and fixed salaries, as well as between exchange rates and fixed salaries. Additionally, interest rates and total compensation are positively related, and exchange rates also have a positive relationship with fixed salaries. Understanding how macroeconomic conditions influence CEO pay helps Compensation Committees contextualize performance. It allows them to differentiate between achievement driven by a CEO’s abilities and that resulting from external factors, ensuring fair compensation and minimizing excessive rewards for “luck”. This knowledge supports the adjustment of incentive plans based on relative performance and economic-adjusted metrics, reducing the cyclical influence of macroeconomic variables on firm performance and, ultimately, CEO compensation. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

The evolution of modern warfare and civil exploration requires platforms that can operate seamlessly across the air–water interface. The folding-wing Hybrid Air and Underwater Vehicle (FUD) has emerged as a transformative solution, combining the high-speed cruising capabilities of fixed-wing aircraft with the stealth characteristics of underwater navigation. This review thoroughly analyzes the advancements and challenges in folding-wing FUD technology. The discussion is framed around four interconnected pillars: the overall design driven by morphing technology, adaptation of the propulsion system, multi-phase dynamic modeling and control, and experimental verification. The paper systematically compares existing technical pathways, including lateral and longitudinal folding mechanisms, as well as dual-use and hybrid propulsion strategies. The analysis indicates that, although significant progress has been made with prototypes demonstrating the ability to transition between air and water, core challenges persist. These challenges include underwater endurance, structural reliability under impact loads, and effective integration of the power system. Additionally, this paper explores promising application scenarios in both military and civilian domains, discussing future development trends that focus on intelligence, integration, and clustering. This review not only consolidates the current state of technology but also emphasizes the necessity for interdisciplinary approaches. By combining advanced materials, computational intelligence, and robust control systems, we can overcome existing barriers to progress. In conclusion, FUD technology is moving from conceptual validation to practical engineering applications, positioning itself to become a crucial asset in future cross-domain operations. Full article

This study addresses the ongoing challenge of accurately classifying beers by fermentation type and product category, an issue of growing importance for quality control, authenticity assessment, and product differentiation in the brewing sector. We applied a multiblock chemometric framework that integrates phenolic profiling obtained via GC–MS, antioxidant and antiradical activity derived from in vitro assays, and complementary colorimetric and physicochemical measurements. Principal Component Analysis (PCA) revealed clear compositional structuring within the dataset, with p-coumaric, gallic, syringic, and malic acids emerging as major contributors to variance. Supervised machine-learning classification demonstrated robust performance, achieving approximately 93% accuracy in discriminating top- from bottom-fermented beers, supported by a well-balanced confusion matrix (25 classified and 2 misclassified samples per group). When applied to ale–lager categorization, the model retained strong predictive ability, reaching 90% accuracy, largely driven by the C* chroma value and the concentrations of tyrosol, acetic acid, homovanillic acid, and syringic acid. The integration of multiple analytical blocks significantly enhanced class separation and minimized ambiguity between beer categories. Overall, these findings underscore the value of multi-block chemometrics as a powerful strategy for beer characterization, supporting brewers, researchers, and regulatory bodies in developing more reliable quality-assurance frameworks. Full article

With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered strategies including machine learning, deep learning, and large language models for identifying cybersecurity vulnerabilities and supporting automated patching. In this review, we conducted a synthesis and appraisal of 29 peer-reviewed studies published between 2019 and 2024. Our results indicate that AI methods substantially improve the precision of detection, scalability, and response speed compared with human-driven and rule-based approaches. We detail the transition from conventional ML categorization to using deep learning for source code analysis and dynamic network detection. Moreover, we identify advanced mitigation strategies such as AI-powered prioritization, neuro-symbolic AI, deep reinforcement learning and the generative abilities of LLMs which are used for automated patch suggestions. To strengthen methodological rigor, this review followed a registered protocol and PRISMA-based study selection, and it reports reproducible database searches (exact queries and search dates) and transparent screening decisions. We additionally assessed the quality and risk of bias of included studies using criteria tailored to AI-driven vulnerability research (dataset transparency, leakage control, evaluation rigor, reproducibility, and external validation), and we used these quality results to contextualize the synthesis. Our critical evaluation indicates that this area remains at an early stage and is characterized by significant gaps. The absence of standard benchmarks, limited generalizability of the models to various domains, and lack of adversarial testing are the obstacles that prevent adoption of these methods in real-world scenarios. Furthermore, the research suggests that the black-box nature of most models poses a serious problem in terms of trust. Thus, XAI is quite pertinent in this context. This paper serves as a thorough guide for the evolution of AI-driven vulnerability management and indicates that next-generation AI systems should not only be more accurate but also transparent, robust, and generalizable. Full article

Background/Objectives: To analyze the parent-perceived quality of life (QoL) in children with cerebral palsy (CP) and to study the relationship between sociodemographic and clinical factors and this perception, under the perspective of the International Classification of Functioning, Disability and Health (ICF). Methods: A cross-sectional study was conducted with 95 participants (ages 5–19 years) with CP. Participants’ parents were asked about sociodemographic and clinical characteristics and compiled Cerebral Palsy Quality of Life (CP-QoL) and Pediatric Disability Inventory-Computer Adaptive Test (PEDI-CAT). Participants were assessed and classified into the following functional domains: gross motor function (GMFM-88, GMFCS), manual ability (MACS), eating and drinking abilities (EDACS), and communication function (CFCS). Correlations between CP-QoL domains and variables were investigated using Spearman’s correlation coefficient and multivariate predictive models were used to investigate the variables predicting CP-QoL scores for each domain. Results: A total of 95 children with a mean age of 12.4 ± 3.5 years (range 5–19 years) were included. Participants demonstrated moderate-high GMFM-88 level (228.8 ± 44.7) and high functional performance across PEDI-CAT domains: Activity (57.2 ± 5.1), Mobility (63.1 ± 5.6), and Social/Cognitive (70.2 ± 4.3). Parent-perceived QoL was significantly higher when children did not require AFOs, botulinum toxin, or recent hospitalizations, and lower among children who attended physical therapy >2 h/week. Moderate correlations were consistently found between the ‘Feelings about Functioning’ domain and functional variables, being positive for GMFM-88 and all PEDI-CAT domains, and negative for GMFCS, MACS, EDACS and CFCS. That domain of CP-QoL was best explained by the regression model (R² = 0.619, p < 0.001), with the combination of three variables: GMFM-88, PEDI-CAT Activity and PEDI-CAT Social/Cognitive. Among them, PEDI-CAT Activity was the strongest predictor (β = 0.1436). Conclusions: In children with CP, to enhance family well-being, interventions should prioritize social participation and carefully balance the intensity and frequency of therapy against family burden and daily life demands, as QoL is primarily driven by manual ability and functional performance. Full article

Lugana DOC is an Italian PDO white wine from the south coast of Lake Garda, produced with ‘Trebbiano di Lugana’ grapes (synonym of ‘Trebbiano di Soave’ and ‘Verdicchio bianco’) and characterised by tropical fruit, citrus, and balsamic notes due to the presence of volatile thiols and methyl salicylate, respectively. To deepen the knowledge of the aromatic profile of these wines and to study how they evolve during aging, the chemical and sensory profile of 12 Lugana DOC wines from the same winery in different consecutive vintages (2008–2019, evaluated in 2023) were analysed. Sensory analysis data were subjected to hierarchical cluster analysis, identifying four main groups that appropriately distinguished the aged wines from the young wines. Younger wines had a greenish yellow colour and were characterised mainly by fruity, citrus, floral, and flinty notes related to thiol compound contribution. Older wines, divided into three different clusters, shifted colour towards orange and were characterised by descriptors related to oxidative aging (e.g., cooked fruit, marsala-like, figs, nuts) or retained pleasant varietal and evolutionary notes (e.g., citrus, white flowers, flint, vanilla) confirmed by their chemical markers detected by GC-MS and LC-MS. Full article

This paper presents a robust data-driven methodology for estimating transmission loss coefficients (B-coefficients) in power systems using linear and quadratic programming (LP and QP), both of which belong to the family of convex optimization models. The first model employs a linear objective function with linear constraints, ensuring computational efficiency for simpler scenarios. The second model utilizes a quadratic objective function, also under linear constraints, to better capture more complex nonlinear relationships. By framing the estimation problem as a parameter identification task, both methodologies minimize the cost functions that quantify the mismatch between measured and modeled power losses. By considering a broad range of operational scenarios, our approach effectively captures the stochastic behavior inherent in power system operations. The effectiveness of both the LP and QP models is validated in terms of their ability to accurately extract physically meaningful B-coefficients from diverse simulation datasets. This study underscores the potential of integrating linear and quadratic programming as powerful and scalable tools for data-driven parameter estimation in modern power systems, especially in environments characterized by uncertainty or incomplete information. Full article

Peptide arrays represent a powerful tool for investigating a wide application field for biomedical questions. This review summarizes recent applications of peptide chips in oncology, with a focus on tumor microenvironment, metastasis, and drug mechanism of action for various cancer types. These high-throughput platforms enable the simultaneous screening of thousands of peptides. We report on recent achievements in peptide array technology for tumor microenvironments, an enhanced ability to decipher complex cancer-related signaling pathways, and characterization of cell-adhesion-mediating peptides. Furthermore, we highlight the applications in high-throughput drug screenings for development of immune therapies, e.g., the development of novel neoantigen therapies of glioblastoma. Moreover, epigenetic profiling using peptide arrays has uncovered new therapeutic targets across various cancer types with clinical impact. In conclusion, we discuss artificial intelligence-driven peptide array analysis as a tool to determine tumor origin and metastatic state, potentially transforming diagnostic approaches. These innovations promise to accelerate the development of precision cancer approaches. Full article

Agricultural soils are the largest anthropogenic source of nitrous oxide (N₂O), primarily due to excessive nitrogen (N) fertilization and inefficient N management. Mitigating N₂O emissions from croplands without compromising productivity is therefore a major global challenge for climate and environmental sustainability. A three-year split-plot field experiment was conducted in an arid maize production region of northwestern China to examine how green manure intercropping combined with reduced chemical N input regulates N₂O emissions and soil N residues. The main plots comprised maize monoculture (M), maize intercropped with common vetch (M/V), and maize intercropped with rape (M/R), while subplots consisted of local conventional N application (N1: 360 kg N ha⁻¹) and a 25% reduced rate (N2: 270 kg N ha⁻¹). Results indicated that intercropping with green manure can offset the reduction in maize grain yield caused by a 25% decrease in N supply. Green manure intercropping significantly decreased cumulative N₂O emissions compared with monoculture maize, and the mitigation effect was further strengthened under reduced N input. The M/V system under reduced N input exhibited the strongest mitigation effect, reducing N₂O emissions per unit of grain yield by 9.2–11.5% compared with the M/R system. This reduction was driven by the ability of M/V to stabilize soil mineral N availability. Notably, the independent maize growth stage contributed 52.6–66.9% of total seasonal N₂O emissions, emphasizing it as a critical period for emission mitigation. Overall, integrating green manure intercropping with reduced chemical N input effectively mitigates N₂O emissions while maintaining maize productivity in arid regions, providing a practical strategy for sustainable and environmentally responsible agricultural intensification. Full article

Radio-frequency interference (RFI) severely degrades the imaging quality of synthetic aperture radar (SAR), especially when the interference energy is strongly coupled with ground backscatter in both the time and frequency domains. Existing algorithms typically rely on energy contrast or component decomposition in transform domains, which limits their ability to cleanly separate complex RFI from high-power echoes. Exploiting the fact that kurtosis is insensitive to ground clutter and background noise, this paper proposes an interference suppression network based on the temporal kurtosis guidance mechanism. Specifically, a statistical prior vector capturing the non-Gaussian characteristics of RFI is constructed using kurtosis in the time–frequency domain and is integrated into a multi-scale attention mechanism, allowing the network to more effectively concentrate on interfered regions. Meanwhile, a systematic framework is established for the quantitative assessment of phase fidelity in the reconstruction of complex-valued SAR echoes. On this basis, by exploiting the strong generalization capability and high processing efficiency of data-driven models, the proposed network achieves improved RFI separation and enhanced reconstruction accuracy of underlying scene features. Ablation experiments validated that the design of a kurtosis-guided module can reduce the mean square error (MSE) loss by 14.87% compared to the basic model. Furthermore, regarding the phase fidelity, the correlation coefficient between the suppressed signal and the original true signal reached 0.99. Finally, GF-3 satellite data are used to further demonstrate the effectiveness and practicality of the proposed method. Full article

The establishment of a sustained human presence on the Moon is critically dependent on the ability to utilize local resources, primarily the production of oxygen for life support and propellant. The ROXY (Regolith to Oxygen and metals conversion) process is a molten salt electrolysis technology designed for this purpose. This paper presents an economic analysis of a ROXY pilot plant capable of producing over one ton of oxygen per year. We evaluate the economic viability by analyzing development, transportation, and operational costs against the potential revenue from selling oxygen and metals within a nascent lunar economy. A key aspect of this analysis is the perspective of an early customer in habitation life support systems preceding that of much higher propellant production demand. The analysis contextualizes this paradigm by recognizing that the primary economic driver for oxygen production is the larger future market for propellant; however, early life support demand may incentivize a paradigm-shift from Earth-based consumable resupply. Scenarios based on varying transportation costs and development timelines are evaluated to determine the internal rate of return (IRR) and time to break even (TTBE). The results indicate that the ROXY pilot plant is economically viable, particularly in near-term scenarios with higher transportation costs, achieving a positive IRR of up to 47.4% when both oxygen and metals are sold. The analysis identifies facility mass, driven by the robotics subsystem, as the primary factor for future cost-reduction efforts, concluding that ROXY is a technically and economically sound pathway toward sustainable lunar operations. Full article