Search Results (938)

Contextual multi-armed bandits (CMABs) are vital for sequential decision-making in areas such as recommendation systems, clinical trials, and finance. We propose a simulation framework integrating Gaussian Process (GP)-based CMABs with vine copulas to model dependent contexts and GARCH processes to capture reward volatility. Rewards are generated via copula-transformed Beta distributions to reflect complex joint dependencies and skewness. We evaluate four policies—ensemble, Epsilon-greedy, Thompson, and Upper Confidence Bound (UCB)—over 10,000 replications, assessing cumulative regret, observed reward, and cumulative reward. While Thompson sampling and LLM-guided policies consistently minimize regret and maximize rewards under varied reward distributions, Epsilon-greedy shows instability, and UCB exhibits moderate performance. Enhancing the ensemble with copula features, GP models, and dynamic policy selection driven by a large language model (LLM) yields superior adaptability and performance. Our results highlight the effectiveness of combining structured probabilistic models with LLM-based guidance for robust, adaptive decision-making in skewed, high-variance environments. Full article

This study aims to clarify how different types of motivation influence employee retention by identifying the distinct roles of intrinsic and extrinsic factors in shaping job satisfaction, particularly under varying levels of urban stress and generational identity. Drawing on Herzberg’s Two-Factor Theory and Self-Determination Theory, we distinguish between intrinsic drivers (e.g., autonomy, achievement) and extrinsic hygiene factors (e.g., pay, stability). Using survey data from 356 Chinese employees and applying PLS-SEM with a moderated mediation design, we investigate how urbanization and Generation Z moderate these relationships. Results show that intrinsic motivation enhances satisfaction, especially in urban settings, while extrinsic factors negatively affect satisfaction when perceived as insufficient or unfair. Job satisfaction mediates the relationship between motivation and retention, although this effect is weaker among Generation Z employees. These findings refine motivational theories by demonstrating how environmental pressure and generational values jointly shape employee attitudes. The study contributes a context-sensitive framework for understanding retention by integrating individual motivation with macro-level moderators, offering practical implications for managing diverse and urbanizing labor markets. Full article

Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in the Ulsan petrochemical complex, South Korea. A vehicle-mounted Me-DOAS system conducted monthly measurements throughout 2024, capturing data during four daily intervals to evaluate diurnal variation. Routes included perimeter loops and grid-based transects within core industrial zones. The highest benzene concentrations were observed in February (mean: 64.28 ± 194.69 µg/m³; geometric mean: 5.13 µg/m³), with exceedances of the national annual standard (5 µg/m³) in several months. Notably, nighttime and early morning sessions showed elevated levels, suggesting contributions from nocturnal operations and meteorological conditions such as atmospheric inversion. A total of 179 exceedances (≥30 µg/m³) were identified, predominantly in zones with benzene-handling activities. Correlation analysis revealed a significant relationship between high concentrations and specific emission sources. These results demonstrate the utility of Me-DOAS in capturing spatiotemporal emission dynamics and support its application in exposure risk assessment and industrial emission control. The findings provide a robust framework for targeted management strategies and call for integration with source apportionment and dispersion modeling tools. Full article

Engineering of Cupriavidus necator could enable the production of various polyhydroxyalkanoates (PHAs); particularly, poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (P(3HB-co-3HH)), a biopolymer with enhanced mechanical and thermal properties compared to poly(3-hydroxybutyrate) (PHB), can be efficiently produced from vegetable oils. However, challenges remain in the recovery process, particularly in removing residual oil and minimizing degradation of the polymer structure during extraction steps. This study investigated the effects of ethanol-based defatting on the recovery and polymeric properties of P(3HB-co-3HH). The proposed method involves the addition of ethanol to the cell broth to effectively remove residual oil. Ethanol improved the separation of microbial cells from the broth, thereby streamlining the downstream recovery process. Using ethanol in the washing step increased the recovery yield and purity to 95.7% and 83.4%, respectively (compared to 87.4% and 76.2% for distilled water washing), representing improvements of 8.3% and 7.2%. Ethanol washing also resulted in a 19% higher molecular weight compared to water washing, indicating reduced polymer degradation. In terms of physical properties, the elongation at break showed a significant difference: 241.9 ± 27.0% with ethanol washing compared to water (177.7 ± 10.3%), indicating ethanol washing retains flexibility. Overall, an ethanol washing step for defatting could simplify the recovery steps, increase yield and purity, and retain mechanical properties, especially for P(3HB-co-3HH) from oils. Full article

Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. The empirical copula transformation, a rank-based nonparametric approach, preprocesses input covariates to better represent the underlying joint distributions before modeling. We compare this method with a baseline CNN-LSTM model lacking copula preprocessing and a nonparametric tree-based approach, the Causal Forest, grounded in generalized random forests for HTE estimation. Our framework accommodates continuous, count, and censored survival outcomes simultaneously through a multitask learning setup with customized loss functions, including Cox partial likelihood for survival data. We evaluate model performance under varying treatment perturbation rates via extensive simulation studies, demonstrating that the Empirical Copula CNN-LSTM achieves superior accuracy and robustness in average treatment effect (ATE) and conditional average treatment effect (CATE) estimation. These results highlight the potential of copula-based deep learning models for causal inference in complex multivariate settings, offering valuable insights for personalized treatment strategies. Full article

Trimethylamine N-oxide (TMAO) is known to increase in human cardiovascular, metabolic, and renal diseases. In human medicine, TMAO has recently been utilized as a diagnostic and prognostic biomarker for renal dysfunction, and research is ongoing regarding its potential as a therapeutic target. This study aimed to evaluate the diagnostic and prognostic potential of TMAO as a supportive biomarker in dogs with chronic kidney disease (CKD). To assess its diagnostic utility, TMAO concentrations were compared between a CKD group (n = 32) and a healthy control group (n = 32). In addition, patients with CKD were subdivided into stages 2 (n = 12), 3 (n = 11), and 4 (n = 9) and compared individually with the healthy controls. For prognostic evaluation, the CKD group was monitored over six months, and the TMAO levels were compared between survivors (n = 18) and non-survivors (n = 14). The TMAO concentrations showed a highly significant difference between patients with CKD and healthy controls (p < 0.0001). Patients with each different CKD stage exhibited statistically significant differences compared with the healthy controls (p < 0.05). Furthermore, the median TMAO levels tended to increase with advancing CKD stage; however, the differences among stages were not statistically significant. In addition, within the CKD group, TMAO concentrations were significantly higher in non-survivors than in survivors at the six-month follow-up (p = 0.0142). This pilot study highlights the potential of TMAO as a supportive renal biomarker for diagnostic and prognostic evaluation in canine CKD. Full article

This study evaluated the effects of early-life creep feeding with a high-protein, high-energy diet on growth performance, ruminal fermentation, and gut microbiota in Hanwoo calves (n = 10). Calves were assigned to control or treatment groups from birth to 6 months of age. No significant differences were observed in body weight, average daily gain (ADG), or feed conversion ratio (FCR), but ADG and dry matter intake (DMI) tended to be higher in the treatment group. Ruminal pH, NH₃-N, and volatile fatty acid (VFA) concentrations showed no significant differences. Fecal VFA profiles exhibited numerical trends suggesting higher propionate at 3 months and lower acetate, butyrate, and total VFA at 6 months in the treatment group, potentially reflecting altered substrate availability or absorption capacity, though these mechanisms were not directly measured. Microbiota analysis indicated stable ruminal alpha diversity, with numerical increases in fecal Bacteroidetes and genera such as Fournierella and Flavonifractor in the treatment group. These results suggest that early creep feeding with high-nutrition diets can support intake and promote potential shifts in hindgut microbiota composition without compromising overall microbial stability. Further research with larger sample sizes is needed to confirm these trends and assess long-term impacts on calf health and productivity. Full article

Background/Objectives: Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. This study validates AiTiALVSD, our previously developed artificial intelligence (AI)-enabled ECG Software as a Medical Device, for its accuracy, transparency, and robustness in detecting LVSD. Methods: This retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. To enhance model transparency, the study employed Testing with Concept Activation Vectors (TCAV), clustering analysis, and robustness testing against ECG noise and lead reversals. Results: The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. Conclusions: AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI-ECG, setting a new benchmark in cardiac care. Full article

This study examines the factors that influence individuals’ intentions to create socially oriented ventures, emphasizing the joint role of social and technical systems. Grounded in Socio-Technical Systems Theory, the research investigates how perceptions of social legitimacy and technological infrastructure shape social entrepreneurial intention (SEI) and how these effects are conditioned by generational cohort, familiarity and intent to use artificial intelligence (AI), and social proximity to entrepreneurial peers. Based on survey data from 388 respondents in China who expressed interest in both entrepreneurship and social problem-solving, the study applies a conditional process structural equation model to capture the complex interplay between external systems and individual-level readiness. The results show that both social and technical systems significantly and positively influence SEI, particularly among younger generations (Millennials and Generation Z). Furthermore, AI familiarity and social proximity operate as moderated mediators, differentially transmitting and shaping systemic influences on SEI. These findings advance the theoretical understanding of socio-technical determinants of social entrepreneurship and offer practical insights for fostering inclusive, generationally responsive entrepreneurial ecosystems. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection. Full article

The clean-label movement has markedly increased consumer demand for meat products free from synthetic additives, such as sodium nitrite, ascorbate, and phosphate. This review summarizes strategies to replace these additives with natural alternatives while preserving the functional and quality properties of traditionally cured meats. Nitrite replacement commonly employs nitrate-rich vegetables, alongside nitrate-reducing starter cultures or pre-converted nitrite powders for adequate nitric oxide production and meat pigment stabilization. Ascorbate substitutes include vitamin C-rich materials and polyphenol-based antioxidants from green tea and rosemary, supporting nitrite reduction and contributing to meat pigment and oxidative stability. To compensate for phosphate functions, natural substitutes such as hydrocolloids, dietary fibers, protein isolates, and calcium powders from eggshells or oyster shells have shown partial success in restoring water-holding capacity, pH buffering, and textural integrity. In addition, non-thermal processing technologies, such as high-pressure processing, ultrasound, and cold plasma are explored as complementary strategies to enhance the efficacy of natural ingredients and support industrial scalability. However, challenges persist regarding ingredient variability, dose-dependent effects, and consistency in functional performance. Future research should focus on synergistic ingredient combinations, formulation standardization, and scalable application in industrial production to ensure the production of high-quality clean-label meat products. Full article

Background/Objectives: Sarcopenia is an age-related disease resulting in muscle mass deterioration and declining strength and functional ability. Muscle protein degradation pathways are activated through the ubiquitin–proteasome system, which is integral to the pathogenesis of sarcopenia. This study examined the capability of Lactobacillus plantarum beLP1 as a postbiotic ingredient of kimchi that prevents sarcopenia. Methods: We evaluated cell viability and measured diameters in a C2C12 myotube damage model and muscle volume, muscle weight, muscle strength, and the expression of muscle degradation proteins MuRF1 and Atrogin-1 in dexamethasone-induced sarcopenic model rats using a heat-killed beLP1 strain. Results: beLP1 had no cytotoxic effects on C2C12 and prevented dexamethasone-induced cellular damage, suggesting its role in muscle protein degradation pathways. beLP1 treatment significantly prevented the dexamethasone-induced reduction in myotube diameter. In a dexamethasone-induced sarcopenic rat model, oral beLP1 significantly mitigated muscle mass decline and prevented grip strength reduction. Microcomputed tomography demonstrated that beLP1 reduced dexamethasone-induced muscle volume loss. beLP1 treatment reduced Atrogin-1 and Muscle RING-finger protein-1 (MuRF1) and the transcription factor Forkhead box O3 alpha (FoxO3α), which triggers muscle protein breakdown. beLP1 exerts protective effects by inhibiting the ubiquitin-proteasome system and regulating FoxO3α signaling. It increased AKT (Ser473) phosphorylation, which affected muscle protein synthesis, degradation, and cell survival, suggesting its potential to prevent sarcopenia. Conclusions: Heat-killed Lactobacillus plantarum beLP1 alleviates muscle mass wasting and weakness in a dexamethasone-induced sarcopenia model by regulating muscle protein degradation pathways and signaling molecules. Thus, postbiotics may be functional ingredients in sarcopenia prevention. Full article

Metabolic dysfunction-associated steatohepatitis (MASH), previously referred to as non-alcoholic steatohepatitis (NASH), which is a progressive non-alcoholic fatty liver disease, is accompanied by hepatic steatosis, inflammation, and fibrosis. Despite its increasing prevalence, available treatment options for MASH are limited. Here, we investigated the protective effects of the Distylium racemosum ethyl acetate fraction (DRE) using MASH models and explored its key physiologically active components. Palmitic acid (PA)-induced AML12 hepatocytes and high-fat methionine- and choline-deficient-fed C57BL/6 mice were used as MASH models. Lipid accumulation was evaluated via triglyceride measurement, oil red O staining, and histological analysis. Lipid accumulation, inflammation, and fibrosis-associated gene expression were evaluated via real-time polymerase chain reaction. The physiologically active components of DRE were identified via high-performance liquid chromatography. Lipid accumulation and triglyceride levels were significantly reduced in PA-treated AML12 cells following DRE treatment. Additionally, DRE inhibited the expression of genes involved in lipogenesis (FAS and SREBP1c), inflammation (CD68, IL-6, and MCP-1), and fibrosis (COL1A1, COL1A2, and TIMP1). DRE reduced the liver weight, liver-to-body weight ratio, and hepatic steatosis in MASH model mice. It increased carnitine palmitoyltransferase-1 levels and decreased CD36 and transforming growth factor-β levels in the MASH mouse liver. High-performance liquid chromatography revealed that the extract contained rutin flavonoid family members. Overall, DRE was involved in lipid metabolism, inflammation, and fibrosis regulation, exerting potent hepatoprotective effects partly attributed to rutin and serving as a potential preventive candidate for MASH. Full article

In this study, we investigated the potential of a three-mushroom complex extract (GMK) to inhibit neuronal cell death induced by the activation of AMPA and NMDA receptors following glutamate treatment in NGF-differentiated PC12 neuronal cells. GMK significantly mitigated glutamate-induced excitotoxic neuronal apoptosis by reducing the elevated expression of BAX, a critical regulator of apoptosis, and restoring BCL2 levels. These neuroprotective effects were associated with redox regulation, as evidenced by the upregulation of SOD, CAT, and GSH levels, and the downregulation of MDA levels. Mechanistic studies further revealed that GMK effectively scavenged ROS by downregulating NOX1, NOX2, and NOX4, while upregulating NRF1, P62, NRF2, HO1, and NQO1. Additionally, in the same model, GMK treatment increased acetylcholine, choline acetyltransferase, and GABA levels while reducing acetylcholinesterase activity. These effects were also attributed to the regulation of redox balance. Furthermore, we investigated the antioxidant and anti-inflammatory mechanisms of GMK in LPS-stimulated BV2 microglia. GMK inhibited the activation of IκB and MAPK pathways, positively regulated the BCL2/BAX ratio, suppressed TXNIP activity, and upregulated NQO1 and NOX1. In conclusion, GMK improved neuronal excitotoxicity and microglial inflammation through the positive modulation of the redox regulatory system, demonstrating its potential as a natural resource for pharmaceutical applications and functional health foods. Full article

The widespread application of pesticides in agriculture has raised increasing concerns regarding their ecological impact, particularly in aquatic environments. Among these, α-cypermethrin, a highly active isomeric form of cypermethrin, has been extensively used due to its potent insecticidal efficacy and low mammalian toxicity. However, its toxicity to non-target aquatic organisms remains insufficiently understood at the metabolic level. In this study, a targeted metabolomics approach was employed to investigate the biochemical effects of α-cypermethrin in adult zebrafish. Acute toxicity was first determined to establish sublethal exposure concentrations (0.15 µg/L and 1.5 µg/L), followed by a 48 h exposure under a controlled flow-through system. GC-MS/MS-based analysis quantified 395 metabolites, and multivariate statistical models (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)) revealed clear dose-dependent metabolic alterations at two time points. Pathway analysis identified disruptions in glycolysis, glycerolipid metabolism, amino acid turnover, and glutathione pathways. Notably, glutamate depletion and associated reductions in GABA (4-Aminobutanoate) and TCA (Tricarboxylic acid) cycle intermediates suggest oxidative stress-induced metabolic bottlenecks. These results provide mechanistic insights into α-cypermethrin-induced toxicity and demonstrate the utility of metabolite-level biomarkers for environmental monitoring. This study contributes to a systems-level understanding of how sublethal pesticide exposure affects vertebrate metabolism, offering a basis for improved ecological risk assessment and pesticide regulation. Full article