Search Results (21,609)

Low temperatures limit the yield and stability of autumn-sown oats; thus, investigating cold resistance physiological responses is essential. In this study, we compared a cold-resistant variety (‘Aiwo’) and a cold-sensitive variety (‘Hewang’). ‘Aiwo’ exhibited a significantly higher overwintering survival rate (96.9%) and superior physiological traits, including elevated levels of soluble proteins, proline, putrescine, unsaturated fatty acids, and glutathione, alongside greater ATPase activity and reduced ROS levels. Exogenous putrescine application suggested a potential role of Put in alleviating lipid peroxidation. Metabolomic analysis showed that the arginine–proline and cysteine–methionine pathways were enriched among DAMs associated with ‘Aiwo’, accompanied by the accumulation of stress-protective metabolites. These metabolic changes may contribute to improved energy balance and membrane stability under low-temperature conditions. Our findings suggest that proline, putrescine, and glutathione are candidate physiological indicators associated with the cold-resistant phenotype, which may facilitate future screening of cold-resistant oat germplasm. Full article

This study investigates the relationship between Environmental, Social, and Governance (ESG) ratings and firm financial performance across a comprehensive global sample of over 10,000 companies from more than 80 countries observed in 2015–2022. Using panel data analysis, we examine how overall ESG scores and their components affect the Return on Assets (ROA), Return on Equity (ROE), and Weighted Average Cost of Capital (WACC). We employ several econometric approaches designed for panel data, including the univariate approaches; static (Pooled OLS; Fixed Effects) and a multivariate approach (Panel Vector Autoregression; PVAR) to address potential endogeneity concerns and provide robust findings. Our results revealed a complex relationship between ESG performance and financial outcomes. While OLS models generally show positive associations between ESG scores and profitability measures, Fixed Effects models indicate some negative relationships, suggesting that unobserved firm-specific factors are crucial. PVAR results highlight important dynamic interactions between ESG performance and financial metrics over time. These findings contribute to stakeholder theory by demonstrating that the financial implications of ESG performance are contingent on methodological approaches, time horizons, and specific contexts. Our research has important implications for corporate managers, investors, and policymakers seeking to understand the financial consequences of sustainability practices. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Background: Polycystic ovary syndrome (PCOS) is a frequently encountered endocrine disturbance with a still poorly defined etiology that arises in women during their reproductive years. Increased apoptosis of granulosa cells has been identified as one of the key factors contributing to abnormal follicular development. This study aimed to elucidate the role of six-transmembrane epithelial antigen of prostate 4 (STEAP4) in granulosa cell function using in vitro and in vivo models relevant to PCOS. Methods: We treated KGN cells (a human granulosa-like cell line) and C57BL/6 mice with dehydroepiandrosterone (DHEA) to establish experimental models mimicking PCOS features. STEAP4 expression was assessed by qRT–PCR, Western blot, and immunohistochemistry. Proliferative capacity and apoptotic rates were gauged with CCK-8 assays, EdU labeling, and flow cytometry. The regulatory mechanisms were investigated through immunofluorescence staining for nuclear factor erythroid–2–related factor 2 (Nrf2) nuclear translocation and immunoprecipitation assays for HIF-1α ubiquitination. Results: Exposure to androgen markedly raised both STEAP4 transcript and protein abundance in KGN cells as well as in PCOS model mice. STEAP4 knockdown resulted in increased proliferation and reduced apoptosis in DHEA–treated KGN cells. Mechanistically, STEAP4 enhanced reactive oxygen species levels, promoted Nrf2 nuclear translocation, and stabilized HIF–1α protein by reducing its ubiquitination, leading to increased TERT expression and subsequent TP53 pathway activation. In vivo, STEAP4 silencing significantly alleviated hormonal imbalances, estrous cycle disorders, and reduced oxidative stress levels in ovarian tissue of DHEA-induced PCOS–like mice. Conclusions: Taken together, evidence from these experimental models indicates that STEAP4 shapes oxidative stress and granulosa cell apoptosis by operating through the ROS–TERT–TP53 axis. The data point to a possible contribution of STEAP4 to PCOS pathogenesis and mark it as a candidate therapeutic target that merits additional clinical study. Full article

Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely risk stratification and personalized clinical management. In the present study, an interpretable machine learning framework for diabetes classification in patients with cardiovascular disease was developed using routinely available clinical, biochemical, renal, and echocardiographic parameters. A retrospective dataset consisting of 131 cardiovascular patients was included in the final analysis, comprising 65 patients with diabetes mellitus and 66 patients without diabetes. Demographic, metabolic, renal, and cardiovascular variables, including age, body mass index (BMI), glycated hemoglobin (HbA1c), glucose concentration, estimated glomerular filtration rate (eGFR), troponin level, heart rate, and left ventricular ejection fraction (EF), were included in the analysis. Multiple supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, were implemented and compared using repeated stratified cross-validation. Among the evaluated models, Random Forest demonstrated the highest classification performance, achieving a mean ROC AUC of 0.880 ± 0.050. Statistical analysis revealed significantly elevated HbA1c, glucose, and troponin levels together with reduced ejection fraction values in diabetic patients. Explainable artificial intelligence analysis using SHAP and partial dependence plots identified glucose concentration, HbA1c, age, and renal function as the dominant contributors to diabetes classification. Nonlinear relationships between metabolic and cardiovascular variables were additionally observed. The obtained findings demonstrate that interpretable machine learning approaches can provide effective discrimination between diabetic and non-diabetic cardiovascular patients while maintaining clinically meaningful interpretability. The proposed framework may contribute to future intelligent clinical decision-support systems and personalized cardiovascular risk assessment strategies. Full article

Continuous full-domain vehicle trajectories are essential for smart highway monitoring, but single-sensor roadside perception is limited by physical coverage, occlusion, and environmental sensitivity. To address continuous trajectory tracking across multiple roadside-sensing domains, this study proposes a real-time, full-domain highway trajectory tracking framework based on radar–camera fusion, improved Deep-SORT, and inverse covariance intersection. At the local perception level, a two-stage object-level and decision-level fusion model is constructed, and Deep-SORT is improved using a CIoU matching strategy and an occluded target tracking controller to enhance local multi-object tracking continuity. At the cross-domain association level, a geometry-motion consistency stepwise calibration method is developed to unify adjacent sensing domains, and a CATS-ICI trajectory stitching strategy is introduced to improve trajectory association and state smoothness during sensor handover. The proposed framework was validated on a real highway test section with roadside radar, video, and drone-based ground-truth trajectories. Experimental results show that the full local method achieves an EMOTA of 92.35%, and the reconstructed full-domain trajectories achieve a successful trajectory matching rate of 98.4% under the 452 vehicles/10 min test condition. Additional ablation experiments further verify the contributions of radar–camera fusion, CIoU, OTTC, GMCSC, CATS, and ICI. These results demonstrate that the proposed framework can provide continuous and reliable full-domain vehicle trajectories for real-world highway monitoring. Full article

This study aims to investigate the effects of heterogeneous microstructure and strain rate on the microstructural evolution and mechanical properties of ultrafine-grained (UFG) austenitic stainless steel during elevated-temperature tension. In this research, 17Cr-10Ni austenitic stainless steel was rolled to a 60% reduction in thickness at room temperature and 200 °C, followed by annealing at 1000 °C and 500 °C, respectively. The microstructural evolution of the annealed samples and high-temperature tensile specimens was characterized using optical microscopy, transmission electron microscopy, scanning electron microscopy equipped with electron backscatter diffraction, and X-ray diffraction. Results show that at room temperature, the heterogeneous twinned UFG (TW-UFG) sample, influenced by hetero-deformation-induced stress strengthening, maintains good ductility while exhibiting higher strength than the uniform UFG sample. During tensile deformation at 600 °C, grain refinement still contributes to strengthening, and the dominant deformation mechanism in the uniform UFG sample is dislocation dynamic recovery, whereas in the TW-UFG sample is detwinning combined with dynamic dislocation recovery. At low strain rates (10⁻⁴ s⁻¹), sufficient dynamic recovery and detwinning in the TW-UFG sample delay plastic instability and improve elongation. Full article

This study examines whether ESG rating disagreement is a leading indicator of corporate greenwashing and how digital transformation (DTI) moderates this relationship through disclosure and performance channels. Using 8111 firm-year observations from Chinese A-share companies (2012–2022), we employ two-way fixed-effects panel regression complemented by Bayesian-optimised machine learning models interpreted through SHAP. Aggregate rating disagreement is a strong and robust predictor of greenwashing. Channel decomposition reveals asymmetric DTI moderation: the disclosure channel amplifies greenwashing risk as digitally advanced firms expand reporting capacity to widen the gap between disclosed and actual ESG performance (bloom_DTI: β = +0.2471, p < 0.01), while the performance channel attenuates greenwashing risk as digital operational monitoring translates substantive performance into a measurable reduction (hua_DTI: β = −0.2804, p < 0.01). This pattern is robust across ownership structure, pollution intensity, and region. Machine learning analysis confirms the econometric findings and reveals nonlinear threshold effects invisible to panel regression. This asymmetric channel mechanism contributes to the ESG rating divergence literature and has implications for disclosure regulation and ESG-based investment screening. Full article

Studying the impact of extreme climate events on vegetation dynamics is crucial for maintaining ecosystem stability. Based on ERA5-Land temperature and precipitation data, as well as MOD13C2 data, this study employs Pearson correlation analysis, wavelet analysis, and the XGBoost-SHAP model to analyze the response of the Normalized Difference Vegetation Index (NDVI) in China to extreme climate variations. The findings reveal the following. (1) NDVI increased steadily at a rate of 0.021/10 yr from 2001 to 2024. Extreme temperature and precipitation indices show an increasing trend in most regions. (2) NDVI was positively correlated with most extreme temperature and precipitation indices, and showed a significant negative correlation exclusively with Frost Days FDO (Frost Days) and CDD (Consecutive Dry Days). (3) R25 (Number of Heavy Precipitation Days) and SDII (Simple daily intensity index) are the primary drivers of nationwide NDVI changes, with contribution rates of 31.6% and 12.5%. Extreme climate indices can significantly affect vegetation growth when surpassing certain thresholds. For instance, the thresholds for R25 and SDII are 1.85 days and 4.35 mm/day. This study provides a scientific basis for understanding and managing vegetation responses to increasing climate extremes. Full article

Despite growing policy support for active travel, the fatality rate of vulnerable road users has remained persistently high in recent years, while the emergence of autonomous vehicles has further increased the complexity of mixed traffic environments. Interactions between cyclists and motorized vehicles are a major contributor to these fatalities, highlighting the urgent need for effective cyclist protection strategies. As one of the most widely adopted active transport modes, cycling safety cannot be assessed solely through crash statistics; understanding cyclists’ perceived safety is equally critical, as it reflects how infrastructure design and dynamic traffic conditions influence cycling behavior. In this study, we propose a cyclist safety perception framework that combines vision–language models with interpretable machine learning to analyze perceived safety in mixed traffic scenarios. A vision–language model is employed to generate semantic descriptions of traffic scenes, while an Explainable Boosting Machine quantifies both individual and interactive contributions of traffic-related features. By integrating visual information with road attributes extracted from OpenStreetMap, the proposed framework achieves a binary safety classification accuracy of 71% and a mean absolute error of 1.01 on a safety score scale ranging from 1 to 9. The results demonstrate the potential of combining multimodal perception and explainable models to support cyclist-centered safety assessment and inform sustainable and intelligent transportation system design. More specifically, the results show that protected cycling infrastructure is the most significant factor in improving perceived safety, whereas road construction has the opposite effect. Full article

Background: Respiratory failure (RF) is a frequently fatal complication of chronic liver disease (CLD), yet population-level data on RF-related mortality trends among adults with CLD are lacking. This study characterized temporal trends and demographic disparities in RF-related mortality among U.S. adults with CLD from 1999 to 2024. Methods: Death certificate data were obtained from the CDC WONDER database for adults aged ≥25 years with both RF (ICD-10: J96) and CLD (ICD-10: K70–K76) listed as an underlying or contributing cause of death. Age-adjusted mortality rates (AAMRs) per 100,000 were calculated using the 2000 U.S. standard population. Joinpoint regression identified temporal inflection points and annual percentage change (APC). Results: Among 241,075 deaths, the overall AAMR increased 3.2-fold from 2.237 (1999) to 7.162 (2021) per 100,000, then declined to 6.132 by 2024. Joinpoint analysis identified four segments: moderate increase (1999–2006; APC +2.40%), accelerated increase (2006–2018; APC +5.37%), late acceleration period (2018–2021; APC +13.10%), and post-pandemic decline (2021–2024; APC −4.32%; all p < 0.001). The 2024 AAMR remained 174.2% above baseline. The male-to-female rate ratio narrowed from 2.02 to 1.50, with females showing steeper acceleration (+14.38% vs. +12.36%). American Indian or Alaska Native individuals had the highest AAMRs and the most dramatic surge (APC +26.90%). Rural areas surpassed urban AAMRs by 2020, with steeper post-2007 acceleration (+8.74% vs. +5.51%). The Western U.S. consistently had the highest regional rates. Younger adults aged 25–34 and 35–44 showed 2.96-fold and 2.37-fold increases in crude mortality rates, respectively. Approximately 80% of deaths occurred in inpatient settings. Conclusions: RF-related mortality among U.S. adults with CLD increased more than threefold from 1999 to 2021, with a dramatic surge followed by incomplete decline. Persistent disparities by sex, race/ethnicity, urbanization, and region highlight the need for targeted interventions, including expanded screening for alcohol-associated and metabolic liver disease and improved access to hepatology services in underserved communities. Full article

Unconventional gas-well production is jointly controlled by fracture conductivity, stimulated-region supply, matrix replenishment, boundary propagation, and low-pressure fluid-property changes. In practice, RTA diagnostic curves are often affected by variable operating schedules, pressure-measurement errors, and production disturbances, making flow-stage boundaries difficult to define consistently. To reduce the subjectivity of manual interpretation and to capture stage evolution rather than whole-well classes, an ordered flow-state identification method based on a five-region analytical model and RTA sliding-window features is developed. A fully random, large-sample production-response library is generated with the five-region model. Each well production curve is divided into local time windows, from which dynamic features, including RNP, material-balance time, local slopes, pseudopressure derivatives, and normalized cumulative gas production are extracted. K-means clustering is then used to identify local states, which are reordered by material-balance time to form an ordered S1–S5 sequence. Results from 10,000 synthetic wells yielded 689,394 RTA windows, an inter-cluster separation of 1.8924, a stage-regression rate of 0.0238, and an average of 4.24 states per well. S1–S5 represent early fracture–stimulated-region response, stimulated-region supply development, matrix composite supply transition, enhanced boundary/control-volume effects, and late low-pressure property response, respectively. Application to Well M1 shows that S4 contributes the most gas (37.83%), followed by S5 (23.47%), indicating dominant mid-to-late effective supply and low-pressure long-tail production. The method converts empirical flow-regime division into reproducible and comparable window-state identification results, supporting stage diagnosis and production-strategy adjustment for unconventional gas wells. Full article

Glioblastoma multiforme (GBM), isocitrate dehydrogenase (IDH)-wildtype, is the most aggressive primary malignant tumor of the central nervous system, characterized by poor prognosis and high recurrence rates despite standard multimodal treatment. This study investigates the molecular response of glioblastoma cells to 5-aminolevulinic acid (5-ALA)-based photodynamic therapy (PDT), focusing on gene expression changes associated with apoptosis, ferroptosis, and oxidative stress. Human glioblastoma T98G cells were treated with 5-ALA followed by light irradiation, and gene expression was analyzed using RT-qPCR. PDT induced moderate upregulation of pro-apoptotic genes (BAX, CASP3, FAS) alongside increased expression of the anti-apoptotic gene BCL2, indicating simultaneous activation of cell death and survival pathways. Ferroptosis-related genes showed mixed responses, with slight upregulation of ACSL4 and downregulation of GPX4, suggesting increased susceptibility to lipid peroxidation. The most significant change was observed in GCH1 expression, reflecting activation of oxidative stress response mechanisms. However, none of the observed changes reached statistical significance, likely due to the limited sample size. These findings demonstrate that PDT induces a complex and dual biological response in glioblastoma cells, involving both cytotoxic and adaptive mechanisms. This may limit therapeutic efficacy and contribute to treatment resistance. The results support the rationale for combining PDT with targeted molecular therapies aimed at inhibiting antioxidant defenses and anti-apoptotic pathways. Additionally, personalized therapeutic strategies based on tumor molecular profiles may enhance treatment outcomes. Further studies with larger sample sizes and functional validation are required to confirm these preliminary observations. Full article

Drought represents one of the major constraints limiting maize productivity in southeastern Europe, particularly under non-irrigated conditions. This study evaluated the effectiveness of foliar biostimulants derived from animal collagen and keratin hydrolysates in mitigating drought stress and improving maize performance in southern Romania during a six-year field experiment (2020–2025). During the screening phase (2020–2022), four formulations (FM1, FM2, KC, and K2) were applied at two rates (5 and 10 L ha⁻¹) and compared with an untreated control. Significant effects of biostimulant formulation and dose were identified for plant height and grain yield (p < 0.001). Duncan’s multiple range test showed that K2 applied at 10 L ha⁻¹ achieved the highest mean grain yield (87.71 q ha⁻¹), significantly exceeding the untreated control (70.94 q ha⁻¹). Based on these results, K2 was selected for long-term validation during 2023–2025 and subsequently evaluated across the entire six-year experimental period. Mean grain yield increased from 52.06 q ha⁻¹ in the untreated control to 58.74 and 64.91 q ha⁻¹ following K2 application at 5 and 10 L ha⁻¹, respectively. Yield improvements were particularly pronounced during years characterized by severe precipitation deficits, when relative yield increases reached up to 41.9%. Economic analysis demonstrated positive net returns in all experimental years, with average profits of 108.6 EUR ha⁻¹ and 206.9 EUR ha⁻¹ for the 5 and 10 L ha⁻¹ application rates, respectively. The results demonstrate that keratin-based biostimulants derived from industrial by-products can improve maize productivity, enhance drought resilience, and contribute to circular-economy approaches in sustainable agriculture. Full article

High-frequency ultrasound-assisted activation of peracetic acid (PAA) was investigated as a tunable advanced oxidation process for the removal of organic pollutants from water. Sunset Yellow FCF (SSY), a representative anionic azo dye, was used as a probe contaminant in a 425 kHz sonoreactor to clarify the roles of PAA speciation, acoustic cavitation, dissolved gases, oxidant dose, acoustic power, and initial pH. UV spectroscopic analysis showed that PAA exhibits pH-dependent far-UV absorbance associated with acid-base speciation and peroxide equilibria, while ultrasonication promoted simultaneous PAA activation and H₂O₂ accumulation. Compared with PAA alone and ultrasound alone, the combined US/PAA process markedly enhanced SSY decolorization. Under natural conditions, 5 mg/L SSY and 5 mM PAA were completely decolorized within 210 min, with an initial rate of 0.116 mg/L·min, compared with 0.078 and 0.0086 mg/L·min for ultrasound and PAA alone, respectively. The corresponding synergy ratio and synergy index were 1.5 and 1.34. The process exhibited tunable reaction-pathway control, with two favorable pH windows: a strongly acidic region, where interfacial HO^•-driven sonochemistry and PAA stability are favored, and a mildly alkaline region, where PAA deprotonation promotes peracetate-driven acyl/peroxyl radical-chain propagation. Oxygen saturation improved performance, whereas CO₂ suppressed cavitation-driven activation. Increasing PAA concentration and acoustic power enhanced removal up to practical limits, beyond which radical scavenging and diminishing sonochemical returns became evident. Beyond demonstrating enhanced decolorization, this study distinguishes US/PAA from previously reported UV/PAA, transition-metal/PAA, and ultrasound-only systems by showing how 425 kHz cavitation converts PAA into a tunable hybrid HO^•/acyl–peroxyl radical network. The main contribution is a mechanistic operating map that links PAA speciation, sonochemical peroxide accumulation, dissolved gas chemistry, acoustic power, oxidant dose, and pH to pollutant-removal performance, thereby defining practical windows for sono-activated PAA treatment of anionic dyes and related recalcitrant contaminants. Full article