Search Results (822)

1,3-butadiene (BD) is a volatile organic pollutant. Upon inhalation, it is metabolically activated to reactive epoxides which alkylate genomic DNA and form potentially mutagenic monoadducts and DNA–DNA crosslinks including N7-(1-hydroxyl-3-buten-1-yl)guanine (EB-GII) and 1,4-bis-(guan-7-yl)-2,3-butanediol (bis-N7G-BD). While metabolic activation resulting in mutagenicity is a well-established mode of action for 1,3-butadiene, characterization of the extent of inter-individual variability in response to BD exposure is a gap in our knowledge. Previous studies showed that population-wide mouse models can be used to evaluate variability in 1,3-butadiene DNA adducts; therefore, we hypothesized that this approach can be used to also study variability in the formation and loss of BD DNA adducts across tissues and between sexes. To test this hypothesis, female and male mice from five genetically diverse Collaborative Cross (CC) strains were exposed to filtered air or 1,3-butadiene (600 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Some animals were kept for two additional weeks after exposure to study DNA adduct persistence. EB-GII and bis-N7G-BD adducts were quantified in liver, lungs and kidney using established isotope dilution ESI-MS/MS methods. We observed strain- and sex-specific effects on both the accumulation and loss of both DNA adducts, indicating that both factors play important roles in the mutagenicity of 1,3-butadiene. In addition, we quantified the intra-species variability for each adduct and found that for most tissues/adducts, variability values across strains were modest compared to default uncertainty factors. Full article

Lithium-ion batteries are widely used in energy storage and power systems due to their high energy density, long cycle life, and stability. Accurate prediction of the state of health (SOH) of batteries is critical to ensuring their safe and reliable operation. However, the prediction task remains challenging due to various complex factors. This paper proposes a hybrid TCN–Transformer–BiLSTM prediction model for battery SOH estimation. The model is first validated using the NASA public dataset, followed by further verification with dynamic operating condition simulation experimental data. Health features correlated with SOH are identified through Pearson analysis, and comparisons are conducted with existing LSTM, GRU, and BiLSTM methods. Experimental results demonstrate that the proposed model achieves outstanding performance across multiple datasets, with root mean square error (RMSE) values consistently below 2% and even below 1% in specific cases. Furthermore, the model maintains high prediction accuracy even when trained with only 50% of the data. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

The inclusion of meat quality traits in breeding programs is a promising strategy to improve beef by selecting animals based on both growth and meat quality. This study aimed to estimate genetic parameters for average daily gain (ADG) and Warner–Bratzler shear force (WBSF), as well as to perform genome-wide association studies (GWAS) to identify genomic regions and transcription factor (TF) binding sites associated with both traits in Nelore cattle. Genetic parameters were estimated using a bi-trait Bayesian model, and GWAS identified key SNPs explaining over 1% of variance in genomic estimated breeding values. Candidate genes near these SNPs were annotated, TF binding sites predicted, and gene–TF networks constructed. Genetic estimates indicated moderate heritability for ADG, low heritability for WBSF, and a small negative genetic correlation between traits. Genomic regions contained 116 and 151 candidate genes for ADG and WBSF, respectively, with 35 shared between traits. Functional analyses highlighted MYBPC1 and PENK for WBSF, and GHRS and NPY for ADG. TF analysis identified 25 TFs, with 3 key ones highlighted. Gene–TF networks revealed candidates including CAPN1 and LTBP3 for WBSF, and CARM1 and GH1 for ADG. Shared candidate genes identified in the combined network provide valuable insights into the genetic architecture of growth and tenderness in Nelore cattle. Full article

Urban soundscapes are increasingly recognized as fundamental for both ecological integrity and human well-being, yet the complex interplay between the vegetation structure, seasonal dynamics, and microclimatic factors in shaping these soundscapes remains poorly understood. This study tests the hypothesis that vegetation structure and seasonally driven biological activity mediate the balance and the quality of the urban acoustic environment. We investigated seasonal and spatial variations in five acoustic indices (NDSI, ACI, AEI, ADI, and BI) within a historical urban garden in Castelfranco Veneto, Italy. Using linear mixed-effects models, we analyzed the effects of season, microclimatic variables, and vegetation characteristics on soundscape composition. Non-parametric tests were used to assess spatial differences in vegetation metrics. Results revealed strong seasonal patterns, with spring showing increased NDSI (+0.17), ADI (+0.22), and BI (+1.15) values relative to winter, likely reflecting bird breeding phenology and enhanced biological productivity. Among microclimatic predictors, temperature (p < 0.001), humidity (p = 0.014), and solar radiation (p = 0.002) showed significant relationships with acoustic indices, confirming their influence on both animal behaviour and sound propagation. Spatial analyses showed significant differences in acoustic patterns across points (Kruskal–Wallis p < 0.01), with vegetation metrics such as tree density and evergreen proportion correlating with elevated biophonic activity. Although the canopy height model did not emerge as a significant predictor in the models, the observed spatial heterogeneity supports the role of vegetation in shaping urban sound environments. By integrating ecoacoustic indices, LiDAR-derived vegetation data, and microclimatic parameters, this study offers novel insights into how vegetational components should be considered to manage urban green areas to support biodiversity and foster acoustically restorative environments, advancing the evidence base for sound-informed urban planning. Full article

Background/Objectives: The execution of clinical trials is increasingly constrained by operational complexity, regulatory requirements, and variability in site performance. These challenges have direct implications for the reliability of trial outcomes. However, standardized methods to evaluate site-level performance remain underdeveloped. This study introduces the Clinical Trial Site Performance Measure (CT-SPM), a novel framework designed to systematically capture site-level operational quality and to provide a scalable short form for routine monitoring. Methods: We conducted a multicenter study across six Italian academic hospitals (January–June 2025). Candidate performance indicators were identified through a systematic review and expert consultation, followed by validation and reduction using advanced statistical approaches, including factor modeling, ROC curve analysis, and nonparametric scaling methods. The CT-SPM was assessed for structural validity, discriminative capacity, and feasibility for use in real-world settings. Results: From 126 potential indicators, 18 were retained and organized into four domains: Participant Retention and Consent, Data Completeness and Timeliness, Adverse Event Reporting, and Protocol Compliance. A bifactor model revealed two higher-order dimensions (participant-facing and data-facing performance), highlighting the multidimensional nature of site operations. A short form comprising four items demonstrated good scalability and sufficient accuracy to identify underperforming sites. Conclusions: The CT-SPM represents an innovative, evidence-based instrument for monitoring trial execution at the site level. By linking methodological rigor with real-world applicability, it offers a practical solution for benchmarking, resource allocation, and regulatory compliance. This approach contributes to advancing clinical research by providing a standardized, data-driven method to evaluate and improve performance across networks. Full article

Predicting the concentrations of air pollutants, particularly PM_2.5, with accuracy and dependability is crucial for protecting human health and preserving a healthy natural environment. This research proposes a deep learning-based, robust prediction system to predict regional PM_2.5 concentrations for the next one to twenty-four hours. To start, the input features of the prediction system are initially screened using a correlation analysis of various air pollutants and meteorological factors. Next, the BiTCN-ISInformer prediction model with a two-branch parallel architecture is constructed. On the one hand, the model improves the probabilistic sparse attention mechanism in the traditional Informer network by optimizing the sampling method from a single sparse sampling to a synergistic mechanism combining sparse sampling and importance sampling, which improves the prediction accuracy and reduces the computational complexity of the model; on the other hand, through the introduction of the bi-directional time-convolutional network (BiTCN) and the design of parallel architecture, the model is able to comprehensively model the short-term fluctuations and long-term trends of the temporal data and effectively increase the inference speed of the model. According to experimental research, the proposed model performs better in terms of prediction accuracy and performance than the most advanced baseline model. In the single-step and multi-step prediction experiments of Shanghai’s PM_2.5 concentration, the proposed model has a root mean square error (RMSE) ranging from 2.010 to 10.029 and a mean absolute error (MAE) ranging from 1.436 to 6.865. As a result, the prediction system proposed in this research shows promise for use in air pollution early warning and prevention. Full article

In this paper, an efficient hybrid photovoltaic (PV) power forecasting model is proposed to enhance the stability and accuracy of PV power prediction under typical weather conditions. First, the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is employed to decompose both meteorological features affecting PV power and the power output itself into intrinsic mode functions. This process enhances the stationarity and noise robustness of input data while reducing the computational complexity of subsequent model processing. To enhance the detail-capturing capability of the Bidirectional Long Short-Term Memory (BiLSTM) model and improve its dynamic response speed and prediction accuracy under abrupt irradiance fluctuations, we integrate a Temporal Convolutional Network (TCN) into the BiLSTM architecture. Finally, a Multi-head Self-Attention (MHA) mechanism is employed to dynamically weight multivariate meteorological features, enhancing the model’s adaptive focus on key meteorological factors while suppressing noise interference. The results show that the ICEEMDAN-TCN-BiLSTM-MHA combined model reduces the Mean Absolute Percentage Error (MAPE) by 78.46% and 78.59% compared to the BiLSTM model in sunny and cloudy scenarios, respectively, and by 58.44% in rainy scenarios. This validates the accuracy and stability of the ICEEMDAN-TCN-BiLSTM-MHA combined model, demonstrating its application potential and promotional value in the field of PV power forecasting. Full article

Unexpected in-hospital cardiac arrest (IHCA) in the emergency department is defined as an unexpected cardiac arrest during the stay in the emergency department with measured vital signs when entering the emergency department, requiring immediate emergency treatment to save a life. Since IHCA is an urgent medical event, especially in the emergency department, this study explored the risk prediction of IHCA events in the emergency department. IHCA not only has a high mortality rate, but is also likely to cause permanent neurological damage. In the emergency environment, due to the complexity and rapid changes in the patient’s condition, traditional assessment tools often fail to identify high-risk cases in a timely manner. In view of this, this study uses both the Power BI visual analysis platform and the binary decision tree model to construct a data-driven risk prediction tool. Power BI analysis successfully presented the dynamic ranking of influencing factors, and the decision tree prediction model showed excellent performance, with an accuracy of 91%, a recall rate of 89%, an F1-score of 89%, and an overall accuracy of 100%; this prediction system is expected to improve the efficiency of emergency medical care, identify high-risk patients in a timely manner, and assist medical staff in intervening in advance and implementing preventive measures. This study provided two different approaches: Power BI and decision tree. Power BI requires no coding and can be used by medical professionals without a programming background, while decision tree is designed for professionals with a programming background. While the structures of Power BI and decision tree differ slightly, they are generally similar and can both serve as intelligent clinical tools. Full article

The construction industry in Pakistan faces persistent challenges due to uncertainties such as behavioral intention, risk identification, and stakeholder perception, which often lead to significant losses in construction activities and human resources. This study aims to quantitatively evaluate these critical factors within the theoretical framework of Building Information Modeling (BIM) and the Technology Acceptance Model (TAM). Specifically, key constructs—Behavioral Intention (BI), Hazard Identification (HI), and Stakeholder Perception (SP)—are analyzed to assess their influence on construction safety management practices. A structured questionnaire was distributed electronically to construction professionals across various ongoing projects in Pakistan. The questionnaire items were based on a five-point Likert scale, and reliability was confirmed with high Cronbach’s alpha values for BI (0.82), HI (0.92), and SP (0.91). To evaluate the relationships between constructs, descriptive statistics and multiple regression analysis were employed. The regression results showed strong model fit for BI and HI (R² = 0.945), and near-perfect fit for SP (R² = 0.998), demonstrating robust predictive power. Significant correlations were found among independent variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), and others. This study further identifies Trust (TR) and Organizational Culture (OC) as critical predictors of stakeholder perception in the BIM context. A conceptual framework was developed incorporating statistical parameters (e.g., p-values, R², t-stats) to categorize the effectiveness of BIM and TAM theoretical integration for safety risk management. This approach is novel in its use of TAM-based constructs to evaluate BIM-related safety outcomes in the Pakistani construction sector—a context where such empirical evidence is limited. The findings provide predictive insights into how behavioral, perceptual, and organizational variables influence construction safety performance, offering practical implications for BIM adoption and safety policy design. Full article

With accelerated urbanization and the pursuit of the “dual carbon” goals, shared bicycles have re-emerged as a green travel option. This study focuses on the central urban area of Nanjing and develops a carbon emissions reduction (CER) estimation model for shared bicycles. By analyzing spatio-temporal dimensions, it systematically assesses carbon reduction benefits and highlights the synergy with metro-connected travel. Key findings are as follows: (1) shared bicycles primarily support short-distance commuting, with a daily cycling pattern exhibiting a bi-modal distribution and a pronounced peak period demand; (2) cycling trips concentrate in densely populated and commercially vibrant zones, with a spatial pattern of central aggregation and multi-point diffusion; (3) each kilometer cycled by a shared bicycle reduces carbon emissions by about 96.19 g, with daily reductions of around 42.72 t and annual reductions up to 15,591.04 t; (4) the CER benefits of bicycle–metro integration are especially pronounced, contributing nearly 45.00% during peak periods; and (5) factors such as travel mode shifts, metro station layouts, and the development of electric vehicles continue to influence the CER benefits of shared bicycles. This work provides scientific evidence to inform urban green travel policies and transportation infrastructure optimization in cities. Full article

Objective: This study aims to explore the mechanism of regulated cell death-related genes in the development of endometrial carcinoma. Methods: Endometrial carcinoma-related datasets were yielded via the Cancer Genome Atlas and Gene Expression Omnibus databases, and regulated cell death-related genes were extracted from the literature. Differential expression analysis, weighted gene co-expression network analysis, and protein interaction analysis were performed to identify critical regulated cell death-related genes. Gene set enrichment analysis was used to identify the functional pathways involved in these critical genes. Afterward, the best clustering approach for tumor samples was yielded via consensus clustering analysis, and nomogram prediction models were built. Shiny Methylation Analysis Resource Tool was used to compare the expression levels of CpG methylation probes for critical genes between tumor and normal samples. Spearman correlation analysis was conducted to investigate the relationship between critical genes and various immune features. Eventually, immuno-infiltrative analysis was implemented, and potential therapeutic agents were screened targeting critical genes. The data were analyzed and visualized by R software using different packages. In addition, the expressions of critical genes were validated by quantitative real-time polymerase chain reaction and immunochemistry. Results: Four critical genes, namely GBP2, SLC11A1, P2RX7, and HCLS1, were identified, and they were involved in various functional pathways such as leukocyte-mediated cytotoxicity. There were substantial differences in CpG methylation in GBP2, SLC11A1, and HCLS1 between tumor and normal samples. As for immune features, all critical genes were positively connected with immunosuppressive factors such as TIGIT and most HLA molecules in endometrial carcinoma. The critical genes high/low expression groups of tumor samples showed different immune responses towards PD-1, PD-L1, and CTLA-4 immunotherapy. The infiltration of 24 immune cells, such as effector memory CD8⁺ T cells, was notably different between tumor and normal samples. Based on sensitivity analysis of chemotherapeutic agents, we found the highest positive correlation between SLC11A1 and “BI.2536” and the strongest passive correlation of HCLS1 and GBP2 with “Ribociclib”, as well as P2RX7 with “BMS.754807”. Quantitative real-time polymerase chain reaction suggested that the expression trends of GBP2, P2RX7, and HCLS1 were consistent with the results of bioinformatic analysis. Conclusions: Regulated cell death-related genes (GBP2, SLC11A1, P2RX7, and HCLS1) may play a role in endometrial carcinoma development, which can provide new ideas for the treatment and prognosis prediction of this disease. Full article

This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes were used, with meteorological data processed by AERMET and terrain elevation data generated using AERMAP. AERMOD performed dispersion calculations using Gaussian and bi-Gaussian models. The simulations identified atmospheric stability classes C and F, which, combined with other external factors, directly influenced the dose rates and the distances reached by the radioactive plume. The dose rate analysis, based on calculated concentrations in the air, water and soil, indicated that, in this scenario, the potential release of radioactive material does not pose a threat to the population. The adopted methodology proved effective in mapping the behavior of the radioactive plume across the three media, providing accurate and reliable results for use in safety assessments and emergency response planning. Full article

The switch open-circuit fault signal of the Vienna rectifier possesses non-stationary characteristics and is also vulnerable to external interference factors, such as sensor noise and load variation. This phenomenon reduces the performance of traditional methods, including model-based and signal-based algorithms. In order to improve the accuracy, convergence rate, and robustness of diagnosis models, a hybrid deep learning Transformer–BiTCN optimized via ISGA (Improved Snow Geese Algorithm, ISGA) is proposed in this paper. Firstly, to assess the Vienna rectifier’s open-circuit fault signal, the time-varying and non-stationary characteristics generation mechanism is analyzed. Then, combining the fault signal characteristics of the Vienna rectifier, the hybrid deep learning model using Transformer–BiTCN, along with multi-scale feature fusion, is presented to extract hierarchical features, including both global temporal dependencies and local characteristics to enhance fault diagnosis accuracy and model robustness. Finally, the ISGA optimization algorithm with the Bloch initialization strategy and the Rime search mechanism is further presented to optimize the hyperparameters of the Transformer–BiTCN model so as to improve convergence and improve accuracy. Finally, the effectiveness of our proposed method is tested by simulations and experiments. It has been verified that the Transformer–BiTCN along with ISGA optimization is robust to non-stationary open-circuit fault signals and can achieve high diagnosis accuracy with a fast convergence rate. Full article

This study explores the application of phosphonium-based ionic liquids (ILs) for the efficient separation of valeric acid (VA) through reactive liquid–liquid extraction. Two hydrophobic quaternary phosphine ILs, trihexyl(tetradecyl)phosphonium decanoate (C103) and trihexyl(tetradecyl)phosphonium bis(2,4,4-trimethylpentyl)phosphinate (C104), were evaluated in combination with heptane as a diluent. Extraction efficiency was experimentally determined at different levels of extraction process factors in terms of aqueous phase pH (3–6), IL concentration (0–120 g/L), and process temperature (25–60 °C). For each IL, extraction efficiency was predicted using a response surface regression model, and the process factors were optimized based on the desirability function approach. Both ILs effectively extracted VA, with optimal mean values of extraction efficiency of 98.61% for C103 and 99.24% for C104 under optimal conditions (pH of 3.8 and 4, respectively, IL concentration of 60 g/L, and temperature of 25 °C). Mechanistic analysis revealed that VA extraction occurs through the formation of IL-acid complexes, involving hydrogen bonding between the non-dissociated acid and the IL anion. Depending on the extractant concentration, 1:1 and 2:1 acid-to-IL stoichiometric ratios were observed. These findings highlight the potential of phosphonium-based ILs, particularly in a heptane-diluted system, as high-performance extractants for carboxylic acid separation. Full article