Search Results (9,769)

In recent years, the rapid development of underground engineering projects has driven a significant increase in the variety and quantity of excavation equipment. The wear of excavation tools significantly increases construction costs and reduces construction efficiency. The wear rate of excavation tools is closely related to the abrasiveness of the rock. The Cerchar abrasivity index (CAI) is the most widely used index for estimating rock abrasiveness. The primary objective of this paper is to develop a novel prediction model for CAI, which is established based on the mechanical properties and petrographic parameters of rocks. These parameters include uniaxial compressive strength, Brazilian splitting strength, quartz content, equivalent quartz content, average quartz size, brittleness indices, rock abrasive index, and Schimazek’s F-abrasiveness. Correlation analysis was used to conduct a preliminary analysis between CAI and single-influence parameters. The results indicated that a single factor is not suitable for directly predicting CAI. In addition, multiple linear regression (MLR) and a non-linear algorithm, gene expression programming (GEP), were used to establish new prediction models for CAI. A statistical comparison was conducted between the prediction accuracy of the GEP-based model and the MLR-based model. In comparison to the MLR-based model, the GEP-based model demonstrates higher accuracy in predicting CAI. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

The Bell regression model (BRM) is a statistical model that is often used in the analysis of count data that exhibits overdispersion. In this study, we propose a Bayesian analysis of the BRM and offer a new perspective on its application. Specifically, we introduce a G-prior distribution for Bayesian inference in BRM, in addition to a flat-normal prior distribution. To compare the performance of the proposed prior distributions, we conduct a simulation study and demonstrate that the G-prior distribution provides superior estimation results for the BRM. Furthermore, we apply the methodology to real data and compare the BRM to the Poisson and negative binomial regression model using various model selection criteria. Our results provide valuable insights into the use of Bayesian methods for estimation and inference of the BRM and highlight the importance of considering the choice of prior distribution in the analysis of count data. Full article

Background/Objectives: The Jiangnan diet—a traditional dietary pattern prevalent in Eastern China—is a newly proposed dietary pattern. This study provides additional epidemiological evidence for the promotion of the Jiangnan diet through examining the association between the Jiangnan diet and mild cognitive impairment (MCI). Methods: A multicenter cross-sectional study was carried out during 2020 among 1084 community-dwelling adults aged 55 years and above across multiple sites in Zhejiang Province, China. Data collection encompassed basic information of the population, cognition (using the Montreal Cognitive Assessment), dietary intake information (using the Food Frequency Questionnaire, FFQ), life pattern, depressive symptoms (using the Mental Health Assessment Scale for the Elderly), and physical examinations (e.g., height, weight). The dietary patterns were assessed using a validated semi-quantitative FFQ. Factor analysis was used to analyze the 16 categories of food intake of the participants, and dietary patterns and the “Jiangnan diet” were extracted. The Jiangnan diet scores were categorized into quartiles: Q1 (lowest) to Q4 (highest). Multivariate logistic regression was employed to examine the association between adherence to the Jiangnan diet and the prevalence of MCI, with results expressed as odds ratios (OR) and 95% confidence intervals (CI). Results: The estimated prevalence of MCI in the study population was 24.6%. The dietary pattern characterized by whole grains, low salt, and low oil was identified as the “Jiangnan diet”. Participants with the highest adherence to the “Jiangnan diet” pattern had 79.2% lower odds of MCI than those with the lowest adherence (odds ratio = 0.208, 95% CI = 0.120~0.362, p < 0.0001) after adjusting for age, frequency of social activities, depression, hypertension, alcohol consumption, and energy intake. Conclusions: High adherence to the Jiangnan diet was associated with lower odds of MCI. To further verify the relationship between the Jiangnan diet and MCI, future studies will focus on longitudinal research exploring different dietary patterns and disease outcomes across various regions. Full article

The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management. Full article

The Global Trade Alert (GTA) website, managed by the United Nations, releases a large number of industrial policy (IP) announcements daily. Recently, leading nations including the United States and China have increasingly turned to IPs to protect and promote their domestic corporate interests. They use both offensive and defensive tools such as tariffs, trade barriers, investment restrictions, and financial support measures. To evaluate how these policy announcements may affect national interests, many countries have implemented logistic regression models to automatically classify them as either IP or non-IP. This study proposes ensemble models—widely recognized for their superior performance in binary classification—as a more effective alternative. The random forest model (a bagging technique) and boosting methods (gradient boosting, XGBoost, and LightGBM) are proposed, and their performance is compared with that of logistic regression. For evaluation, a dataset of 2000 randomly selected policy documents was compiled and labeled by domain experts. Following data preprocessing, hyperparameter optimization was performed using the Optuna library in Python. To enhance model robustness, cross-validation was applied, and performance was evaluated using key metrics such as accuracy, precision, and recall. The analytical results demonstrate that ensemble models consistently outperform logistic regression in both baseline (default hyperparameters) and optimized configurations. Compared to logistic regression, LightGBM and random forest showed baseline accuracy improvements of 3.5% and 3.8%, respectively, with hyperparameter optimization yielding additional performance gains of 2.4–3.3% across ensemble methods. In particular, the analysis based on alternative performance indicators confirmed that the LightGBM and random forest models yielded the most reliable predictions. Full article

Background/Objectives: The development of anti-drug antibodies (ADA) significantly diminishes the clinical efficacy of infliximab (IFX) in Crohn’s disease (CD). This study aimed to develop and validate an interpretable machine learning (ML) framework for predicting ADA risk during IFX induction therapy using multidimensional clinical and laboratory data. Methods: We conducted a retrospective analysis of 606 CD patients who initiated IFX induction between January 2023 and August 2024 at the Sixth Affiliated Hospital of Sun Yat-sen University. Predictor selection was performed through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, with significant features further evaluated via multivariate logistic regression. Seven ML models were developed and evaluated mainly based on area under the curve (AUC), F1 score, and Brier score. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Results: Among the 606 CD patients, 145 (23.93%) developed ADA during IFX induction. Independent predictors included serum trough levels of IFX (TLI), erythrocyte sedimentation rate (ESR), history of delayed treatment, prior exposure to anti-TNF agents, and concomitant use of immunosuppressants (IMM). The XGBoost algorithm outperformed others, with an AUC of 0.899, accuracy of 0.851, F1 score of 0.640, and Brier score of 0.102 in validation. SHAP analysis identified TLI and ESR as the most influential predictors, with history of delayed treatment and prior exposure to anti-TNF agents showing moderate impact, while concomitant use of IMM was associated with a protective effect. Conclusions: We developed an interpretable ML model that effectively predicts ADA formation in CD patients undergoing IFX induction therapy, facilitating early risk stratification and personalized treatment planning. This approach integrates advanced analytics with clinical practice to support precision medicine in CD management. Full article

Background and Objectives: To evaluate the prognostic value of the Clinical–Pathologic Stage–Estrogen receptor status and Grade (CPS+EG) staging system, which combines clinical staging, pathological staging, oestrogen receptor (ER) status, and tumour grade in predicting survival outcomes in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer receiving neoadjuvant therapy (NACT). Materials and Methods: A retrospective review was performed on 245 female breast cancer patients who received anti-HER2 therapy alongside NACT at the Medical Oncology Department of Kartal Dr Lütfi Kırdar City Hospital, University of Health Sciences, from April 2012 to June 2024. The CPS+EG score was calculated using the MD Anderson Cancer Centre neoadjuvant treatment response calculator. Patients were categorised into two groups based on their CPS+EG score < 3 and ≥3. The primary outcomes assessed were disease-free survival (DFS) and overall survival (OS). Kaplan–Meier and log-rank tests were utilised for time-to-event analysis; Cox regression was used for multivariate analysis. A significance level of ≤0.05 was considered. Results: The median age of the patient cohort was 51 years (range: 27–82 years). Among these patients, 183 (74.6%) had a CPS+EG score less than 3, while 62 (25.3%) exhibited a score of 3 or higher. The median follow-up duration was 37.6 months. The pathological complete response (pCR) rate across the entire cohort was 51.8%. Specifically, the pCR rate was 56.3% in the group with CPS+EG scores below 3, and 38.7% in those with scores of 3 or higher (p = 0.017). Patients with CPS+EG scores less than 3 demonstrated superior overall survival (OS), which reached statistical significance in univariate analysis. Multivariate analysis identified the CPS+EG score as an independent prognostic factor for both overall survival and disease-free survival (DFS), with hazard ratios of 0.048 (95% CI: 0.004–0.577, p = 0.017) and 0.35 (95% CI: 0.14–0.86, p = 0.023), respectively. Conclusions: The CPS+EG score is an independent and practical prognostic marker, particularly for overall survival, in patients with HER2-positive breast cancer who have received neoadjuvant therapy. Patients with a CPS+EG score < 3 have higher pCR rates and survival rates. When used in conjunction with pCR, it can improve risk categorisation and contribute to the individualisation of adjuvant strategies in the post-neoadjuvant period. Due to its ease of calculation and lack of additional costs, this score can be instrumental in clinical practice for identifying high-risk patients. Our findings support the integration of the CPS+EG score into routine clinical decision-making processes, although prospective validation studies are necessary. Full article

This study investigates the determinants of Romanian workers’ migration intentions towards Italy, integrating economic, social, and psychological perspectives. Based on a sample of 358 respondents, four hypotheses were tested concerning perceived living standards, working conditions, quality of public services, and anticipated integration difficulties. Data were analysed using descriptive statistics, Spearman’s rho correlation, Mann–Whitney U, Chi-square, ANOVA, and ordinal logistic regression. The results confirm that higher perceived living standards and better working conditions in Italy significantly increase the likelihood of expressing migration intentions, while favourable evaluations of healthcare and education act as additional pull factors. Conversely, anticipated integration difficulties, particularly language barriers and cultural adaptation, reduce migration intentions, indicating that socio-psychological obstacles can counterbalance economic incentives. By combining non-parametric and multivariate analyses, the study demonstrates that migration is a multidimensional process shaped not only by structural opportunities but also by behavioural and psychological appraisals. These findings are consistent with recent research on European labour mobility and contribute to the literature by highlighting the role of subjective perceptions in shaping migration decisions. Implications for policy include the need to address both economic disparities and integration barriers to support more balanced mobility within the European space. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Background: The Healthy Japan 21-Phase III dietary recommendations comprise a staple food, main dish, and side dish to maintain nutritional balance and support healthy child growth. The relationship between the frequency of such balanced meals and early adiposity rebound (AR), a predictor of obesity, remains unclear. Objective: This study aimed to examine the association between the frequency of balanced meals (staple food, main dish, and side dish) and early AR in preschool children. Methods: In this cross-sectional secondary analysis of nationwide online survey data of 688 mothers of children aged 3–6 years, dietary habits were assessed using a validated NutriSTEP-based 22-item Japanese Nutrition Screening Questionnaire. AR constituted a body mass index (BMI) increase from the 18- to 36-month health checkups recorded in the Maternal and Child Health Handbook. Risk scores reflecting lower frequency of balanced meals were calculated for staple foods, main dishes, and side dishes. Logistic regression evaluated associations between dietary risk scores and AR, adjusting for the child’s sex, age, gestational age, birth weight, daycare attendance, and parental obesity. Results: Among 688 children, 193 (28.1%) exhibited early AR and had significantly higher BMI at age 3 and the most recent measurement (both p < 0.01). A higher total dietary risk score was independently associated with AR (adjusted odds ratio; 2.58 [95% CI: 1.08–6.16]). In addition, the absolute risk difference between high- and low-risk groups was 8.5% (95% CI: 1.7–15.2%). Conclusions: A lower frequency of balanced meals is associated with early AR. These findings suggest that a simple, meal-balance screening tool could potentially aid in the early identification of the risk of later obesity and timely nutritional guidance. Full article

Predicting how vegetation–debris interactions reshape alternate sandbars under a steady subcritical flow remains poorly understood in laboratory-to-field scaling. This study quantified how vegetation density and layout interact with debris geometry to control scouring and deposition and developed an empirical tool to predict normalized bed-level changes. Flume experiments investigated how vegetation–debris interactions regulate the hydromorphodynamics of non-migrating alternate sandbars under a steady subcritical flow (Q = 0.003 m³/s; slope = 1/200). Vegetation patches were configured in two spatial layouts—upstream (apex) and river line (edge), at varying densities, with and without debris (I-type: wall-like; U-type: horseshoe-shaped). Results indicated that dense upstream vegetation combined with I-type debris produced the strongest morphodynamic response, generating maximum scour, corresponding to the maximum bed-elevation changes (Δz) normalized by water depth (h) (dimensionless Δz/h) values of −1.55 and 1.05, and sustaining more than 70% of the downstream morphodynamic amplitude. In contrast, U-type debris promoted distributed deposition with a milder scour, while sparse vegetation yielded weaker, more transient responses. Debris geometry-controlled flow partitioning: the I-type enhanced frontal acceleration, whereas the U-type facilitated partial penetration and redistribution. To integrate these findings into predictive frameworks, an empirical regression model was developed to estimate Δz/h from the vegetation density, distribution, and debris geometry, with an additional blockage index to capture synergistic effects. The model achieved 87.5% prediction within ±20% error, providing a practical tool for anticipating scour and deposition intensity across eco-hydraulic configurations. These insights advance intelligent water management by linking morphodynamic responses with predictive modeling, supporting flood-resilient river engineering, adaptive channel stability assessments, and nature-based solutions. Full article

This study advances the unmanned aerial vehicle (UAV) localization technology within the framework of a low-altitude economy, with particular emphasis on the accurate and real-time identification and tracking of unauthorized (“black-flying”) drones. Conventional YOLOv8s-based target detection algorithms often suffer from missed detections due to their reliance on single-frame features. To address this limitation, this paper proposes an improved detection algorithm that integrates a long-short-term memory (LSTM) network into the YOLOv8s framework. By incorporating time-series modeling, the LSTM module enables the retention of historical features and dynamic prediction of UAV trajectories. The loss function combines bounding box regression loss with binary cross-entropy and is optimized using the Adam algorithm to enhance training convergence. The training data distribution is validated through Monte Carlo random sampling, which improves the model’s generalization to complex scenes. Simulation results demonstrate that the proposed method significantly enhances UAV detection performance. In addition, when deployed on the RK3588-based embedded system, the method achieves a low false negative rate and exhibits robust detection capabilities, indicating strong potential for practical applications in airspace management and counter-UAV operations. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

Corporate transparency is vital for sustainable governance. However, detecting financial misstatements remains challenging due to their rarity and resulting class imbalance. Using financial statement data from Korean firms, this study develops an integrated AI framework that evaluates the joint effects of sampling strategy, model choice, and interpretability. Across multiple imbalance ratios, NearMiss undersampling consistently outperforms random undersampling—particularly in recall and F1-score—showing that careful data balancing can yield greater improvements than algorithmic complexity alone. To ensure interpretability rests on reliable predictions, we apply Shapley Additive Explanations (SHAP) and Permutation Feature Importance (PFI) only to high-performing models. Logistic regression emphasizes globally influential operating and financing accounts, whereas Random Forest identifies context-dependent patterns such as ownership structure and discretionary spending. Even with a reduced feature set identified by explainable AI, models maintain robust detection performance under low imbalance, highlighting the practical value of interpretability in building simpler and more transparent systems. By combining predictive accuracy with transparency, this study contributes to trustworthy misstatement detection tools that reinforce investor confidence, strengthen responsible corporate governance, and reduce information asymmetry. In doing so, it advances the United Nations Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions) by supporting fair, accountable, and sustainable economic systems. Full article