Search Results (1,585)

The compressive behavior of carbon fiber-reinforced polymer (CFRP)-confined ultra-high-performance concrete (UHPC) short columns involves nonlinear interactions among concrete properties, confinement characteristics, and specimen geometry. In this study, a database of 144 circular specimens was compiled using nine input variables: specimen diameter ($D$), height ($H$), CFRP thickness ($t$), number of CFRP layers ($Layers$), unconfined concrete compressive strength ($f_{co}^{\prime }$), concrete ultimate strain (${\epsilon }_{co}$), CFRP tensile strength ($f_f$), CFRP ultimate strain (${\epsilon }_f$), and CFRP elastic modulus ($E_f$). Eight regression algorithms were evaluated using an 80:20 training–test split, with ten-fold cross-validation conducted within the training subset for hyperparameter selection. ANN achieved the best test performance, with an $R^2$ of 0.96, an MAE of 8.37 MPa, and an RMSE of 11.32 MPa, while SVM and CatBoost also showed competitive predictive accuracy. The selected ML models exhibited substantially lower prediction errors than seven existing empirical equations. SHAP analysis further identified CFRP layer number, unconfined concrete strength, specimen height, and CFRP thickness as influential predictors and revealed interactions among confinement, matrix deformability, and specimen geometry. The proposed framework provides an accurate and interpretable supplementary approach for assessing the compressive strength of CFRP-confined UHPC within the parameter range represented by the database. Full article

This study investigates psychological profiles associated with trust in artificial intelligence (AI) among university students using an interpretable machine learning (ML) approach. Although AI tools are becoming increasingly embedded in higher education, limited research has examined how students’ psychological resilience, stress levels, and trust in AI interact to shape their perceptions of AI systems. This study analyzes cross-sectional survey data from 107 students at a Historically Black College and University (HBCU) in the United States. K-means clustering was applied to identify student profiles based on psychological resilience, perceived stress, and AI trust. Clustering solutions were evaluated using multiple internal validation metrics and stability analysis, followed by ANOVA and chi-square analyses to characterize psychological and demographic differences across the identified profiles. The analysis identified three student profiles: (i) moderately stressed AI-positive students, (ii) high-resilience low-stress AI-adopters, and (iii) psychologically resilient AI skeptics. Significant differences were observed across all psychological variables (p < 0.001), with stress and AI trust demonstrating the greatest differentiation across profiles. Gender was significantly associated with profile membership (χ² = 10.34, p = 0.006), whereas age group, academic level, employment status, and STEM background were not significantly associated with profile membership. Overall, the findings highlight heterogeneity in students’ psychological characteristics and AI-related perceptions, suggesting that psychological factors may provide additional insight into variations in AI trust beyond demographic characteristics. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming the landscape of rheumatological and systemic inflammatory disease management, offering unprecedented capacity to integrate complex, multidimensional data for diagnostic support, disease monitoring, and therapeutic decision-making. This comprehensive narrative review, based on a non-systematic literature search of PubMed/MEDLINE and Google Scholar combined with the authors’ clinical expertise, provides a clinically oriented synthesis of current and emerging AI applications across the full spectrum of immune-mediated inflammatory diseases—including rheumatoid arthritis, systemic lupus erythematosus, vasculitis, inflammatory bowel disease, psoriatic arthritis, systemic sclerosis, inflammatory myopathies, and sarcoidosis—with particular attention to applications that have demonstrated or are approaching clinical utility. We discuss deep learning-based image analysis, natural language processing of electronic health records, multi-omic biomarker discovery, and the application of Fourier transform-based signal processing to biological time series as a novel approach to continuous disease monitoring. Fourier transform methods—already foundational in MRI reconstruction, cardiac electrophysiology, and clinical neurophysiology—are here systematically extended to rheumatological and inflammatory disease signals, including accelerometry, electromyography, heart rate variability, and longitudinal biomarker time series. The phenomenon of large language model hallucination—particularly critical in rare inflammatory diseases—is addressed alongside retrieval-augmented generation as a mitigation strategy. We further argue that AI-driven methods do not merely improve the interpretation of clinical data, but fundamentally expand what is observable—with profound epistemological implications for clinical knowledge transmitted through generations of medical tradition. Ethical considerations and future directions toward precision inflammatory disease medicine are outlined. Full article

The environmental and economic impacts of nitrate leaching from agricultural soils highlight the need for accurate prediction to support effective nitrogen management. This study evaluated six machine learning (ML) models—Multiple Linear Regression, Elastic Net, K-Nearest Neighbors, Decision Trees, Extra Trees, and Multi-Layer Perceptron—using 2993 field observations to predict nitrate leaching. The models incorporated crop sequence, manure and fertilizer inputs, soil, and percolation and are benchmarked against the empirical NLES5 model used in Danish nitrogen regulation. Four ML models achieved higher predictive accuracy than NLES5 when evaluated on independent test data, with the Extra Trees model achieving the best performance (R² = 0.63, RMSE = 23.3 kg N ha⁻¹), exceeding NLES5 (R² = 0.39, RMSE = 29.8 kg N ha⁻¹). Model interpretability analyses identified winter percolation, winter vegetation cover, and soil type as key drivers of nitrate leaching. The Extra Trees model was further evaluated using scenario analyses of long-term leaching trends, marginal responses to spring-applied mineral nitrogen, and spatial patterns within the Bolbro Bæk catchment. Findings highlight the potential of ML models to improve nitrate leaching predictions in ungauged areas. Future research could incorporate additional variables, including crop yield and tillage practices, to enhance model accuracy and support sustainable agricultural practices that maintain productivity while reducing nitrate leaching. Full article

Background/Objectives: Pediatric uroflowmetry is volume dependent, and low-volume voids can yield recordings that require repetition or cannot be interpreted confidently. To evaluate whether documented bladder volume-guided timing was associated with first-attempt pediatric uroflowmetry process adequacy in children undergoing evaluation for suspected non-neurogenic lower urinary tract dysfunction. Methods: This single-center retrospective workflow cohort included 110 toilet-trained children aged 5–12 years who underwent uroflowmetry for suspected non-neurogenic lower urinary tract dysfunction. The exposure was classified from contemporaneous pre-test documentation as bladder volume-guided timing (n = 55) or standard urge-based timing (n = 55). Expected bladder capacity (EBC) was calculated as (age + 1) × 30 mL. The primary process outcome was first-attempt voided volume ≥ 50% EBC. Repetition after an inadequate first attempt was treated as a deterministic workflow consequence rather than an independent endpoint. Analyses were observational and effect estimates were interpreted as associations. Results: Adequate first-attempt voided volume was documented in 50/55 children (90.9%) with bladder volume-guided timing and 39/55 (70.9%) with standard urge-based timing (unadjusted risk ratio 1.28, 95% confidence interval [CI] 1.06–1.55; Newcombe risk difference 20.0 percentage points, 95% CI 5.3–34.0). After adjustment for age, baseline urgency score, and time since last void, the association remained (adjusted risk ratio 1.27, 95% CI 1.06–1.53; p = 0.011), and the model converged without numerical warnings. Using the age-specific lowest acceptable voided volume, adequacy occurred in 53/55 children (96.4%) versus 46/55 (83.6%) (adjusted risk ratio 1.15, 95% CI 1.01–1.31; p = 0.040). Repetition after an inadequate first attempt occurred in 9.1% versus 29.1% and was treated as a direct consequence of primary-threshold failure; it was not tested independently. Workflow-time measures were exploratory. Conclusions: Documented bladder volume-guided timing was associated with greater first-attempt process adequacy. Adjustment for age, baseline urgency score, and time since last void did not materially change the estimate. The association was attenuated but remained directionally consistent when the age-specific lowest acceptable voided volume was used. Because the timing rule deliberately targeted the same volume construct as the primary outcome, this finding does not establish improved diagnostic accuracy, clinical decision making, or patient outcomes. Retrospective exposure classification and routine documentation further preclude causal interpretation. Documented bladder volume-guided timing was associated with higher first-attempt achievement of a prespecified voided-volume threshold than standard urge-based timing. Because the pathway targeted the same volume construct used to define the primary outcome and was non-randomized, these findings are interpreted as hypothesis-generating workflow data rather than evidence of improved diagnostic accuracy or downstream clinical benefit. Full article

Leptoglossus chilensis is a native South American coreid bug of economic and quarantine relevance whose chemical ecology remains undescribed. In this exploratory study, we characterised adult volatile and cuticular chemistry using dynamic headspace sampling (DHS), cuticular wash (CW), solid-phase microextraction (SPME), supercritical fluid extraction (SFE), and quantified electroantennographic (EAG) dose–responses in males and females in relation to a panel of eight literature-informed compounds (hexanal, hexyl acetate, hexyl formate, β-caryophyllene, (E)-β-farnesene, ocimene, benzyl alcohol and 2-phenylethanol). EAG responses were evaluated at six concentrations using a Bayesian hierarchical four-parameter Hill model. Across the four methods, 128 compounds were detected, of which 69 met a ≥20% detection-frequency threshold and were retained as the representative profile. Of these, hexanal, hexyl acetate, hexyl formate, and β-caryophyllene were detected in L. chilensis extracts, the latter only in male DHS collections; this compound is more likely to be a host plant volatile than an insect-produced signal. All eight compounds elicited concentration-dependent antennal responses in both sexes. Among these four compounds, β-caryophyllene yielded the lowest fitted EC₅₀ in both females (9 µg mL⁻¹ [90% CrI: 5–16]) and males (9 µg mL⁻¹ [90% CrI: 5–15]), although curves remained unsaturated; because delivered antennal dose was not measured and volatility differs substantially among the tested compounds, this cannot be interpreted as evidence of greater antennal sensitivity to β-caryophyllene specifically, and cross-compound EC₅₀ comparisons throughout this study are restricted to this caveat. All four Hill parameters were identifiable in both sexes for benzyl alcohol, (E)-β-farnesene, hexyl formate, and 2-phenylethanol. These results provide the first chemical and peripheral olfactory reference for L. chilensis and identify targets for behavioural and field evaluation. Full article

Forest fires remain a major threat to biodiversity, human settlements, and the climate. This study presents Nuthur, an intelligent wildfire prediction and early-warning system for Al-Soudah, Saudi Arabia, integrating near-real-time environmental data with artificial intelligence models. The system used the Algerian Forest Fire dataset and a newly created local Saudi Arabian dataset. L1 regularization and Recursive Feature Elimination with Cross-Validation (RFECV) were used to examine relevant environmental variables, while oversampling, undersampling, and (Conditional Tabular Generative Adversarial Network) CTGAN-based synthetic augmentation were evaluated to address class imbalance. Multiple ML and DL models were evaluated, including Random Forest (RF), SVM, XGBoost, CatBoost, ensemble models, MLP, TabNet, and exploratory LSTM and CNN models, which were not interpreted as temporal or spatial models. Under random five-fold cross-validation, ML models achieved accuracy values from 0.89 to 0.97, with XGBoost with oversampling achieving the highest accuracy of 0.97. Deep-learning models achieved accuracy values from 0.75 to 0.94, with TabNet using RFECV achieving the best deep-learning result. A separate spatial cross-validation analysis of the Saudi dataset showed lower geographic generalization performance. CatBoost without oversampling achieved the highest mean spatial accuracy (0.8317) and weighted F1-score (0.7782), while logistic regression with oversampling achieved the highest fire-class recall (0.5616). In contrast, XGBoost with oversampling had a fire-class recall of 0.1096. These results highlight the need for further geographically and temporally diverse Saudi data before operational deployment. Full article

This study evaluated the dose-dependent effects of a nodule-associated bacterial isolate subsequently identified by whole-genome sequencing as Kosakonia cowanii on nodulation, nitrogen status, growth, phenology, and yield components of common bean (Phaseolus vulgaris L. cv. Centenario) under greenhouse conditions in northern Ecuador. A randomized complete block design with five treatments and three replicates was used, including three inoculation doses (6 × 10⁹, 6 × 10^4.5 and 6 × 10^2.25 CFU mL⁻¹), a non-inoculated control and a nitrogen-fertilized control. Significant differences were detected for plant height, SPAD index, phenological development, nodulation and 100 seed weight. The lowest inoculation dose (T3) produced the strongest biological response, reaching 227.67 nodules plant⁻¹, approximately 6.5 times more than the non-fertilized control, and the highest SPAD value at 60 days after sowing (41.16), exceeding both the non-fertilized control (26.84) and the nitrogen-fertilized treatment (33.14). T3 also achieved the greatest plant height (80.31 cm) and accelerated flowering and pod formation by up to two days compared with higher inoculation doses. Although grain yield did not differ significantly among treatments because of high experimental variability and limited statistical power, inoculated plants maintained yields comparable to the nitrogen-fertilized control while improving seed filling. Whole genome sequencing confirmed the identity of the dominant genome as K. cowanii (ANI = 96.73%; completeness = 99.03%; contamination = 0.63%), supporting the interpretation that this bacterium acts primarily as a plant growth-promoting and nodule-associated bacterium rather than a classical nitrogen-fixing symbiont. These findings highlight the potential of native K. cowanii as a component of sustainable biofertilization strategies for Andean bean production systems. Full article

Rectangular concrete-filled steel tube (RCFST) columns are widely adopted as primary load-bearing components in engineering structures, and making reliable estimation of their axial compressive capacity crucial to structural design and safety assessment. However, existing theoretical and design equations generally rely on a series of simplifying assumptions and empirical modification coefficients, which limit their prediction accuracy and applicability. In this context, a data-driven strategy is proposed in which capacity prediction and model interpretation are integrated within a unified procedure. The proposed approach establishes the nonlinear mapping between high-dimensional input features, including geometric and material properties, and axial compressive capacity, while employing the SHapley Additive exPlanations (SHAP) method to quantify the contributions of critical input parameters to the model predictions. An experimental database comprising 745 axial compression tests on RCFST columns is established through an extensive literature survey. Prior to training, correlations within the candidate inputs are examined using Pearson coefficients. An SMA-LSSVM–ANN hybrid prediction model is then developed, in which SMA iteratively searches for the optimal combination of the LSSVM-ANN hyperparameters using mean squared error as the fitness function. Comparisons with several benchmark machine learning (ML) models and existing design-code formulations validate the predictive performance of the proposed model, which achieves an R² of 0.9571 and an RMSE of 302.84 kN on the testing dataset. Furthermore, SHAP analysis is employed to interpret the proposed model from global and feature-dependency perspectives. Global SHAP analysis identifies concrete compressive strength and cross-sectional dimensions as the dominant features, while feature-dependence analysis reveals generally positive effects of cross-sectional dimensions and material strengths and a negative effect of column length on the predicted capacity. Full article

Objectives: Manual surgical staplers remain widely used in minimally invasive lung surgery, while powered devices have been introduced to improve tissue compression, staple formation, and device handling. This study compared clinical outcomes associated with manual and powered endoscopic staplers in patients undergoing minimally invasive lung cancer surgery, with postoperative air leak as the main outcome of interest. Methods: We retrospectively reviewed 400 consecutive patients who underwent VATS segmentectomy or lobectomy for lung cancer between January 2023 and June 2025. After 20 exclusions, 380 patients were analyzed: 190 treated with manual staplers (Group A), representing a historical control cohort, and 190 with powered staplers (Group B). Postoperative air leak, operative time, intraoperative blood loss, postoperative complications, and length of stay were compared. Surgeon-perceived ergonomic benefit was assessed after each powered-stapler procedure using a dichotomous yes/no question. Results: Postoperative air leak occurred less frequently with powered than manual staplers (10.0% vs. 17.9%; p = 0.0382; OR = 0.510, 95% CI: 0.279–0.931). No patient developed prolonged air leak (>5 days) or required additional intervention for air leak. Operative time (65.32 ± 2.52 vs. 66.74 ± 3.07 min; p < 0.0001) and intraoperative blood loss (86.09 ± 4.12 vs. 88.3 ± 4.47 mL; p < 0.0001) were statistically lower with powered staplers, although absolute differences were small. No significant differences were observed in postoperative atelectasis or length of stay. All 190 surgeon assessments indicated a perceived ergonomic advantage with the powered stapler. Conclusions: Powered staplers were associated with a lower incidence of postoperative air leak. Operative time and blood loss were also statistically lower, although the absolute differences were of limited clinical relevance and did not translate into shorter hospitalization. Powered staplers were consistently perceived as ergonomically advantageous, although this assessment was subjective and non-validated. These findings warrant cautious interpretation given the retrospective, single-center design and historical-control comparison. Full article

Diversified crop rotations and cover crops are increasingly promoted as climate-smart management strategies for improving soil organic carbon (SOC) while minimizing nitrous oxide (N₂O) emissions in semiarid agroecosystems. However, regional-scale assessment of SOC–N₂O trade-offs remains computationally challenging because process-based simulations across large spatial and climatic domains are highly demanding. This study combined long-term DSSAT simulations with machine-learning (ML) surrogate models to quantify and spatially predict SOC and N₂O responses to diversified rotations and cover-crop systems across the Texas High Plains (THP). Random Forest models were trained using DSSAT outputs representing multiple crop rotations, climate scenarios, and soil conditions. Two complementary modeling frameworks were developed: (i) BAU-relative responses, which quantified SOC and N₂O changes relative to baseline management, and (ii) added cover-crop effects, which isolated the additional benefits of diversified cover-crop systems relative to a simplified no-cover crop improved rotation system. Model interpretation was conducted using permutation importance and SHAP analysis to identify the dominant environmental and management controls. The surrogate models accurately reproduced DSSAT-derived responses, particularly for BAU-relative SOC and N₂O changes reaching R² ≈ 0.90, while added cover crop SOC was less predictable than added N₂O. Spatial predictions revealed strong geographic variability in climate-smart response zones, with larger SOC benefits generally observed under future climate conditions, particularly during the 2070s. However, some regions also exhibited stronger N₂O trade-offs, highlighting the need for spatially targeted management strategies. The results demonstrate the potential of combining process-based simulations with ML surrogates to generate computationally efficient decision-support tools for climate-smart agriculture in semiarid cropping systems. Full article

Rigid carbon frameworks in organic semiconductors restrict the tunability of their electronic and spin properties. Peri-metalation of polyacenes with coinage metals, particularly gold and copper, offers a route to electronic regimes not attainable in conventional organic systems, yet navigating this hybrid space often requires exhaustive quantum chemical sampling. This study integrates density functional theory with a small-data scientific machine learning framework to show that global electronic trends, including bandgaps, ionization energies, and electron affinities, can be captured by minimalist, coordinate-free descriptors. The hybrid architecture combines an analytical baseline defined only by inverse ring size and Au/Cu counts with coordinate-free residual learning that utilizes discrete metal sequence and topology descriptors. The original 53-descriptor residual model offers a chemically comprehensive representation, whereas a reduced 4-descriptor model assesses the persistence of principal predictive trends following significant dimensionality reduction. By circumventing explicit atomic coordinates, geometric parameters, orbital energies, wavefunctions, and interaction energies as model inputs, both models successfully recover chemically meaningful electronic properties and trends across the polyacene series while remaining sensitive to subtle local sequence effects. Systematic model–DFT deviations serve as diagnostic indicators, revealing that Cu-rich extended acenes represent a regime where the learned size and composition scaling is quantitatively insufficient, thereby necessitating further electronic structure analysis. While gold metalation yields stable, predictable electronic structures, copper incorporation drives the system into “emergent” regimes characterized by extreme bandgap narrowing and near-degenerate singlet–triplet states. This work establishes a framework in which machine learning performance itself marks the boundary of simple chemical trends, offering a rational approach to the discovery of low-bandgap, spin-sensitive hybrid semiconductors. Full article

Earthquakes remain among the most destructive natural hazards, necessitating reliable monitoring and early warning systems for effective risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved seismic signal analysis, enabling more accurate event detection, phase picking, classification, and magnitude estimation. This study presents a systematic review of ML- and DL-based approaches for earthquake monitoring, with particular emphasis on Distributed Acoustic Sensing (DAS) as an emerging technology for high-resolution, real-time seismic observation. Following the PRISMA 2020 guidelines, a systematic literature search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar, yielding 252,223 initial records. After applying the predefined publication period, removing duplicate records, conducting relevance screening, and performing eligibility assessment, 138 peer-reviewed studies published between 2021 and 2025 were retained for detailed analysis and synthesis. The review reveals a significant transition from conventional signal-processing techniques to advanced artificial intelligence-based approaches, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Bidirectional Long Short-Term Memory (BiLSTM) networks, Transformer-based architectures, hybrid models, and Bayesian learning methods for uncertainty quantification. The findings further demonstrate that Distributed Acoustic Sensing (DAS) has emerged as a transformative sensing technology because of its dense spatial coverage, high spatial resolution, and continuous monitoring capability. However, several challenges remain, including the lack of standardized datasets, limited model generalization across diverse geological settings, insufficient model interpretability, high computational complexity, and the limited integration of uncertainty-aware approaches for real-time seismic monitoring. This review identifies these critical research gaps and highlights promising future research directions, including multimodal data fusion, interpretable artificial intelligence, physics-informed learning, self-supervised learning, and robust uncertainty quantification for next-generation intelligent seismic monitoring systems. Unlike previous review studies that primarily focus on individual machine learning techniques or conventional seismic monitoring, this review provides a comprehensive and systematic synthesis of recent advances in machine learning, deep learning, and Distributed Acoustic Sensing (DAS), identifies current research gaps, and offers practical recommendations to guide future research on intelligent earthquake monitoring systems. Full article

Reservoir operation during flood events is a critical component of water resources management, requiring the balancing of flood risk mitigation, water supply, and system safety under significant uncertainty. Traditional rule-based and optimization methods often struggle to adapt to dynamic hydrological conditions and forecast errors. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing reservoir operation by improving inflow forecasting, learning operational policies, and enabling real-time decision-making. This review synthesizes recent advances in ML-based reservoir operation, including deep learning, ensemble learning, and reinforcement learning approaches. Special attention is given to integrating ML with hydrological models and optimization frameworks, along with effectively addressing uncertainty in flood control operations. The review highlights that while ML significantly improves predictive accuracy and operational flexibility, challenges remain in model interpretability, generalization, and real-world implementation. Future research directions are identified, focusing on physics-informed ML, explainable artificial intelligence, and adaptive real-time systems. This study provides a comprehensive understanding of the current state-of-the-art and outlines pathways toward intelligent and resilient reservoir operation systems. Full article

Bond strength degradation of corroded reinforced concrete (RC) structures involves complex mechanical interactions that are difficult to capture through experimental or purely data-driven methods alone. This study proposes a physics-informed neural network (PINN) framework that embeds a thin-walled cylinder rust expansion model as differentiable soft constraints in the loss function, achieving deep integration of mechanical priors with data-driven learning. The PINN predicts the bond strength τ_u alongside intermediate physical quantities including rust expansion stress σ_r, hoop stress σ_θ, and damage variable D_θ. A four-stage progressive training strategy is adopted to balance data fitting and physical consistency. The results show that the proposed PINN improves R² by 5.49–15.66% and reduces SMAPE by 33.91–58.87% compared to XGBoost, RF, and SVM, while achieving an average prediction error of 1.66% in experimental validation. Under the cover thickness extrapolation scenario (15 mm → 10 mm), the PINN maintains prediction errors within 1.5%, significantly outperforming both ML models and empirical formulas. The framework’s interpretability is further demonstrated through decomposition of the bond strength into frictional and mechanical interlock components, providing mechanistic insight into the corrosion-induced failure process. Full article