Search Results (931)

Glass Fiber Reinforced Polymer (GFRP)-reinforced concrete beams have gained significant prominence in structural engineering due to their advantageous mechanical and durability characteristics. However, the influence of training–testing data partitioning on machine learning (ML)-based deflection prediction for such members remains insufficiently explored. This study addresses this gap by evaluating the predictive performance of the K-Nearest Neighbors (KNN) regression algorithm in estimating the load–deflection behavior of GFRP-reinforced high-strength concrete beams. The experimental program comprised nine beams manufactured with concrete strength classes C45, C50, and C65, followed by ML-based deflection analyses using multiple data-splitting strategies. Findings indicate that the KNN model employing an 80:20 training–testing ratio provides the most accurate deflection predictions, achieving approximately 80% agreement with experimental results, while a higher prediction accuracy of approximately 85% was observed for beams with the highest concrete compressive strength (C65). Experimentally recorded deflections ranged from approximately 20 mm to values exceeding 50 mm, depending on the concrete strength class and loading level. Owing to its superior performance, the KNN model with an 80:20 training–testing ratio is recommended for predicting the deflection capacities of GFRP-reinforced high-strength concrete members. The study further examined the structural response associated with the use of GFRP as longitudinal tensile reinforcement. A consistent failure mechanism was observed across all beams, characterized by the formation of a single, wide vertical crack initiating at the beam’s soffit, regardless of concrete strength class. These observations contribute to a deeper understanding of the flexural behavior and fracture characteristics of GFRP-reinforced high-strength concrete beams and provide a foundation for future modeling efforts. Full article

Background: Type 2 diabetes (T2D) is a growing public health problem in Mexico. Lipid profile alterations have been shown to appear years before changes in glycemic biomarkers, and some of the latter are limited in availability, especially in underserved settings. Therefore, anthropometric variables and lipids represent relevant early indicators for the early detection of the disease. This study evaluates the capacity of non-glycemic clinical data—including lipid profile and anthropometric indicators—to detect T2D using machine learning, and compares the performance of different feature engineering approaches. Methods: Using more than a thousand clinical records of Mexican adults, three experiments were developed: (1) a distribution and normality analysis to characterize the variability of lipid variables; (2) an evaluation of the predictive power of multiple atherogenic indices (Castelli I, Castelli II, TG/HDL, and AIP); and (3) the implementation of statistical transformations (logarithmic, quare-root, and Z-standardization) to stabilize variance and improve feature quality. Logistic regression, SVM-RBF, random forest, and XGBoost models were trained on each feature set and evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve. Results: The AIP index showed the greatest discriminatory power among the atherogenic indices, while normality-based transformations improved the performance of distribution-sensitive models, such as SVM. In the final experiment, the SVM-RBF and XGBoost models achieved AUC values greater than 0.90, demonstrating the feasibility of a diagnostic approach based exclusively on non-glycemic data. Conclusions: The findings indicate that the transformed lipid profile and anthropometric variables can constitute a solid and accessible alternative for the early detection of T2D in clinical and public health contexts, offering a robust methodological framework for future predictive applications in the absence of traditional glycemic biomarkers. Full article

Background/Objectives: Notwithstanding the declaration by the World Health Organization in May 2023 regarding the conclusion of the COVID-19 pandemic, new cases of this potentially lethal infection continue to be documented globally, exerting a sustained influence on the worldwide economy and social structures. Contemporary SARS-CoV-2 variants, while associated with a reduced propensity for severe acute pathology, retain the capacity to induce long-term post-COVID syndrome, including in ambulatory patient populations. This clinical phenomenon may be attributable to potential autoimmune reactions hypothetically triggered by antiviral antibodies, thereby underscoring the need for developing novel, universal vaccines against COVID-19. The nucleocapsid protein (N), being one of its most conserved and highly immunogenic components of SARS-CoV-2, presents a promising target for such investigative efforts. However, the protective role of anti-N antibodies, generated during natural infection or through immunization with N-based vaccines, alongside the potential adverse effects associated with their production, remains to be fully elucidated. In the present study, we aim to identify potential sites of homology in structures or sequences between the SARS-CoV-2 N protein and human antigens detected using hyperimmune sera against N protein obtained from mice, rabbits, and hamsters. Methods: We employed Western blot analysis of lysates from human cell lines (MCF7, HEK293T, THP-1, CaCo2, Hep2, T98G, A549) coupled with mass spectrometric identification to assess the cross-reactivity of polyclonal and monoclonal antibodies generated against recombinant SARS-CoV-2 N protein with human self-antigens. Results: We showed that anti-N antibodies developed in mice and rabbits exhibit pronounced immunoreactivity towards specific components of the human proteome. In contrast, anti-N immunoglobulins from hamsters showed no non-specific cross-reactivity with either hamster or human proteomic extracts because of the lack of autoreactivity or immunogenicity differences. Subsequent mass spectrometric analysis of the immunoreactive bands identified principal autoantigenic targets, which were predominantly heat shock proteins (including HSP90-beta, HSP70, mitochondrial HSP60, and HSPA8), histones (H2B, H3.1–3), and key metabolic enzymes (G6PD, GP3, PKM, members of the 1st family of aldo-keto reductases). Conclusions: The results obtained herein highlight the differences in the development of anti-N humoral responses in humans and in the Syrian hamster model. These data provide a foundational basis for formulating clinical recommendations to predict possible autoimmune consequences in COVID-19 convalescents and are of critical importance for the rational design of future N protein-based, cross-protective vaccine candidates against novel coronavirus infections. Full article

This review surveys the recent literature on the fire resistance of reinforced concrete (RC) columns based on a bibliometric analysis of publications to reveal research trends and focus areas. The collected studies are synthesized from the perspectives of materials, structural behaviors, parameter influences, and predictive modeling. From the material aspect, the review summarizes the degradation mechanisms of conventional concrete at elevated temperatures and highlights the improved performance of ultra-high-performance concrete (UHPC) and reactive powder concrete (RPC), where dense microstructures and fiber bridging effectively suppress spalling and help maintain residual capacity. In terms of structural behavior, experimental and numerical studies on RC columns under fire are reviewed to clarify the deformation, failure modes, and effects of axial load ratio, slenderness, cover thickness, reinforcement ratio, boundary restraint, and load eccentricity on fire endurance. Parametric analyses addressing the influence of these factors, as well as the heating–cooling history, on overall stability and post-fire performance is discussed. Recent advances in thermomechanical finite element analysis and the integration of data-driven approaches such as machine learning have been summarized for evaluating and predicting fire performance. Future directions are outlined, emphasizing the need for standardized parameters for fiber-reinforced systems, a combination of multi-scale numerical and machine-learning models, and further exploration of multi-hazard coupling, durability, and digital-twin-based monitoring to support next-generation performance-based fire design. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

In this paper, we consider Cover’s and Pombra’s formulation of feedback capacity of additive Gaussian noise (AGN) channels, with jointly Gaussian nonstationary and nonergodic noise. We derive closed-form feedback capacity formulas, using Karush–Kuhn–Tucker (KKT) conditions and convergence properties of difference Riccati equations to limiting algebraic Riccati equations of filtering theory, for unstable and stable autoregressive (AR) noise. Surprisingly, the capacity formulas depend on the parameters of the AR noise, its pole $c\in (-\infty ,\infty )$ and noise variance $K_W\in (0,\infty )$, and the total transmit power $\kappa \in [0,\infty )$, indicating substantial gains for the unstable noise region $\forall c^2\in (1,\infty ),\forall \kappa >{\kappa }_{min}\stackrel{▵}{=}{\displaystyle \frac{K_W\left(1+\sqrt{4c^2-3}\right)}{2{\left(c^2-1\right)}^2}}$ compared to its complement region. In particular, feedback capacity is distinguished by three regimes, as follows. Regime 1, $\forall c^2\in (1,\infty ),\forall \kappa >{\kappa }_{min}$: the optimal channel input includes an innovations part, the capacity increases as $\left|c\right|>1$ increases, while ${\kappa }_{min}$ and the allocated transmit power decrease. Regime 2, $\forall c^2\in (1,\infty ),\forall \kappa \le {\kappa }_{min}$, Regime 3, $\forall c\in [-1,1],\forall \kappa \in [0,\infty )$ (complement of Regime 1): the innovations part of the optimal channel is asymptotically zero and the capacity is fundamentally different compared to Regime 1. The differences of capacity formulas for Regimes 1, 2 and 3 are directly related to their operational meaning: (i) Regime 1 is an ergodic capacity while Regimes 2 and 3 are nonergodic capacities; (ii) Regime 1 is achieved by an asymptotically stationary channel input with a non-zero innovations part, while Regimes 2 and 3 are achieved by an asymptotically zero innovations part. The gains of capacity for Regime 1 are attributed to the high correlation of noise samples compared to stable noise and the use of an informative innovations part by the optimal channel input, which make possible the prediction of future noise samples from past samples, unlike memoryless noise. Our results provide answers to certain open questions regarding the validity of capacity formulas of stable noise that appeared in the literature. Full article

The paper investigates the seismic vulnerability of single-story precast industrial buildings constructed in Romania during the 1970s, with particular reference to the damage observed following the 1977 Romanian earthquake. More than 800 structures were analytically assessed using a displacement-based evaluation procedure grounded in their original design specifications. Several displacement capacity models for flexure-controlled concrete columns were applied, and their suitability for the analyzed buildings is critically discussed. The study also includes a detailed case study that illustrates the practical application of the assessment methodology and highlights specific structural behaviors under seismic loading. The results demonstrate that the displacement-based assessment provides realistic predictions of seismic performance, consistent with observations from similar buildings constructed after the 1977 Vrancea earthquake. The conclusions indicate that the analyzed buildings generally exhibit favorable seismic behavior, with flexural hinging preceding shear failure and displacement-based methods offering more realistic and less conservative assessments than traditional force-based approaches. The scientific contribution of this work lies in using a comprehensive framework for evaluating the seismic response of existing precast industrial structures, offering insights into the effectiveness of different column capacity models, and establishing a foundation for future research on retrofitting strategies and the interaction of structural and non-structural components under seismic actions. Full article

Objective: This study aimed to apply the Random Forest machine learning model using oxidative stress biomarkers to classify breast cancer status and assess sentinel lymph node (SLN) metastasis, a pathology of high incidence and mortality that represents a major public health challenge. Methods: The breast cancer classification cohort included 188 women with infiltrating ductal carcinoma and 78 healthy volunteers. For SLN metastasis assessment, a subset of 29 women with metastases and 57 controls (n = 86) was used. Data preprocessing and the SMOTE technique were applied to balance the classes in the metastasis set, achieving a perfect balance of 171 examples (57 per class). Random Forest model with a leave-one-out validation strategy was employed and oxidative stress biomarkers (e.g., lipid peroxidation, total antioxidant capacity, superoxide dismutase, catalase, glutathione peroxidase) were used. Results: The model achieved high accuracy (0.996) in classifying breast cancer, representing a substantial improvement over current screening methods such as mammography. In contrast, its performance in detecting SLN metastases was more limited (accuracy = 0.854), likely reflecting the inherent complexity and heterogeneity of the metastatic process. Moreover, these estimates derive from a retrospective case–control cohort and should not be viewed as a substitute for, or a direct comparison with, population-based mammography screening, which would require dedicated prospective validation. Conclusions: The findings underscore the model’s robust performance in distinguishing women with breast cancer from healthy volunteers, but highlight significant gaps in its ability to diagnose metastatic disease. Future research should integrate additional biomarkers, longitudinal data, and explainable artificial intelligence (XAI) methods to improve clinical interpretability and accuracy in metastasis prediction, moving towards precision medicine. Full article

Local scour around support structures has remained a critical barrier to tidal stream turbine deployment in energetic marine channels since loss of embedment and bearing capacity has undermined stability and delayed commercialization. This review identifies key mechanisms, practical implications, and forward-looking strategies related to local scour. It highlights that rotor operation, small tip clearance, and helical wakes can significantly intensify near-bed shear stress and erosion relative to monopile foundations without turbine rotation. Scour behavior is compared across monopile, tripod, jacket, and gravity-based foundations under steady flow, reversing tides, and combined wave and current conditions, revealing their influence on depth and morphology. The review further assesses coupled interactions among waves, oscillatory currents, turbine-induced flow, and seabed response, including sediment transport, transient pore pressure, and liquefaction risk. Advances in prediction methods spanning laboratory experiments, high-fidelity simulations, semi-empirical models, and data-driven techniques are synthesized, and mitigation strategies are evaluated across passive, active, and eco-integrated approaches. Remaining challenges and specific research needs are outlined, including array-scale effects, monitoring standards, and integration of design frameworks. The review concludes with future directions to support safe, efficient, and sustainable turbine deployment. Full article

A promising approach to alleviate the emerging capacity limitations in backbone optical networks is to employ the Super C+L-band, which provides an available spectrum of roughly 12 THz. Network throughput is a key metric for analyzing the performance of such networks; however, evaluating this metric is a complex task due to the interplay between physical and network layer aspects. Physical modeling, which accounts for signal impairments, is particularly complex in these scenarios due to the presence of Stimulated Raman Scattering (SRS), which transfers energy from the C to the L band. On the other hand, network layer modeling is also challenging due to the influence of numerous factors, including physical topology, routing, and traffic characteristics. For the networks considered here, we propose a machine learning approach to predict both the network throughput and the average channel capacity for the Shannon and real cases, and to investigate how these metrics depend on various physical topology parameters. The approach relies on an Artificial Neural Network (ANN) model, whose predictions are interpreted using the SHapley Additive exPlanations (SHAP) method to identify the importance of various topological parameters. Furthermore, the ANN is trained using data obtained from a previously developed simulator that takes into account both physical and network aspects. The analysis provides valuable insights for designing future ultra-high-capacity optical backbone networks. Full article

Social media is an essential part of people’s lives worldwide. This study aimed to analyze the predictive capacity of social media addiction on academic engagement among students enrolled in the Faculty of Health Sciences at the National University of Chimborazo during the first academic period of 2023. The Social Media Addiction Questionnaire (ARS) and the Utrecht Work Engagement Scale (UWES-S-17) were applied to 1200 participants during an analytical study. According to the simple linear regression model, 11.2% of the variance in academic engagement levels was explained by social media addiction, with statistical significance (p < 0.05). The multiple linear regression model was significant, although it showed a low capacity to explain and predict the level of academic engagement, considering the dimensions of the level of addiction to social media (obsession, lack of control, and excessive use). The ROC curve parameters showed statistical significance, showing a moderate ability to discriminate insufficient academic commitment. The results serve as a basis for future studies and as a diagnostic basis for establishing policies and strategies in the institution where the research was conducted to increase academic engagement and reduce social media addiction. Full article

Diceraeus melacanthus (Dallas, 1851) (Hemiptera: Pentatomidae) has become a key pest in Brazilian maize production, particularly during seedling establishment. This study estimated its lower and upper developmental thresholds (Tb and Tsup), thermal constant (K), and degree-day requirements, and used these parameters to model the potential annual generations (PAG) across the Mato Grosso do Sul, Paraná, and São Paulo states. Biological parameters were calculated from controlled laboratory assays, and historical meteorological datasets were combined with regression models and spatial analyses to generate phenology maps of PAG. Results indicated marked regional differences: Mato Grosso do Sul presented the highest potential, averaging eleven generations per year, São Paulo showed intermediate values with nine generations, and Paraná exhibited the lowest, with approximately seven generations annually. Latitude exerted the strongest influence on PAG, while altitude contributed the least. These findings are consistent with the known adaptability of D. melacanthus to warmer climates and highlight its capacity to persist in no-tillage soybean–maize systems and areas with volunteer plants. The results provide a predictive framework for assessing population risk and may support decision-making in integrated pest management. Further studies on host range, phenology, and distribution are required to anticipate future expansions across South America. Full article

Wastewater-derived microplastics (WW-MPs) are increasingly recognised as reactive vectors for aromatic organic contaminants (AOCs), yet their role in contaminant fate remains insufficiently constrained. This review synthesises current knowledge on the transformation of microplastics in wastewater treatment plants, including fragmentation, oxidative ageing, additive leaching, and biofilm formation, and links these processes to changes in sorption capacity toward phenols, PAHs and their derivatives, and organochlorine pesticides (OCPs). We summarise the dominant adsorption mechanisms-hydrophobic partitioning, π-π interactions, hydrogen bonding, and electrostatic and, in some cases, halogen bonding-and critically evaluate how wastewater-relevant parameters (pH, ionic strength, dissolved organic matter, temperature, and biofilms) can modulate these interactions. Evidence in the literature consistently shows that ageing and biofouling enhance WW-MP affinity for many AOCs, reinforcing their function as mobile carriers. However, major gaps persist, including limited data on real wastewater-aged MPs, lack of methodological standardisation, and incomplete representation of ageing, competitive sorption, and non-equilibrium diffusion in existing isotherm and kinetic models. We propose key descriptors that should be incorporated into future sorption and fate frameworks and discuss how WW-MP-AOC interactions may influence ecological exposure, bioavailability, and risk assessment. This critical analysis supports more realistic predictions of AOC behaviour in wastewater environments. Full article

Understanding the dynamics of Land Resource Carrying Capacity (LRCC) under rapid urbanization requires not only retrospective assessment but also operational tools for supporting future planning. This study develops a spatially explicit LRCC framework for Xuchang City (2000–2020) that integrates economic, land-use, and resource subsystems, and encodes key policy instruments as time-varying variables. Entropy weighting is used as a data-driven baseline for indicator aggregation, with robustness checks conducted using equal weighting and the analytic hierarchy process (AHP). Results show that Xuchang’s citywide LRCC increased from 0.56 in 2000 to 0.81 in 2020, corresponding to a 44.6% rise over the study period. This growth exhibits marked spatial heterogeneity across the urban area and is closely associated with changes in urbanization level, industrial restructuring, and land development intensity. The overall pattern of LRCC change is robust to alternative weighting schemes, supporting the comparability of the estimates across time. To explore potential futures, a scenario-based simulation is conducted in which a +2% increase in urbanization rate and a −10% reduction in land development intensity are translated into LRCC responses, yielding a predicted increase of about +0.023 (from 0.81 to approximately 0.833). This scenario illustrates how the framework can be used to evaluate trade-offs and synergies among development and conservation objectives. By quantifying the contributions of policy-related and socio-economic indicators to LRCC dynamics, the study provides an evidence-based tool for optimizing land governance and promoting sustainable urban development in rapidly urbanizing regions. Full article

Compared to traditional single/double-row concrete cast-in-place piles or concrete walls commonly used in foundation pit engineering, pre-tensioned prestressed hollow concrete-filled steel tube piles (referred to as prestressed Steel Cylinder Piles, or prestressed SC piles) demonstrate superior advantages including high bearing capacity, light weight, enhanced stiffness, excellent crack resistance, and cost-effectiveness, indicating a promising future in foundation pit engineering. However, current research has paid limited attention to such piles. Only a few experimental studies have focused on their flexural performance. No studies have presented bearing behavior investigations considering soil–pile interactions and the differences between these kinds of piles and traditional piles. To address this gap, this paper conducts a systematic investigation into the bearing performance of prestressed SC piles. A refined finite element analysis model capable of accurately characterizing pile–soil interactions is developed to analyze the mechanical behavior. Subsequently, the elastic foundation beam method recommended by design codes is employed to analyze the internal forces and displacement variations of these piles during excavation. Finally, the predictions by the design code are compared against those from the refined model. Results shows that the established finite element model presents reasonable predictions on monitoring data and experimental results, with deviations in bending moments and deformations within the range of 10–15%; a comparative analysis of different pile types reveals that prestressed SC piles exhibit smaller horizontal displacements and higher bearing capacities; the bending moments and deformations predicted by design methods (elastic foundation beam method) are conservative, with the predicted values significantly higher than those predicted by the refined model. Full article