Seismic bearing capacity is a critical factor in designing shallow foundations, particularly in earthquake-prone regions, where traditional analytical methods often fall short. This study explores machine learning (ML) techniques to predict the seismic bearing capacity factor of shallow strip footings, explicitly considering the influence of both primary (P) and secondary (S) seismic waves. Multiple evolutionary ML models - Decision Trees (DT), Random Forest (RF), Adaptive Boosting (AdaB), and CatBoost (CB) - were developed and evaluated using robust statistical metrics. Among these, the CatBoost model emerged as the most accurate and reliable, delivering strong performance with R 2 (coefficient of determination) values of 0.957 in training and 0.936 in testing, alongside low mean absolute error (MAE) and root mean square error (RMSE). Notably, CatBoost surpassed the standard analytical formulas, underscoring its superior predictive power. A comprehensive parametric analysis further confirmed the model's reliability, showing strong agreement with trends reported in established literature. These findings demonstrate CatBoost's effectiveness as both a research tool for soil-structure interaction analysis under seismic conditions and a practical solution for geotechnical engineers requiring reliable bearing capacity assessments. • ML captures soil's nonlinear seismic response, replacing conventional analyses • Gradient boosting outperforms classical models in predicting seismic bearing capacity • CatBoost shows high robustness and accuracy in seismic bearing capacity prediction • CatBoost shows stable, generalizable performance for static and seismic loading cases

Damaged RC structures are typically strengthened in the load-carrying state in practice, which limits the improvement of their mechanical performance due to stress lag in the strengthening components. In this study, the shear performance of damaged thin-walled web RC beams strengthened with an ultrahigh-performance concrete (UHPC) layer and postinstalled adhesive bolts under secondary loading was experimentally and numerically investigated. Direct shear tests were first conducted to study the interfacial shear behavior between UHPC and normal concrete with different interface treatments. Subsequently, the three-point bending tests were performed to assess the performance evolution of the strengthened beams under secondary loading, i.e., strengthening under sustained loading conditions. The results demonstrated that even under secondary loading, the UHPC layer could still effectively constrain crack propagation and improve the shear performance of the damaged RC beams. However, due to the strain lag of UHPC induced by secondary loading, the UHPC strength was not fully utilized, leading to 15.7% and 10.8% reductions in the stirrup yielding load and ultimate load, respectively, compared with the strengthened beam without secondary loading. Finite-element analysis further indicated that the detrimental effect of secondary loading intensified with increasing sustained load levels but was mitigated as the UHPC layer thickness and postinstalled bolt ratio increased. Based on the experimental and numerical results, a combined surface treatment of mechanical chiseling followed by high-pressure water jetting at 80 MPa is recommended for effective UHPC-based shear strengthening of thin-walled web RC beams.

This study investigates how second-language (L2) listeners from five first-language (L1) backgrounds—English, Dutch, Mandarin, Spanish, and Korean—perceive English lexical stress, focusing on their use of vowel quality, pitch, and duration cues. Participants completed a cue-weighting perception task (Tremblay et al., 2021) in which two acoustic dimensions were manipulated orthogonally while the third was neutralized. Data for Dutch listeners come from the original study. Predictions about cross-lin-<br/>guistic transfer were based on the functional weight of each cue in the L1. The following L1 effects were predicted: For vowel quality: English, Mandarin &gt; Dutch &gt; Spanish, Korean; for pitch: Mandarin &gt; Korean &gt; Dutch, Spanish &gt; English; for duration: English, Mandarin&gt; Dutch, Spanish &gt; Korean. Bayesian mixed-effects models tested the effects of cues and L1 with L2 proficiency (Lemh€ofer &amp; Broersma, 2012) as a covariate. The results aligned broadly with our predictions: for vowel quality, English-&gt; Mandarin &gt; Dutch &gt; Korean &gt; Spanish; for pitch: Mandarin &gt; Korean, Dutch &gt; Spanish &gt; English; for duration: English, Mandarin, Dutch &gt; Spanish &gt; Korean. These findings support a cue-weighting typology shaped by L1-specific cue prominence, with implications for theories of transfer and perceptual learning in L2 acquisition.

The development of small‐scale wind turbines with composite materials continues to gain momentum due to their cost‐effectiveness, high energy conversion efficiency, and ease of deployment. Despite these advantages, such composite structures are susceptible to operational failures such as fiber rupture, matrix cracking, and delamination. This research introduces a comprehensive design and analysis methodology for a 30 kW‐class small wind turbine blade engineered for low noise and enhanced durability. The blade incorporates a sandwich composite structure, utilizing E‐glass, S‐glass, and carbon fiber face sheets combined with a balsa wood core to improve weight efficiency and mechanical stability. To determine the most effective structural configuration and understand potential failure modes, nine composite sandwich variants were analyzed, considering core and layer failure limits, fiber orientation, and laminate stress distribution. Finite element analysis (FEA) was applied to evaluate stress responses and deformation behavior under static loads. Among the configurations tested, the one employing epoxy S‐glass unidirectional face sheets with a multidirectional fiber layup exhibited the lowest peak stress and superior resistance to deformation. An experimental tensile test on dog‐bone specimens further supported the numerical outcomes, with the unidirectional carbon fiber sample achieving the highest tensile strength of approximately 92 MPa. The FEA results for the optimized configuration remained safely within this failure limit. This study establishes a robust, data‐driven framework for optimizing composite blade structures, ensuring both performance and structural integrity in small wind turbine applications.

Thermal drying (100–300 °C) is usually required to reduce moisture from chromium (Cr)-containing sludge before incineration, storage, or other resource recovery processes. Yet, part of trivalent chromium (Cr(III)) is oxidized to the toxic hexavalent chromium (Cr(VI)) during thermal drying. Currently, the molecular-level Cr(III) oxidization pathways during thermal drying remain poorly understood. In this paper, the molecular reaction pathway and the influence of Fe(III) substitution on CrxFe1–x(OH)3 oxidation in thermal drying are clearly elucidated. CrO3 is identified as a formed Cr(VI) product, and the oxidation pathway is heavily dependent on temperature and CrxFe1–x(OH)3 hydration. CrxFe1–x(OH)3 is oxidized through a well-defined pathway involving CrO3 as a key metastable intermediate, which sequentially decomposes into Cr5O12 and CrnFe2–nO3 at higher temperatures. The substitution of Fe(III) enhanced CrxFe1–x(OH)3 oxidation and lowered initial oxidation temperature, because Fe3d orbital hybridization with exogenous O2p orbitals facilitated electron transfer between Cr–O systems, and expanded electron transition regions. Since Cr(OH)O is an essential intermediate product during the Cr(VI) formation process, PO43–, SO42–, and Cl– ions were introduced to preferentially combine Cr(III) before the CrxFe1–x(OH)3 dehydration process, thereby preventing the generation of Cr(OH)O and subsequent Cr(VI) products. This study provides fundamental insights into the molecular-level mechanism of Cr(III) oxidation during thermal drying of Cr-containing sludge.

In this chapter, a numerical study to investigate the seismic vulnerability of the two storey colonnade of the Forum in Pompeii has been conducted. Software based on the Distinct Element Method (DEM) of analysis has been used. The colonnade was represented as an assemblage of distinct blocks connected together by zero thickness interfaces which could open and/or close depending on the magnitude and direction of stresses applied to them. Both static and non-linear static analyses have been undertaken. Also, a sensitivity study has been performed to investigate the effect of frictional resistance of the joints on the structural response of the colonnade. This was to simulate potential joint degradation effects and/or possible water lubrication at the joint.

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is a relatively new material known for its superior mechanical properties, particularly its compressive strength (CS), making it suitable for advanced structural applications. Traditional experimental methods for predicting CS are time-consuming and costly. In this study, a dataset of 276 samples with 12 input parameters was compiled from existing literature to develop predictive analytical models. The input variables include cement, sand, water, superplasticizer, silica fume, fiber content, water–binder ratio, water–cement ratio, curing age, fiber aspect ratio, temperature, and fiber volume. The reported CS values range from 90 to 186 MPa. Five modeling techniques—Linear Regression (LR), Log Base Regression (LBR), Nonlinear Regression (NLR), M5P-tree, and Artificial Neural Network (ANN)—were employed to predict the compressive strength of UHPFRC. Among these models, ANN demonstrated the highest prediction accuracy across all evaluation criteria, followed by the M5P-tree model. Residual error analysis confirmed that the ANN produced the lowest prediction error. Sensitivity analysis revealed that temperature, curing age, and superplasticizer content significantly influence CS. Optimization results indicated that a fiber content between 2.05% and 2.09% yields maximum compressive strength. These findings provide valuable insights for optimizing UHPFRC mix design using machine learning approaches.

This pioneering research involved an in-depth experimental evaluation of the mechanical properties of ambient-cured alkali-activated mortar (AAM), while assessing an innovative machine learning (ML) driven solution for sustainable construction. A comprehensive dataset was used, comprising 635 compressive strength and 94 flexural strength data points, including data from previous studies. The performance of six ML algorithms in predicting the compressive and flexural strengths of AAM was evaluated. Hyperparameter optimisation was performed with Optuna and ten-fold cross-validation. Multi-objective optimisation aimed to maximise compressive strength while minimising the carbon dioxide footprint. The findings highlight the significant impact of ground granulated blast-furnace slag (GGBS) content on strength, with higher GGBS improving compressive and flexural strengths but reducing workability. The highest compressive strength was 56.28 MPa at 28 days, for the AAM with 100% GGBS. The highest flexural strength was 0.580 MPa at 28 days, with 75% GGBS. Extreme gradient boosting was found to be the most reliable model in predicting the compressive strength, achieving a coefficient of determination (R2) of 98.1% on training data and 86.8% on testing data. Extra tree regression showed high accuracy in predicting the flexural strength of the AAM, achieving R2 = 90% on the testing dataset. A user-friendly interface was developed for predicting the mechanical properties of AAMs.

This study investigates how second-language (L2) listeners from five first-language (L1) backgrounds—English, Dutch, Mandarin, Spanish, and Korean—perceive English lexical stress, focusing on their use of vowel quality, pitch, and duration cues. Participants completed a cue-weighting perception task (Tremblay et al., 2021) in which two acoustic dimensions were manipulated orthogonally while the third was neutralized. Data for Dutch listeners come from the original study. Predictions about cross-lin-<br/>guistic transfer were based on the functional weight of each cue in the L1. The following L1 effects were predicted: For vowel quality: English, Mandarin &gt; Dutch &gt; Spanish, Korean; for pitch: Mandarin &gt; Korean &gt; Dutch, Spanish &gt; English; for duration: English, Mandarin&gt; Dutch, Spanish &gt; Korean. Bayesian mixed-effects models tested the effects of cues and L1 with L2 proficiency (Lemh€ofer &amp; Broersma, 2012) as a covariate. The results aligned broadly with our predictions: for vowel quality, English-&gt; Mandarin &gt; Dutch &gt; Korean &gt; Spanish; for pitch: Mandarin &gt; Korean, Dutch &gt; Spanish &gt; English; for duration: English, Mandarin, Dutch &gt; Spanish &gt; Korean. These findings support a cue-weighting typology shaped by L1-specific cue prominence, with implications for theories of transfer and perceptual learning in L2 acquisition.

The growing demand for concrete poses a significant environmental challenge, but alkali-activated high-performance concrete (AA-HPC) offers a more sustainable alternative by potentially reducing carbon emissions and ecological harm. This study explores the latest developments in machine learning (ML) applications aimed at predicting the compressive strength of AA-HPC, with a focus on minimizing experimental expenses, construction duration, and environmental impact. Among nine evaluated ML models, the combination of extreme gradient boosting (XGBoost) with the African vultures optimization algorithm (AVOA) emerged as the most effective. AVOA proved highly efficient in optimizing model parameters, achieving the lowest root mean square error (RMSE) during hyperparameter tuning. On the training dataset, XGB-AVOA reached an R2 of 0.994 and an RMSE of 2.368, while on the testing dataset, it maintained superior performance with an R2 of 0.975 and an RMSE of 5.664. These findings highlight AVOA’s strength in fine-tuning XGBoost compared to alternative optimizers such as grey wolf optimizer (GWO), whale optimization algorithm (WOA), social spider optimization (SSO), and gorilla troops optimizer (GTO). To support practical implementation, a graphical user interface (GUI) has also been developed, allowing researchers to efficiently utilize the XGB-AVOA model for accurate, cost-effective, and time-saving predictions in laboratory environments.

I PEBA sono strumenti che servono alle amministrazioni pubbliche per individuare ed eliminare le barriere architettoniche, rendendo gli spazi accessibili a tutti. Anche gli ospedali sono obbligati a dotarsi di questi piani, non solo per rispettare la legge, ma soprattutto per migliorare la qualità dei servizi per gli utenti. In questo caso, l’Azienda Ospedaliero-Universitaria Pisana ha collaborato con l’Università di Firenze per sviluppare un piano di accessibilità per l’ospedale di Cisanello. L’obiettivo non era solo rispettare le norme, ma anche creare un metodo pratico per organizzare e pianificare gli interventi nel tempo. Il progetto è partito dall’analisi della situazione esistente e ha previsto la raccolta di dati, coinvolgendo anche persone con disabilità per avere una valutazione più concreta. Tutte le informazioni sono state poi inserite in una piattaforma digitale, così da poter aggiornare e monitorare gli interventi nel tempo. In sintesi, il progetto serve sia a capire lo stato attuale delle strutture, sia a definire delle linee guida per migliorare quelle future, formando anche persone in grado di gestire questi processi. È organizzato in diverse fasi e aiuta a passare dall’analisi alla realizzazione concreta del piano.

It is plausible that under organic rice farming conditions, with Monochoria vaginalis as the dominant weed, the Indica rice ‘Takanari’ (Tak) outperform the Japonica rice ‘Koshihikari’ (Kos) in terms of nitrogen (N) uptake and biomass production. However, how N uptake, biomass, and yield in Tak and Kos are affected by weeds under organic rice farming paddy fields across multiple growing seasons remains unclear. To investigate this, we conducted a two-year field experiment (2022 and 2023) at Yamagata University Farm, Tsuruoka, Japan. Tak and Kos were transplanted individually (four seedlings/hill) or interplanted as Tak + Kos (2 + 2 seedlings/hill), with weeding and no-weeding as the main treatments. Rice and weed biomass and N uptake, as well as rice yield, were measured at harvest, with significant differences observed among all treatments. Weeding practices and growing seasons significantly affected on biomass and N uptake in both Tak and Kos. In 2023, aboveground rice biomass under weeding conditions was significantly lower than that in 2022, whereas no significant difference was observed under no-weeding conditions. Tak consistently showed higher biomass, N uptake, and yield compared with Kos across all treatments, including both seasons, weeding practices, and planting modes. The aboveground biomass ratios between Tak and Kos in interplanting mode were higher than those in individual planting mode under both weeding conditions across both years. These results suggest that Tak exhibited stronger competitive ability in terms of N uptake, biomass, and yield when interplanted with Kos. In conclusion, this two-year organic rice farming field experiment indicates that Tak may sustain higher N uptake and yield compared with Kos under both weeding and no-weeding conditions.