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.

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.

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.

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.

Ultra-High-Performance Concrete (UHPC) is an advanced cementitious material with exceptional strength and durability, widely used in high-performance structural applications. However, predicting its compressive strength remains a challenge due to the complex nonlinear interactions among its mix constituents. This study employs machine learning (ML) models—Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—to develop predictive models for UHPC compressive strength. To further enhance predictive accuracy, four advanced metaheuristic optimization algorithms were integrated —Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Giant Trevally Optimizer (GTO), and Mountain Gazelle Optimizer (MGO)—to fine-tune hyperparameters of the ML models. A dataset of 810 UHPC mix samples with 15 input variables was used to train and evaluate the models. Among the tested approaches, the MGO-optimized XGBoost (MGO-XGB) model achieved the highest accuracy, with an R² of 0.9966 in training and 0.9839 in testing, along with the lowest root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate that integrating metaheuristic optimization significantly improves ML model performance, with MGO-XGB emerging as the best predictor. Additionally, SHAP analysis identified key influencing factors, including silica fume content, superplasticizer dosage, and curing age, which play a critical role in UHPC strength development. The findings indicate that ML-assisted optimization can reduce reliance on extensive experimental testing, offering a cost-effective and efficient approach for UHPC mix design and quality control. To enhance practical usability, a graphical user interface (GUI) was developed, allowing engineers and researchers to input mix parameters and obtain immediate strength predictions. This study contributes to data-driven advancements in concrete technology, enabling more efficient and sustainable UHPC design and construction. • Revealing of material’s nature. • Hybrid ML models and metaheuristic optimization were used for UHPC strength prediction. • XGBoost optimized with MGO demonstrated the best predictive performance. • SHAP analysis identified key mix components influencing UHPC strength. • A user-friendly GUI was developed for real-time strength estimation.

This study evaluates the performance of foamed geopolymer concrete (FGC) incorporating rigid polyurethane (PU) waste as a partial sand replacement and aluminum powder (AP, 1%) as a foaming agent. The mixtures were based on metakaolin, fly ash, and silica fume. Fresh and hardened properties were assessed, including workability, setting time, density, compressive strength, flexural strength, splitting tensile strength, elastic modulus, water absorption, porosity, gas permeability, and chloride ion penetration. Microstructural characteristics were examined using scanning electron microscopy (SEM). The results show that moderate PU incorporation significantly enhances mechanical performance. The optimal mixture (PU30) achieved a compressive strength of 47.25 MPa at 180 days, representing a 15.6% increase compared to the control. Flexural and splitting tensile strengths improved by 19.9% and 16.7%, respectively, while the elastic modulus increased by 33.8% to 0.95 GPa. These improvements are attributed to enhanced particle packing and more efficient stress transfer within the matrix. In contrast, higher PU contents (>30%) reduced mechanical performance due to increased total porosity and weakened interfacial bonding. Durability-related properties indicated that mixtures PU20–PU30 exhibited reduced permeability and optimized pore structure, characterized by lower pore connectivity. SEM observations confirmed a denser matrix with uniformly distributed pores at optimal PU levels. Additionally, the integration of Random Forest regression with GLCM-based texture analysis demonstrated strong capability in predicting mechanical properties from SEM images. Overall, the combined use of PU waste and AP enables the production of lightweight, structurally efficient, and sustainable FGC with improved mechanical and durability performance.

A reactive-transport model for chloride binding in cementitious materials is developed and validated over four temperatures (5, 21, 35, and 80°C) and three chloride concentrations (5, 10, and 20 g.L−1). Diffusive transport is coupled with surface complexation reactions (SCRs), in which the equilibrium constants (logK) are described by a nonlinear temperature-dependent formulation, and with kinetic laws for Friedel salt precipitation and dissolution that depend explicitly on temperature and chloride activity. Compared with equilibrium-only approaches, the proposed model avoids over-prediction of early-age bound chloride at elevated temperature. The nonlinear logK formulation provides a more consistent representation of SCR behavior from 5 to 80°C, while the chloride-activity-dependent Friedel salt kinetics is required to capture the delayed precipitation observed at 80°C under high chloride exposure. . The simulations further show that Friedel salt should be retained as the governing chloride-binding AFm phase in the final model formulation, whereas Kuzel salt does not reproduce the observed bound-chloride level satisfactorily under the investigated high-temperature conditions. In addition, kinetic Portlandite dissolution regulates Ca2+ supply and shifts the balance among competing phases. An additional assessment using CEM V indicates that the framework remains applicable up to 35°C, whereas the discrepancy at 80°C reveals a binder-specific limitation of the current high-temperature mineralogical submodel.

Speech perception relies on multiple acoustic cues whose relative weighting varies across languages. The present study examines how long-term language experience shapes cue weighting in second-language (L2) speech perception, refining an attentional-learning account of cross-linguistic transfer. Native speakers of English, Dutch, Spanish, Korean, and Mandarin completed a cue-weighting task targeting English lexical stress, in which vowel quality, pitch, and duration were orthogonally manipulated. Results revealed robust, dimension-specific differences across first-language (L1) groups that could not be explained solely by the presence or absence of lexical stress or lexical tone in the L1. Instead, cue weighting reflected how acoustic dimensions function within the L1 cue ecology, including their relative contribution to lexical distinctions and the stability and interpretability of these mappings across contexts. Cue redundancy constrained relative cue strength without eliminating attentional sensitivity to secondary dimensions. Machine-learning classification further showed that L1-linked attentional profiles were sufficiently structured to support prediction, even among L2 listeners with substantial English proficiency, demonstrating the persistence of L1-shaped attentional tuning. These findings support a view of cue weighting as reflecting durable, multidimensional attentional priors shaped by long-term experience and highlight the importance of L1 cue ecologies in understanding cross-linguistic transfer in speech perception.

Chloride-induced corrosion remains one of the main durability concerns for reinforced concrete exposed to marine or de-icing environments. Conventional diffusion-based models often neglect the chemical form of chloride and the role of counter-cations in altering hydrated cement. In practice, chloride transport is a reactive process controlled by simultaneous diffusion, binding, dissolution/precipitation, and pH buffering within the evolving cement matrix. This study investigates how different cations Na⁺, K⁺, Ca²⁺, and Mg²⁺ affect chloride ingress, binding, and hydrate stability in saturated concrete. A reactive transport model is developed that couples diffusion, aqueous speciation, mineral equilibrium, kinetic reactions, and surface complexation on C-S-H. The simulations reproduce and extend the experimental results of literature for four boundary solutions: 0.5 mol/l NaCl, 0.5 mol/l KCl, 0.25 mol/l CaCl₂, and 0.25 mol/l MgCl₂, over exposure periods up to ten years in saturated concrete. Under NaCl and KCl, the pore network remains stable, alkalinity is maintained, and binding is moderate producing deep free-chloride penetration. Under CaCl₂ and MgCl₂, strong near-surface reactions occur: AFm phases convert into Kuzel-type compounds, and portlandite dissolution with C-S-H decalcification produces brucite or M-S-H. These transformations trap chloride near the surface, limit transport, and reduce pH in the outer zone. Consequently, monovalent salts lead to transport-controlled ingress, while divalent salts cause binding/microstructure-controlled accumulation. Reliable prediction of corrosion risk requires evaluating free chloride, total chloride, and alkalinity together. Reactive transport modeling thus provides a physically consistent and predictive framework for performance-based durability design of concrete under diverse chloride environments.

Autologous hematopoietic cell transplantation (HCT) has been introduced for patients with severe systemic sclerosis (SSc). We aimed to assess the safety and long-term efficacy of HCT modality for severe SSc, refractory to conventional therapy, in 17 patients who were referred to our - The Joint Accreditation Committee of the International Society for Cellular Therapy (ISCT) and the European Group for Blood and Marrow Transplantation (EBMT)-accredited Unit from 2005 to 2024. Peripheral blood stem cells were collected using cyclophosphamide and GCSF. An immunoablative conditioning regimen of cyclophosphamide and anti-thymocyte globulin was administered. Disease assessments were done before and after mobilization treatment and post-transplant, focusing on skin sclerosis, pulmonary function, cardiac involvement, gastrointestinal manifestations, the necessity for additional immunosuppressive therapy, and overall patient well-being. Before transplantation, 13/17 (76%) of the patients had diffuse skin involvement with a mean mRSS of 31 (2-49), 2/17 (12%) had pulmonary hypertension, and 14/17 (82%) had gastrointestinal manifestations. The median follow-up period was 9.1 (0. 5-14. 3) years. Improvement of skin sclerosis was observed, with a decrease in mRSS before transplantation from 31 (2-49) to 7 (2-22) post-HCT. Lung function remained stable in 8/15 (53%) patients, improved in 5/15 (33%), and deteriorated in 2/15 (13%). Gastrointestinal manifestations were improved in 12/14 (86%) patients, while all patients (16/16, 100%) reported a great impact on their quality of life. Ten out of the 16 (63%) patients were free of immunosuppressive drugs after the HCT. Overall survival was 16/17 (94.2%). Concerning TRM, there was one (1/17, 5.8%) death early post-transplant. In this specific cohort of selected patients with severe SSc refractory to immunosuppressive medications, autologous HCT led to improvements in the outcomes assessed.