The durability of reinforced concrete structures in chloride-rich environments remains a major concern in infrastructure design, particularly in coastal regions. While standardized laboratory procedures provide reliable quantification of chloride ingress resistance, they are often time-consuming, costly, and unsuitable for early-stage mix design. This study proposes a data-driven framework for predicting the chloride resistance level of concrete using tree-based machine learning (ML) classifiers. A comprehensive experimental dataset was utilized to train and validate three ML models: Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and CatBoost Classifier (CatBC). Extensive hyperparameter tuning was performed using the Optuna framework with 2000 trials per model to enhance predictive performance. Among the tested models, CatBC outperformed its counterparts with a test accuracy of 0.95 and weighted F1-score of 0.85. Feature importance analyses using SHAP values, Prediction Values Change, and other CatBoost interpretability tools consistently identified the water-to-binder ratio, superplasticizer content, test age, and aggregate proportions as key predictors of chloride resistance. The findings demonstrate that machine learning offers a fast, cost-effective, and accurate alternative for classifying concrete’s chloride resistance, supporting informed decision-making in mix design and service-life assessment.

Đặt vấn đề: Sự kết hợp giữa vi khuẩn H. pylori và ung thư dạ dày (UTDD) cùng với sự gia tăng tỷ lệ lây nhiễm trên toàn thế giới, cho thấy sự cấp thiết của việc tìm ra các chiến lược phòng ngừa bệnh. Việt Namhiện nay là một trong những nước có tỷ lệ nhiễm H. pylori cao. Gen cagA, vacA được đặc biệt chú ý trong UTDD. Hiện nay, ở nước ta chỉ mới có một số nghiên cứu làm sáng tỏ một phần mối liên quan chủng H.pylori có cagA, vacA ở bệnh nhân UTDD. Tuy nhiên, cho đến nay còn ít nghiên cứu đề cập đến việc phân tích biểu lộ gen iceA liên quan với các gen cagA, vacA của H. pylori ở bệnh nhân ung thư dạ dày.Mục tiêu: Tìm hiểu mối liên quan kiểu gen iceA, cagA, vacA của H. pylori và mô bệnh học ở bệnh nhân (BN) ung thư dạ dày.Đối tượng và phương pháp: Đối tượng nghiên cứu: Gồm 91 bệnh nhân UTDD (nhóm bệnh) và 92 bệnh nhân viêm dạ dày mạn tính (nhóm chứng), được chọn trong số những người đã đến nội soi dạ dày và đượcchỉ định sinh thiết niêm mạc dạ dày để chẩn đoán xác định tại Khoa Thăm dò chức năng.Kết quả: Các bệnh nhân UTDD có hình ảnh mô bệnh học (MBH) biệt hóa kém chiếm tỷ lệ cao nhất ở cả hai nhóm cagA và vacA dương tính là 55,4% và 54,5%. Không có sự khác biệt các kiểu gen iceA1 và iceA2 giữa thể tuyến ống và thể tế bào nhẫn ở bệnh nhân UTDD với p > 0,05. Không có thể MBH tuyến chế nhày có H. pylori mang gene iceA. Kiểu gen iceA1 chiếm 54% ở nhóm MBH UTDD biệt hóa kém, 32% ở nhóm biệt hóa vừa. Kiểu gen iceA2 chiếm 50% ở nhóm biệt hóa kém và 40% ở nhóm biệt hóa vừa. Sự khác biệt giữa các kiểu gen A1 và A2 ở các nhóm MBH trên bệnh nhân UTDD ở nhóm biệt hóa vừa và kém có ý nghĩa thống kê với p < 0,05.Kết luận: Không có mối liên quan giữa các týp cagA, vacA; các kiểu gen với các đặc điểm mô bệnh học của ung thư dạ dày theo WHO năm 2010. Sự khác biệt giữa các kiểu gen iceA1 và iceA2 ở các bệnh nhânung thư dạ dày ở nhóm biệt hóa vừa và kém có ý nghĩa thống kê (p < 0,05).

SYMBOLIC MATRIX STRUCTURAL ANALYSIS OF TRUSSES: STATIC AND DYNAMIC APPLICATIONS - 10th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering - 15-18 June 2025, Rhodes Island, Greece

Reinforced concrete (RC) structures are among the most prevalent in the construction industry. However, various in-service conditions such as loading, environmental exposure, and construction practices can lead to the formation of concrete cracks. In extreme cases, these cracks may propagate through the cross-section of structural members, creating section pre-cracks. This study examines the influence of section pre-cracks on the shear failure behavior of RC deep beams (with a shear span-to-depth ratio of 1.57) through three-point bend-loading tests and 3D RBSM analysis. The primary experimental variables include the number of pre-cracks (one or two) and their width (0.5 mm or 1.0 mm). The findings reveal that pre-cracks significantly reduce the initial stiffness and shear strength of deep beams, primarily by disrupting the transmission of axial compressive stress in the concrete, thereby diminishing the contribution of arch action to shear strength. Furthermore, it is observed that the greater the total width and number of pre-cracks, the more significant the reduction in shear strength. In addition, combining both experimental tests and numerical simulations, a total of 48 deep beams were subjected to shear failure tests. Based on the shear strength data, two degradation models for shear strength (one representing the average trend and the other a conservative lower-bound envelope model) were developed in relation to the total crack width.

Abstract Vowel glottalization, as voice quality variation, occurs at phrase edges or under prominence, often in conjunction with resolving onsetless syllables or vowel-vowel hiatus. This study examines whether and how voice quality in two varieties of English, American and Australian, is manifested at phrase edges and under prominence in vowels that are neither strictly initial nor final. Analyses of spectral and noise measures show that both varieties utilize voice quality to signal prominence and boundaries, even in non-edge contexts. Specifically, increased glottalization reflects stronger laryngeal articulation in line with domain-initial and prominence-induced strengthening. Conversely, phrase-final positions are characterized by phrase-final creak, which is often observed with phrase-final weakening. While both varieties show similar vowel glottalization usage, Australian English tends to use voice quality more extensively to mark focus than American English, with a greater overall tendency toward glottalization. This suggests that the impressionistic difference perceived in the general use of voice quality between the varieties (more robust glottalized vowels in Australian English) stems from differences in the use of glottalization in marking prosodic structure. These findings underscore the role of non-contrastive voice quality in shaping prosodic structure across varieties while also revealing dialect-specific interactions between phonetics and prosody in its manifestation.

ABSTRACT This study investigates the effectiveness of steel‐reinforced grout (SRG) for strengthening continuous reinforced concrete (RC) beams. A more sustainable alternative to conventional cementitious mortars, geopolymer mortar was employed in SRG‐strengthened RC beams while maintaining structural efficiency. An extensive experimental program involving 15 two‐span continuous RC beam specimens was conducted. The experimental parameters considered include the overall SRG stiffness (by varying the number of fabric layers and fabric density), the span coverage ratio, and the locations of strengthening. Additionally, the performance of steel fabrics in SRG was compared with other types of fabrics, including carbon, glass, and polyparaphenylene benzobisoxazole (PBO) fabrics. The results revealed that strengthening significantly enhanced the flexural capacity of the beams, with improvements ranging from 24% to 81%. Steel fabrics in SRG outperformed all other fabric types in terms of load‐carrying capacity. Low‐density SRG demonstrated superior bonding with the concrete substrate, leading to enhanced strengthening effects compared to high‐density SRG. Several failure modes were observed, including steel yielding, concrete cover separation, fabric rupture and slippage, and SRG debonding. A theoretical model based on SRG effective strain was utilized to predict the maximum load capacity of the strengthened beams.

The project is to share an database on mechanical and rheological Properties of Regolith Simulants, particularly for 3D printing. More than 1000 data are collected from different resources for different types of regolith simulants, including their mechanical properties such as compression, tensile, bending strength, hardness, different printing conditions, and different types of printing methodologies. Their rheological properties of regolith simulants are also included, such as their mix ratios, their activator solutions, their setting times, and their viscosities. This database will provide an overview for future researchers on how they could select regolith simulants for their research and how their data compared to the existing data. The simulants included are JSC-1AC, JSC-1A, NU-LHT-2M, Basalt, PA12, KLS-1, CLRS-1 + 4.6% ilmenite, CLRS-1 + 28.5% ilmenite, FJS-1, CUG-1A, CUG-MT, CUG-HT, CUG-1A, CUG-MT, CUG-HT, CUG-1A, CUG-MT, CUG-HT, JSC-1A without ilmenite, JSC-1A + 5 weight% ilmenite, JSC-1A + 10 weight% ilmenite, JSC-1A + 5 weight% fine ilmenite, JSC-1A + 20 weight% ilmenite, JSC-2A, DNA. DNA-1 lunar regolith simulant (analog to lunar mare regolith), LHS-1 lunar highlands simulant, LMS-1 lunar mare simulant, GVS (Ground Volcanic Scoria) lunar regolith simulant, LRS-1 lunar regolith simulant; targets Apollo-17 samples; mean particle diameter 44 μm; contains plagioclase, olivine and glass, LRS-2 lunar regolith simulant; used as a lunar simulant targeting Apollo-14 samples, LRS-3 lunar regolith simulant; used as a lunar simulant targeting Apollo-12 samples, Lunar regolith simulant (AGK-2010), quartz powder (QP), standard sand Lunar regolith simulant (EAC-1a) + PEEK powder, BH-1 lunar regolith simulant + NaOH solution (alkali activator), GCD-1 lunar regolith simulant (basalt, anorthite, albite, pyroxene, ilmenite, slag, fly ash), BH-1 lunar soil simulant (volcanic scoria, feldspar-rich), LHS-1 (lunar highlands simulant) + LMS-1 (lunar mare simulant), Lunar regolith (highland, mare, simulants) Lunar regolith (various simulants across multiple studies), DNA-1 lunar regolith simulant + NaOH + urea (superplasticizer), LHS-1 lunar highlands simulant; particle size 0.04–1000 μm, median 98 μm; bulk density 1.27 g/cm³, LMS-1 lunar mare simulant; particle size 0.04–300 μm, median 45 μm; bulk density 1.56 g/cm³, TJ-1 simulated lunar soil, Basaltic volcanic slag lunar soil simulant (albite, anorthite, augite, olivine phases) Lunar aggregate simulant (LAS) based on ilmenite rock; reference mix with standardized sand, HIT-L-1 lunar regolith simulant (volcanic scoria) 10 lunar soil simulants (LHS-1, AGK2010, OPRL2N, JSC-1A, CHENOBI, LMS-1, ESA 06-A, ESA 01-E, UoM-B, UoM-W), Lunar regolith simulant BH-1 lunar regolith simulant, HIT-LRS-1 lunar regolith simulant, Lunar regolith simulant, BH-2 lunar regolith simulants (from volcanic scoria, China), Metakaolin-based geopolymer

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.