Hakim Sabzevari University (HSU) (Persian: دانشگاه حکیم سبزواری, Daneshgah-e Hakim Sabzevari) is one of the most developed public universities in Sabzevar city at Razavi Khorasan province, Iran. Hakim Sabzevari University is one of the most prestigious public universities in Iran and the oldest university in Sabzevar. HSU was established in 1973 when its name was Kar University. After the Iran revolution in 1979, this university was halted until 1987. It was reestablished in 1987 as a branch of Tarbiat Moallem University of Tehran but later became independent and it was renamed to Hakim Sabzevari University in 2011, after Hadi Sabzevari, a prominent Iranian philosopher and theologian. HSU offers 139 bachelors, masters, and Ph.D. programs to more than 8152 male and female students studying under about 263 faculty members in 10 departments. HSU is known as the dynamic in science and the leading in development university in Iran. Hakim Sabzevari University has more than 264 international students studying from different countries at this university. According to the Islamic World Science Citation Center statistics, HSU is ranked 41st in Iran amongst other universities of Ministry of Science, Research and Technology. The university currently consists of 10 faculties.
The effect of different entrance walls (including graphene, graphene oxide (GO), boron nitride (BN), silicone carbide (SiC), and Ti2C MXene layer) has been examined on water and ion fluxes in desalination process using carbon nanotube by molecular dynamics simulations. Our results indicated that the kind of entrance wall has significant effect on the desalination process. The Ti2C MXene layer showed higher water and ion fluxes than the other systems at 300 and 320 K at high pressure of 250 MPa. The GO surface exhibits the least water and ions fluxes at all pressures and temperatures which is due to the strong water-wall interactions. Therefore, the GO surface is the best choice of the entrance wall if the goal is only the greatest rejection rate. It is also shown that there are not significant differences between graphene, BN, and SiC surfaces in desalination process.
Fly ash-based alkali-activated concrete is a promising alternative to ordinary Portland cement concrete because it utilizes industrial by-products and offers potential environmental benefits. However, predicting its compressive strength remains challenging because of the nonlinear relationships between mixture composition, curing conditions, and mechanical performance. Although machine learning methods have been increasingly applied to this task, the integration of comparative ANN modeling, cross-validation, explainable prediction, and practical software implementation within a unified framework warrants further investigation. To address this need, this study develops an explainable artificial intelligence framework for predicting the compressive strength of fly ash-based alkali-activated concrete. A dataset comprising 162 concrete mixtures and 16 input variables was used to develop and compare three models: ANN-LM, ANN-GWO, and ANN-ARO, based on the Levenberg–Marquardt algorithm, Grey Wolf Optimizer, and Artificial Rabbits Optimization, respectively. Model performance was evaluated using the coefficient of determination (R²), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute deviation (MAD), and the a20 index. Ten-fold cross-validation was conducted to examine predictive performance across different data partitions, while SHapley Additive exPlanations (SHAP) was used to interpret the influence of input variables on model predictions. Among the three models, ANN-LM achieved the best testing performance, with R² = 0.9296, RMSE = 0.1879, MSE = 0.0353, and MAE = 0.1390. In ten-fold cross-validation, ANN-LM achieved a mean R² of 0.7419, with mean RMSE, MAE, and MAD values of 0.45492, 0.33599, and 0.32469, respectively, and a mean a20 index of 18.64%. The difference between the testing and cross-validation results indicates that predictive performance varies across data partitions, highlighting the need for further assessment using independent datasets. SHAP analysis identified curing temperature, alkaline activator characteristics, and fly ash characteristics as influential predictors of compressive strength. Finally, a MATLAB-based graphical user interface, GeoMixAI, was developed to facilitate preliminary compressive strength estimation from specified mixture inputs. The main contribution of this study is the integration of comparative ANN evaluation, cross-validation, SHAP-based interpretability, and accessible software implementation within a unified framework, linking predictive performance assessment with interpretation and practical application.
Biopolymeric 3D-printed scaffolds promote cell adhesion, proliferation, and differentiation for tissue regeneration. This study addresses the mechanical limitations of natural polymers and enhances interfacial bonding and bioactivity by fabricating polycaprolactone-lignin (PCL-Lig) copolymer scaffolds via ring-opening copolymerization, achieving a 71
Background: Despite growing interest in artificial intelligence (AI)-mediated second or foreign language (L2) learning, little is known about the role of AI-driven informal digital learning of English (AI-IDLE) in shaping learners' communicative intention and motivation. Furthermore, how L2 learner traits such as grit may influence the relationship between AI-IDLE and communicative outcomes remains largely underexplored.Purpose: This study examined the association between AI-IDLE and two communicative variables - L2 willingness to communicate (WTC) and speaking motivation - with a particular focus on the mediating role of L2 grit.Methods: Participants were 244 English as a foreign language (EFL) learners (123 males, 121 females). Structural equation modeling was employed to examine the direct and indirect associations among AI-IDLE, L2 grit, WTC, and L2 speaking motivation.Results: Findings revealed that AI-IDLE significantly and directly predicted grit and WTC but not speaking motivation. L2 grit emerged as a strong predictor of both WTC and speaking motivation, and functioned as a full mediator in the relationships between AI-IDLE and the two communicative outcomes.Conclusion: These results highlight L2 grit as a key factor linking AI-driven informal learning with learners' communicative readiness and motivation, suggesting that fostering grit may maximize the benefits of AI tools for L2 learning.
Educational settings are crucial for fostering children's cognitive development and promoting positive mental health. Additionally, children's willingness to explore and ask questions can greatly enhance these qualities. This study aimed to investigate the mediating role of critical thinking in the relationships between attitudes towards curiosity, resilience and anxiety control. A total of 573 elementary-school students were recruited through stratified sampling. The instruments used in this study were the Children's Attitudes Towards Curiosity Scale, the Critical Thinking Scale, the Anxiety Control Scale, and the Child and Youth Resilience Scale. Correlation results indicated that attitudes towards curiosity was associated with critical thinking, anxiety control, and resilience. Using structural equation modeling (SEM), mediation analyses showed that attitudes towards curiosity exerted an indirect effect on anxiety control through critical thinking. However, critical thinking did not mediate the relationship between attitudes towards curiosity and resilience. Preliminary analyses showed no significant gender differences in the study variables (p > 0.05). The findings highlight the important role of critical thinking in attitudes towards curiosity, particularly in its beneficial effects on children's anxiety control in educational settings.
