Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

This study investigates the performance assessment of methanol and water as working fluid in a solar-powered vapour absorption refrigeration system. This research clarifies the system’s performance across a spectrum of operating conditions. Furthermore, the HAP software was utilized to determine and scrutinize the cooling load, facilitating a comparative analysis between software-based results and theoretical calculations. To empirically substantiate the findings, this research investigates methanol-water as a superior refrigerant compared to traditional ammonia- water and LiBr-water systems. Through experimental analysis and its comparison with previous research, the methanol-water refrigeration system demonstrated higher cooling efficiency and better environmental compatibility. The system’s performance was evaluated under varying conditions, showing that methanol-water has a 1% higher coefficient of performance (COP) compared to ammonia-water systems, proving its superior effectiveness in solar-powered applications. This empirical model acts as a pivotal tool for understanding the dynamic relationship between methanol concentration (40%, 50%, 60%) and system performance. The results show that temperature of the evaporator (5–15 ℃), condenser (30 ℃–50 ℃), and absorber (25 ℃–50 ℃) are constant, the coefficient of performance (COP) increases with increase in generator temperature. Furthermore, increasing the evaporator temperature while keeping constant temperatures for the generator (70 ℃–100 ℃), condenser, and absorber improves the COP. The resulting data provides profound insights into optimizing refrigerant concentrations for improved efficiency.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Functions are the core of algebra, and the teaching of function concepts is also the main task of high school mathematics Students' learning of functions and their concepts shifts from understanding specific quantitative relationships to understanding abstract quantitative relationships The monotonicity of functions, as the property of the first function that students learn in high school, lays a certain foundation for learning function related knowledge in the future.

This study examines aggressive behavior among adolescents in school settings, focusing on its associations with mental health dimensions such as dysfunctional negative emotions and anxiety. A total of 403 adolescents (234 girls and 169 boys) aged 12 and 13 years participated in the study. Self-report questionnaires assessed aggressive tendencies and mental health symptoms, while demographic variables such as age and gender were also collected. Data analysis revealed a non-normal distribution, as determined by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Consequently, non-parametric statistical methods were employed, including the Spearman correlation coefficient to explore relationships between variables and the Mann-Whitney U test to analyze gender differences. The results demonstrated significant positive correlations between aggressive behavior and dysfunctional negative emotions (r = 0.191, p < 0.01) and between aggression and anxiety (r = 0.275, p < 0.01). Additionally, gender differences emerged, with females reporting higher levels of mental health symptoms than males (p < 0.05). These findings highlight the complex relationship between mental health challenges and aggression, emphasizing the significant roles of gender and emotional regulation in shaping these dynamics. The study calls for the development of tailored psychological interventions that not only address aggressive behaviors but also consider the unique mental health needs and emotional profiles of adolescents, ensuring a more personalized and effective approach to support their well-being.

In the present and future of education, fostering complex thinking, especially in the context of the Sustainable Development Goals (SDGs), is critical to lifelong learning. This study aimed to analyze learning scenarios within the framework of a model that promotes complex thinking and integrated design analysis, to identify the contributions of linking design models to the SDGs. The research question was: How does the open educational model of complex thinking link to the SDGs and scenario design? The analysis examined a pedagogical approach that introduced 33 participants to the instructional design of real-life or simulated situations to develop complex thinking skills. The categories of analysis were the model components, the SDGs, and scenario designs. The findings considered (a) innovative design capacity linked to SDG challenges, (b) linking theory and practice to foster complex thinking, and (c) the critical supporting tools for scenario design. The study intends to be of value to academic, social, and business communities interested in mobilizing complex thinking to support lifelong learning.