In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

Surrogacy has opened new doors for many people who need children but are infertile or unable to have children. Through modern scientific technology, couples or mothers can find women to ask them to be surrogates using their eggs or sperm. The nature of surrogacy is reproductive support, but the complexity of the surrogacy procedure causes a lot of controversy not only in the field of criminal law but also regarding its implementation in practice. The article uses qualitative analysis to study current commercial surrogacy formulas. The main goal of this study is to clarify the legal aspects of commercial surrogacy in the world and in Vietnam. The article also concludes that Vietnam and other countries need to agree or develop common principles to avoid cross-border surrogacy as well as establish legal tools to prevent surrogacy for sexual purposes trade to protect human rights and prevent child trafficking.

The decentralization of the NHIS’s implementation to states intended to hasten progress towards universal health coverage, has not effectively addressed healthcare disparities, particularly in Lagos State. The implementation of the Lagos State Health Insurance Scheme appears to perpetuate structural violence, evident in increased out-of-pocket expenses, discrimination based on insurance type, and substandard healthcare delivery. The study therefore examined how structural violence has affected the policy outcomes of the Lagos State Health Insurance Scheme, with a specific emphasis on junior officers in grade level 01–07 in five selected ministries situated within Lagos State. Both primary and secondary data were collected using questionnaire, interview and literature search. Data gathered were analysed statistically and thematically. The findings of the study indicate that the policy outcome of the scheme has been adversely affected by structural violence, resulting in dissatisfaction, compensation claims for unresolved health issues and a shift in health insurance providers among enrolled junior officers.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.