In the context of establishing businesses in a new region, neglecting environmental orientation may lead to the omission of crucial motives for entrepreneurs’ migration and the subsequent course of their businesses. This present study aims to investigate the effect of green space quality (GSQ), green campaign (GC), and green attitude (GA) on green entrepreneurship pioneering intention (GEPI). Further, national pride (NP) was added as a moderator. This study utilized a cross-sectional approach using a survey method targeting small and medium-sized enterprise (SME) owners who will be relocated to the new capital city. Partial least square structural equation modeling was employed in the data analysis. The results revealed that GSQ, GC, and GA positively influence GEPI. Also, NP moderates the positive influences of GC and GA on GEPI. Entrepreneurs were motivated to pioneer green entrepreneurship in the new region due to environmental factors. Furthermore, their nationalism reinforces the connection between environmental motivations and the aspirations to undertake such pioneering endeavors. The findings present valuable insights for governments to formulate policies that encourage entrepreneurs to migrate internally and establish new economic nodes. Further, the results demonstrate how nationalism encourages green business pioneering endeavors in an untapped market.

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.

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.

The study aims to explore the extent to which Jordanian e-news sites rely on artificial intelligence applications in their news content. The researchers will use a media survey methodology, and the sample will consist of 45 editors-in-chief and editors from 10 Jordanian news sites, namely: Ammon, Khabrny, Joe24, Saraya, Amman Net, Jafra, Crown News, Petra, Kingdom, and Roya. The researcher will use an electronic questionnaire, which led to several findings, the most significant of which are: Many news and media sites have introduced artificial intelligence systems to enhance the services they provide to the public. A significant number of journalistic and electronic media websites have shown interest in data analysis tools for their media services. Electronic news sites are clearly striving to improve their capabilities in using artificial intelligence technologies to enhance the services they provide to the Jordanian audience. Additionally, most electronic media websites have expressed a willingness to develop a plan to improve cybersecurity systems to protect against hacking and intrusion attempts, safeguarding their data and the AI systems that operate continuously.AI systems in media organizations also aim to enhance the news experience for users by enriching media services with modern, communicative content.

Indonesia has ratified United Nations Convention on the Law of the Sea 1982 (UNCLOS 1982) through Law No. 17 of 1985 concerning the ratification of the 1982 Law of the Sea Convention, thus binding Indonesia to the rights and obligations to implement the provisions of the 1982 convention, including the establishment of the three Northern-Southern Indonesia’s Archipelagic Sea Lane (ALKI). The existence of the three ALKI routes, including ALKI II, has led to various potential threats. These violations not only cause material losses but, if left unchecked and unresolved, can also affect maritime security stability, both nationally and regionally. The maritime security and resilience challenges in ALKI II have increased with the relocation of the capital, which has become the center of gravity, to East Kalimantan. The research in this article aims to identify and analyze the factors influencing the success of maritime security and resilience strategies in ALKI II. The factors used in this research include conceptual components, physical components, moral components, command and control center capabilities, operational effectiveness, command and control effectiveness, and the moderating variables of resource multiplier management and risk management to achieve maritime security and resilience. This study employed a mixed-method research approach. The factors are modeled using Structural Equation Modeling (SEM) with WarpPLS 8.0 software. Qualitative data analysis used the Soft System Methodology (SSM). The results of the study indicate that the aforementioned factors significantly influence the success of achieving maritime security and resilience in ALKI II.