Tangerang City is characterized by its dense residential, commercial, and industrial activities and strategic proximity to Jakarta. This study aims to evaluate the strategic planning and implementation of innovative city initiatives in Tangerang, Indonesia, focusing on integrating blockchain, Internet of Things (IoT) big data technologies and innovation in urban development. This study has employed explanatory survey data from a structured questionnaire distributed to a diverse Tangerang community sample, including users and non-users of the “Smart City Tangerang Live” application. The survey was conducted for 2-months March to April 2022, included 71 and the sample included individuals across 13 districts, utilizing cluster sampling to ensure representativeness. The findings reveal a positive community response towards the smart city initiatives, with significant Engagement and interaction with the “Tangerang Live” application. However, technology access and usage disparities among different community segments were noted. The study highlights the critical role of intelligent technologies in transforming urban infrastructure and services, improving the quality of life, and fostering sustainable urban development in Tangerang. The implications of this study are multifaceted. For urban planners and policymakers, the results underscore the importance of strategic planning in innovative city development, emphasizing the need for inclusive and accessible technological solutions. The study also suggests potential areas for improvement in community engagement and public awareness campaigns to promote the adoption and efficient use of smart technologies.

This study scrutinizes the allocation of financial aid for climate change adaptation from OECD/DAC donors, focusing on its effectiveness in supporting developing countries. With growing concerns over climate risks, the emphasis on green development as a means of adaptation is increasing. The research explores whether climate adaptation finance is efficiently allocated and what factors influence OECD/DAC donor decisions. It examines bilateral official development assistance in the climate sector from 2010 to 2021, incorporating climate vulnerability and adaptation indices from the ND-GAIN Country Index and the IMF Climate Risk Index. A panel double hurdle model is used to analyze the factors influencing the financial allocations of 41,400 samples across 115 recipient countries from 30 donors, distinguishing between the decision to select a country and the determination of the aid amount. The study unveils four critical findings. Firstly, donors weigh a more comprehensive range of factors when deciding on aid amounts than when selecting recipient countries. Secondly, climate vulnerability is significantly relevant in the allocation stage, but climate aid distribution does not consistently match countries with high vulnerability. Thirdly, discerning the impact of socio-economic vulnerabilities on resource allocation, apart from climate vulnerability, is challenging. Lastly, donor countries’ economic and diplomatic interests play a significant role in climate development cooperation. As a policy implication, OECD/DAC donor countries should consider establishing differentiated allocation mechanisms in climate-oriented development cooperation to achieve the objectives of climate-resilient development.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

The SMARTER model, an innovative educational framework, is designed for blended learning environments, seamlessly integrating both online and face-to-face instructional components. Employing a flipped classroom methodology, this model ensures an equitable division between online and traditional classroom interactions, aiming to cultivate a dynamic and collaborative learning atmosphere. This research focused on developing and rigorously evaluating the SMARTER model’s validity, practicality, and effectiveness. Adopting a research and development (R&D) approach informed by the methodologies of Borg, Gall, and Gall, this study utilized a mixed-methods strategy. This encompassed a robust validation process by experts in design, content, and media, alongside an empirical analysis of the model’s application in actual educational settings. The aim was to comprehensively assess its effectiveness and practicality. The findings from this study affirm the SMARTER model’s validity, practicality, and effectiveness in improving students’ information literacy skills. Comparative analysis between a control group, taught using a traditional expository approach, and an experimental group, educated under the SMARTER model, highlighted significant improvements in the latter group. This effectiveness underscores the model’s capacity not only to efficiently deliver content but also to actively engage students in a collaborative learning process. The results advocate for the model’s potential broader adoption and adaptation across similar educational contexts. They also establish a foundation for future research aimed at exploring the SMARTER model’s scalability and adaptability across diverse instructional environments.

This study aims to compare investment in human capital, equality of gender education in Kuwait before and after adopting SDG 4 and SDG 5 in 2015. It also aims to assess the effect of women’s empowerment on economic growth. To achieve this objective, published data on the State of Kuwait were collected from the World Bank DataBank between 1992 and 2022 and from the Central Bank of Kuwait. The study employed autoregressive distributed lag (ARDL) to determine the impact of women’s empowerment on economic development. The analysis results revealed that the State of Kuwait provided high-quality education for both genders. The results also showed that women are more educated than men. However, this was not reflected in the role of women in the country’s politics, as their participation in parliament and government is still limited. Similarly, women’s participation in business and economic activities is still limited. Finally, the results of the ARDL test showed that women’s education and their political, business, and economic empowerment affect economic development in the short and long run.