Poverty is a major challenge caused by various situations as well as cultural, social, economic, and political interactions. Therefore, poverty alleviation programs and strategies require an integrated approach carried out in consistent and organized stages. It required the participation of all parties, both regional heads, Regional People’s Representative Assembly (RPRA) members, entrepreneurs, and other elements of society. This study aimed to investigate the effect of local spending efficiency on public welfare in Indonesia, using a quantitative and explanatory method. The analysis method used in this study is the panel data regression model. The research population in all provinces in Indonesia was 34 provinces, and a purposive sampling method was used, where a total of 26 provinces were selected. The research period is 2017–2021. The efficiency of local spending (education, health, and infrastructure) is estimated using the Stochastic Frontier Analysis (SFA) cost function approach. The results showed that the higher the efficiency of education spending, the more it will increase public welfare in Indonesia. Meanwhile, the health spending efficiency and the infrastructure spending efficiency do not affect public welfare. The implications of this study for the development of science are that the efficient allocation of education spending will be able to improve the quality of education which is a long-term solution to overcome poverty in Indonesia and for policymakers to be able to optimize education spending to achieve the expected educational goals.

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.

This study considers the relationship between investment in the manufacturing and processing industries and economic growth in Vietnam. This study applies an autoregressive distributed lag (ARDL) model to reassess the long- and short-term relationships between industrial investment and economic growth from 1998 to 2023. It has been found that in both the long and short term, investments in this sector have a positive and significant effect on economic growth. The results further show that labor negatively affects growth in the long run, but is favorable in the short run. The verdict for the role of exports is that more evidence is required before any conclusive analysis can be conducted. Reinvestment in the manufacturing and processing industries for further economic growth is evident in the foregoing analysis. On the other hand, this research provides insight into the optimization of the utilization of resources and future sustainability by the government.

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.

In the face of growing competition, industrial and commercial firms need more effective strategies to gain competitive advantages. This study investigates the role of enterprise risk management (ERM) as a mediator in highlighting the significance of innovation capability on profitability in industrial and commercial firms listed on the Amman Stock Exchange (ASE). Data were collected from 244 respondents using a standardized questionnaire and analyzed with SPSS software. The results indicate that the innovation capability has an impact on profitability in industrial and commercial firms, as well as their ERM practices. Additionally, ERM mediates the relationship between innovation capability and profitability. Firms that adopt distinctive innovation strategies tend to maintain formal ERM strategies, which in turn enhance market superiority and profitability. This research offers some significant managerial ramifications that may be essential for business owners, executives, and decision-makers involved in the development of firms.

The objective of this study is to examine the impact of decentralization on disaster management in North Sumatra Province. Specifically, it will analyze the intergovernmental networks, local government resilience, leadership, and communication within disaster management agencies. The study used a hybrid research approach, integrating qualitative and quantitative methodologies to investigate the connections between these factors and their influence on disaster response and mitigation. The study encompassed 144 personnel from diverse government tiers in North Sumatra and performed a meta-analysis on the implementation of disaster management. Intergovernmental networks were discovered to enhance collaboration in disaster management by eliminating regulatory gaps and efficiently allocating logistics. Nevertheless, local governments have obstacles as a result of limited resources and inadequate expertise, notwithstanding the progress made in infrastructure technology. The F test results reveal that leadership and communication have a substantial impact on the performance of BPBD personnel. The meta-assessment classifies its impact as extraordinarily high, suggesting comprehensive evaluation and successful achievement of goals in disaster management planning. Efficient cooperation among relevant parties is essential in handling calamities in North Sumatra. The government, commercial sector, NGOs, universities, and society have unique responsibilities. To improve effectiveness, governments should encourage private sector involvement, while institutions can increase their research contributions.