This study aims to evaluate the relationship between financial resilience, exchange rate, inflation, and economic growth from 1996 to 2022 using secondary data from the World Bank. The analysis method uses vector autoregressive to understand the causality dynamics between these variables. The results show that past economic growth positively impacts current economic conditions, but an increase in the exchange rate can hinder economic growth. The exchange rate also tends to be influenced by previous values, but high economic growth does not always increase the exchange rate. Previous conditions significantly affect financial resilience and can be strengthened by a strong currency. Meanwhile, inflation has an inverse relationship with economic growth, where past inflation seems to suppress current inflation, which price stabilization policies can cause. From an institutional economics perspective, this study provides an understanding of the interaction between various economic factors in the structural framework and policies that regulate economic activities. The impulse response function (IRF) shows that economic growth can react strongly to sudden changes, although this reaction may not last long. The exchange rate fluctuates with economic changes, reflecting market optimism and uncertainty. Financial resilience may be strong initially but may weaken over time, indicating the need for policies to strengthen the financial system to ensure economic stability. Furthermore, the role of social capital in economic resilience is highlighted as it can amplify the positive effects of a robust institutional framework by fostering trust and collaboration among economic actors. Inflation reacts differently to economic changes, challenging policymakers to balance growth and price stability. Overall, the IRF provides insights into how economic variables interact with each other and react to sudden changes, albeit with some uncertainty in the estimates. The forecast error decomposition variance (FEVD) analysis in this study reveals that internal factors initially influence economic growth, but over time, external factors such as the exchange rate, financial resilience, and inflation come into play. The exchange rate, which was initially volatile due to internal factors, becomes increasingly influenced by economic growth, indicating a close relationship between the economy and the foreign exchange market. From an institutional economics perspective, financial resilience, which was initially stable due to internal factors, becomes increasingly dependent on global economic conditions, suggesting the importance of a solid institutional framework for maintaining economic stability. In addition, inflation, which was initially explained by economic growth and exchange rates, has gradually become more influenced by financial resilience, indicating the importance of effective monetary policy in controlling inflation. This study highlights the importance of understanding how economic variables influence each other for effective economic governance. Integrating institutional economics and social capital perspectives provides a comprehensive framework for enhancing financial resilience and promoting sustainable economic development in Indonesia.

In developing metropolitan cities, the expansion of urban areas due to the urbanization phenomenon has resulted in massive transport infrastructure development in suburban areas. This development has prompted many governments to begin introducing Transit-Oriented Development (TOD) to organize emerging transit hubs in suburban areas into their city plans. The approach adopted to introduce TOD may differ, depending on the existing context. Countries with similar socio-cultural background typically adopt a uniform approach, but not Jakarta and Kuala Lumpur as the most developing metropolitan cities in Southeast Asia with similar urbanization and socio-cultural Based on the situation, through the examining documents and spatial analysis, this study seeks to examine the impact of different policy approach between Jakarta and Kuala Lumpur on the progressions of transport infrastructure and TOD areas in suburban. The results showed that Kuala Lumpur had a more rapid progression in transport infrastructures development, accompanied by the establishment of several transit zones in urban and suburban areas. Meanwhile, Jakarta’s approach comprised the gradual development of infrastructures, initially focusing on TOD in central urban areas and only a limited number of suburban areas with significant commuter traffic. These results indicate that differences in policy approaches in the two regions with similar urbanization and socio-cultural contexts influence the evolution of transport infrastructure and TOD areas development. Several factors contribute to these discrepancies, including efficiency, synchrony, bias, clarity of organizational structure, and conceptual comprehension. At macro basis, policy makers must underline that the characteristics suitability between the approach and region critically determines the success of urban development.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Nanoparticle drug delivery systems are engineered technologies that use nanoparticles for the targeted delivery and controlled release of therapeutic agents. Cisplatin-loaded nanoparticle formulations were optimized utilizing response surface methods and the central composite rotating design model. This study employed a central composite rotatable design with a three-factored factorial design with three tiers. Three independent variables namely drug polymer ratio, aqueous organic phase ration, and stabilizer concentration were used to examine the particle size, entrapment efficiency, and drug loading of cisplatin PLGA nanoparticles as responses. The results revealed that this response surface approach might be able to be used to find the best formulation for the cisplatin PLGA nanoparticles. A polymer ratio of 1:8.27, organic phase ratio of 1:6, and stabilizer concentration of 0.15 were found to be optimum for cisplatin PLGA nanoparticles. Nanoparticles made under the optimal conditions found yielded a 112 nm particle size and a 95.4 percent entrapment efficiency, as well as a drug loading of 9 percent. The cisplatin PLGA nanoparticles tailored for scanning electon microscopy displayed a spherical form. A series of in vitro tests showed that the nanoparticle delivered cisplatin progressively over time. According to this work, the Response Surface Methodology (RSM) employing the central composite rotatable design may be successfully used to simulate cisplatin-PLGA nanoparticles.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

The St. Peter Sandstone of the American Midwest is presented today in textbooks as a simple and unproblematic example of “layer-cake geology.” The thesis of this paper is that the very simplicity of St. Peter Sandstone has made it challenging to characterize. In widely separated states, the sandstone appeared under different names. Several theories about how it formed began to circulate. The story of the St. Peter is not only the story of the assemblage of a stratigraphic unit over a vast area during three centuries, but also the role the study of the provenance of this unit played in the development of sedimentology in the early twentieth century, research that was made all the more challenging by its “simple” mineralogy. Indeed, the St. Peter has been controversial since it was first described.