Rapid population growth and inadequate adherence to scientific and managerial principles in urban planning have intensified numerous challenges, pushing major Iranian cities toward instability. Tehran, as the capital and one of the most urbanized regions in the country, faces significant sustainability threats that require immediate attention. These challenges are not unique to Tehran but represent a broader issue faced by rapidly urbanizing cities worldwide, particularly in developing countries. Addressing such challenges is critical to fostering sustainable development on a global scale. While urban sustainability has been extensively studied, limited research has focused on the indicators of urban instability and their tangible impacts on sustainable urban planning. This study aims to bridge this gap by identifying and analyzing key factors contributing to urban instability across economic, environmental, and social dimensions, with Tehran serving as a representative case. The findings reveal that economic instability is driven by uncertainty in economic policies, fluctuating housing prices, non-standard housing conditions, income disparity, unemployment, and cost of living pressures. Environmental instability is exacerbated by climate change, urban heat islands, floods, transportation mismanagement, energy insecurity, pollution, and insufficient green infrastructure. Social instability arises from limited social interaction, unequal access to services, weak community participation, social harms, and diminished urban safety and welfare. By framing these local challenges within a global context, the study underscores the interconnectedness of these dimensions and highlights the necessity for integrated, evidence-based approaches that combine local insights with global best practices. The findings aim to contribute to the broader discourse on sustainable urban development by offering actionable insights and strategies that can be adapted and implemented in other rapidly urbanizing cities. This research serves as a guide for policymakers, urban planners, and stakeholders worldwide, emphasizing the importance of holistic and resilient urban strategies to address the multifaceted challenges of sustainability and instability.

This research aims to delineate the ecocity indicators from the local perspectives in urban communities in the Northeast of Thailand. The research was quantitative survey research. Data was collected from a sample of 400 people who live in Khon Kaen Municipality and Udon Thani Municipality using a questionnaire. Data was analyzed by descriptive statistics and factor analysis. We found that the eco-city indicators from the perspective of people in the urban communities in the Northeast of Thailand were divided into three main criteria: a) economic perspectives; b) social perspectives; and c) environmental perspectives. When considering each aspect, it was found that the economic perspective had a total of 9 issues with an average of 3.06 out of 5.00, the social perspective had a total of 16 issues with an average of 3.76 out of 5.00, and the environmental perspective had a total of 14 issues with an average at 3.00 out of 5.00.

Private banking institutions serve the financial sector’s wealthiest clientele via a dedicated value proposition. Based on the relevant tendencies and statistics, a remarkable expansion can be outlined since the mid-1990s. The aim of this study is to elaborate the Hungarian private banking market’s development as a case study. The paper also intends to add to the literature on this unique segment of the financial market. Based on the available statistics, the analysis primarily focuses on the Hungarian private banking market’s rapid development process. This can be underpinned by the clientele’s savings, number of accounts and respective segmentation limits of the institutions. Referring to the amount of savings, a correlation analysis indicates significant co-movements with specific social and economic variables. The growth rate of the Hungarian clientele’s savings outperformed the respective indicator in Western Europe during the review time period (2007–2020). The current paper also includes a section that summarises general challenges that private banking managers need to address during the development process. Generally, the literature on private banking can still be considered scarce, whereas there is a lack of studies on the Central-Eastern European region. The analysis of the Hungarian sector’s development path can serve with relevant information to any financial expert in the field.

The Human Development Index, which accounts for both net foreign income and the total value of goods and services generated domestically, illustrates how income becomes less significant as Gross National Income (GNI) rises by using the logarithm of income. South Africa ranks 109th out of 189 countries in the Human Development Index (HDI) within the Brazil, Russia, India, China and South Africa (BRICS) economic bloc, raising long-term sustainability concerns. The study explores the relationship between economic, demography, policy indicators and human development in South Africa. South Africa’s unique status as a developing country within the BRICS economic group, alongside its lengthy history of racial discrimination, calls for a sophisticated approach to understanding Human Development. Existing research considered economic, demography, policy indicators independently; the gap of understanding their interconnection and long-term effects in the South African contexts exists. The study addresses the gap by using Autoregressive-Distributed Lag (ARDL) approach to investigate the short-term and the long-term relationship between economic, demography, policy indicators and human development in South Africa. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa. The findings indicate that growth in GDP is a key factor in the HDI since it shows that there are more financial resources available for human development. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa.

Despite the efforts of public institutions and government spending, progress on the SDGs is mixed at the midpoint of the 2030 timeframe-some targets are off track and some have even regressed. ICT-related indicators, on the other hand, stand out for their strong progress. The author notes this progress, but questions its relationship to the implementation of the 2030 Agenda. He argues that the growth in internet and mobile network penetration is due to the economic characteristics of communications development. The objectives of the article are to review the impact of the ICT sector on economic growth, to consider the role of government spending in the development of this sector in the context of fostering the achievement of the Sustainable Development Goals, and to identify the prerequisites for significant progress towards SDG targets in communications. Achievement of these objectives will make it possible to determine whether this progress is a consequence of targeted efforts to achieve the SDGs, or whether, in accordance with the author’s hypothesis, it is based on the specifics of the ICT sector’s development, allowing for the accelerated spread of mobile communications and the Internet, which is reflected in the SDG indicators.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.