Online shopping has eliminated the need to visit physical commercial centres. As a result, trips to these centres have shifted from primarily shopping-motives to leisure, companionship, and dining. The shifting in consumer behaviour is implicated in the growing spatial agglomeration of restaurants/cafes within commercial centres in European cities. Conversely, in southern cities, various casual restaurants/cafes also serve as leisure and companionship hubs. However, their spatial patterns are less explained. This article aims to elucidate the spatial pattern of these diverse restaurants/cafes in a typical southern city, Surabaya City. In this study, we employ the term ‘food services’ to encompass the various types of restaurants/cafes found in southern cities. We gather Points of Interest (POIs) data about food services via web scraping on Google Maps, then map out their spatial distribution across 116 spatial units of Surabaya City. Utilising k-means cluster analysis, we classify these 116 spatial units into six distinct clusters based on the composition of food service variants. Our findings show that City Centres and Sub-City Centres are locations for different types of restaurants/cafes. The City Centre is typically a location for fine dining restaurants and cafes, whereas Sub-City Centres are locations for fast casual dining and fast food restaurants. Cafes and fast food restaurants are centralised throughout downtown areas. Casual food service restaurants, such as casual style dining, coffee shops, and food stalls, are dispersed along business, residential zones, and periphery areas without intense domination of any specific variant.

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.

The aim of this study is to investigate the effect of tourist resources, conditions and opportunities of sacral tourism in Kazakhstan using panel data (time series and cross-sectional) regression analysis for a sample of 14 regions of Kazakhstan observed over the period from 2004 to 2022. The article presents an overview of modern methods of assessment of the tourist and recreational potential of sacral tourism, as used by national and foreign scientific works. The main focus is on the method of estimating the size and effectiveness of the tourist potential, which reflects the realization and volume of tourist resources and their potential. The overall results show a significant positive effect in that the strongest impact on the increase in the number of tourist residents is the proposed infrastructure and the readiness of regions to receive tourists qualitatively. This study is expected to be of value to firm managers, investors, researchers, and regulators in decision- making at different levels of government.

Women play a pivotal role in national development, and it is essential for every country to harness their skills to promote economic growth and comprehensive development. The purpose of the current study is to analyze and evaluation the impact of the most recent legislatives and legal reforms in the Saudi Arabia laws in the women’s empowering and economic growth. In addition, the research method is used is analyzing laws, regulations, and reports documents related to women rights in Saudi Arabia to clarify its impact on the women’s empowerments and economic developments. The study’s results indicate a significant and positive impact of recent legal and legislative reforms in Saudi Arabia on women’s empowerment and economic growth. Legal reforms have expanded employment opportunities and fostered entrepreneurship among women, resulting in increased workforce participation and a rise in women-owned businesses. Social empowerment has been enhanced through greater autonomy and improved access to education and vocational training, equipping women with competitive skills. Additionally, reforms have facilitated women’s participation in governance that creating a safer and more equitable environment. These changes have contributed positively to the economic incomes and diversification that reflecting the efforts undertaken by the Kingdom to enhance women’s empowerment and ensure the sustainability of reforms to achieve the ambitious goals of the Kingdome Vision 2030.

The study employed a qualitative approach to determine the influence and effectiveness of storytelling in shaping the Alpha generation’s buying decisions and consumption behaviours. The students of the University of Lagos Junior Secondary School were selected for the study. The interview questions were set to focus on factors like experiences, sources of storytelling communication, the outcomes and the affective effects. Twenty-five students were purposively selected out of one hundred and twelve (112) population for the interview based on the conditions for selection. Thematic analysis was used and a total of 244 themes were identified. Four (4) major themes were later identified in thematic synthesis through coding translation. The findings revealed that storytelling is effective and strategic in brands targeted at the Alpha generation, hence, the generation relied on storytelling to choose brands in convenience, impulsive and shopping products, and radio and television were the main sources of storytelling campaigns among the generation. Storytelling wrapped in songs, entertainment, dancing, drama, etc. captivated and influenced the generation, and children used the information from the storytelling campaigns to influence family purchase decisions and parents’ buying decisions and behaviours.

This paper explores how compassion can be defined as a transformative moral technology through analysis of Martha Nussbaum’s idea. Nussbaum contends that compassion goes beyond just feeling pain for others’ suffering; it also involves acknowledging the severity of suffering, understanding that it is not solely the victim’s fault, and recognizing the suffering individual as one of our most important goals and projects. Through a literature review that considers reductive explanations, we establish that compassion encompasses cognitive, affective, and conative capacities that are crucial for moral reasoning, knowledge, and judgment, all stemming from the experience of human suffering. These capacities of cognition, affection, and conation are supported by the system of reasoning and moral perspective known as techne, episteme, and oikeiosis as systems of reasoning and morality perspective. We argue that compassion is more than just an emotion or feeling, it is catalyst for moral action, as its essence lies in “suffering with; suffering together.”