Historically, transportation projects and urban mobility policies overlook the dimension of social sustainability, mainly focusing on economic and environmental criteria. This neglect, seen enhanced in the Global South, leads to long travel times, growing congestion, reliance on motorcycles, high traffic accident rates, and limited access to public transport, jobs, and urban facilities, especially for the more vulnerable population. In light of these issues, this paper proposes the Social Sustainability of Urban Mobility (SSUM) approach as an analytical framework that assesses the state of social sustainability in urban mobility by applying a Systematic Literature Review where three gaps were found. First, by tailoring the SSUM approach to the context of the Global South, it is possible to address the population-focused gap in urban mobility. Second, in the literature review, a theoretical gap defining social sustainability in urban mobility and its three primary categories has yet to reach a consensus among practitioners and academics. Finally, more empirical research should be conducted to discuss methodological aspects of operationalizing the SSUM approach through the three main categories: accessibility, the sustainability of the community, and institutionality. The SSUM approach promotes implementing a sustainable urban agenda that builds inclusive, equitable, and just cities in urban mobility.

Baribis Fault disasters caused the loss of human lives. This study investigates the strategies local communities employ in Indonesia to cope with disasters. A qualitative study was conducted on various cultural strategies used to mitigate disasters in relevant areas. These strategies were selected based on the criteria of locally based traditional oral and written knowledge obtained through intensive interviews. The study reveals that technological and earth science solutions are insufficient to resolve disasters resulting from Baribis Fault activity. Still, local culture and knowledge also play a crucial role in disaster mitigation. The study contributes to a deeper understanding of how cultural strategies avoid disasters and highlights the need to transform local knowledge regarding effective cultural strategies for mitigating such disasters. This transformation can have positive psychological implications and enhance community harmony.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

The growing interconnectedness of the world has led to a rise in cybersecurity risks. Although it is increasingly conventional to use technology to assist business transactions, exposure to these risks must be minimised to allow business owners to do transactions in a secure manner. While a wide range of studies have been undertaken regarding the effects of cyberattacks on several industries and sectors, However, very few studies have focused on the effects of cyberattacks on the educational sector, specifically higher educational institutions (HEIs) in West Africa. Consequently, this study developed a survey and distributed it to HEIs particularly universities in West Africa to examine the data architectures they employed, the cyberattacks they encountered during the COVID-19 pandemic period, and the role of data analysis in decision-making, as well as the countermeasures employed in identifying and preventing cyberattacks. A total of one thousand, one hundred and sixty-four (1164) responses were received from ninety-three (93) HEIs and analysed. According to the study’s findings, data-informed architecture was adopted by 71.8% of HEIs, data-driven architecture by 24.1%, and data-centric architecture by 4.1%, all of which were vulnerable to cyberattacks. In addition, there are further concerns around data analysis techniques, staff training gaps, and countermeasures for cyberattacks. The study’s conclusion includes suggestions for future research topics and recommendations for repelling cyberattacks in HEIs.