This research investigates the impact of modern technological methods of knowledge management (KM) and total quality management (TQM) on the performance of faculty members in educational colleges in Jordan. Drawing on a survey conducted with 306 faculty members, the study examines the influence of technology on teaching methodologies and academic quality within the Jordanian higher education context. The study utilizes the Technology Acceptance Model (TAM) to back up the modern technological methods of knowledge management (KM) and total quality management (TQM) models. The findings reveal a generally positive perception among respondents regarding the beneficial effects of modern technological tools on teaching effectiveness, collaboration, and innovation. Additionally, technology-enhanced TQM practices were found to contribute to improvements in curriculum design, student engagement, and administrative processes. Regression and correlation analyses support significant relationships between technology-enabled KM and TQM practices and faculty performance, highlighting the transformative role of technology in shaping the future of higher education in Jordan. Recommendations are provided for educational institutions to enhance the integration of technology and foster a culture of innovation and continuous improvement among faculty members.

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 intersex person’s social isolation condition is the leading concern in inclusive educational practices. It provides for the relevance of intersex communities with the influence of social isolation on their education and well-being. Given the underlying problem, this paper stresses the isolation-free condition of the intersex community by facilitating inclusive education. The Atkinson and Shiffrin Model and Behaviorism-Based Intersex Theory supports inclusive education by extending the desire to significantly manage stereotypes, quality teaching, parental beliefs, expressions, physique, and intersex attribution. The qualitative research method analyses the reducing role of social isolation for inclusive education. The semi-structured interview research instrument is used for the data collection from the Ministry of Human Rights, Educational Institutions, and inter-sex Representatives. The results show that managing directors and heads of educational institutions frame policy management for the free social isolation of intersex persons, which is relevant through inclusive education. This paper aims to provide a better social condition for intersex persons and promote inclusive education through effective policy management.