This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

Orientation: Rewards are integral to keeping employees happy, efficient and engaged in their work. Thus, the engagement of academic staff within higher education institutions has become a top priority for organisational productivity and competitiveness. Research purpose: This study investigated the impact of total rewards on work engagement among the academic staff at a South African higher education institution. Motivation for the study: Engagement of academic staff is vital as higher education institutions are influential in the country’s development. Literature, however, has shown that most studies on total rewards and work engagement focus on sectors such as financial institutions, the mining industry and others. However, few reports have been on total rewards and work engagement in higher education. Research design, approach and method: This study employed a cross-sectional survey design, following a quantitative approach. From a population of 100 academic staff, 74 respondents responded to a self-administered questionnaire. Main findings: The results show a positive relationship between two dimensions of total rewards (work-home integration and quality work environment) and work engagement. However, no relationship was found between base pay, benefits, performance and career management, and work engagement. From the five dimensions of total rewards, a quality work environment was the only significant predictor of work engagement. Contribution: The study provides theoretical contributions through new literature and possible recommendations. The study may guide management in developing a rewards strategy that can promote staff work engagement.

The tourism sector is exponentially expanding across the globe. Despite different forms of tourism, community-based tourism has evolved with new dimensions of development. Assessing the sustainable development of the sector is a top priority in order to adopt the new forms. Therefore, in this study, the association between community-based tourism and its sustainable development was measured under the lens of collaborative theory and social exchange perspective. Non-probabilistic judgmental sampling techniques were applied, and 201 respondents were assessed. Data analysis was conducted using structural equation modeling (SEM). The study grounded with residents’ perspectives and attested that community-based tourism directly enhanced residents’ economic conditions with a better environment, and the relationship between residents and tourists enhanced the tourism industry’s sustainable development. Stakeholders like government and local administrations play a significant role in exploring community-based tourism. This outcome of the research will be a substantial resource for local administrations, governments, researchers, policymakers and practitioners.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

The aim of this study is to determine how bank diversification affects bank stability. To this end, it examines data of 136 commercial banks operating in 14 MENA (Middle East and North Africa) countries observed from 2005 to 2021, using the System Generalized Method of Moments (GMM) panel data regression analysis. The selected countries are Bahrain, Egypt, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Morocco, Lebanon, Algeria, Tunisia, Iran, Iraq, and the United Arab Emirates. The main results point to the enhancing effect of income diversification on bank stability. Our results underline the “Bright Side” of banking income diversification in the MENA region. However, this stabilizing income diversification effect is not always maintainable. The results also point to a non-linear relationship between interest/non-interest income and financial stability, suggesting that higher diversification reduces risk. We use a dynamic panel threshold model to determine income diversification thresholds that stabilize banks in the MENA region.

The UN agenda of Sustainable Development Goals (SDGs) 2015–2030 is a holistic approach. Universities play an important role in dissemination of quality knowledge, developing the skills and attitudes of a large number of youth across the world. Though the emphasis on Education for Sustainable Development (ESD) started as early as 1992, yet Universities adopted the concept of Green Campus integrating the environmental, social and economic aspects of sustainability quite recently. In developing countries including Pakistan, the Green Campus Initiatives (GCI) have not been implemented in the majority of the Universities. Northern Pakistan comprising Azad Jammu & Kashmir (AJ&K) and Gilgit Baltistan (GB) faces multiple challenges including Climate Impacts at the top. The fragile ecosystem of the region requires more sustainable initiatives at the University and community levels. In this research, the readiness of the seven universities located in Northern Pakistan have been assessed for GCI on the basis scanning of the websites and questionnaire survey of the relevant stakeholders. The results have shown that there is little commitment of resources for sustainability from senior management, lack of awareness in faculty & staff and less research focus on the related themes of green campus. The co-curricular activities in universities are not linked with sustainability and there are no incentives for faculty, staff and students to this end. It has been recommended that Green Campus Framework may be developed for Pakistani Mountain Universities, with commitment from leaders of the universities and allocation of sufficient resources for development of sustainable campuses. The Higher Education Commission of Pakistan (HEC) needs to allocate special funds for promoting GCI across universities in Pakistan.

Environmental Education (EE) programs are of crucial importance. EE are aimed at global citizenship to generate new knowledge and new, more participatory and conscious ways of acting in the environment. This study, therefore, wants to verify the effectiveness of a training intervention that is based on education on climate change issues and on the active participation of subjects in the dimension of the small psychological group. At the intervention 309 students took part, equally distributed by gender (52.1% males), 64.4% enrolled in primary school, 35.6% enrolled in lower secondary school. A quantitative protocol was administered to evaluate the effectiveness of the intervention. The study shows an increase in pro-environmental behaviors and their stability even after 15–30 days. The intervention seems to be effective in triggering pro-environmental behaviors and maintaining them in the following weeks. The results of this study highlight the need to develop environmental education pro-grams in schools to increase levels of knowledge and awareness on the issue of climate change.