The Malaysian government has been actively strengthening the information and communication industry’s ecosystem through talent retention to realize Malaysia 5.0 and transform the country into a developed human-centered society that balances economic advancement with the resolution of talent problems. This is done to recognize the significance of emerging in building a vibrant and dynamic economy for the country. Few of these studies, however, had developed comprehensive policy recommendations for keeping information specialists in Malaysia’s information businesses. To address this gap, a comprehensive literature review was conducted to understand the factors driving information professionals to leave the sector. The findings aim to inform talent retention strategies that will strengthen the industry’s sustainability and attract skilled leaders, ensuring the information sector’s readiness for a successful digital transition.

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.

The purpose of the study was to examine the role of personalization in motivating senior citizens to use AI driven fitness apps. Vroom’s expectancy theory of motivation was applied to examine the motivation of senior citizens. The responses from participants were collected through structured interviews. The participants belonged to South Asian origin belonging to India, Bangladesh, Nepal and Bhutan. The authors adopted a content analysis approach where the gathered interview responses were coded in the context of elements of Vroom’s theory. The findings of the study indicated that a highly personalized approach in the context of motivation, expectancy, instrumentality and valence will motivate senior citizens to use AI based fitness apps. The study contributes to the personalization of AI fitness apps for senior citizens.

Lifelong learning (LLL) is progressively recognized as a crucial component of personal and professional development, particularly for adult students. As a heavily populated developing country, China requires profound national education reform to support its economic development and maintain its competitive advantage on the global economic stage. The governmental policy endorses the execution of diverse forms of lifelong learning programs to bolster the national education reform. However, implementing such programs can be challenging for all the stakeholders of the programs, especially for adult students. The weaker foundational knowledge and insufficient online learning abilities of adult students particularly highlight the academic challenges they face. This study explores the academic challenges faced by adult learners in a Chinese vocational college’s LLL program. Focusing on ex-soldiers, unemployed individuals, migrant workers, and new professional farmers (aged 22–44), data were collected from 16 adult students via purposive sampling. Semi-structured interviews and document analysis revealed recurring thematic academic challenges. Additionally, the study found that adult student attributes (highest education level, age) significantly influenced the unique academic challenges they encountered. This research provides practical solutions to improve LLL programs and promote successful lifelong learning experiences for adult students. 

The COVID-19 pandemic had an adverse impact on the mental health of frontline workers including firefighters. To better understand this occurrence, this cross-sectional study evaluated the prevalence of depression, anxiety, and stress among 105 operational team and elite team firefighters in Kota Bharu, Kelantan State, Malaysia before and after the pandemic. The Depression, Anxiety and Stress Scale-21 (DASS-21), a validated self-reporting survey tool, was used to assess symptoms of depression, anxiety, and stress among the survey respondents. Findings revealed that firefighters had an increased level of anxiety and depression during the post-pandemic period compared to the pre-pandemic period. However, there was a decrease in the stress levels (20%) reported by study participants. Respondents belonging to the operational team had a higher reported level of depression, anxiety, and stress than those from the elite team. This may be attributed the operational team being more exposed to the risk of COVID-19 infection on account of their routine and more voluminous workload. The findings of this study suggest that firefighters, in general, are at an increased risk of mental health problems as a result of the COVID-19 pandemic. Knowing this, it is important to consider these findings when addressing the prevention and management of mental health among firefighters. This includes providing additional support and devoting more resources to those who are most at risk for experiencing symptoms of mental health such as firefighters performing functions aligned with that of an operational team.