This study addresses the critical issue of employee turnover intention within Malaysia’s manufacturing sector, focusing on the semiconductor industry, a pivotal component of the inclusive economy growth. The research aims to unveil the determinants of employee turnover intentions through a comprehensive analysis encompassing compensation, career development, work-life balance, and leadership style. Utilizing Herzberg’s Two-Factor Theory as a theoretical framework, the study hypothesizes that motivators (e.g., career development, recognition) and hygiene factors (e.g., compensation, working conditions) significantly influence employees’ intentions to leave. The quantitative research methodology employs a descriptive correlation design to investigate the relationships between the specified variables and turnover intention. Data was collected from executives and managers in northern Malaysia’s semiconductor industry, revealing that compensation, rewards, and work-life balance are significant predictors of turnover intention. At the same time, career development and transformational leadership style show no substantial impact. The findings suggest that manufacturing firms must reevaluate their compensation strategies, foster a conducive work-life balance, and consider a diverse workforce’s evolving needs and expectations to mitigate turnover rates. This study contributes to academic discourse by filling gaps in current literature and offers practical implications for industry stakeholders aiming to enhance employee retention and organizational competitiveness.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Students from different cultures possess varying levels of skills in learning, remembering, and understanding concepts. Some terms and their explanations may seem easy for one group of students but difficult for another. Therefore, delivering educational content that aligns with student’s learning capabilities is a challenging task based on cultural orientations. This study addresses the learning challenges by developing a Thesaurus Glossary E-learning (TGE) framework method. This study introduces the TGE method which is a multi-language tool with visual associations that adapts to students’ capabilities. It also examines cultural differences and native languages, particularly aiding Arab Native to visualize appropriate terms (thesaurus) and their explanations (glossary) based on students’ learning capabilities. TGE learns from students’ term selection behavior and displays terms at a simple or advanced level that matches their learning ability. Additionally, TGE demonstrated its effectiveness as an e-learning tool, accessible to all students anytime and anywhere. The study analyzed 314 records related to student performance, out of which 114 students were surveyed to evaluate the effectiveness of the TGE method. This work presents TGE as a novel e-learning tool designed to enhance conceptual thinking within the context of modern educational practices during the digital transformation. TGE is based on artificial intelligence algorithms and associative rules that simulate the human brain, establishing logical connections between related key terms and sketching associations among diverse facets of a situation. An experiment was conducted at a private university in the Sultanate of Oman to assess the effectiveness of the proposed TGE tool. TGE was integrated with selected subjects in information systems and used by the students as a resource for e-learning methods and materials. The results show that 85% of students who used TGE improved their performance by 19%. We believe this work could establish a new smart e-learning teaching method and attract modern and digital universities to enhance student learning outcomes linked with conceptual thinking.

The increased awareness of the environmental effects of petroleum based plastics has stimulated the coffee price emergence of biodegradable polymers such as polylactic acid (PLA). In a bid to increase the sustainability of PLA agricultural residues of animal feeds (corn stover, rice straw, and soybean hulls) have been explored and examined as reinforcing fillers to PLA composites. The consideration of such applications is suitable to the goals of the circular economy as it recycles low-value agricultural products. The current review critically evaluates lately carried out life cycle assessment (LCA) studies on PLA composites that have implemented such waste fillers with the full focus being on their environmental performance as well as methodological consistency. The review shows that these fillers have a potential of reducing the amount of greenhouse emission, energy usage, and other environmental effects, compared to pure PLA. However, unevenness in LCA methodology, especially in functional units, the system boundaries, and impacts categories obstructs direct LCA comparisons. The 1997 State of the Market report also has limited options of feedstocks and the lack of appraisals in the socio-economic front, so the overall sustainability analysis is restricted. Some of the remaining limitations that can be critical are to have generalized LCA frameworks, extended exploration of waste-based fillers, as well as combination of techno-economic analysis and social impact. Future inquiries ought to devise design considerations that would optimize both the functional characteristics and the performance of the environment and improve the reliability of sustainability measures. This review is evidence to the potential of agricultural waste reinforced PLA composites in the progress towards environmentally friendly materials and the need of integrative evaluation in the sustainable maturation of bioplastics.

This study investigates non-academic employees’ perceptions of their line managers’ leadership styles at a private university in Malaysia and how these perceptions influence their intention to remain employed. Employing a qualitative approach and the path-goal theory as a theoretical framework, data were collected through purposive sampling from 10 non-academic employees and analyzed thematically using NVivo 12 software. The findings reveal that a supportive and participative leadership style fosters an informal leadership dynamic between line managers and subordinates. Informal leadership behaviors encompass affective qualities and effective communication that enable the development of close relationships outside the workplace, facilitating increased employee engagement and motivation levels. Consequently, this approach notably improves employee retention. This study offers a comprehensive understanding of informal leadership styles contributing to enhanced human resource management at the private university while providing an inclusive perspective on employees’ perceptions and their intention to remain employed. Finally, we propose a model of employees’ perception of leadership styles as the main driver that better serves their intention to stay in organizations.

This study investigates the multifaceted challenges and barriers to implementing public auditor recommendations in Ghana’s public sector over an eighteen months period, aiming to enhance governance and accountability. Utilizing a qualitative research approach, the study involved semi-structured interviews with key stakeholders, including officials from the Ghana Audit Service, government ministries, and civil society organizations. The findings reveal a complex interplay of organizational, political, and attitudinal factors that impede effective implementation. Key challenges identified include the lack of clear implementation plans, insufficient resources, weak political commitment, and a pervasive culture of mistrust towards audit recommendations. The research underscores the necessity for a comprehensive and holistic approach to address these barriers, advocating for strengthened political leadership, enhanced accountability mechanisms, and improved stakeholder coordination. Additionally, fostering a sense of ownership and buy-in among implementation stakeholders is crucial for successful reform. The study contributes valuable insights into the systemic issues affecting public sector governance in Ghana and offers practical recommendations for overcoming the identified challenges, ultimately aiming to empower citizens and enhance governmental accountability. By addressing these barriers, the research highlights the potential for transformative change in the governance landscape of Ghana’s public sector.