Improving educational outcomes in subjects such as English and mathematics remains a significant challenge for educators and policymakers. Strategic Human Resource Management (SHRM), which aligns human resource practices with organizational goals, has proven effective in business sectors but is less explored in educational contexts, especially from students’ perspectives. Existing studies often focus on teacher development, overlooking direct impacts on student performance. This research addresses the gap by examining how SHRM influences students’ performance in English and mathematics, incorporating student feedback to assess SHRM’s effectiveness. In the quantitative study, 200 students were analyzed to explore the relationship between SHRM practices and academic outcomes. The findings indicate that SHRM significantly affects student performance, with high predictive relevance and explanatory power in both subjects. The results suggest that strategic HR practices, such as professional development, performance management, and resource allocation, are critical to academic success. These insights provide valuable implications for educators and policymakers, highlighting the importance of integrating strategic HR management into educational frameworks to enhance curriculum design and resource distribution. The study demonstrates the broad applicability of SHRM across different academic disciplines, suggesting a need for comprehensive HR strategies that focus on both teacher and student performance. Future research should explore how SHRM influences educational outcomes and identify contextual factors that moderate its impact, enhancing effective HR practices in diverse academic settings.

In the process of X-ray transmission imaging, the mutual occlusion between structures will lead to the image information overlap, and the computed tomography (CT) method is often required to obtain the structure information at different depths, but with low efficiency. To address these problems, an X-ray focused on imaging algorithm based on multi-line scanning is proposed, which only requires the scene target to pass through the detection area along a straight line to extract multi-view information, and uses the optical field reconstruction theory to achieve the de-obscured reconstruction of the structure at a specified depth with high real-time. The results of multi-line scan and X-ray reconstruction of the target show that the proposed method can reconstruct the information of any specified depth layer, and it can perform fast imaging detection of the mutually occluded target structures and improve the recognition of the occluded targets, which has a good application prospect.

The Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.