Pneumonia remains the leading infectious cause of death globally, particularly in low-resource settings where delayed diagnosis and limited radiologist availability exacerbate mortality. Existing AI-based radiograph interpretation systems often demand high computational resources and lack robustness across imaging projections. This study presents a proof-of-concept convolutional neural network diagnostic tool optimised for projection-invariant pneumonia detection under constrained conditions. Using a DenseNet-121 backbone trained on 2,000 curated images from the MIMIC-CXR dataset, our model achieved an AUC of 0.7310 and F1-score of 0.6207, with 66.36% validation accuracy. The model’s performance was consistent across posteroanterior and anteroposterior projections, though lateral view evaluation is pending. Preprocessing included CLAHE and DICOM standardisation, while augmentation improved generalisation. Though early-stage, this work shows the potential of lightweight, projection-tolerant CNNs in offline diagnosis pipelines. Future work will validate deployment feasibility on edge devices and expand evaluation across diverse patient demographics.

Bioactive compounds in mushrooms are recognized for their therapeutic potential and are seen as promising alternatives to synthetic medications, which these synthetic medications may lead to adverse health effects. Commercially available mushrooms are particularly noteworthy for their potential to produce extracellular and intracellular polysaccharides. This research investigates the bioactive compounds in various commercial mushrooms to assess their potential as safer alternatives to synthetic drugs. The study aims to extract and analyze bioactive compounds from different commercial mushrooms, addressing the need for safer medicinal alternatives. Five mushroom types—Monkey mushroom, Abalone mushroom, Yellow oyster mushroom, Enoki mushroom, and Shiitake mushroom, were selected for analysis. Tests were conducted to assess carbohydrate content, antioxidant capacity, and the presence of polysaccharides and phenolics using the Phenol Sulphuric Acid Assay. Phytochemical screenings were also performed to determine the overall phytoconstituents. The Monkey mushroom showed the highest intracellular polysaccharide content, while the Yellow oyster mushroom had the lowest. Additionally, the Monkey mushroom exhibited the highest phenolic content, whereas the Enoki mushroom had the lowest. These results suggest that commercial mushrooms, especially the Monkey mushroom, contain significant levels of bioactive compounds, supporting their potential use as safer alternatives to synthetic medications. Future research should explore the mechanisms underlying the bioactive properties of these compounds and their clinical applications, while expanding the scope to include a broader range of mushroom species and extraction methods.

Understanding functions is a crucial aspect of the Form 4 Additional Mathematics curriculum under the KSSM syllabus. However, many students struggle to grasp its core concepts, highlighting potential gaps in instructional effectiveness. This study aims to assess students' proficiency in the Functions subtopic through a diagnostic pretest administered to a group of talented students in secondary school. Methods included the analysis of pretest results to identify common misconceptions and knowledge gaps, using statistical tools to interpret student performance. The results revealed significant areas where students lacked understanding, particularly in inverse functions and composite functions. These findings underscore the need for targeted interventions in teaching methods. In conclusion, this study provides insights into students' challenges with functions and suggests strategies for improving learning outcomes. Future research may extend this approach to other mathematical topics to enhance overall student competency.

In recent years, Education 4.0 has brought about a revolution in the field of education. By innovative teaching methods, such as gamification and gamebased learning, the learning experience has been taken to a whole new level. Among these methods, gamification has emerged as an up-and-coming technique for enhancing students' engagement and motivation in various subject areas. Gamification involves incorporating game-like elements, such as point systems, rewards, and challenges, into the learning process. By leveraging the principles of game design, educators can create engaging and immersive learning experiences that help students develop a deeper understanding of the subject matter and ultimately succeed in their studies. However, implementing the gamification method effectively and efficiently for specific subjects requires careful consideration and exploration, especially in the subject of Additional Mathematics. This subject has long been acknowledged as one of the most challenging subjects in Kurikulum Standard Sekolah Menengah (KSSM). Students must devote significant time and effort to mastering the subject matter. Therefore, this research focuses on developing an educational game that leverages the Scratch platform to enhance students' learning experience. Specifically, the game will focus on Functions, a crucial topic in Additional Mathematics. The research design involves two main stages. The first stage involves creating comprehensive notes and designing multilevel questions based on credible sources. In the second stage, an immersive learning game will be developed using the Scratch platform using the features in Scratch. The findings of this research indicate that the combination of gamification, detailed note-taking, and a variety of games is an effective tool in connecting the gap between conventional teaching methods and contemporary learning preferences. This innovative approach can transform the traditional education system by making learning more engaging and effective for students of all ages