Tunku Abdul Rahman University of Management and Technology

We simulated the manufacturing production line in a micro, small, and medium enterprise (MSME) to assess the efficiency of a helmet product organization, using ARENA simulation modelling software version 15.10.00000.

Betacyanins are bioactive compounds found in Caryophyllales, including red beetroots (Beta vulgaris), amaranths (Amaranthus sp.), and red dragon fruits (Hylocereus polyrhizus). Their biosynthesis requires several enzymes, including tyrosinase, 4,5-DOPA-extradiol-dioxygenase (DOD), and 5-O-glucosyl transferase (5GT). Environmentally friendly extraction techniques, such as ultrasound-assisted, microwave-assisted, supercritical fluid, and pulsed electric field extraction, improve the recovery of betacyanins from natural resources. Betacyanins have commercial value as food coloring agents, for smart food packaging, and in the nutraceutical, pharmaceutical, and cosmetic industries. Industrial application is expanding as more innovative methods are discovered. Several factors affect the absorption of betacyanins, including gastrointestinal degradation, the nature of the ingested betacyanins, and the food matrix. Betanin, the main betacyanin, is excreted through urine when given intravenously. However, orally administered betanin showed limited urinary excretion, suggesting extensive modification or digestion in the gastrointestinal tract. Biologically, betacyanins are shown to have antioxidant, anti-inflammatory, antidiabetic, hypolipidemic, gut-microbiome-modulatory, antiproliferative, and antimicrobial properties. Recent molecular docking developments showed this compound group’s potential in modulating key target enzymes and proteins.

Indonesia has extensive biodiversity and a rich tradition of medicinal knowledge that has been passed down from generation to generation. These traditional medicines, derived from native plants and cultural practices, have significant potential to contribute to national health. However, the National Health Insurance System (SJKN) has not integrated traditional medicine into its financing scheme. This exception not only limits public access to alternative therapies but also hinders state support for the development and formal recognition of traditional medicine. At the same time, the national pharmaceutical industry is still heavily dependent on imported raw materials for medicines (Drug Raw Materials/BBO), making it vulnerable to global supply chain disruptions and the influence of drug import cartels, all of which are exacerbated by weak regulatory enforcement. This research uses a normative approach and case studies on the importance of legal protection through Intellectual Property Rights (IPR) and the integration of traditional medicine in JKN. The findings show that suboptimal regulation and weak IPR protections, particularly in the form of patents, trade secrets, and geographical indications, act as major barriers to innovation, commercialization, and investment. Strengthening the legal framework can encourage research and development, protect traditional knowledge from exploitation, and promote the sustainable use of local resources. Integrating traditional medicine into SJKN is not only culturally valuable, but also has a significant influence on reducing dependence on imported medicines.

Accurate detection of vehicle damages such as dents, scratches, and cracks is essential for improving the efficiency, consistency, and scalability of insurance claim assessment. Conventional inspection procedures rely heavily on manual evaluation, making them time-consuming, subjective, and costly. To address these limitations, this paper presents a three-stage progression of mixture-of-experts (MoE)–based classification models trained on the CarDD dataset, which contains 4,000 COCO-annotated vehicle damage images. The study begins with a baseline RawMoE model operating on flattened image representations, followed by ResMoE-50, which incorporates ResNet-50 for deep feature extraction. Building upon these foundations, we propose CropResMoE-50, a region-aware hybrid architecture that integrates object-level cropping with scale-specific analysis to enhance spatial localization and classification accuracy. Extensive experiments on the CarDD benchmark, together with comparisons against established baselines including ResNet-50, EfficientNet-B0, and Swin-T, demonstrate thatCropResMoE-50 achieves strong performance with favorable computational efficiency (24.8M parameters, 4.13 GFLOPs, and approximately 0.006 s inference latency). The model attains 89.30% test accuracy and average precision scores of 87.70%, 93.45%, and 98.12% for small, medium, and large objects, respectively. To extend practical applicability, a semi-automated labeling pipeline integrating ChromaDB is introduced to support retrieval-augmented learning and pseudo-labeling under uncertainty. Additional validation on a real-world insurance dataset from a real-world insurance company confirms robust generalization, achieving 83.33% accuracy. Overall, the proposed framework offers a scalable, interpretable, and deployment-ready solution for automated vehicle damage assessment.

ABSTRACT The Indo‐Pacific Economic Framework for Prosperity (IPEF) is an unconventional economic initiative in that it excludes tariff reductions and market access. Instead, it focuses on trade, supply‐chain resilience, sustainability, and fair practices. However, without conventional trade incentives, the IPEF may struggle to secure long‐term commitment for costly reforms, which is a paradox that requires some significant unraveling. This study contributes to the search for solutions to the paradox by attempting to apply the Theory of Inventive Problem Solving (TRIZ) to resolve this contradiction. TRIZ identifies and addresses conflicts within systems by balancing opposing objectives. In the context of the IPEF, the challenge is incorporating trade incentives to enhance commitment without undermining its non‐traditional design. Using TRIZ's “engineering contradiction” approach, the study proposes four innovative solutions by strengthening the IPEF's effectiveness while preserving its unique structure. The findings offer strategic insights to ensure sustained engagement and drive inclusive economic growth in the Indo‐Pacific region.

The Malaysian estate agency industry, which has been regulated since 1981, has been experiencing long-term problems of breaching ethics, conflicts of interest, and a lack of transparency. The purpose of this paper is to enhance the level of ethical conduct and improve general performance in the Malaysian estate agency industry. The mixed-methods approach (surveys and comparative studies) is used in the paper. The results reveal the necessity to implement sustainable methods and offer changes in regulatory systems to promote the development and professionalism of the industry. Although the structure of the estate agency in Malaysia is similar to that of the developed countries, there is a potential for improvement that can be realized to ensure that it becomes sustainable. The article highlights the need to change the regulatory systems to match the modern business dynamics and industry needs to ensure that Malaysia keeps progressing and that the profession of the estate agency is developed.

Tubule identification is a critical task in histopathology cancer grading, as the presence and architecture of glandular structures are closely associated with cancer differentiation. This study presents an ontology-based framework for the detection of tubules in breast, prostate, and colon cancer histopathology images. The framework integrates domain knowledge through spatial and morphological analysis, emulating the diagnostic process used by pathologists. The proposed method consists of three main stages: segmentation of lumen candidates, segmentation of nuclei using an improved Fuzzy C-Means clustering approach, and a biologically informed three-phase spatial validation process for tubule detection. This includes nuclear density assessment, boundary conformity analysis, and area overlap verification to ensure reliable classification of glandular structures. The dataset comprises 60 annotated images collected from Malaysian medical institutions, including 20 images from each cancer type. Experimental results demonstrate robust performance, with breast cancer achieving the highest detection accuracy (0.95) and Dice score (0.93), followed by colon (0.91) and prostate cancer (0.89). The framework shows strong generalizability across cancer types and offers an interpretable, automated solution that supports histopathological analysis and grading. The findings highlight the potential of ontology-guided image analysis in improving diagnostic precision and reproducibility in computational pathology.