MIMOS (Malaysia)

The presence of free fatty acids (FFAs) in edible oil varies and presents challenges in visual quantification due to inconsistent colour transitions during titration. In conventional colourimetric titration, the endpoint is often subjectively identified. As per the Malaysian Palm Oil Board (MPOB) standard, neutralisation occurs when a faint pink hue persists for 30 s. However, this definition lacks an objective reference, which impacts its reproducibility. To address this limitation, this study developed an objective RGB (red, green, and blue) sensor-based multi-wavelength colourimetric titration method for quantifying FFAs in edible oils. Two pH indicators were evaluated: red cabbage extract and phenolphthalein. In the first experiment, red cabbage extract was used with acetic acid (CH3COOH) and sodium hypochlorite (NaClO), and colour changes were monitored using an RGB colour sensor. Data were processed through minimum-maximum normalisation and equal interval classification. In the second experiment, phenolphthalein was employed to detect FFAs in cooking and olive oils, with illumination provided by multi-wavelength lasers and RGB light-emitting diodes (LEDs). RGB changes were recorded to determine the neutralisation point and compute FFA content using MATLAB. Results showed that laser-based illumination produced sharper spectral transitions and reduced signal noise. Linear regression analysis revealed a positive linear correlation between RGB values and FFA concentration in cooking oil, while olive oil displayed weaker yet similar trends. Measured FFA contents were 0.33% for cooking oil and 0.45% for olive oil. The findings confirm that RGB sensor systems, combined with tailored illumination and data analysis, can enhance endpoint accuracy in titration and support the development of low-cost, portable diagnostic tools for oil quality monitoring. This approach shows strong potential for further development into real-time, in situ monitoring platforms for industrial and consumer applications. © 2026 Society of Chemical Industry.

Human umbilical cord mesenchymal stem cell-derived exosomes have gained attention as a promising, cell-free regenerative therapy due to their immunomodulatory and tissue-repair properties. This study retrospectively evaluated the safety and efficacy of nebulized human umbilical cord mesenchymal stem cell exosome therapy in three patients with asthma and/or chronic obstructive pulmonary disease who underwent weekly nebulized exosome therapy for 5 weeks at ALPS Medical Centre, Kuala Lumpur, Malaysia. Clinical outcomes were assessed using pulmonary function tests, inflammatory markers, and laboratory parameters. Post-therapy, pulmonary function improved, with increases in forced expiratory volume in 1 s from 2.59 to 3.4 L (p = 0.07) and forced vital capacity from 2.48 to 3.32 L (p = 0.11), while the forced expiratory volume in 1 s/forced vital capacity ratio significantly increased from 82.57% to 92.9% (p = 0.05), indicating reduced airflow limitation. Inflammatory markers, including C-reactive protein and eosinophil count, remained stable, and no significant changes were observed in hepatic, renal, or haematological parameters. Additionally, no adverse reactions or safety concerns were reported. In conclusion, nebulized human umbilical cord mesenchymal stem cell-derived exosome therapy demonstrated potential in improving pulmonary function and alleviating respiratory symptoms in patients with asthma and chronic obstructive pulmonary disease while maintaining a favourable safety profile. These preliminary findings support further investigation of exosome therapy as an adjunctive treatment for chronic respiratory diseases.

Abstract To prevent operational breakdowns, it is essential to inspect drill bits for defects following the manufacturing process. Traditional methods to inspect manufacturing defects are not suitable for the drill bits having helical oil holes. These methods are capable to examine surface characteristics but unable to detect internal defects formed during oil hole manufacturing. In this paper, an Operational Modal Analysis (OMA) technique is applied as a Non-Destructive Testing (NDT) to overcome this limitation in detection of manufacturing defects. In this method, the specimen is excited by broadband random vibration and its structural dynamics is analysed by OMA that segregate un-defected and defected drill bits. The proposed algorithm was verified on multiple sets of specimens, and correctly classified all specimens, achieving 100% detection accuracy. The proposed method offers a solution for a more comprehensive inspection by acquiring the dynamic characteristics of the specimen. Nonetheless, its sensitivity in identifying very small defects requires further exploration.

We present an illustrated checklist of the liverwort flora of Mount Tebu Forest Reserve, Tereng-ganu, providing diagnostic features for each species. Our study recorded 80 species belonging to 28 genera and 10 families, representing 10% of 804 species and infraspecific taxa, 23% of 120 genera, and 25% of 40 families reported for the Malaysian liverwort flora.

Accurate tree segmentation in unstructured outdoor environments is essential for autonomous navigation in agricultural robotics. To address this need, this study proposes a deep learning-based method for segmenting palm oil trees in unstructured outdoor environments. This study evaluates the performance of the YOLOv8 segmentation network in comparison with Mask R-CNN and DeepLabV3 for oil palm trees under real-world plantation conditions. Under natural field conditions, images were collected and annotated in instance segmentation format to train and evaluate deep learning models for oil palm tree detection. Performance was assessed based on accuracy, segmentation quality, and inference speed. Among the models tested, YOLOv8m demonstrated the best trade-off between segmentation accuracy and computational efficiency. It achieved the fastest inference time (0.0119 s per image), the shortest training time (3.36 hours), and maintained a competitive mean Pixel Accuracy (mPA) of 97.45%, a respectable mean Intersection over Union (mIoU) of 70.98%, and high precision at 81.90%. These results highlight YOLOv8m as the most suitable model for real-time segmentation in robotic applications operating in unstructured environments with limited computational resources.

This work explores the enhancement of the Velo2Cam LiDAR-camera calibration algorithm by introducing a LiDAR rotation step to improve adaptability across different sensor configurations. The study also investigates optimal sensor-target-backwall poses and distances for effective calibration. The proposed method was validated using an Ouster OS0-64 LiDAR and a ZED 2i stereo camera in a lab setting. This method was validated using an Ouster OS0-64 LiDAR sensor and a ZED 2i stereo camera in a lab setting. The findings demonstrate successful calibration within specific distance ranges for various target sizes. The calibration is effective within 0.6-0.9 m for small targets and 0.6-1.8 m for medium targets, with optimal target-wall distances of 0.2-0.3 m and 0.2-0.7 m, respectively. This work contributes to a better understanding of LiDAR-camera calibration and provides practical insights for achieving user-friendly calibration processes. This approach simplifies calibration, reducing the need for recalibration after sensor misalignment.

A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.

This study examines the role of agency managers in driving the success of waqf-takaful death compensation products in Malaysia. The research identifies the challenges agency managers face and the strategies they use to promote the integration of Islamic financial products with waqf, a charitable endowment. A qualitative approach was employed, involving in-depth interviews with eight agency managers from various takaful companies. Thematic analysis was used to identify key themes related to product knowledge, customer education, marketing strategies, and incentives. The findings reveal that agency managers are pivotal in educating potential clients, overcoming skepticism, and bridging the gap between religious values and financial products. However, challenges such as limited market awareness, product misconceptions, and the need for improved promotional efforts remain significant obstacles. The study suggests that enhanced training for agency managers, better marketing strategies, and clearer communication of product benefits could contribute to the wider adoption of waqf-takaful products. This research offers valuable insights into the practical implications for takaful providers and policymakers in Malaysia, emphasizing the importance of agency managers in promoting financial inclusion and social responsibility through waqf-takaful products.

Training accurate and robust license plate recognition models is often challenged by the scarcity of diverse and comprehensive real-world data. Traditional datasets may lack the variations needed to ensure generalization across different environments, lighting conditions, and plate designs, especially when new license plates are introduced. To address this issue, we propose a novel method that leverages Generative Adversarial Networks (GANs) to synthesize realistic license plate images that closely mimic the characteristics of real-world plates. Our approach involves training a GAN to generate high-fidelity synthetic license plates, incorporating variations in font styles, colors, backgrounds, and plate formats. These generated images are then used to create a large-scale synthetic dataset that augments the limited real-world data. By simulating diverse conditions and variations, our synthetic dataset significantly improves the training process, enabling faster convergence and enhancing the model’s ability to generalize to unseen data. In this paper, we describe the architecture and training process of our GAN model, including the design of the generator and discriminator networks, as well as the loss functions and training parameters used to optimize the generation of realistic license plates. Experimental results demonstrate that models trained on our synthetic dataset, combined with a small amount of real-world data, achieve comparable or superior performance to models trained solely on real data. Our approach not only accelerates the training process but also reduces the dependency on large, labeled datasets, making it a scalable solution for developing license plate recognition systems.