Transactions on Science and Technology

Ibrahim Mohammed Dibal; Yeak Su Hoe.

This study introduces a novel single-step hybrid block method with four intra-step points that attains six-order accuracy, ensures A-stability, consistency, and provides an efficient, accurate, and computationally economical tool for solving ordinary differential equations. The scheme incorporates intra-step points, which provide richer information within each integration step and significantly improve both precision and stability. When function values are not naturally defined at the chosen nodes, suitable interpolation techniques are introduced to approximate the missing terms without compromising accuracy. A detailed theoretical framework is established, including the analysis of convergence behavior and the derivation of local truncation error expressions. The stability of the method is further examined by identifying its stability regions and proving zero-stability under practical constraints on the step size. These theoretical guarantees ensure that the scheme is not only accurate but also reliable for long-time numerical integration. To complement the analysis, a series of comprehensive numerical experiments are conducted on benchmark problems frequently used in literature. The experimental results consistently demonstrate the superiority of the proposed method over existing approaches in terms of accuracy, efficiency, and overall robustness.

Ammielle Akim Kerudin; Siti Nur Athirah Binti Othman.

Colorectal cancer (CRC) remains a major global health burden with high mortality in advanced stages, highlighting the urgent need for more effective and safer therapies. Aberrant β-catenin stabilization and nuclear accumulation promote oncogenic transcriptional programs and remains an attractive yet challenging therapeutic target. Here, an in silico screen of 25 naturally derived compounds was performed against β-catenin (PDB: 1JDH) using AutoDock Vina 1.2.5. Ligands and receptors were prepared in PyMOL 3.0 and AutoDockTools 1.5.7. Blind docking was conducted using a whole-protein search space centered at x = −2.857, y = 9.859, z = 40.811 with a box size of approximately 107 × 59 × 121 Å in triplicate runs. Binding poses and interaction patterns were visualized in PyMOL 3.0 and BIOVIA Discovery Studio 2024 (3D and 2D interaction maps). Nine compounds achieved predicted binding affinities (ΔG_bind) of ≤ −7.0 kcal/mol, led by silibinin (−9.9 kcal/mol), followed by quercetin (-7.8 kcal/mol), luteolin (-7.7 kcal/mol), ellagic acid (-7.5 kcal/mol), garcinol (-7.5 kcal/mol), betulinic acid (-7.4 kcal/mol), ursolic acid (-7.4 kcal/mol), derricin (-7.0 kcal/mol) , and epigallocatechingallate (EGCG) (-7.0 kcal/mol). Silibinin showed a consistent predicted pose with multiple hydrogen-bond and pi-alkyl hydrophobic contacts within a putative pocket. Drug-likeness analysis using Lipinski’s Rule of Five indicated that most of the top hit ligands complied with criteria for molecular weight, hydrogen bond donors/acceptors, and lipophilicity, suggesting favorable oral bioavailability, while EGCG exceeded the recommended limits for hydrogen bond donors and acceptors, and garcinol surpassed the molecular weight and cLogP thresholds. Additionally, ADMET predictions highlighted potential concerns for quercetin due to a predicted mutagenic/tumorigenic risk. Overall, by applying a curated ligand set under a single standardized docking–ADMET workflow, this study reports novel screening outputs, including docking scores, predicted binding poses, and residue-level interaction profiles, together with an ADMET-informed prioritization. Based on these in silico results, silibinin emerged as the leading scaffold for prioritized experimental validation.

Amira Batrisyia Shariman; Puteri Hanis Nafisah Megat Kamil; Fatin Syahirah Othman; Shaza Eva Mohamad; Nadiah Abu.

Extracellular vesicles (EVs) are nano-sized particles that are naturally released by cells. Recently, increasing attention has been directed toward EVs derived from natural sources such as plants and fruits. This is due to their sustainability and cost-effectiveness compared to mammalian-derived EVs. Microalgae are photosynthetic unicellular organisms that have also been reported to secrete EVs. In this study, we aimed to isolate EVs from the well-known microalga Chlorella vulgaris and evaluate their cytotoxicity in Jurkat T cells. EVs were isolated using a combination of ultracentrifugation and size-exclusion chromatography (SEC). Characterization was conducted using MicroBCA protein assay, dynamic light scattering (DLS), and nanoparticle tracking analysis (NTA). Subsequently, the EV-enriched population was co-cultured with Jurkat T cells, and cell viability and apoptosis were assessed. Our results demonstrated that the EV-enriched population had a mean size below 200 nm. Moreover, the EVs did not cause any significant reduction in cell viability or induce apoptosis. Overall, our preliminary study has shown that microalgae-derived EVs are non-cytotoxic and are biocompatible in a mammalian system.

Janice Lynn Ayog; Venus Tan.

Urbanization and climate change have increased the frequency and intensity of urban flooding, particularly in tropical cities such as those in Malaysia. Green roofs offer a promising low-impact development (LID) strategy for stormwater management by reducing runoff volume and peak discharge; however, there remains limited understanding of how roof slope influences green roof hydrological performance under tropical rainfall conditions. This study evaluates the effectiveness of green roofs with different slopes in reducing peak runoff and assesses the capability of the EPA Storm Water Management Model (SWMM) to simulate slope-dependent runoff responses. Experimental runoff data were obtained from a laboratory-scale green roof model with slopes of 2% and 6%, subjected to controlled simulated rainfall events, and were used for calibration and validation of EPA SWMM, focusing on key indicators including peak discharge and time to peak. Results show that green roofs significantly reduced peak discharge compared to conventional roofs, with reductions of 97.2% at 2% slope and 95.4% at 6% slope, with the shallower slope exhibiting greater runoff attenuation associated with delayed runoff response. SWMM simulations demonstrated satisfactory agreement with observed data, with Nash–Sutcliffe Efficiency (NSE) values between 0.50 and 0.65 and RMSE–observations standard deviation ratio (RSR) values between 0.58 and 0.68. Overall, the findings indicate that green roofs, including those on steeper slopes, are effective in attenuating stormwater runoff, and that EPA SWMM is a suitable tool for comparative modelling of green roof hydrology under tropical conditions. These insights support the integration of green roofs into urban stormwater planning in Malaysia and similar environments.

Xiuhua Huang; Suhaila Abd Halim; Normi Abdul Hadi.

This study investigates the development and clinical applications of Large Deformation Diffeomorphic Metric Mapping (LDDMM) in medical image registration. Through systematic comparison between conventional optimization-based methods and contemporary deep learning techniques, we evaluate their respective performance in registration accuracy, computational efficiency, and clinical utility. Our methodology encompasses a thorough examination of both mathematical foundations and neural network implementations in LDDMM. Results demonstrate that traditional approaches maintain superior precision for complex anatomical variations via rigorous variational optimization, whereas deep learning methods achieve substantial computational acceleration (reducing processing time from hours to seconds) through learned deformation patterns. Critical analysis reveals important trade-offs: while deep learning offers remarkable speed improvements, traditional methods preserve accuracy advantages in specialized clinical scenarios. We identify key challenges including computational complexity, implementation difficulties, and domain adaptation limitations, while proposing hybrid architecture and transfer learning as potential solutions. The study concludes that integrating the mathematical robustness of conventional LDDMM with the computational efficiency of deep learning presents the most viable path forward. Such synergistic approaches promise to advance medical image analysis pipelines and promote wider clinical implementation of sophisticated registration technologies.

Wei Sheng Chong; Izz Khalisah Nashir; Imelus Nius; Pei Zhao Liew; Fikri Akmal Khodzori; Muhammad Aiman Mohd Azseri; Muhammad Dawood Shah.

Precise benthic habitat mapping is essential for the efficient management and preservation of tropical marine ecosystems. This study assesses the effectiveness of Unmanned Aerial Vehicle (UAV) technology for high-resolution mapping of the benthic habitats adjacent to Mantanani Besar Island, Malaysia. Employing a Support Vector Machine (SVM) algorithm, high-spatial-fidelity imagery (GSD = 3.72 cm/pixel) was classified into two hierarchical tiers: a binary Level 1 (L1) scheme (live coral and non-living substrate) and a multi-class Level 2 (L2) scheme consisting of six distinct classes (branching coral, massive coral, patch coral, sand, coral rubble, and submerged rock). The UAV-derived classification attained an Overall Accuracy (OA) of 88.60% (κ = 0.8625), illustrating the efficacy of this platform for detailed habitat description. The findings reveal that live coral occupies roughly 37.55 ha (30%) of the examined area, whilst non-coral substrates, mainly sand and rubble, constitute the remaining 88.86 ha (70%). The occurrence of fragmented benthic substrates, especially in settlement locales, indicates considerable past reef degradation possibly caused by sedimentation and human activities. This study highlights the use of UAVs as a precise instrument for marine surveillance, delivering essential spatial data for the conservation of Mantanani's biodiversity.

Awang Bono; Zykamilia Kamin; Dona Stacy Petrus; Muhamad Afif Naqiudien Aladin; Mohd Hardyianto Vai Bahrun.

The amount of moisture content in drying process is crucial. Determining accurate process control, product quality and energy efficiency in food processing is necessary to ensure the quality of the drying food. In this study, Response Surface Methodology (RSM), a feedforward Neural Network (NN), and a Random Forest (RF) model were developed for moisture content of avocado pulp dried under controlled convective conditions. A Central Composite Design with number of experiments = 30 was used to explore four factors which are hot-air temperature (denoted as A, ranging from 40 to 70 °C), drying time (B, 13–20 h), wind speed (C, 0.12–0.26 m/s), and raw material thickness (D, 0.5–1.25 cm). A second-order polynomial model which was developed using RSM, was fitted to the experimental data and compared with other models, NN and RF using cross-validation to improve robustness. The results showed that RSM model performed significantly better than the other two models. The RSM model achieved coefficient of determination of 0.80 followed by NN, 0.43 and RF, 0.34. Feature-importance was used on ML models to determine the ranking of the factors based on how significant the factors influence the drying process. It was identified that wind speed and drying time as the main factors to represent the final moisture content, directly affect mass-transfer control in this application. The results highlight that small datasets can outperform the machine learning model especially for characterizing food drying processes.

Muhammad Irfan Herlan; Borhan Abdul Haya; Ilmas Abdurofi; Muhamad Askari; Nur Aainaa Hasbullah; Lum Mok Sam; Mohd Rakib Mohd Rashid.

This study evaluated the effects of fermentation agents (EM4 and yeast), fermentation duration (7, 14, and 21 days), and water volume (1 L and 2 L) on the physicochemical properties of liquid organic fertilizer (LOF) derived from cow dung. A Completely Randomized Design (CRD) with three replications was employed. Parameters analyzed included pH, carbon (C), nitrogen (N), hydrogen (H) concentrations, and C/N ratio. Results indicated that both fermentation agent and duration significantly influenced LOF properties. The yeast-based treatment with 7 days fermentation and 2 L water (Yeast + 7 days + 2 L) emerged as the most practical and efficient combination, achieving a near-neutral pH (6.53), balanced C/N ratio (9.98), and acceptable nitrogen content (0.40%) within the shortest time. In contrast, EM4 treatments, particularly EM4 + 21 days + 2 L, yielded higher nitrogen concentrations (0.52%) but required extended fermentation. A strong positive correlation (r=0.916) was observed between carbon and nitrogen, indicating synchronized nutrient release during decomposition. The findings suggest that yeast serves as a rapid, resource-efficient, and sustainable fermentation agent for small-scale LOF production, offering a viable alternative to more complex microbial consortia such as EM4.

Jacynthia Charmentie Anak Cha’ir; Yanty Noorzianna Abdul Manaf; Fan Hui Yin.

Palm oil is known as one of the important vegetable oils enriched with health beneficial constituents. Despite using hydraulic pressing in palm oil recovery, enzymatic pretreatment prior to pressing was investigated for its effects on the fatty acid profile and micronutrient contents. An enzyme blend of pectinase, cellulase and tannase was used to pretreat the palm mesocarp prior to hydraulic pressing. Enzymatic pretreatment significantly affected the predominant fatty acids contents in the hydraulically pressed palm oil but the overall fatty acid profile (MUFAs > SFAs > PUFAs) remained unchanged compared to the untreated palm oil sample. α-tocopherol and all four forms of tocotrienols (α-, β-, γ- and δ-) were significantly lower in enzymatic treated sample. In contrast, the enzymatic pretreatment markedly influenced the total carotene content with two-fold higher in β-carotene content in the hydraulically pressed palm oil. Additionally, enzymatically treated palm oil showed reduced cholesterol and raised β-sitosterol contents compares to the untreated sample. Overall, enzymatic pretreatment influenced micronutrient contents in hydraulically pressed palm oil without altering its fatty acid profile.

Liu Yi; Sharifah Md Yasin; Mohd Izuan Hafez Ninggal; Aziah Asmawi.

The security core of blockchain technology relies on digital signature schemes. However, existing schemes face numerous challenges when applied on a large scale, such as complex key management, reliance on certificate infrastructure, potential key escrow risks, and lack of resistance to quantum computing capabilities. To address these issues, this paper proposes a novel enhanced certificateless identity-based digital signature scheme. This scheme ingeniously integrates certificateless cryptosystem and lattice cryptography, aiming to simultaneously achieve identity-friendly public key management, effectively mitigate the key escrow problem, and lay theoretical foundation for post-quantum security. This paper first presents the formal definition and detailed construction of the scheme. Then, under the random oracle model, the security of its existence being unforgeable is reduced to the computational difficulty of the short integer solution problem on the lattice. The performance evaluation of the system shows that, compared with the traditional scheme based on bilinear pairing, this scheme significantly improves security while maintaining reasonable computational overhead. Experimental results show that at the 128-bit security level, the signing time is 4.8 ms and the verification time is 2.1 ms. Finally, this paper elaborates in detail on the application model of this scheme in secure blockchain transactions, demonstrates how it simplifies the transaction process by using human-readable identity identifiers, and through its anti-quantum and decentralized trust characteristics, provides a powerful cryptographic primitive for building the next generation of secure, efficient and user-friendly blockchain systems.