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.