Universiti Teknologi MARA

Herein, chitosan (CS) and watermelon seed shell (WSS) were blended to yield a new biocomposite (CS/WSS) via sonication with an ultrasonic bath of 40 kHz for 25 min at 20 W. Thus, CS/WSS adsorbent was applied for the removal of reactive orange 16 dye (RO16) from the aqueous environment. The CS/WSS characteristics were evaluated using XRD, SEM–EDX, FTIR, and pHpzc methods. The adsorption efficiency of CS/WSS with the RO16 dye was optimized using a Box-Behnken design (BBD). The three independent experimental parameters include the CS/WSS dosage (A 0.02–0.1 g/100 mL), contact time (B 10–60 min), and RO16 solution pH (C 4–10). The kinetics of adsorption analysis validates that the adsorption of RO16 with the CS/WSS biocomposite adopts a pseudo-second-order (PSO) adsorption profile. In addition, the Freundlich and Langmuir isotherm models were assessed to obtain best-fit results for the isotherm profiles. The maximum adsorption capacity of CS/WSS biocomposite (qmax) for RO16 was found to be 158.6 mg/g in an acidic pH environment at pH 4 and 25 °C. The mechanism of RO16 adsorption onto the CS/WSS biocomposite surface has several contributions that include electrostatic forces, hydrogen bonding, n-π and π -π interactions. Thus, the CS/WSS biocomposite exhibits favourable RO16 dye removal in aqueous media.

Culture broth with secreted macromolecules and culture broth of filamentous fungi showing disperse growth exhibit elevated viscosity, usually with shear‐thinning flow behavior. High viscosity, however, poses a serious challenge in the production and research of these compounds and organisms. It commonly causes insufficient mixing and oxygen transfer in large‐ and small‐scale bioreactors. Computational Fluid dynamics (CFD) has been proven to be a valuable tool for the computation of important bioprocess parameters. The published literature for small‐scale shaken bioreactors, especially shake flasks, however, almost exclusively focuses on water‐like viscosity. In this paper, a previously published CFD model for 250 mL shake flasks was used to simulate experiments at high viscosities of up to 100 mPa·s. Compared to experimental data, the CFD model accurately predicted the liquid distribution and computed the volumetric power input with a deviation of less than 7% and the kLa value within a factor of two, compared to the kLa correlation from Henzler and Schedel. Furthermore, a novel approach to compute the shear rate was tested. Lastly, new insights into the out‐of‐phase phenomenon were gained. The presented data confirms the usefulness of the already established critical phase numbers of 0.91 and 1.26, while underlying the fundamentally smooth transition from in‐phase to out‐of‐phase operating conditions.

Leukemia is a heterogeneous disease in terms of cytogenetics, with four primary subtypes: acute lymphoid leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and chronic lymphoid leukemia (CLL). As cytogenetic heterogeneity increases, the disease prognosis worsens, highlighting the significance of cytogenetic profile in disease diagnosis. In this study, we conducted cytogenetic profiling of 105 leukemia cases referred to the Clinical Diagnostic Laboratory (CDL) at the Advanced Medical and Dental Institute (AMDI) in northern Malaysia between 2006 and 2021. Of these cases, 50.47% were ALL, 37.14% were AML, and 12.38% were CML. Most of the patients, approximately 57.15%, were cytogenetically normal, while the rest, 42.85%, were cytogenetically abnormal. Overall, the most common cytogenetically abnormal karyotypes detected in patients were chromosomal translocation (20.95%), followed by complex karyotypes (13.33%), and chromosomal addition (4.76%). The majority of ALL patients were under 14 years old, whereas most AML and CML patients were older than 14. The correlation between the ages and the karyotype abnormalities in ALL, AML, and CML showed a negative moderate correlation (r=-0.501, p=0.312). In conclusion, cytogenetic profiling provides valuable insights into the disease's underlying mechanism, which may help strategize the treatment of leukemia patients.

In a consequence of climate change’s adverse effects, Malaysia’s road infrastructure faces significant challenges, particularly during both dry and rainy seasons, which weaken the natural bonds of the laterite soil. This research, therefore, outlines a laboratory study aimed at assessing the impact of cement stabilisation on the compressibility characteristics of laterite soil, subject to both saturated and unsaturated conditions. This study reveals that a 6% cement dosage is optimal for stabilising the laterite soil, proving the minimum 7-day strength requirement of 800 kPa, as specified by the Malaysia Public Works Department (MPWD) for stabilised subgrade material in low-volume roads. Consequently, the research involved conducting saturated tests (utilising a conventional oedometer) on soil specimens stabilised with 3%, 6%, 9%, and 12% cement dosages. Meanwhile, only the 6% cement-stabilised soil is used in unsaturated tests with a modified suction-controlled oedometer. The findings of this study highlighted that cement-stabilised laterite soil exhibits significantly lower compressibility in comparison to unstabilised laterite soil. Furthermore, the unsaturated oedometer test demonstrated that soil’s compressibility is notably decreased at higher suction levels (drying conditions) compared to lower suction levels (wetting conditions). In summary, this research contributes valuable insights, emphasising the potential of cement as an effective soil stabiliser by reducing soil settlement and enhancing the durability of Malaysia’s roads in response to climate-related challenges.

Urgent remediation is needed to degrade the low-biodegradability dye molecules in dye-polluted water from textile industries, as this contamination has been recognized as a serious environmental issue, causing a range of harmful effects on both human health and ecosystems. In this milieu, the present study investigates the biofabrication of tin oxide nanoparticles (SnO2 NPs) using leaves extract from Morinda citrifolia and Pandanus amaryllifolius for the degradation of methylene blue (MB), benefaction an alternative solution to the issue of dye-polluted water. The synthesis method integrates tin chloride pentahydrate with the leaves extract, followed by calcination. Comprehensive characterization via FTIR, XRD, FESEM, EDX, HRTEM, and UV-Vis spectroscopy confirmed the successful formation of SnO2 NPs, revealing distinct morphological and crystalline properties. Photocatalytic tests demonstrated that SnO2 NPs derived from M. citrifolia achieved a superior degradation rate of 97%, compared to 80% from P. amaryllifolius, with optimal activity under neutral pH. While radical scavenger experiments identified electrons as the primary active species to accelerate the degradation and reusability tests indicated a gradual decline in efficiency over five cycles, demonstrating its stability. These findings underscore the superiority of biofabricated SnO2 NPs as a sustainable and efficient photocatalyst using these two plants, compared to other plant templates, in which pronounce promising avenues for environmental conservation and resource management.

The past decade has seen a rapidly changing landscape in priority areas for public health globally and, as such, across the teaching and learning curriculum for tertiary education in health sciences. The nature of some of these changes has led to pedagogical challenges in higher education that require transformative, interactive, and virtual modes of delivery and knowledge facilitation not previously seen. The COVID-19 pandemic, climate change, increasing health disparities, and a shift to a focus on noncommunicable diseases has merged with the changing nature of social, cultural, and technological preferences of the generations living through such times to see an increasing need in more viable teaching solutions for these “wicked problems.” This article outlines key innovations empirically demonstrated to meet these challenges through nuanced responses to increasingly disrupted approaches to linear delivery of content and a shift toward bite-sized, interactive, reflexive modes of achieving learning objectives.

Introduction: In Asian countries, warfarin is still widely used for stroke prevention in non-valvular atrial fibrillation compared to non-vitamin K antagonist oral anticoagulants (NOACs) due to its affordability. A tool such as the SAMe-TT 2 R 2 is needed to determine the probability of achieving and maintaining good anticoagulation control with warfarin therapy. However, it requires validation in the Malaysian cohort. Therefore, the objective of our study is to validate the SAMe-TT 2 R 2 score in predicting poor anticoagulation control in Malaysia. A time in therapeutic range (TTR) < 65% was used to determine poor anticoagulation control. Method: This retrospective cohort study was conducted from July 2022 to July 2023. Patients were enrolled in 2020 from 49 facilities located across Malaysia resulting in a total of 957 included patients. TTR was calculated using Roseendaal’s method. Results: The mean (SD) TTR and SAMe-TT 2 R 2 score in the overall cohort is 65.2% (±24) and 5.5 (±0.9) respectively. Almost half of the population (43.7%) has the SAMe-TT 2 R 2 score of 5. Having diabetes, ischemic heart disease, and increasing HAS-BLED and SAMe-TT 2 R 2 score affects anticoagulation control on univariate analysis. However, after adjusting for demographics and clinical variables on multivariate analysis, only the SAMe-TT 2 R 2 score as a continuous variable persists in predicting poor anticoagulation control. A SAMe-TT 2 R 2 score cut-off point of >5 best predicts poor anticoagulation control with a sensitivity of 0.49 and a specificity value of 0.68. Conclusion: The SAMe-TT 2 R 2 score, especially when exceeding 5, was associated with a higher likelihood of poor anticoagulation control, emphasizing its relevance in clinical assessment. However, its limited predictive capability, reflected by a C-statistic of 0.548, suggests the need for cautious interpretation and consideration of additional factors in anticoagulation management decisions. Continuous monitoring and personalized strategies are crucial for optimizing outcomes in this population.

A cost-effective and straightforward biorecognition platform based on an anodic aluminum oxide (AAO) membrane has been developed to detect fluorescent-tagged complementary target DNA. This platform utilizes a cross-linked DNA probe bearing a triazole compound functionalized on the surface of the AAO membrane. The proposed structure of the as-synthesized triazole compound is supported by proton and carbon nuclear magnetic resonance spectra. Additionally, the Fourier-transform infrared spectrum of the modified AAO membrane surface reveals emerging peaks corresponding to the C-H stretching of the aromatic methyl group of the triazole compounds. The X-ray photoelectron spectroscopy survey scan showed carbon, nitrogen, oxygen, and silicon traces on the surface of the silanized AAO membrane. The performance of the DNA sensor array was validated using fluorescence confocal microscopy. Images obtained through confocal microscopy confirm the successful hybridization of fluorescent-tagged complementary target DNA on the AAO membrane biosensor, with a remarkable lowest detection limit of 0.029 nM. In conclusion, the as-synthesized triazole compounds are an alternative cross-linker with high efficiency for DNA detection.

In literature, color words serve as important carriers for writers to convey their emotions. In this paper, color words in literary works written by over 30 famous writers from different countries and regions in world literature are selected as the research object. According to the frequency of their use in the selected literary works, our analysis divides the different color symbols into two types: high-frequency and low-frequency. Among the various color symbols, red, blue, white, black, and yellow are classified as high-frequency color symbols. Purple, gray, green, and brown belong to low-frequency color symbols. Comparatively speaking, the metaphorical meanings of high-frequency color symbols are richer and more diverse, while the symbolic meanings of low-frequency color symbols are relatively simple. Through the analysis of the color symbols in the literary works, we find that there is a benign interaction between color symbols and literary works. On the one hand, writers convey their pursuit of art and aesthetics through color symbols. Color symbols become a perfect medium for them to express ideas, show themes, and construct characters. On the other hand, literary works also expand and enrich the metaphorical meaning of color symbols.

The study of projected rainfall data across multiple future scenarios is a key factor in developing sustainable water resource management plans. This paper presents an analysis of projected rainfall series in the Sabah and Sarawak region, Malaysia, against the bias-corrected GCM simulated rainfall data. Three Shared Socioeconomic Pathways (SSP) of SSP126, SSP245, and SSP585 were used to retrieve rainfall simulations of three Global Climate Models (GCMs) of Access-CM2, HadGEM, and UKESM1. The SSPs provide different pathways through which they can affect the rainfall trend. This investigation helps to illustrate the complex interactions between socio-economic developments and climatic changes, underlining the need for adaptive strategies in regional planning. The GCM outputs were downscaled using the quantile-based bias correction method for the future projections. The annual and monthly rainfall data were divided into two periods of 2021–2055 and 2056–2090 for detailed analysis of the future rainfall in the study area. This division allows for a clearer understanding of short-term versus long-term climatic impacts. The non-parametric Mann–Kendall (MK) test and the Sen’s Slope estimator were used to study the trend in the rainfall series. The rainfall data simulated using the Access-CM2 and the HadGEM showed a negative trend, while it was positive in the UKESM1 simulations. Generally, a positive trend in the projected rainfall series was observed. The rainfall series and the rainfall variability index (RVI) chart were plotted to compare the rainfall series of all the SSPs. The drought Severity-Duration-Frequency analysis for the return periods of 2-year, 5-years, 10-year, 20-year, and 50-year was also developed based on the RVI, to estimate the temporal trend of drought severity. These analyses are crucial for preparing effective drought management and mitigation strategies. Results demonstrated that as the drought duration increases its intensity and severity increases as well.

With universities continually producing data in their laboratories, effective data management has become increasingly crucial. To meet this demand, the application of laboratory information management systems (LIMS) is becoming increasingly widespread. LIMS is a complex computing system used to manage laboratory data. There are currently a variety of LIMS to choose from, but most LIMS use proprietary codes, so the development cost is very high. At the same time, existing LIMS models usually only focus on functional requirements and ignore the specific means of functional implementation, which leads to difficulties for users in management. In addition, since LIMS are very complex, they are often designed to meet the needs of specific laboratories, which makes their versatility and reusability very poor. Therefore, this paper presents the Collaborative Laboratory Management Model(CLMM), which introduces advanced management ideas based on the LIMS model, aiming to make the model focus on both function and technology. CLMM integrates workflows, and users can create and manage workflows themselves. This not only improves the flexibility of the model, but also increases reusability, which can meet the needs of different types of laboratories. With the development of the model, we hope to improve the management efficiency of laboratories by managing the data of university laboratory information, equipment, instruments, etc., and contribute to the complex management work of universities.

Given the increasing demand for laboratory equipment management in universities, especially the increasingly complex equipment management, the traditional equipment management system can no longer meet the management needs of universities. Therefore, it is very important to optimize the university’s equipment management system. The allocation of laboratory equipment maintenance tasks in the laboratory equipment management system of universities is a very critical link. Its effective solution is crucial to ensure the normal operation of laboratory equipment and the reasonable allocation of maintenance resources. This study proposes a double coding adaptive genetic algorithm to optimize the allocation of laboratory equipment maintenance tasks in universities to achieve the optimal allocation of resources and minimize maintenance costs. The work allocation scheme is iteratively optimized by a dual-coding strategy and definition of adaptive crossover and mutation operators. The experimental results of this study show that the algorithm can find the approximate optimal task allocation scheme within a reasonable time, which improves the efficiency and accuracy of laboratory equipment maintenance. In addition, compared with the traditional allocation method, the algorithm in this paper shows stronger flexibility and robustness when dealing with large-scale complex problems.

This study explores sentiment analysis research framework based on the Mandarin social media dataset, focusing on the Transformer model. The paper first reviews the background of sentiment analysis, emphasizing the importance of this task in text classification and the relative lack of sentiment analysis in Mandarin Chinese. The study used three public datasets from the CSDN platform, including positive and negative reviews from different social media platform, explores how word order impacts sentiment classification in Mandarin. The study completed the experiment through four stages: preprocessing, text embedding, feature extraction, and sentiment classification, and used a pre-trained Transformer model for analysis. The results demonstrate the effectiveness of Transformer models for sentiment analysis with high accuracy on certain datasets, although challenges persist with specific data due to complexity. Future work aims to refine the model’s performance on diverse datasets and address limitations in sentiment feature extraction for Mandarin texts. The results confirm that there is still much room for improvement in Transformer models in improving sentiment classification in non-English languages such as Mandarin.

Surface defect detection plays a pivotal role in ensuring product quality in industrial production, as defects like cracks, scratches, and dents can compromise product performance and durability. Traditional detection methods, such as manual inspection and Non-Destructive Testing (NDT), are limited by inefficiency, reliance on human expertise, and susceptibility to errors, which restrict their application in large-scale production. With advancements in artificial intelligence, deep learning models, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have emerged as promising solutions for automated surface defect detection. This paper provides a comprehensive review of surface defect detection technologies, starting from traditional methods to modern deep learning-based techniques. The advantages and limitations of each approach are analyzed, highlighting key advancements in deep learning, including recent models like Faster R-CNN, Cascade R-CNN, and YOLOv4. Furthermore, challenges such as handling complex defects and improving detection accuracy in real-world industrial environments are discussed, along with potential directions for future research. Experimental evaluations using the Few Steels Classification (FSC) dataset demonstrate the effectiveness of modern detection methods in industrial applications, offering insights into enhancing defect detection systems.

Smoked fish is a fishery product that is widely produced by the community. Fishery products require halal certification. Implementing a halal assurance system can start by identifying critical points. Small and medium industries widely produce smoked fish. There are still not many SMEs that carry out halal certification. The aim is to design the implementation of a halal assurance system for fish-smoking SMEs in the Bangkalan district. The halal assurance system (HAS) preparation refers to the five halal pillars listed in HAS 23000. Based on the study results, the criteria for halal commitment and responsibility and monitoring and evaluation are not by the halal assurance system. The criteria for ingredients, halal product processes, and products as a whole are by the HAS. Recommendations given to SMEs are creating and socializing halal policies, forming a halal management team, conducting regular training involving all workers, creating a material matrix and compiling halal product documents, writing procedures for critical activities, creating traceability procedures that can trace the materials and facilities used, creating procedures for handling products that are not by the halal assurance system, name the product following the product criteria, and creating procedures for conducting internal meetings for halal assurance evaluation.

Aspirasi ialah ciri linguistik yang asing dalam dialek Malayik di Semenanjung Malaysia. Walau bagaimanapun, terdapat kekecualian bagi pernyataan ini kerana sebahagian kecil daripada dialek tempatan yang dituturkan di bahagian utara Semenanjung Malaysia, khususnya di kawasan sempadan Malaysia–Thailand, ternyata memperlihatkan ciri aspirasi dalam sistem fonologinya. Antara dialek Malayik Semenanjung Malaysia yang mendapat ciri tersebut termasuklah dialek Melayu Baling. Dengan berdasarkan gelintaran terhadap kajian terdahulu, faktor utama yang mewujudkan ciri aspirasi dalam dialek Melayu Baling ialah kontak linguistik. Oleh itu, dialek sejatinya tidak mempunyai aspirasi. Contohnya, dialek Melayu Baling menerima ciri tersebut setelah berkontak dengan bahasa lain, iaitu bahasa Siam. Dalam makalah ini, kontak yang mewujudkan ciri aspirasi dibincangkan dengan terperincinya menerusi paparan latar sejarah dan sosial. Selain kontak, makalah ini menampilkan perbincangan yang mengaitkan penyebaran aspirasi secara meluas dan tekal dalam dialek Melayu Baling dengan mekanisme inovasi dalaman linguistik, terutamanya yang melibatkan proses penyingkatan suku kata dan analogi. Perbincangan makalah ini diakhiri dengan jangkaan tentang ketekalan ciri aspirasi dalam dialek Melayu Baling yang mungkin terhakis akibat dua faktor. Pertama, persempadanan geopolitik yang membataskan kontak dengan bahasa Siam. Kedua, intensiti kontak yang tinggi dengan dialek Melayu Kedah ekoran persempadanan geopolitik yang berkemungkinan menghadirkan semula konsonan hentian tidak bersuara tanpa sebarang elemen hembusan sebagai penanda ciri aspirasi. Dapatan kajian ini menegaskan keperluan penggabungan unsur dalaman dan luaran linguistik bagi menjelaskan fenomena dialek agar sebarang teori yang terbit daripadanya lebih berpada dan bertepatan dengan kenyataan penyebaran dialek secara geografi dan histori.

With the rapid development of artificial intelligence technology, recommendation systems have been widely applied in various fields. However, in the art field, art similarity search and recommendation systems face unique challenges, namely data privacy and copyright protection issues. To address these problems, this article proposes a cross-institutional artwork similarity search and recommendation system (AI-based Collaborative Recommendation System (AICRS) framework) that combines multimodal data fusion and federated learning. This system uses pre-trained convolutional neural networks (CNN) and Bidirectional Encoder Representation from Transformers (BERT) models to extract features from image and text data. It then uses a federated learning framework to train models locally at each participating institution and aggregate parameters to optimize the global model. Experimental results show that the AICRS framework achieves a final accuracy of 92.02% on the SemArt dataset, compared to 81.52% and 83.44% for traditional CNN and Long Short-Term Memory (LSTM) models, respectively. The final loss value of the AICRS framework is 0.1284, which is better than the 0.248 and 0.188 of CNN and LSTM models. The research results of this article not only provide an effective technical solution but also offer strong support for the recommendation and protection of artworks in practice.

Rivers are often contaminated with metals. This study was conducted on the Jempul River in Jengka, Pahang, Malaysia, due to the agricultural activities carried out along this river. The aims are to investigate the levels of certain metals (namely Al, Fe, Mn, and Pb), identify their likely sources, assess their toxicity loads, and estimate the associated health risks. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine metal concentrations. The concentrations were discovered in the following order: Fe > Al > Mn > Pb, for both dry and wet conditions. The highest concentration in water sample was observed for Fe (1.28 mg/l) in the dry condition and the lowest was detected for Pb (0.04 mg/l) in the wet condition. Principal component analysis (PCA) indicated that the metals originate from natural and anthropogenic sources. Based on metal toxicity load (MTL) calculations, it is recommended that approximately 73% of Pb, 60% of Mn, and 58% of Al be eliminated from the river water to ensure its safety. Children are more susceptible to non-cancer and cancer hazards than adults. This study suggests that extensive exposure assessment and detailed monitoring of water quality indicators should be carried out.