Malaysian Journal of Science Health & Technology
Journal title Malaysian Journal of Science, Health & Technology Initials MJoSHT Abbreviation Malaysian J. of Sci. H. and Tech eISSN 2601-0003 (online) Frequency Two issues, approx. 22 articles per year (April and October) DOI Prefix 10.33102/mjosht - Crossref linked Editor-in-chief Professor Ts. Dr. Mohd Ikmar Nizam Mohamad Isa , Universiti Sains Islam Malaysia, Malaysia Managing Editor Dr. Azira Khalil , Universiti Sains Islam Malaysia, Malaysia Publisher USIM Press Citation Analysis Scopus | Google Scholar Peer Review Double Blind Review
Penerbit Universiti Sains Islam Malaysia
The copyright of this article will be vested to author(s) and granted the journal right of first publication with the work simultaneously licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, unless otherwise stated.
Enhancing Cryptographic Resilience through Symmetric Encryption Algorithm Utilizing Variable Length Chromosomes Genetic Algorithm
Symmetric cryptography, particularly the Advanced Encryption Standard (AES), is widely used for secure data transmission due to its computational efficiency and strong encryption structure. However, limited internal randomness in AES makes it susceptible to advanced cryptanalytic attacks. To address this limitation, this research proposes an enhanced encryption approach that integrates Genetic Algorithm (GA) techniques into the AES framework to enhance randomness. The GA employs variable-length chromosomes and entropy-based fitness evaluation to evolve dynamic binary outputs through selection, crossover, and mutation operations. These evolved outputs are used to introduce controlled randomness into the encryption process, resulting in unpredictable and robust ciphertext. The expected outcome includes improved increased randomness, and stronger resistance to differential and linear attacks. In conclusion, the proposed GA-AES enhanced model offers a mechanism to strengthen symmetric encryption by introducing evolutionary randomness, making it more secure for modern data protection needs.
Copyright (c) 2025 Ahmed Jobaer, Nur Hafiza Zakaria, Farida Ridzuan, A H Azni https://creativecommons.org/licenses/by/4.0
Knowledge Integration Model of Blockchain Technology in Higher Learning Institutions: A Rasch Measurement Model Approach for Pilot Study
Blockchain technology has emerged as a transformative tool in higher education, offering secure, transparent, and tamper-proof systems for credential verification, academic record management, and institutional data governance. Despite its potential, adoption remains limited due to technical, financial, and organizational barriers, alongside a lack of validated measurement tools to assess readiness and integration. This pilot study aims to validate an instrument grounded in the Knowledge Integration Model of Blockchain Technology for Higher Learning Institutions using the Rasch Measurement Model. A 50 item survey was administered to 31 purposively sampled stakeholders, including academics, researchers, and postgraduate students with blockchain related experience. Data analysis using WINSTEPS 3.92.1 assessed reliability, item-person fit, unidimensionality, and targeting through Wright Map analysis. The results showed satisfactory reliability (person = 0.82; item = 0.77). Four items (B5, B6, D8, C7) exceeded the acceptable outfit MNSQ threshold (>1.5) and were revised to enhance instrument quality. However, several items showed misfit and require refinement, and the Wright Map indicated a need for more challenging items to better target high ability respondents. Unidimensionality testing revealed a dominant construct with minor secondary dimensions, suggesting further refinement for conceptual clarity. These findings provide preliminary evidence the instrument’s potential for large scale application, providing a foundation for assessing blockchain knowledge integration and guiding policy and strategic implementation in higher education.
Mathematics, Statistics and Actuarial Science
Copyright (c) 2025 Nur Ilyana Ismarau Tajuddin, Nurafifah Mohd Nor, Nor Aziyatul Izni, Nur Idayu Ah Khaliludin, Aeshah Mohd Ali https://creativecommons.org/licenses/by/4.0
Investigation of 0debug Stealer Using Assisted Static Analysis Method
This study investigates the capabilities of the identified 0debug Stealer malware through qualitative static analysis and reverse engineering. This study primarily employs non-executional static techniques, such as fingerprinting, decompiling, and string extraction, with a bit of dynamic analysis used primarily for deobfuscation. Analysis confirms that obfuscation hinders direct examination, yet reveals core functionalities of 1) anti-tampering, anti-VM, and anti-sniffer to evade detection 2) data theft targeting important victim information, and 3) Command and Control exfiltration. Findings demonstrate 0debug Stealer’s capabilities in bypassing traditional security measures, emphasising the critical role of static analysis in dissecting advanced threats. This paper contributes to the forensic analysis of emerging threats by providing a detailed examination of the 0debug Stealer's manner, revealing its evasion techniques and comprehensive data theft capabilities.
Copyright (c) 2025 Taqiyuddin Anas, Farida Ridzuan, Sakinah Ali Pitchay, Charles Lim https://creativecommons.org/licenses/by/4.0
Human Dimensions of Cybersecurity Operations: Survey Insights from SOC Professionals in Malaysia
Security Operations Centres (SOCs) form the operational core of cyber defence strategies across the world. They are tasked with ensuring continuous monitoring, triage, detection, and response to ever-evolving threats. However, conventional performance metrics used to evaluate SOC efficacy—such as Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), and false positive rates—primarily assess system throughput while overlooking the psychosocial realities of the human analysts who underpin these metrics. This paper presents the findings of a quantitative workforce study conducted among 175 Malaysian SOC professionals across internal, hybrid, and managed security service provider (MSSP) environments. Using a Likert-based adaptation of the NIOSH Worker Well-Being Questionnaire (WellBQ), the study investigates stress, burnout, alert fatigue, task autonomy, psychological safety, tooling efficacy, and career development perceptions. Findings reveal that 73.1% of respondents experience emotional tiredness at least “Sometimes,” while 78.9% agree that more automation would improve their well-being. Notably, 68.0% reported having opportunities to grow and develop their careers within their SOC environments. These insights highlight the need for a paradigm shift in SOC performance measurement—one that integrates human-centric indicators alongside traditional technical KPIs. The paper concludes with empirically grounded recommendations for embedding well-being frameworks into operational security management to ensure sustainable, high-performing cybersecurity teams.
Security Operations Center (SOC); Human-Centric, Cybersecurity; Mean Time to Detect (MTTD); Mean Time to Respond (MTTR).
Copyright (c) 2025 Mohd Haffezal Md Yahaya, Najwa Hayaati Mohd Alwi https://creativecommons.org/licenses/by/4.0
Cancer Classification Challenges in High-Dimensional Microarray Data: An In-Depth Exploration of Machine Learning Models
Microarray gene expression profiling has transformed biomedical research by enabling large-scale, parallel analysis of thousands of genes. Despite its promise, cancer classification using Machine Learning (ML) on microarray data continues to face critical challenges, particularly due to high dimensionality, limited sample sizes, and severe class imbalance. These factors contribute to overfitting, poor generalization, and inflated performance metrics, hindering the clinical translation of models. This Structured Literature Review (SLR) examines ML-based cancer classification studies published between 2015 and 2025. This period was marked by the emergence of deep learning, synthetic data generation, and biologically informed modeling. Using a transparent selection protocol, we synthesize findings from over 20 peer-reviewed studies. The review focuses on three methodological pillars: biologically grounded feature selection, constrained data augmentation, and robust performance evaluation. We identify a growing trend toward hybrid feature selection methods that balance statistical relevance and biological interpretability. However, comparative benchmarking across datasets remains limited. Data augmentation techniques, such as Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN)s, are increasingly being adopted. However, they often lack biological validation. This raises concerns about the plausibility of synthetic gene profiles. To address this, we recommend integrating pathway-level constraints and gene ontology checks during the augmentation process. Furthermore, we observe that many studies disproportionately emphasize accuracy. This can misrepresent the model's efficacy in imbalanced settings. Metrics such as Matthews Correlation Coefficient (MCC), F1-score, and precision-recall curves offer more reliable insights. These metrics should be standardized across evaluations. External validation using independent datasets is also essential to assess generalizability. In addition, it helps mitigate dataset-specific bias. Based on the findings, we present a conceptual hybrid framework that integrates biologically informed feature selection, biologically constrained data augmentation, and balanced evaluation protocols. This framework is intended to enhance reproducibility, biological fidelity, and translational reliability in machine learning-based cancer diagnostics, thereby contributing to the advancement of precision oncology.
Copyright (c) 2025 Wafa' , Sakinah Ali Pitchay, Farida Ridzuan, Muhammad Harith Noor Azam https://creativecommons.org/licenses/by/4.0
An Improved Maturity Model for Electronic Court Case Management System from the Perspective of End Users in Malaysia
Many judicial systems are currently undergoing a significant digital transformation; however, there is no clear way to measure whether their Electronic Court Case Management Systems (ECCMS) are truly mature or not. In Malaysia, several nationwide court modernization programs have been underway. Since 2013, the end-user evaluation has remained somewhat fragmented. In this study, we propose an improved maturity model for an ECCMS and, after filtering out noisy and incomplete responses, test it on 300 active users from various Malaysian courts; 287 were used. The paradigm builds upon earlier e-government maturity models with four underlying constructs: second-order (1) human behaviour, (2) technological, (3) organisational factors, and (4) legal issues. Data were gathered using descriptive statistics, validity tests, and structural equation modelling (PLS-SEM), with a 5-point Likert scale. The structural model earned a Q²_predict value of 0.483 and an R² score of 0.8927, showing predictive power good but not perfect. Legal and regulatory aspects revealed, up to 0.713, the highest path coefficients, whereas technical and behavioural elements were marginally weaker. Although for further research there are still a few gaps, the accepted methodology provides a useful foundation for evaluating the development of e-justice. It can inform decisions about court digitalization, more data-driven.
Copyright (c) 2025 Adham M Alankar, Nurzi Juana Mohd Zaizi , Hanifah Abdul Hamid https://creativecommons.org/licenses/by/4.0
Identification of Key Parameters for Cover Audio Generation in Imperceptible Audio Steganography
The security of data during communication is an ever-growing challenge, as outdated algorithms face increasing vulnerability. Audio steganography offers a promising alternative by embedding data within audio signals in a manner that is imperceptible to human listeners. Recent advancements in cover selection as well as cover generation techniques have improved the quality and suitability of audio for embedding, yet the parameters influencing imperceptibility remain underexplored. This research aims to identify and evaluate key audio parameters which are energy, the total and value distribution of silent samples. These parameters can be assessed prior to the embedding process which offer a potential advantage over existing metrics, typically require post-embedding analysis to accurately assess imperceptibility. The methodology of this research consists of five main stages: parameter identification, dataset preparation, parameter extraction, embedding, and evaluation. Signal-to-Noise Ratio (SNR) was employed as the primary metric to assess perceptual transparency, with 20% data capacity embedded in each audio of total 220 audio. The first experiment revealed a positive correlation as high as r = 0.706 between energy and SNR, indicating that higher-energy audio benefit from auditory masking, resulting in improved imperceptibility. The second experiment showed a negative correlation, r = –0.787 between total silent sample and SNR, confirming that embedding in silent sample increases the risk of perceptible artifacts. Silent sample that has higher value exhibited superior SNR values compared to those with extremely lower value. These findings highlight the importance of pre-embedding analysis in identifying suitable samples for embedding. By identifying and evaluating these influential parameters, this research contributes to the development of intelligent cover generation strategies that enhance the transparency of audio steganography systems. The insights presented offer a foundation for future design, optimization, and real-world application of robust data-hiding techniques.
Copyright (c) 2025 Muhammad Harith Noor Azam, Farida Ridzuan, A H Azni, Sakinah Ali Pitchay, Nur Hafiza Zakaria https://creativecommons.org/licenses/by/4.0
Rapid Authentication of Pig Leather and Synthetic AlternRapid Authentication of Pig Leather and Synthetic Alternatives using NIR Spectroscopy and Chemometrics Approachatives using NIR Spectroscopy and Chemometrics Approach
This study investigates the discrimination between pig leather and Polyurethane (PU) leather using Near-Infrared (NIR) spectroscopy combined with chemometric techniques. Genuine leather derived from pig hides presents unique biochemical signatures linked to its collagen structure. In contrast, PU leather is a synthetic polymer engineered to replicate the visual and tactile properties of natural hides. Differentiating between these two materials is particularly crucial in industrial and cultural contexts, such as halal certification, where accurate authentication is crucial. Principal Component Analysis (PCA) was first employed as an unsupervised method to explore natural clustering. The PCA score plot revealed a clear separation between pig leather and PU leather, with the first principal component (PC-1) explaining 96% of the total variance, confirming that the dominant chemical features are sufficient for preliminary discrimination. For supervised classification, Partial Least Squares Discriminant Analysis (PLS-DA) revealed clear separation in the score plot. The model was supported by robust calibration and validation statistics, with high coefficients of determination (R² > 0.99) and low Root Mean Square Error (RMSE). Overall, these findings demonstrate that portable NIR spectroscopy, coupled with chemometrics, provides a rapid, non-destructive, and reliable authentication method with significant potential for industrial quality control, fraud prevention, and regulatory compliance.
Near-infrared spectroscopy; Chemometrics; Leather authentication, Halal authentication.
Copyright (c) 2025 Mohd Fared Abdul Khir, Ainil Hawa Mohamad Fauzi, Dzulfadli Rosli, Mohd Sukri Hassan, Norzaliman Mohd Zain https://creativecommons.org/licenses/by/4.0
Actuarial Reserve for Accumulated Leave Benefits (GCR) with Enhanced Mortality and Interest Rate Modeling
The 2023 Malaysian Budget increased the maximum compensable unused leave for public employees from 160 to 180 days, significantly raising future liabilities for public sector employers. Under the Malaysian Private Entities Reporting Standard (MPERS), universities must recognise these benefits, which are payable upon retirement or death, as actuarial liabilities. Existing tools, such as the Jabatan Perkhidmatan Awam (JPA) calculator, offer limited accuracy as they do not account for mortality risk or interest rate fluctuations. This study introduces an enhanced actuarial reserve model that incorporates both uncertainties. The model considers two payment casesw which are upon retirement and upon death, using survival probabilities, interest discounting, and assurance factors to determine the expected present value of future cash awards. To evaluate model performance, a simulation study using a non-parametric bootstrap is conducted on 5,000 simulated employee records. Bootstrap resampling is repeated at increasing levels to measure bias and consistency. Results show that both bias and variance decrease as bootstrap iterations increase, indicating model stability and accuracy. This improved model offers a more realistic and reliable method for estimating future cash awards, ensuring better financial planning, policy compliance, and reserve adequacy for higher education institutions.
Copyright (c) 2025 Nur Idayu Ah Khaliludin, Haslinawati Mohd Mustapha, Nur Ilyana Ismarau Tajuddin, Muhammad Nuruddin Sudin https://creativecommons.org/licenses/by/4.0
Toward Quantum-Resilient PKI: A Systematic Literature Review of Post-Quantum Certificates Model
The rise of quantum computing presents a critical threat to classical public-key cryptographic systems, necessitating a global shift toward post-quantum cryptography (PQC). While algorithmic standardisation has seen substantial progress since the National Institute of Standards and Technology (NIST) initiated its call for PQC proposals in 2017, the integration of these algorithms into certificate-based infrastructures remains fragmented and underexplored. This gap creates uncertainty for secure communication, particularly in transitioning legacy systems into quantum-resilient environments. This systematic literature review (SLR) focuses on PQC certificate architectures studied between 2017 and 2025, encompassing the evolution of the field since NIST's formal standardisation efforts began. The review employs the PRISMA 2020 methodology to identify and analyse six distinct post-quantum certificate models: Pure PQC, Hybrid, Composite, Chameleon, Parallel, and Wrapped. Each model is evaluated across several criteria, including structure, backwards compatibility, tooling support, standardisation status, and production readiness, drawing evidence from scholarly publications, draft standards, and open-source implementations. Findings highlight the Hybrid certificate as the most viable short-term solution due to its high interoperability and maturity in deployment. Composite and Parallel architectures offer enhanced security assurances that are suitable for critical infrastructure, although they come with increased implementation complexity. Pure PQC certificates, while future-proof, are still limited to constrained or greenfield environments. Chameleon and Wrapped models are identified as emerging alternatives for blockchain and legacy scenarios, respectively, with limited but growing support. This review enlightens the design and deployment of quantum-safe public key infrastructures by outlining the trade-offs and maturity levels of each architecture. It also emphasises the need for continued development in standardisation and toolchain support to facilitate scalable and secure PQC adoption.
Post-Quantum Cryptography (PQC), Public Key Infrastructure; X.509 Certificates; Certificate Verification; Hybrid Cryptographic Models
Copyright (c) 2025 Nor Azeala Mohd Yusof , A H Azni , Farida Ridzuan, Nur Hafiza Zakaria, Sakinah Ali Pitchay, Abdul Alif Zakaria, Tasnuva Ali https://creativecommons.org/licenses/by/4.0
Enhancing Audio Steganography Through CGAN-Generated Cover Audio and Adaptive LSB Embedding: A Hybrid Approach
Audio steganography hides secret messages inside audio files, enabling covert communication without drawing attention. Audio steganography methods aim to achieve high imperceptibility, robust performance, and high payload capacity. While traditional techniques like Least Significant Bit (LSB) coding offer good imperceptibility, they are highly vulnerable to statistical steganalysis and signal manipulation. Existing hybrid methods suffer from maintaining quality across diverse audio and inadequate robustness mechanisms, struggling to balance imperceptibility, payload capacity, and robustness. This paper proposes a novel hybrid approach that combines Conditional Generative Adversarial Networks (CGANs) with LSB coding to address these limitations. The CGAN is trained with the LibriSpeech dataset to generate audio patterns that simulate spontaneous speech for use as adaptive covers. The model was implemented using PyTorch, with performance evaluated based on Signal-to-Noise Ratio (SNR), Perceptual Evaluation of Speech Quality (PESQ), Bit Error Rate (BER), and robustness to audio transformations. Experimental results showed a PESQ value of 4.05 and a mean SNR of 33.1 dB, representing excellent audio quality given the substantial payload capacity of 1.27 kbps. The method achieved a BER value of 2.23% and 87% robustness to compression, filtering, and resampling operations. The effectiveness of the CGAN-LSB hybrid method for enhancing capacity, imperceptibility, and robustness is achieved through the CGAN's ability to generate statistically natural audio covers, while adaptive LSB integration preserves data integrity during signal processing operations, making it highly suitable for secure audio communication and audio watermarking applications. While the generated covers exhibit distributional properties similar to genuine audio, direct validation against specific steganalysis detectors remains an important direction for future empirical evaluation.
Copyright (c) 2025 Usman Ibrahim Musa, Farida Ridzuan, A H Azni, Nur Hafiza Zakaria, Ahmed A. AlSabhany https://creativecommons.org/licenses/by/4.0
Evaluation of User-Centred Design in the User Interface (UI) and User Experience (UX) of a Gamified Edutainment Application on Disaster Preparedness
Recent years have witnessed a rise in the frequency and intensity of natural disasters, disproportionately affecting vulnerable communities, as reported by the United Nations Office for Disaster Risk Reduction (UNDRR, 2022). Public preparedness, particularly among youth and rural populations, remains limited due to the absence of accessible and engaging educational tools. Conventional approaches often lack interactivity, reducing knowledge retention and behavioural change. To address these gaps, this study developed DisasterPrep, a user-centred mobile edutainment solution designed to enhance disaster awareness and preparedness. The research pursued three objectives: (i) to identify knowledge gaps among target users, (ii) to design a gamified mobile application tailored to their needs, and (iii) to evaluate its effectiveness in improving preparedness and resilience. A mixed-methods approach was adopted, including surveys, focus groups, and co-design workshops, followed by prototype testing with 30 participants in Malaysia. Quantitative data were collected through a structured survey measuring User Interface (UI) and User Experience (UX) using a 5-point Likert scale, with descriptive statistics computed for usability and engagement. Results revealed strong usability, with UI mean scores ranging from 4.04 to 4.60 and UX outcomes between 4.20 and 4.56. Tutorials, navigation flow, and progress tracking emerged as the most valued features, while simulation realism and quiz integration revealed more variable responses. Findings confirm alignment with Malaysia’s MySTIE strategic goals and contributions to SDG 3 (Good Health) and SDG 4 (Quality Education). This research demonstrates that gamified, user-centred mobile tools can significantly enhance disaster preparedness education, offering a scalable, inclusive model to strengthen community resilience. The novelty lies in integrating edutainment with structured evaluation, establishing a replicable framework for disaster education that bridges gaps in interactivity, engagement, and scalability.
Copyright (c) 2025 Sakinah Ali Pitchay, Farida Ridzuan , Najwa Hayaati Mohd Alwi, Azni A H https://creativecommons.org/licenses/by/4.0
Advanced NLP Techniques for Generating Contextual and Grammatical Arabic Exam Questions
This paper outlines the development of an Arabic exam question generator that utilizes advanced Natural Language Processing (NLP) techniques and a comprehensive Arabic corpus. The primary aim is to aid educators in automating the process of crafting exam questions tailored specifically for A1-level Arabic learners. By harnessing the capabilities of NLP, the system integrates sequence-to-sequence (seq2seq) models and template-based methods to generate educationally appropriate questions. The seq2seq models are designed to predict the next word in a sequence, ensuring that the generated questions are natural and contextually fitting. This approach enables the system to produce logically coherent questions that align with the given context. Moreover, the template-based method guarantees grammatical accuracy, which is essential for educational purposes. The templates use as structured guidelines that steer the seq2seq models, ensuring that the questions adhere to proper grammatical rules and structures. A vital aspect of the system is the incorporation of the AraBERT pre-trained model. AraBERT, a transformer-based model customized for Arabic, undergoes fine-tuning with a specifically annotated dataset to adapt it to the task of generating questions from simple Arabic sentences, thereby enhancing its ability to handle the intricacies of the Arabic language. By combining seq2seq models for contextual relevance and template-based methods for grammatical precision, this dual approach effectively addresses the unique challenges associated with Arabic NLP. The richness of Arabic morphology and its syntactic complexity pose significant hurdles for NLP applications. Through the integration of these methodologies, the system ensures that the generated questions are not only contextually relevant but also grammatically correct, making it a valuable tool for educators. In conclusion, the paper discusses an innovative application of advanced NLP techniques and Arabic corpus utilization, providing a robust solution for automated Arabic exam question generation. This system holds significant potential for enhancing the efficiency and effectiveness of language instruction for Arabic learners.
Copyright (c) 2025 A H Azni, Farida Ridzuan, Najwa Hayaati Mohd Alwi , Sakinah Ali Pitchay, Zainur Rijal Abd Razak, Hanif Ridzwan Ahmad Rodzi, Ahmad A AlSabhany https://creativecommons.org/licenses/by/4.0
Speakable Me: The Development Of Speech Assistance Tools For Patients With Speech Disabilities
This project involves analyzing and designing a system that functions as an Augmentative and Alternative Communication (AAC) to help individuals with speech and language disorders (e.g., aphasia and dysarthria) who are unable to use verbal speech to communicate by producing sounds based on the words entered by users. The main objective of this project is to assist patients with various types and levels of speech disabilities in communicating their needs, achieving a higher rate of Words Per Minute (WPM) with minimal financial cost. This system is developed using React Native, Visual Studio Code, and Firebase. Speakable Me provides three categories of speech assistants. In particular, Category 1 allows patients to enter the words with full spellings, and the system produces sounds based on the words entered. Meanwhile, Category 2 uses Morse code, which is a combination of 1 and 0 that has been pre-determined and assigned to a word in the library. This category aims to help patients with limited movement since it only requires patients to use two fingers to press the keys. Following this, Category 3 is a category that uses pictograms and has limited keys. Each key represents basic sentences and words that patients usually use. Patients need to press one key, and the system will voice out the whole sentence. Speakable Me also provides a feature that allows patients or their caregivers to add their own words and pre-recorded voices to the library for Categories 1 and 2. While sentences and words for Category 3 can be replaced, they are limited to the ten keys of the numbered keyboard.
augmentative and alternative communication
Copyright (c) 2025 Noor Hayani Abd Rahim, Nadia Natasha Abd Aziz, Nur Izyan Rosli; Muhammad Hafizuddin Hamid https://creativecommons.org/licenses/by/4.0
The Role of Inflammatory Markers for Risk Stratification and Prognosis in Dialysis Patients: A Narrative Review
Chronic inflammation is a prevalent condition among dialysis patients and is associated with adverse health outcomes. This narrative review aims to provide a comprehensive analysis of the types and roles of inflammatory markers in risk assessment and their impact on the prognosis of dialysis patients. The associations between various inflammatory markers, including C-Reactive Protein (CRP), anti-inflammatory markers (IL-4, IL-10), and pro-inflammatory markers (TNF-α, IL-1, IL-6, IL-12), are evaluated in relation to their ability to predict mortality and cardiovascular complications in this population. Additionally, the potential of these markers for individualized risk stratification is discussed, aiding proper clinical decision-making and improving patient care. This review synthesizes current knowledge on inflammatory markers and their impact on the outcomes of dialysis patients, offering insights for improved prognostic assessment and therapeutic strategies.
Copyright (c) 2025 Siti Liyana Mohd Nor, Mohd Radzniwan A. Rashid, Faizul Helmi Addnan, Siva Gowri Pathmanathan, Nizam Baharom, Sharifah Najwa Syed Mohamad, Ruslinda Mustafar, Saharudin Ahmad, Arifa Mustika, Minidian Fasitasari, Lusito Lusito https://creativecommons.org/licenses/by/4.0
Microbial Diversity in Landfills through Metagenomics Profiling: A Review
Microbial communities are play a crucial role in the breakdown of organic waste, the production of biogas, and the reduction of environmental impact at landfills, which are complex ecosystems more than just places to dispose of waste. This study provides a comprehensive overview of the current knowledge of microbial diversity in landfills based on metagenomics studies. Metagenomics studies show that various microorganisms, including bacteria, archaea, and fungi, are found in landfills and interact to break down waste. Environmental factors such as pH, temperature, and waste composition impact these microbial communities. This review investigate how landfill age, waste composition, and operational methods impact microbial communities. Important microbial group functions in the waste breakdown, greenhouse gas emission, and soil leachate composition are also highlighted in this paper. The review examine how technologies, such as bioinformatics tools and high-throughput sequencing, enhance our knowledge of the landfill microbiome. Through metagenomics, researchers can identify the microbial genes responsible for important activities, including pollution bioremediation, production of methane, and organic matter breakdown. High-throughput sequencing methods such as 16S rRNA sequencing and shotgun metagenomics have improved the current understanding of landfill microbiomes by identifying new microbes with specialized metabolic roles. Identifying the plastic-degrading microbes and their enzymes is an emerging area of interest with potential for that may help develop biotechnological solutions to landfill plastic pollution. Metagenomics provides the opportunity to develop sustainable landfill management techniques and microbial-based waste treatment methods by identifying these microbial processes. This review emphasis the need of using the microbial processes for a better waste management and the significance of metagenomics in optimising landfill operations.
Copyright (c) 2025 Farah Aisha Mohd Haniff, Hasdianty Abdullah, Mohd Fadzli Ahmad, Maegala Nallapan Maniyam, Hazeeq Hazwan Azman, Nazahiyah Sulaiman, Nor Suhaila Yaacob https://creativecommons.org/licenses/by/4.0
Harnessing Green Nitrogen-Doped Zinc Oxide for Efficient Methylene Blue Dye Photocatalytic Degradation in Wastewater Treatment
Photocatalysis remains a promising solution for water treatment due to its efficiency, sustainability, and ability to degrade a wide range of contaminants. This study explores the green synthesis of zinc oxide (ZnO/GS) doped with varying weight percent of nitrogen (N) as a photocatalyst. ZnO/GS was synthesized via co-precipitation with zinc nitrate hexahydrate and papaya leaf extract in an aqueous solution, which will be further doped using urea as nitrogen doping. Characterization techniques, including FESEM, EDX, FTIR, and XRD, were employed to analyze the morphology, structural characteristics, surface, and pore volumes of nitrogen-doped green synthesized zinc oxide (N-ZnO/GS). The photocatalytic performance of these samples was evaluated through the photodegradation of methylene blue dye under visible light irradiation. The sample with 10wt%, which is 1 g of dopant, exhibited the optimal results, indicating a homogeneous surface and high crystallinity of N-ZnO/GS synthesized via co-precipitation, demonstrating the best photodegradation efficiency in the photocatalysis process. The findings suggest potential applications in environmental remediation through the degradation of organic pollutants, enhancing wastewater treatment efficacy.
Copyright (c) 2025 Ummu Humairah Hashim, Shahzulaikha Aiman Mohmed, Syahida Suhaimi, Maisurah Mukhtar, Nur Athirah Mohd Taib, Imam Tazi https://creativecommons.org/licenses/by/4.0
Application of Oven-Dried Durian Seed Powder and Freeze-Dried Durian Seed Powder on Extending Probiotics Longevity
Durian seed is often wasted but it can be utilized as a biopolymer and encapsulating agent for probiotics. This is good since it is a natural and sustainable biopolymer source. However, durian seed contains a lot of impurities that may hinder the biopolymer’s functional properties. This study aims to extract gum from durian seeds, subject them to oven-drying and freeze drying to obtain durian seed powder, study both oven-dried and freeze-dried durian seed’s characteristics and the potential to use them to enhance the growth of probiotics. Freeze-dried durian seed powder has higher yield percentage, higher water holding capacity, and higher oil holding capacity than oven-dried durian seed powder. Freeze-dried durian seed powder also has more pores and more uniform structure compared to oven-dried durian seed powder, which can be seen through scanning electron microscope. Both oven-dried durian seed powder and freeze-dried durian seed powder manage to support probiotics’ growth. The probiotics’ concentration can be seen to increase after 3 days of incubation upon encapsulation via dried durian seed flour for both encapsulated probiotics with oven-dried durian seed powder and encapsulated probiotics with freeze-dried durian seed powder. Freeze-dried durian seed powder support probiotics’ growth better than oven-dried durian seed powder since the concentration of probiotics after being encapsulated with freeze-dried durian seed powder is higher than the concentration of probiotics after being encapsulated with oven-dried durian seed powder. Based on observation via scanning electron microscope, encapsulated probiotics with freeze-dried durian seed powder also has better appearance, less wrinkles and less dents compared to encapsulated probiotics with oven-dried durian seed powder.
Copyright (c) 2025 Ahmad Naim Rohaizan, Latiffah Karim, Siti Radhiah Omar, Syamila Mansor, Nur Huda Faujan, Jeehan Nusair https://creativecommons.org/licenses/by/4.0
Sleep Duration and BMI: A Study Among First-Year Students of a Malaysian Muslim Gifted and Talented Institution
Sleep is widely recognized as a public health concern due to its impact on societal well-being and healthcare expenses. Emerging evidence suggests that insufficient sleep may significantly increase the risk of obesity and cardiovascular event. To date few studies have examined sleep duration and patterns within Malaysian boarding school particularly in Muslim Gifted and Talented institution. This study aims to identify sleep duration and investigate its relationship with body mass index (BMI) among first-year students of Kolej PERMATA Insan. Demographic data including gender, height, and weight were analysed using descriptive analysis. A linear regression test was applied to explore the relationship between BMI and sleep duration. Result indicated that male students slept significantly longer compared to female students. However, no association was found between sleep duration and BMI. This postulates that the structured environment and healthy lifestyle prepared by the Muslim Gifted and Talented Institution may mitigate potential negative impacts of insufficient sleep on BMI.
Copyright (c) 2025 Nur Fazidah Asmuje, Muhammad Nashwan Mohd Nasarudin, Abdul Razzak Khan Rustum Ali Khan, Rahayu Ahmad https://creativecommons.org/licenses/by/4.0
The Relationship Between Brain Wave Power Value Of Pain Perception And Empathy Using The Electroencephalogram (EEG)
Pain is said to be influenced by many psychological factors such as emotional state that manipulated the level of empathy. However, there are limited studies exploring the neuro-correlation connecting all these aspects. The current study aims to assess the dominant brain wave power value of pain perception and empathy by determining the area activated associated with the imaginary pain under three different categories which are stranger, one-self and loved-one. The study utilized a tool of brain signal detector (electroencephalography (EEG) device) which is Emotiv Insight focusing the relevant area of the brain (AF3, AF4, T7, T8 and Pz) to record the electrical signal of healthy subjects during the pain-visual-stimuli presentations. Images showing the pain condition were used as stimulus material during the EEG brain scanning session. The analysis was performed using Emotive Launcher and Brain Vision Analyzer version 2.2. Findings indicates the imaginary pain has activated the prefrontal area of the brain, specifically the medial frontal gyrus (MFG), superior frontal gyrus (SFG), orbital gyrus, subcallosal gyrus, and rectal gyrus; and revealing theta power band as the most dominant brain wave in this research. These power values also relate with the pain perception that influenced by the empathy level of individuals as observed, individual with high empathy do feel more pain for others. This outcome will help in understanding the mechanism of empathy and pain perception especially when imagining their loved one in pain that could enhance the research development in examining psychological processes.
EEG; Imaginary Pain; Empathy; Brain Wave; Power Value
Copyright (c) 2025 Siti Nur Aisyah Sazali, Muhammad Zaim Kashfi Zaman, Sofina Tamam, Eizan Azira Mat Sharif, Muhammad Ali Mustofa Kamal https://creativecommons.org/licenses/by/4.0
Analysis of Trust Models in Public Key Infrastructure: A Systematic Literature Review of Interoperability Challenges
Public Key Infrastructure (PKI) guarantees secure communication and authentication in digital contexts. With the expansion of digital ecosystems, the ability of PKI systems to work together smoothly becomes increasingly important, allowing multiple trust models to be compatible. Nevertheless, several significant challenges must be overcome when merging different trust models. The current body of research does not thoroughly examine these difficulties in various PKI trust models. This study aims to conduct a comprehensive assessment and analysis of the difficulties related to PKI interoperability. The focus will be on trust models such as Cross Certification, Bridge CA, Hierarchical, Hybrid, Cross Recognition, and Certificate Trust Lists. This systematic literature review (SLR) uses the PRISMA technique to analyse trust models in PKI interoperability. The review focuses on peer-reviewed studies published between 2000 and 2024, ensuring transparency and rigor. Eligibility criteria included studies using quantitative methodologies and sourced from major academic databases. This systematic review identified critical challenges in the interoperability of PKI trust models, particularly in operational complexity, security, liability, and scalability. Trust models like Cross Certification, Bridge CA, and Hierarchical systems each present unique challenges when integrated into diverse digital ecosystems. Moreover, gaps in the current research suggest the need for more standardised, scalable solutions that can accommodate the growing complexity of digital infrastructures. Future research should focus on developing a universal model for PKI interoperability, with a particular emphasis on large-scale environments such as E-commerce and E-government systems.
Key management System (KMS), Interoperability, Public Key Infrastructure (PKI), Comparative analysis
Copyright (c) 2025 Rozlinda Radzali, A.H. Azni, Farida Ridzuan, Nur Hafiza Zakaria, Tasnuva Ali https://creativecommons.org/licenses/by/4.0
A Maturity Model for the Accredited Food Laboratory in Malaysia: A Case Study
A Maturity Model (MM) is essential for laboratories aiming to improve and compete globally. Despite being accredited for over 20 years, some laboratories lack evidence of system maturity necessary for international competitiveness. A higher maturity level indicates a robust quality management system, leading to improved efficiency, accuracy, and customer satisfaction, while also assuring stakeholders, such as customers and regulatory bodies, of the laboratory's commitment to continuous improvement. The MM is designed to assess the success of laboratory processes, management styles, and the development of quality management practices, based on the 4M model (Manpower, Method, Machine, Material). The MM was developed using Analytes Accurate Certain Score (AACS) for the ten most common tests and System Maturity Scores (SMS), which incorporate proficiency testing and audit scores over two accreditation cycles (six years). While the model identifies inefficiencies, it helps organizations pinpoint areas of improvement and devise strategies to enhance their operations. This study applied the MM to a commercial laboratory, ABC (anonymized), accredited since the early 1990s and having undergone eight assessment cycles. The laboratory was found to be at a “leading” maturity level with a score above 80%, although improvements are still needed. Key areas for improvement include: 1) **Manpower**: maintaining competent staff by adjusting management strategies, 2) **Method**: validating all in-house methods according to Analytical Laboratory Accreditation Criteria Committee (ALACC) guidelines, 3) **Machine**: applying good laboratory practices (GLP) for equipment sharing, especially for specific analytes, and 4) **Material**: ensuring metrologically traceable reference materials and proficiency testing for all analytes. These improvements will help the laboratory further enhance its global competitiveness.
Computational Modeling & System Development
maturity model, analytes accurate certain scores, proficiency testing, audit/assessment scores
Copyright (c) 2025 Li Hui Ling, Mohd Sukri Hassan, Che Wan Zanariah Che Wan Ngah, Mehmet Ozturk https://creativecommons.org/licenses/by/4.0
The Impact of Capital Market Instruments on Malaysian Economic Growth
The relationship between capital market instruments and economic growth in countries has been quite a topic of debate and research in recent years. Financial markets, whether they are based on capital markets or banks, are essential to one nation’s growth. Its major purpose of serving as the government’s and enterprises’ primary source of finance gives it prominence and authority. This study analyses the causal-effect relationship of stock, bond, sukuk, and foreign exchange markets towards Malaysian economic growth. It aims to appraise the role of capital markets instruments in advancing Malaysian economic growth using the Autoregressive distributed lag (ARDL) method. Although many have studied how capital market instruments affect economic growth, this research intends to fill the absence of that research by offering a comprehensive study on the cumulative effects of capital market instruments towards Malaysian economic growth. A thorough examination that considers the significant and positive influence of stock, bond, Sukuk and foreign exchange markets on Malaysian economic growth will offer a deeper knowledge of their actual meaning to economic growth and insightful analysis that will help academics, investors, and policymakers create strategies and policies that would promote Malaysia’s sustainable economic growth. The findings of this study are stock, bond, Sukuk and foreign exchange markets have a positive impact on economic growth both in the long and short runs. On the other hand, this study found that the sum of bond and sukuk market is statistically significant in the short run.
Economic Growth; Capital Markets; Stock; Bond; Sukuk; Foreign Exchange; Unemployment Rate; ARDI
Copyright (c) 2025 Norizarina Ishak, Siti Nur Syafiqah Nordin, Siti Raihana Hamzah, Ahmad Fadly Nurullah Rasedee, Sukono https://creativecommons.org/licenses/by/4.0
Using 5WH Competency Questions for Validating Information Retrieval from Al-Quran: A Methodological Approach
Various methods are used to validate information retrieved (IR) from unstructured text. Measuring the effectiveness of IR systems is an important focus of IR research, based on sample retrieval for testing combined with evaluation measures. Evaluation measures include both quantitative and qualitative aspects. Quantitative measures assess precision, recall, and benchmark scores, while qualitative measures focus on relevance, user satisfaction, and usability. As the quality of information retrieved is of importance to humans, the use of competency questions (CQs) has been introduced, particularly, in the case of information retrieved from Al-Quran - to validate the output. As the design of CQs usually relies on expert support, which can limit usability for general users seeking guidance, this research proposes using basic fact-finding 5WH questions. These are fact finding why, what, how, where, when and who questions. It is proposed to use these as CQs to test the relevance of Verses/Ayats retrieved on the basis of common text-patterns found in Al-Quran. We propose this methodological approach using 5WH CQs to test the competency of retrieved information, focusing on popular concepts like Salat, Zakat, and the Sun. It is found that sample Ayats retrieved on the basis of text-patterns found answer fundamental questions such as WHY? For example, why pray Salat? or why was the Sun created? Results show that this method evaluates the relevance and usability of IR systems. Further work based on repeating text-patterns in the Al-Quran is recommended to test whether this evaluation approach can help ontology learning.
Copyright (c) 2025 Nazia Nishat, Rosalina Abdul Salam, Yusuf Mahbubul Islam, Zulkifly Mohd Zaki https://creativecommons.org/licenses/by/4.0
Forecasting Public Health Risks in Malaysia: Insights from SEIR Modelling of Infectious Diseases
During the global pandemic of 2020, the Malaysian government implemented a Movement Control Order (MCO) as a mitigation plan to control the spreading of COVID-19. This effort demonstrates a significant decrease in active cases. However, it is disturbing the nation economically. The lack of a visualization hub to predict the spread of infectious diseases in Malaysia disrupts decision-makers’ efficiency in optimizing health sectors. Therefore, this paper aims to forecast infectious diseases in Malaysia using the Susceptible, Exposed, Infectious, Removed (SEIR) model. The SEIR Model predicts the projection of disease-spread cases based on the sample of previous cases. The samples used public health data from the COVID-19 Open Data Repository, which covers 12 states in Malaysia. The finding revealed that the SEIR model demonstrates a sharp decline in susceptible individuals after three months into 2022, a peak in exposed and infectious individuals around the same time, and a steady rise in recovered individuals as most of the population becomes immune. This forecast data can provide earlier insights on the infection trend, allowing actionable recommendations for policymakers and healthcare.
Copyright (c) 2025 Nuha Rahim, Sakinah Ali Pitchay, M. A.B. Sahbudin, A.H. Azni, Ilfita K. Sahbudin https://creativecommons.org/licenses/by/4.0
BRAINIAC: The Planning Process for the E-Learning Platform with Text Summarisation Tools
This paper presents the planning process for the BRAINIAC, an e-learning platform with text summarisation tools. The evolution of technology in education happens gradually throughout daily lives, from the old traditional method to the digital process, making the educational experience easier and more convenient. E-learning, which uses electronic devices and digital resources, has become essential, especially during the COVID-19 pandemic. Despite the widespread use of e-learning platforms such as Moodle, Google Classroom, Blackboard, and Udemy, it still lacks an integrated text summarisation tool. This tool is vital for helping learners efficiently process and comprehend extensive textual information. This project, Brainiac, aims to develop an e-learning platform, which is a web-based application that incorporates Artificial Intelligence (AI) and Natural Language Processing (NLP) for text summarization. The methodology used to ensure the development process runs smoothly for this project is the Agile Model. The research objectives are to identify gaps in existing e-learning platforms, propose a robust security framework for Brainiac, and evaluate its accuracy and compatibility through thorough testing. Hence, Brainiac aims to enhance learning, improve comprehension, and save time for users, thus significantly enhancing the overall effectiveness of digital education. The implication of Brainiac could revolutionise digital education by improving information processing and comprehension, ultimately enhancing learning efficiency and user experience. In conclusion, Brainiac’s text summarisation tools significantly advance e-learning, offering a transformative approach to improving information processing, comprehension, and overall educational efficiency.
Copyright (c) 2025 Siti Nur Fatehah Nordin, Nurdiana Azizan, Noraizah Abu Bakar, Dini Onasis https://creativecommons.org/licenses/by/4.0
CYBERLEAP: The Planning Process for the Security Awareness Game
This paper presents the planning process for CyberLeap, a security awareness game for adolescents. In today's digital age, adolescents navigate a world filled with both opportunities and threats. CyberLeap adopts a novel approach to cybersecurity education in recognition of the significance of providing youngsters with the necessary skills to navigate safely (aligned with Sustainable Development Goals (SDG) 4: Quality Education and 16: Peace, Justice, and Strong Institutions). This casual online game is aimed towards teenagers, drawing them in with interactive tasks that simulate real-life situations, such as password management and phishing attempts. CyberLeap makes learning enjoyable, whether by solving a challenging quiz on secure password creation or identifying a phishing email that appears legitimate. CyberLeap is an educational and entertaining platform that empowers teenagers to become responsible digital citizens by providing a range of tasks, clear feedback explaining correct and incorrect responses, and a rewarding system with points, awards, or even leaderboards. As a result, there may be a safer online atmosphere due to their increased awareness of cyber threats and the encouragement of appropriate online conduct. In the end, CyberLeap advances a more promising cyber world in which teenagers can prosper and confidently explore the immense possibilities of the internet. The implication of the research suggests that integrating engaging, game-based cybersecurity education like CyberLeap into adolescent learning can significantly enhance digital safety and responsibility, contributing to a more secure online environment and supporting SDGs related to quality education and strong institutions. The research concludes that CyberLeap effectively enhances adolescents' cybersecurity awareness and accountable online behaviour through interactive, game-based learning, fostering a safer digital environment and supporting quality education and strong institutions.
Copyright (c) 2025 Nur Anis Aqilah Muhamad Azahar, Nurdiana Azizan, Noraizah Abu Bakar, Dini Onasis https://creativecommons.org/licenses/by/4.0
EVENTCIBLE: The Planning Process for the Event Navigation and Tracking Mobile Application
This paper presents the planning process for the EVENTCIBLE, the event navigation and mobile application tracking. In today's digitally driven world, where access to information shapes individuals and societies, the emergence of mobile technology has revolutionised news consumption. This research delves into creating a campus event application tailored to students of Universiti Sains Islam Malaysia (USIM). Designed to cater to the dynamic university life and students' affinity for technology, the USIM event app is a centralised hub for students to discover, engage with, and participate in campus activities. The app aims to bridge the gap between active participants and those less connected by capitalising on the prevalence of smartphones among students. Offering a user-friendly interface, students can seamlessly explore upcoming events across various faculties, book, and receive timely notifications, fostering a vibrant campus community. This endeavour showcases the potential of mobile technology in enhancing student engagement and connectivity within higher education institutions, contributing to the ongoing discourse on the transformative role of mobile applications in academia. The methodology used is a hybrid methodology that integrates both waterfall and agile approaches. This combined approach ensures structured planning and execution while allowing flexibility. The implication of the research enhances student engagement and connectivity, illustrating how technological solutions can bridge gaps in campus participation and foster a more integrated university community. The significance of the research is the potential of mobile technology in promoting student engagement and bridging participation gaps within university communities. The research concludes that the EVENTCIBLE app will hopefully enhance student engagement and connectivity by leveraging mobile technology to bridge participation gaps and foster a more cohesive and vibrant campus community.
Copyright (c) 2025 Abdullah Azzam Syafrizal, Nurdiana Azizan, Noraizah Abu Bakar, Dini Onasis https://creativecommons.org/licenses/by/4.0
Advancing Localized Public Health Surveillance in Malaysia by Enhancing EIOS with Google COVID-19 Data Integration
Epidemic intelligence has evolved from traditional manual reporting and field investigation methods to dynamic, real-time surveillance, driven by the 21st-century surge in digital data sources. Infectious diseases pose a significant global health threat, with traditional surveillance methods often facing delays in detecting and responding due to reliance on structured clinical data. The Covid-19 pandemic has emphasized the need for precise and actionable data to inform public health decisions. The current categorization of the Epidemic Intelligence from Open Sources (EIOS) system by country limits its ability to precisely track and monitor infectious diseases at more localized levels. This study focuses on enhancing the EIOS system by improving geographic data specifically for Malaysia. Currently, the EIOS platform, which incorporates data sources from Johns Hopkins University (JHU), the World Health Organization (WHO), and the Worldometers (WOM), provides country-level data that limits the effectiveness of localized interventions. This enhancement involves integrating state-level data in Malaysia from the Google Covid-19 Open Data Repository, which collects data automatically from authoritative sources, volunteers and contributors into the EIOS system. This paper presents the focus in Malaysia for better precision and effectiveness, in public health interventions. The suggested EIOS system will assist in sorting data based on dates,cases numbers,fatalities and data origins resultng in a more intricate and adaptable depiction of the pandemics advancement. The results of this study will offer insights and improvements for public health experts in Malaysia regarding the management and containment of infectious diseases at a local level.It will help optimize resource distribution and readiness efforts to mitigate the effects of outbreaks effectively.This improvement is intended to support targeted lockdowns and other public health interventions, in geographic areas with greater precision by enhancing geographical tracking capabilities.
Copyright (c) 2025 Hazeeqah A.K. Aryffin, Sakinah Ali Pitchay, Murtadha A.B. Sahbudin, A.H. Azni, Ilfita K. Sahbudin https://creativecommons.org/licenses/by/4.0
Equity-based Financing and Credit Risk: A Monte Carlo Analysis of Economic Crises in Malaysia
Financial instruments play a critical role in influencing the performance of a corporation. Corporate management may choose between debt and equity-based financing instruments to ensure the continuity of their business operations. Even so, the management will need to consider the credit risk exposure associated with the financing instruments as they tend to affect corporate performance, especially during an economic fluctuation. Therefore, this study employed Monte Carlo simulation to analyze and compare the credit risk exposure associated with debt and equity-based financing across various sectors in Malaysia, particularly in the context of significant economic crises. The sectoral data were used and segregated into two different phases of economic crises, which are from 2007 until 2018 (this period involves the Global Financial Crisis (GFC)) and from 2010 until 2021 (this period involves the COVID-19 pandemic). Through the simulation, this study finds that equity-based financing recorded consistent results of providing low credit risk exposure regardless of sectors and economic crises. Meanwhile, debt financing recorded high credit risk by all sectors during the economic crises. Therefore, this study suggests that equity-based financing is more reliable than debt in promoting corporate financial sustainability by offering lower exposure to credit risk and minimizing reliance on debt issuance.
Equity-based financing; Debt financing; Credit risk; Monte Carlo; Economic Crises.
Copyright (c) 2025 Umul Ain’syah Sha’ari, Siti Raihana Hamzah, Karmila Hanim Kamil, Putri Swastika https://creativecommons.org/licenses/by/4.0