Controlled double-diffusive Marangoni convection in a rotating nanofluid layer, heated from below, is studied. Various types of lower-upper boundary conditions, including free-free and rigid-free, are considered. The nanofluids model incorporates the mechanisms of Brownian motion and thermophoresis in nanofluids. The stability of the nanofluids model is analysed using a linear stability analysis based on the normal mode technique. The eigenvalue problem is numerically solved using the Galerkin technique and computational simulations are carried out using Maple software. The influences of several parameters are examined and presented graphically, including modified diffusivity ratio, nanoparticles concentration, solutal Rayleigh number and Soret effects. These effects are found to contribute to the advancement of Marangoni convection, a phenomenon that occurs due to variations in surface tension along the interface of a nanofluid layer. Conversely, the presence of the Coriolis force (due to the rotation), controller gain, and Dufour effects are observed to slow down the process of Marangoni convection.
Web caching offers several advantages, such as increasing cache hit rates, lowering the workload on origin servers, and minimizing network traffic. Nevertheless, limited cache capacity poses a major challenge in web caching systems. Moreover, repeatedly fetching same media objects from origin servers leads to unnecessary bandwidth consumption. Furthermore, traditional caching policies, including Least Recently Used (LRU), are vulnerable to cache pollution. This study introduces a collaborative caching policy based on the Naïve Bayes (NB) Machine Learning (ML) algorithm. The proposed policy exploits structured peer-to-peer architectures, allowing cache contents to be shared among peers to improve the efficiency of LRU web caching policy. Performance evaluation is conducted through simulations using two real-world datasets obtained from YemenNet Internet Service Provider (ISP) and the IRCache network. The results show that the proposed policy outperforms the traditional LRU policy in terms of Hit Ratio (HR), Byte Hit Ratio (BHR), and Cost Throughput (CT).
The vulnerability of Natural Language Processing (NLP) models to adversarial attacks remains a critical challenge in the field of cybersecurity and AI robustness. While deep learning models have achieved high performance in sentiment analysis, they are susceptible to subtle input perturbations that induce misclassification. This study presents the design and practical implementation of a web-based system (Proof of Concept) that automates the generation of textual adversarial examples using the Bigram Unigram-Semantic Preservation Optimization (BU-SPOF) algorithm. Rather than proposing a novel attack algorithm, our primary contribution is the architectural integration of a dual-source candidate generation strategy (WordNet and OpenHowNet) and a Probability Weighted Word Saliency (PWWS) mechanism to perturb input text while maintaining linguistic coherence. The system was evaluated against a Long Short-Term Memory (LSTM) sentiment classifier using the IMDB dataset.
Information system development involves a lot of tools, infrastructures, and humans. The many phases in system development need humans to interact and work with each other to obtain the goal: to complete information system development. These interactions of humans require the right ethics to ensure that the information system development can be successful. For example, ethical leadership is a vital component in determining the success of any project related to information system development in organizations. The aim of this study is to identify the ethical values which are important in information system development methodology phases. This systematic literature review (SLR) comprises of four databases, and these are IEEE Xplore, ProQuest Dissertations and Theses, Scopus, and Emerald Insight, using the PICOC framework and applying several inclusion and exclusion criteria. 172 papers from 2018 to present were found to have discussed the ethical values that are needed in the system development methodology phases. The study finds that there are 10 ethical values highlighted in those papers, for example: ethical leadership, ethical team dynamics, trust, consideration, and so on. The findings from this paper are important for the people who are involved in system development methodology to practice the right ethical values to ensure the success of the system development.
In e-commerce predictive modeling, imbalanced data is a common challenge especially when both class and demographic attributes are unequally distributed. This study explores the combination of Synthetic Minority Oversampling Technique (SMOTE) and demographic resampling to improve the performance of models predicting online purchasing behavior in Malaysia. The first step is to use SMOTE to handle class imbalance in the five-point purchase intention scale classification. The second step is followed by gender imbalance, which is caused by a higher number of female respondents than male, this problem is solved through a combination of down-sampling and up-sampling. Further adjustments were made to balance other demographic factors such as age, employment status, and ethnicity. For this study the dataset, encompassing 1,126 survey responses, was analyzed using WEKA tools with six classifiers: J48, Random Tree, REPTree, JRip, PART, and OneR. Each classifier was evaluated in three stages: original data, after SMOTE, and after both SMOTE and demographic balancing. The results displayed clear improvements in model performance. For example, J48’s accuracy increases from 62.85% (unbalanced) to 98.69% (fully balanced), while Random Tree reached 99.29%. These results highlight the importance of addressing both class and demographic imbalances to develop better and more reliable models. The study improves to the limited research that combines SMOTE with demographic resampling in Malaysian e-commerce and offers practical insights for building better predictive models to support customer segmentation, targeting, and personalization. Future work could explore balancing more attributes and applying the method to ensemble or deep learning models for improved performance and interpretability.
Few-shot learning (FSL) aims to enable deep models to generalise from extremely limited labelled data, yet unstable metric matching, distribution imbalances, and weak structural representations in low-data regimes often constrain its performance. This paper proposes a conceptual framework that unifies metric-based similarity learning, Unbalanced Optimal Transport (UOT) via Unbalanced Sinkhorn Distance (USD), and self-supervised Transformer representations to conceptually address the theoretical and structural limitations of existing FSL approaches. The framework theoretically unifies distribution-aware USD matching, SSL-enhanced ViT/Swin feature representations, and metric-based inference within a coherent pipeline. This work aims to provide a theoretical foundation and research roadmap for future empirical studies on robust few-shot learning under realistic, distributionally complex conditions.
This study explores how digital technology is integrated into mathematics education, focusing on the views of secondary school mathematics teachers and using a quantitative research method through a survey. Descriptive and inferential statistical analyses were performed to examine the factors influencing technology adoption, identify its barriers, and suggest strategies for enhancement. The novelty of this study lies in its development of an integrated perspective that simultaneously examines teachers’ readiness, experiential background, and attitudes alongside systemic constraints such as training, resource availability, and institutional support within the context of mathematics education. However, challenges such as inadequate training, limited resources, and lack of institutional support impede effective implementation. Therefore, to overcome the issues, the study seeks the perceptions of 108 mathematics teachers in secondary schools using a questionnaire based on a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The study contributes to existing literature by offering empirical evidence on the interconnected nature of these factors and by proposing targeted, context-specific strategies to enhance digital integration in mathematics classrooms. Key recommendations include tailored professional development programs, the promotion of collaborative learning environments, and better infrastructure to equip teachers with the necessary skills and resources for smooth technological integration. By addressing the challenges, educators can foster more enriching teaching and learning experiences in mathematics, ultimately improving outcomes in the digital era.
Portfolio optimization aims to balance risk and return by identifying an effective mix of assets. In this study, we integrate principal component analysis (PCA) and hierarchical clustering for stock selection with the Barzilai–Borwein (BB) gradient method for portfolio optimization. Forty-eight U.S. stocks from the Kaggle fundamental stock dataset were initially collected, and 42 stocks were retained after preprocessing. Financial ratios from 2006 and adjusted closing prices from 2016–2017 were analysed, with one representative stock from each cluster selected using the Sharpe ratio. The BB method was then applied to determine optimal weights, ensuring full capital allocation without short selling. Among the tested approaches, the Barzilai–Borwein gradient method 1 (BB1) step size achieved strong performance, producing an annual return of 25.6% while maintaining relatively low volatility. The portfolio also generated a Jensen’s alpha of 1.55, confirming the presence of positive abnormal returns beyond market expectations. These results suggest that combining PCA-based clustering with the BB optimization method offers a practical and efficient way to construct diversified portfolios. The study highlights the BB algorithm’s potential as a lightweight yet effective alternative to more complex optimization techniques in financial decision-making.
The housing market is a critical driver of economic growth and a source of financial stability. In Malaysia, residential property prices are influenced by a range of macroeconomic factors, including Gross Domestic Product (GDP), exchange rates, unemployment rates, and wage levels. The COVID-19 pandemic introduced significant disruption and volatility to the Malaysian economy, making it essential to understand the relationships between these factors and property prices. The pandemic's economic challenges, coupled with rising property prices, have exacerbated housing affordability issues. While previous studies focused on long-term trends (1991–2019), there is a gap in understanding the high-frequency (monthly) volatility specifically during the COVID-19 shock. As such, this study aims to investigate the short- and long-term effects of GDP, exchange rates, unemployment, and wages on Malaysian residential property prices from January 2020 to March 2023 (a period of 39 months). Using the Autoregressive Distributed Lag (ARDL) model, monthly data are analyzed to examine how these variables have influenced the housing market. The results indicate that in the long term, GDP growth has no significant effect on property prices, while exchange rates and wages show significant negative impacts. Short-term findings reveal a positive influence of unemployment, potentially reflecting government interventions during the crisis. A robust adjustment mechanism toward equilibrium is confirmed by the error correction term. The study concludes that macroeconomic variables play a significant role in determining property prices during periods of crisis, with exchange rate depreciation and wage disparities intensifying affordability challenges. The positive short-term impact of unemployment likely stems from government stimulus and loan moratoriums, which temporarily buoyed the market.
Information technology (IT) has become increasingly critical in today's digital world, supporting, sustaining, and driving growth in public organizations. The board of directors and senior management have traditionally delegated, disregarded, or avoided IT decisions. The majority of public sector organizations have realized that such a mindset is outdated and must be avoided. Being a foundation for effective enterprise governance, the IT risk management domain seeks to guarantee that a structure exists for identifying, analyzing, mitigating, monitoring and communicating an organization's IT risk to ensure successful IT project implementation. This research, therefore, sought to examine the relationship between risk management of IT projects and the hypothesized benefits from IT investments, namely Return on Investments (ROI), number of customers served and the level of customer service delivery. A total of 309 out of 517 public sector organizations in Kenya were sampled, and data were collected from heads of departments, including IT, Finance, HR, Audit, and Technical among others, through an online research tool. The correlation results revealed that IT risk management and ROI had a weak and negative correlation. The correlation between the management of IT risk and the number of customers served was equally found to be weak and negative. However, the correlation between IT risk management and customer service delivery was positive and significant. Results from the multivariate regression test revealed that risk management of IT projects significantly improved customer service delivery but had no significant effect on both ROI and number of customers. The study concluded that there was a significant effect of IT risk management on the customer service delivery. Therefore, senior management had a responsibility to ensure that IT risk management was incorporated in all major IT projects to ensure that improved customer service delivery benefit was realized.
Community engagement and efficient information dissemination are essential for well-functioning neighbourhoods. This project focused on the development of a community-based system in Taman Lapangan Jaya using Activity Theory to guide requirement identification and analysis. The current communication method, which relies on group chats managed by the neighbourhood president, has led to fragmented information, difficulty in accessing documents such as committee contact lists and annual reports, and frequent miscommunication. To address these issues, this study aimed to design and implement a centralized system that meets the residents’ needs. A prototyping approach was employed, beginning with planning and requirement elicitation through stakeholder interviews and the application of Activity Theory. The iterative development process involved quick design, prototype creation, user feedback, and refinement. The system design incorporated class diagrams, a data dictionary, and database structures, leading to a fully developed and operational platform. The final system provides a user-friendly, structured solution for information sharing and communication within the community. This study contributes to the field of requirement engineering by demonstrating the application of Activity Theory in the development of a neighbourhood-focused system and offers a model that can be replicated in similar community settings.
Online grocery shopping or e-grocery has becoming more relevant nowadays when consumers' shopping habit changed due to pandemic COVID-19 while e-Commerce rapidly transformed consumers’ lifestyle and buying behaviour in recent years, Consumers’ expectation for faster, better and cheaper delivery put e-grocers under rising pressure to improve delivery speed, achieving environmentally friendly delivery methods and addressing issues of making profit. There have been studies worldwide on development of more efficient e-grocery delivery system. However, studies concerning e-grocery delivery in Malaysia are still lacking especially on those utilizing mathematical programming models for delivery optimization. Our study focuses on the formulation of mixed integer goal programming (MIGP) models for vehicle routing problem with time windows for homogeneous and heterogeneous fleet of vehicles. This paper presents a structured review of past studies and gap analysis on some selected mathematical programming models. The review and gap analysis provide vital information on main characteristics for models of our study. Results presented would be useful for studies that concern with finding optimal solutions, innovative approaches and the most practical techniques for urban e-grocery deliveries. These strategies could lead to time and costs savings and enhance the effectiveness and efficiency of delivery operations that benefits both e-grocers and consumers.
This research presents a novel cybersecurity framework aimed at improving threat detection and incident response in today's complex digital environment. The framework integrates three key components: advanced threat detection, accelerated incident response, and continuous risk assessment, adopting a holistic and adaptive approach. It leverages machine learning (ML) and artificial intelligence (AI) to proactively identify and counter evolving cyber threats, moving beyond traditional reactive systems. The advanced threat detection element utilizes AI-driven analytics to spot anomalous patterns and forecast potential vulnerabilities, thus enhancing threat visibility. The accelerated incident response streamlines automated responses to common threats, significantly cutting response times. Complementing these is a comprehensive risk assessment, which provides quantifiable resilience metrics for ongoing monitoring and improvement. The framework's effectiveness is validated through extensive testing and real-world case studies across various sectors, including finance, education, healthcare, and manufacturing. Results indicate substantial improvements in key performance indicators, such as reduced false positives and minimized downtime during security incidents. Despite its advancements, the research identifies implementation challenges, including resource intensity, the need for adaptable components across different organizations, and the importance of human factors like employee training. Future research will address these issues, focus on enhancing the framework's adaptability, and explore the integration of emerging technologies, such as blockchain, to bolster its effectiveness in combating sophisticated cyber threats. Ultimately, this initiative seeks to promote innovation and growth in the global digital economy by proactively managing cybersecurity risks.
One of the important processes involved when a disaster happens is to allocate the victims to the navigation center. Current evacuation practices may cause further damage as the victims must wait for the rescue team to come. A self-navigation system, called Banjir Rescue, would allow the victims to save their lives and belongings sooner by determining the appropriate evacuation center. In this approach, the appropriate evacuation center is determined based on two critical parameters, which are the water level and distance. First, the near-real-time data retrieved from the water level sensor set up at the evacuation centers is sent to the system and compared with the evacuation centers' water threshold value. The algorithm will then proceed with the second phase, that is, checking for the distance between the victim’s current location and the evacuation center if the water level found in the first phase is less than the threshold. Finally, the selection is made where the algorithm will choose the nearest evacuation center to the victims. The system will get the coordinates of the appropriate evacuation center that passes both phases and send the coordinates to the installed navigation applications on the mobile phone, such as Google Maps and Waze. The desired navigation app will be launched with the coordinates passed from the system to guide victims to reach the appropriate evacuation center. The result shows that the proposed approach could provide a near-real-time water level reading of each sensor and near-accurate distance calculation to the system to determine the appropriate evacuation center.
Port operations, while critical to global trade and economic growth, contribute significantly to environmental pressures through activities such as vessel traffic, cargo handling, dredging, and on-dock transportation, leading to emissions of greenhouse gases and other pollutants. Hence, it is of priority to have a systematic evaluation and prioritization of sustainability measures across port functions. This study employs a multi-criteria decision-making (MCDM) approach, the Analytic Hierarchy Process (AHP), to evaluate and prioritize key functional roles and subfunctions that contribute to port sustainability performance. By engaging expert judgment from two leading ports in Malaysia, the analysis identifies stakeholder priorities on the emphasis among landlord, operational, regulatory and community functions in sustainable port management. The structured AHP framework ensures transparency and reproducibility of the weighting process while remaining adaptable to incorporate emerging sustainability measures, such as green hydrogen bunkering. The results show that the experts from two major ports expressed differing perspectives on the highest priority function with the Landlord function (at 0.370) being the top priority for Northport and the Regulatory Function (at 0.664) to be ranked highest for Johor Port. Details analysis indicates that the difference is not significant. The findings may offer useful insights for guiding resource allocation, encouraging consensus-building workshops to align stakeholder perspectives, and providing a preliminary foundation for future research on sustainability frameworks. Moreover, this study can contribute to supporting policymakers, port practitioners, and researchers in making more informed and balanced decisions toward advancing environmentally sustainable port operations.
This paper proposes a distance measure for generalised L-R intuitionistic fuzzy number (GLRIFN) which is Hamming distance, aiming to enhance the theoretical and practical tools available for decision-making under uncertainty. The properties of the Hamming distance of generalised L-R intuitionistic fuzzy number are also discussed in this study. GLRIFN extends traditional L-R intuitionistic fuzzy number by incorporating confidence level for both membership and non-membership functions, making them more reliable in the evaluation process. To demonstrate the practical utility of the proposed measure, it is applied within the Generalised L-R Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (GLRIF-TOPSIS), a multi-criteria decision-making (MCDM) method. A real-world case study on river water pollution classification is conducted, wherein the proposed model effectively evaluates the pollution levels of different rivers by capturing the nuances of imprecise, vague, and conflicting environmental data. The results show that the River 5 J is the cleanest river, while the River 1 J is the most polluted river. The integration of the Hamming distance with GLRIF-TOPSIS offers a structured and adaptable decision-making framework, capable of addressing complex multi-criteria problems across domains characterised by high levels of ambiguity. This contribution not only enriches the existing body of fuzzy set theory but also opens avenues for further applications in environmental assessment and other areas that require robust fuzzy modeling.
In the era of digital transformation, human-computer interaction has become a critical factor in shaping customer experiences and loyalty, particularly in the hospitality industry. This study investigates the relationship between human-computer interaction experiences and customer loyalty in Chinese hotels, with a focus on the moderating role of brand trust. Drawing on the Technology Acceptance Model, the research explores how perceived ease of use and perceived usefulness of human-computer interaction systems influence customer loyalty while considering the impact of brand trust as a moderating variable. Using a quantitative research design, data were collected from 350 hotel guests in China who had interacted with human-computer interaction systems such as mobile apps, chatbots, and self-service kiosks. The findings reveal that human-computer interaction experience significantly enhances customer loyalty, and this relationship is positively moderated by brand trust. Specifically, customers with higher levels of brand trust are more likely to remain loyal despite potential technical issues or usability challenges. The study underscores the importance of user-centric human-computer interaction design, trust-building initiatives, and cultural sensitivity in enhancing customer loyalty in the Chinese hospitality market. These insights offer valuable implications for hotel managers and technology developers aiming to optimize human-computer interaction systems to foster long-term customer relationships.
This paper sets out to propose a unified theoretical framework for knowledge tracing (KT) that combines graph convolutional networks (GCNs) with neural sequence architectures in intelligent tutoring systems. While existing methods have achieved some success, they face limitations in modelling relational dependencies among concepts and the temporal progression of learner behaviour. Building on socio-constructivist views of knowledge as a network of relations and connectionist accounts of learning as adaptation over time, the framework integrates graph-based relational reasoning with sequence-based temporal modelling. The argument advanced here is that the integration offers interpretable representations of knowledge states while preserving predictive performance. The paper draws together recent developments in graph-enhanced KT and attention-based models and outlines design heuristics for scalable deployment. Key issues are identified, including computational cost, data sparsity, and explain ability for classroom use. It is anticipated that the framework will inform the design of more systems and provide a tractable agenda for empirical validation across multiple domains and learner populations.
Efficient lecturer-to-course assignment is crucial for ensuring both faculty satisfaction and optimal teaching outcomes in higher education institutions. This study presents an advanced optimization model based on the Modified Hungarian Method (MHM) to address this challenge by integrating lecturers' preference levels and competency scores. While previous research has primarily focused on the traditional Hungarian Method (HM), limited attention has been given to its modified version. Moreover, the incorporation of preference-competency-based criteria in lecturer assignments is still lacking. To bridge these gaps, this study develops a mathematical programming approach to refine the MHM framework. The proposed model, called the Preference-Competency Multi-Objective MHM (PC MO-MHM), aims to achieve two key objectives: maximizing lecturers’ preferences and maximizing lecturers’ competencies. Competency is assessed across three elements: knowledge, skills, and teaching motivation. Data were gathered through an online survey involving Mathematics lecturers teaching undergraduate courses at the public university in Malaysia. By utilizing the collected data on preference levels and competency scores, the PC MO-MHM model was implemented using MATLAB’s intlinprog function to generate an optimized lecturer-to-course assignment plan, limiting each lecturer to a maximum of three courses. The findings highlight that the PC MOMHM model effectively determines the most suitable course assignments based on lecturers’ preferences and competencies. The enhanced MHM framework provides a practical tool for optimizing course-teaching assignment planning. The model potentially not only improves teaching quality but also minimizes mismatches between lecturers and courses, fostering better academic outcomes and increased faculty satisfaction. Ultimately, this study contributes towards refining lecturers’ assignment processes, paving the way for more effective and efficient resource management in academia.
The paper examines the effects of mathematical software on the academic performance of secondary school students, along with its roles in facilitating technological adaptation among the students in mathematics education. The study is motivated by persistent challenges in students’ mathematics achievement and the limited integration of technology in classroom practices, despite the increasing availability of digital tools in schools. Two main objectives guided the research are to evaluate the effectiveness of mathematical software in improving students’ comprehension of mathematics, and to measure the extent of technology integration among students in mathematics learning. A quantitative research design was employed, involving 100 student respondents from Sekolah Menengah Kebangsaan Meru, Klang. Data were collected through questionnaires administered via Google Forms after obtaining the necessary approvals. The findings reveal that students using mathematical software reported a 22% improvement in their comprehension levels, along with enhanced flexibility, critical thinking, learning interest, and classroom interactivity. These results underscore the potential of mathematical software not only to strengthen students’ academic performance but also to build their readiness to embrace technology in learning mathematics. The study highlights the importance for educators and policymakers to integrate mathematical software more systematically into pedagogical practices to maximize its educational benefits.
