Journal of Advanced Research Design

Representative Volume Element in Photopolymerization Additive Manufacturing Techniques for Mold Production: A Comprehensive Structured Review

Syah Mohd Amin Omar — Wed, 03 Sep 2025 00:00:00 +0800

In recent years, the use of Representative Volume Elements (RVE) in photopolymerization additive manufacturing (AM) for mold production has attracted significant attention for its potential to enhance material performance and structural reliability. This systematic literature review (SLR) provides a structured analysis of recent developments in RVE applications within photopolymerization techniques. It focuses on their effectiveness in addressing the challenges of dimensional precision, mechanical strength, and thermal stability in AM molds. The review addresses the need for a consolidated understanding of RVE’s role in optimizing photopolymerization processes to achieve superior mold quality for industrial applications. A comprehensive search was performed following the PRISMA guidelines across established databases, for instance, Scopus as well as Web of Science (WoS), emphasizing research published from the year 2022 to 2024. A total of 26 relevant articles were analyzed, categorizing findings into three key themes: (1) hybrid and multi-material manufacturing techniques, (2) material-specific AM and characterization, and (3) applications and performance enhancements in AM. Results indicate that RVE integration in photopolymerization AM techniques can improve mold properties by up to 30%, with advancements in fiber orientation and controlled curing processes contributing significantly to performance. This review highlights RVE’s critical role in advancing photopolymerization AM for mold production and suggests further research into standardized RVE methodologies for scalable and high-performance mold applications. The findings offer valuable insights for industries seeking reliable and efficient manufacturing solutions through AM innovations.

Kuala Selangor Estuary Salinity Intrusion Modeling during Extreme Flood Events

Wed, 10 Sep 2025 00:00:00 +0800

The occurrence of extreme flood events causes flood plain inundation and sedimentation. In the estuary, these processes lead to salinity changes, which adversely affect the estuarine ecosystem. A shallow water model (SWM) had been used for the Selangor River estuary up to Kg. Asahan station, the limit of saline water intrusion. The performance of the model has been verified using two decades of observed data. The model was used to simulate flood events in Kuala Selangor to investigate salinity intrusion attributed to transverse flow during extreme flood events. Analysis shows that as the annual recurrence interval increases, the flood depth increases, but the salinity level reduces due to the dilution effect from the riverine fresh water inflow.

Alzheimer’s Disease Prediction using ANOVA with t-SNE Feature Selection Techniques and Ensemble Learning

Mon, 08 Sep 2025 00:00:00 +0800

Alzheimer’s disease stands as one of the most common neurodegenerative disorders, and currently, there is no cure for it. Early identification is pivotal for delaying disease continuation. The current approaches to Alzheimer's disease early detection rely on handwriting activities, which provide a significant quantity of data. Because of its great dimensionality, the final data obscures the significance of pertinent features. The challenge of dimensionality in data arises when there are too many features but not enough data samples, making it difficult for a model to discover a pattern in the data, affecting the many approaches used to diagnose or classify Alzheimer's disease. In this study, a way has been provided to overcome the curse of dimensionality by applying t-SNE and improve the efficacy of early Alzheimer's diagnosis by selecting key features using ANOVA; apart from that, seven machine learning algorithms have been used as base classifiers. These base classifiers were then used to create voting classifier results. The results of the studies indicate that the voting ensemble technique (approximately 94.28%) had the highest classification testing accuracy. Our approach has demonstrated its effectiveness by surpassing the latest benchmarks with our proposed technique. To comprehend how different features influence the model’s outcome, we utilized Explainable AI (XAI) techniques, specifically SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Our proposed method has the potential to significantly improve the accuracy of early Alzheimer’s disease diagnosis, laying the foundation for timely interventions and better patient outcomes.

Evaluating User Experience (UX) of Virtual Reality for Sewang Dance

Wed, 10 Sep 2025 00:00:00 +0800

Sewang Dance virtual reality (VR) is a virtual reality that showcases the dance traditions of the Orang Asli people. The objective of the research is to evaluate significant elements of user experience, including efficiency, stimulation, attractiveness, dependability, novelty, and perspicuity. The User Experience Questionnaire (UEQ) framework is used in this research together with the design of the survey questionnaire. The main research objectives are then developed once the data has been retrieved and examined using the User Experience Questionnaire (UEQ) Analysis framework, tool, and analysis. Data gathering from 384 respondents and the setting for the research in Selangor, Malaysia. Different design aspects and user experience (UX) concerns should be considered to boost the Sewang Dance VR in terms of efficiency, stimulation, attractiveness, dependability, novelty, and perspicuity. Future work may include such as one-on-one interviewing or focus groups, to further enhance the UX of the Sewang Dance VR.

Applying Concurrent Engineering Concepts in Video Production for Resource-Limited Settings: A Case Study

Wed, 10 Sep 2025 00:00:00 +0800

The escalating importance of digital video content, particularly in areas like online learning, presents significant challenges in producing high-quality material efficiently, especially within resource-limited settings. Concurrent Engineering (CE), a systematic approach emphasising integrated, simultaneous design, offers a powerful model for improving efficiency and quality in product development. This study explores the potential application of Concurrent Engineering (CE) concepts in the video production process within such resource-constrained environments. Conducted as an exploratory case study, the research is based on real-world production data collected over seven years from 693 videos. The methodology involved embedding selected CE principles, such as performing tasks in parallel, ensuring early information exchange and verification before subsequent stages, fostering frequent communication and cross-functional collaboration, and strategically leveraging low-cost tools like mobile phones, free editing software, and cloud storage. This approach functions as a process improvement by aiming to streamline the workflow, reduce errors, minimize rework, and enhance overall efficiency and quality. The main findings, derived from the analysis of this extensive production data, demonstrate that applying these CE concepts led to significant reductions in production time. Furthermore, evaluation of output metrics, including viewer engagement and retention, indicates that videos facilitated by this CE approach tended to perform better, suggesting a positive contribution to the perceived quality and effectiveness of the content. This study provides foundational, real-world evidence for the practical effectiveness of applying CE principles in a non-traditional, resource-limited creative context, offering valuable insights for educational institutions and content creators facing similar production constraints.

Comparative Study of Topsis and Fuzzy Topsis for the Determination of Water Allocation in an Urbanized River Basin

Lee Wei Koon — Thu, 04 Sep 2025 00:00:00 +0800

The water allocation decision-making model developed by Lee et al. (2023a, 2023b) provides a robust framework that integrates economic, social, and environmental factors with basin water availability to prioritize water allocation. However, the model's reliance on extensive data collection poses significant challenges in data-scarce regions. To mitigate this issue, this study proposes an alternative using a fuzzy TOPSIS algorithm, designed to yield similar outcomes while requiring less data. A comparative analysis between the traditional TOPSIS and fuzzy TOPSIS methods demonstrates analogous trends, with both identifying Option 1 as optimal despite minor ranking differences. Fuzzy TOPSIS stands out for its ability to handle imprecise data, simplifying the evaluation process in uncertain contexts. Nonetheless, its success depends on the strategic perspective of decision-makers. This research underscores the necessity for ongoing refinement and evaluation of the models to effectively tackle uncertainties in water resource management. The enhanced model significantly advances water governance by promoting transparency, stakeholder inclusivity, and informed decision-making. By incorporating multiple criteria and stakeholder perspectives, it fosters equitable and efficient water resource utilization. Ultimately, the model contributes to a more sustainable and resilient future for societies and the environment.

Comparative analysis of Machine Learning Models for Predicting Particle Size Parameters from Drilling Data

Wed, 03 Sep 2025 00:00:00 +0800

The efficiency of drilling operations is determined by numerous aspects, including the particle size of the material being drilled. To achieve efficiency, drilling engineers must take into consideration the size, shape, and density of the cuttings generated during the drilling process. Ineffective drilling can result in increasing expenses and delays for projects involving the extraction of natural resources. The objective of this study is to enhance drilling efficiency by investigating the correlation between drilling parameters such as weight on bit, revolutions per minute, torque, and rate of penetration and features of particle size distribution such as mean particle size and coarseness index as well as mechanical specific energy (MSE). The influence of particle size on drilling has been evaluated through the application of machine learning techniques and comprehensive datasets. The study highlighted relationships between particle size characteristics and the effectiveness of drilling, offering valuable insights into the optimal particle size for tonalite formations that are bordered by mica gneiss. Three machine learning techniques were employed to determine the closest relationship between drilling characteristics and particle size, with the Random Forest approach exhibiting the strongest correlation. This technique may be employed to forecast the size attributes of particles for data points that are not available within the usual range of drilling parameters. This work successfully emphasizes the significance of particle size in drilling operations and showcases the practical use of machine learning in enhanced drilling efficiency.

Evaluating The Adsorption Efficiency Of Pelletized And Powdered Clinoptilolite For Methylene Blue

Thu, 04 Sep 2025 00:00:00 +0800

The widespread use of dyes in various industries such as clothing, manufacturing, cosmetics, and printing has raised concerns about their negative impact on the environment. Adsorption is a practical method used in industries to treat this type of discharge on a large scale. However, the conventional powder format raises practical concern. This paper examines the adsorption capacity of clinoptilolite prepared as both pellets and powders for removing methylene blue. Both formats showed a consistent increase in adsorption capacity as the mass increased due to the increased number of adsorption sites attracting the methylene blue. However, compared to the powder format, the adsorption capacity of the pelletized clinoptilolite was significantly lower as the mass increased. The difference in adsorption capacity between the pellet and powder widened with increasing mass. This is because increasing the pellet mass only contributes to small increases in surface area from the slight increase in pellet thickness, whereas the powder format effectively uses all the exposed surface area of each powder unit as adsorption sites. Additionally, the methylene blue does not completely penetrate the inner structure of the pellet, further lowering its adsorption capacity. This study suggests that the adsorption capacity of clinoptilolite pellets can be enhanced to match the performance of the powder format if the mass is decreased. It shows that there is significant potential for improving clinoptilolite pellets to perform as well as the powder format.

A Robust Location Model through Median-based Estimators for Handling Outliers

Kartini Kasim — Wed, 10 Sep 2025 00:00:00 +0800

The location model (LM) is a well-known statistical method for mixed variable classification problems. This method is commonly used to differentiate between the two observed groups based on classical estimation techniques. However, the presence of outliers can substantially distort classical parameter estimation, leading to inaccurate classification results, particularly a high misclassification error rate. To overcome this limitation, this paper proposes a robust LM called RLMmed, which employs the median as a robust location estimator. This median estimator is paired with a robust covariance matrix derived from the product of the median absolute deviation (MADn) and Spearman rank correlation. Simulation analyses were conducted under two sample sizes ( and ) and three binary variable conditions ( and) with a fixed number of continuous variables (). For comparative purposes, simulated datasets were tested with two mean group separations (0.5 and 1.0) and five different levels of contamination (0%, 10%, 20%, 30% and 40%). Thus, a total of 60 simulation datasets were used to assess the performance of the proposed RLMmed. The results were then validated against the classical LM using real data (heart data). The simulation results and real data result consistently demonstrated that the RLMmed outperforms the classical LM, achieving lower misclassification error rates across most contamination levels in all conditions tested. Moreover, RLMmed revealed the best achievement among the contaminated data inspected with a sample size of 400 and two measured binary variables. In conclusion, the developed RLMmed model can effectively handle mixed variable classification problems in the presence of outliers, which is crucial before conducting further classification analysis.

The Characterization Of Conversion Low-Rank Coal From East Kalimantan Indonesia Through Physical - Chemical Activation To Be Adsorbent

Thu, 04 Sep 2025 00:00:00 +0800

One of the largest coal reserves in Indonesia is in the Kalimantan region, specifically in East Kalimantan. Generally, there are three types of coal, namely anthracite, bituminous, and lignite. Lignite is a type of coal that is widespread and has low economic value as an energy source. Its relatively low calorific value makes this type of coal unsuitable for use as fuel. One way to increase the added value of low-rank coal is to convert the coal into active carbon which can act as an adsorbent medium. The adequate carbon value found in this type of coal makes it possible to convert it into an absorbent material. One of the important parameters of the absorbent material is the iodine number. The process of converting coal into an absorbent material is through an activation process which is carried out through a carbonization stage at a temperature of 600°C for 3 hours then followed by chemical-physical activation of the carbonization material which is soaked in a chemical mixture of phosphoric acid and sodium carbonate in 2.5 M for 8 hours. and physically activated at 800 °C for 1.5 hours. The results of increasing the carbon content were obtained when the low-rank coal was initially 38.68%w, then increased in the carbonization process to 68.62%w, and at the time of activation, the amount of carbon reached 73.79%w, indicating that the low-rank coal conversion process was fulfilled in terms of the quality of the results obtained.

Measuring User Experience of Mobile Game-Based Learning about Mah Meri People (Mah Meri Application)

Thu, 11 Sep 2025 00:00:00 +0800

The Mah Meri mobile application is a game-based learning tool developed to support the preservation and dissemination of the cultural heritage of the indigenous Mah Meri community in Malaysia. The primary aim of this study is to evaluate the user experience (UX) of the application and propose design recommendations for further enhancement. A quantitative research approach was adopted, employing the standardized User Experience Questionnaire (UEQ) to assess six core UX dimensions: novelty, stimulation, dependability, efficiency, perspicuity, and attractiveness. Data were collected through an online survey distributed via Google Forms to residents of Selangor, Malaysia, with a total of 400 valid responses analyzed using the UEQ Data Analysis Tool. The findings revealed that the Mah Meri application received overall positive evaluations, particularly in attractiveness, stimulation, dependability, and efficiency, while novelty obtained a more neutral response. These results suggest that although the application effectively engages users and delivers meaningful cultural learning, further improvements are required to enhance creativity and innovative features. This study highlights the potential of mobile game-based learning as an effective medium for cultural education and preservation. Future work should incorporate qualitative methods to capture deeper insights into user perceptions, enabling iterative refinements and ensuring long-term sustainability of the application.

Aesthetic Experience of Local Cultural Tourism Meron to Encourages Creativity Among Early Childhood in Indonesia

Nur Fajrie — Wed, 03 Sep 2025 00:00:00 +0800

This study explores the integration of local cultural elements and environmental materials (loose parts) in fostering aesthetic development in early childhood education. The research aims to analyze aesthetic experiences of early childhood in Sukolilo related to the Meron cultural tradition, assess how culture-based learning enhances these experiences, and identify supporting factors. Using a mixed-methods approach, the study includes Playgroup students aged 4-5 years from Sukolilo, with data collected through surveys, interviews, observations, and document analysis. Results indicate moderate aesthetic experiences, with children successfully recreating Meron shapes, highlighting the effectiveness of integrating culture and loose parts in promoting creativity.

Assessing Ergonomic Risks in Laser Cutting Sheet Metal Operations Using REBA Method: A Case Study

Muhammad Izzuan Abd Rahman, Shahrul Azmir Osman, Saliza Azlina Osman — Tue, 02 Sep 2025 00:00:00 +0800

The laser cutting of sheet metal, which requires human participation, frequently introduces ergonomic challenges that can negatively impact both worker health and manufacturing productivity. Thus, conducting ergonomic assessments is crucial to identifying and addressing risks associated with workplace design and practices, particularly those related to lifting sheet metal and the potential for musculoskeletal disorders (MSDs). This study uses the Rapid Entire Body Assessment (REBA) method and the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) to investigate ergonomic risk assessment in laser cutting operations. Data were collected from participants familiar with the tasks through questionnaires and observations, focusing on lower and upper back disorders. The REBA analysis revealed that lifting sheet metal is linked to poor posture and a high risk of lower and upper back disorders, highlighting the need for improvements. Concurrently, CMDQ results highlighted issues related to lower back discomfort and upper back and forearm discomfort, which can detrimentally affect process efficiency. Integrating ergonomic principles into the design process can enhance worker safety and health, increasing productivity and fostering a more sustainable work environment that benefits both employees and the organization.

Architecting Robust Connectivity: Unveiling The Blueprint for A Resilient Telecommunication Network Design

Sabiroh Md Sabri, Haryani Haron, Mohd Idhamm Ibrahim, Saleh A. Alkhabra — Tue, 02 Sep 2025 00:00:00 +0800

The telecommunication industry has seen rapid growth over the years. This phenomenon is a result of changes in people’s lifestyle, an increase in governmental defence needs as well as in the banking sector and transport control. To ensure all aspects from business to people’s lives run smoothly without any disruptions, the communication network must stay efficient at all times. Network designers are continuously faced with the challenge of keeping a communication network running smoothly. The diffusive nature of design knowledge, such that it is scattered among organizations and individual designers because of project-specific variances, makes the task at hand difficult. This distribution creates knowledge silos that pose barriers to access and reuse, therefore the repeated initiation of projects and escalation of costs. These concerns are captured by this study’s ambition to systematize and document the knowledge regarding network design in a systematic, easily accessible format, increasing reusability and reducing project duration. The research follows a qualitative approach and focuses on the processes and activities involved in network design. Data gathering included interviews and observations that were interpreted through thematic analysis. The research identifies three knowledge types: definitions of “telecommunication network resilient”, “resilient network design knowledge” and “resilient network design process knowledge”. This study concludes with a proposed process model for developing a resilient telecommunication network and advice to future users.

Trend In Publication in Chemical Engineering in Indonesia: A Bibliometric Analysis Perspective Relating to Particle Technology

Asep Bayu Dani Nandiyanto, Dwi Novia Al Husaeni — Tue, 02 Sep 2025 00:00:00 +0800

This research analyzed publication trends in the field of Chemical Engineering in Indonesia from a bibliometric perspective. Using data from the Scopus database, this research explored various aspects such as the number of publications, annual distribution, most popular topics, most productive authors, as well as document types and publication sources. The analysis results showed fluctuations in the number of publications over the last five years, with the highest peak in 2020 with a total of 18 documents. Journal articles were the type of document and source most often chosen by researchers. In addition, international collaboration was confirmed to be very important because it increased the chances of producing articles that were highly cited by other researchers. Therefore, chemical engineering was a topic of great interest to researchers in Indonesia. It is hoped that the findings from this study can contribute to the development of research strategies and academic policies in the field of Chemical Engineering in Indonesia, especially relating to particle technology.

Tin/Indium Electrodes for Liquid Ethanol Sensing at Various Concentrations

Tue, 02 Sep 2025 00:00:00 +0800

Ethanol is widely used in medical, industrial and environmental sectors, prompting a growing demand for accurate and real-time detection technologies. However, conventional tin (Sn)-based ethanol sensors face major limitations, including low selectivity, poor stability and delayed response times, especially at higher ethanol concentrations. To address these challenges, this study aims to enhance the sensing performance of Sn electrodes by modifying them with indium (In) through an electrodeposition method at three concentrations: 0.01 M, 0.03 M and 0.05 M. The fabricated Sn/In electrodes were characterized and evaluated using chronoamperometry under different ethanol concentrations (25%, 50% and 95% v/v). Results revealed that the 0.05 M In-modified electrode achieved the highest current response (~0.75 mA) and response ratio (~600 Rt/Ro) at 95% ethanol. In comparison, the 0.03 M In electrode exhibited superior sensitivity at lower concentrations. All electrodes demonstrated rapid response times within two minutes of exposure. These findings suggest that indium-modified Sn electrodes are highly promising for liquid-phase ethanol sensing applications, offering enhanced sensitivity and tunability based on dopant concentration.