Journal of Advanced Research in Applied Sciences and Engineering Technology
Journal of Advanced Research in Applied Sciences and Engineering Technology (ARASET) is an international forum for the publication and dissemination of original work which contributes to greater scientific understanding in the area of applied sciences and engineering technology. The Journals reports principally the achievements spanning the interdisciplinary field of applied researches. Scope of the journal includes: biology, chemistry, physics, environmental, health science, mathematics and statistics, geology, engineering, computer science, natural and technological sciences, medicine, and architecture. EVENTS UPDATE Innovate, Write & Publish: An International Research Challenge (IWP-IRC2026) – MyCite and International indexed Journal (IWP-IRC2026) 30 April 2026, Online https://submit.confbay.com/conf/iwp-irc2026 Most cited articles (Source: SCOPUS) Renewable Energy of Rice Husk for Reducing Fossil Energy in Indonesia, Lubis Global Energy Security and European Union: A Review, Khattak et al MHD Flow and Heat Transfer of Double Stratified Micropolar Fluid over a Vertical Permeable Shrinking/Stretching Sheet with Chemical Reaction and Heat Source, Khan et al
Factors Influencing Housing Developers’ Decisions in the Development of Gated and Guarded Housings
Guarded neighbourhood and gated community are two forms of gated and guarded housing that emerged in Malaysia’s housing market. Although both appear similar physically, but the latter has been the result of various legal and planning issues brought by the development of guarded neighbourhood in the country. Unlike guarded neighbourhood, the development and management of gated community are well governed by the strata laws. However, it does not affect housing developers’ interest towards guarded neighbourhood until now. Therefore, this paper aims to investigate the reasons why housing developers have still opted for guarded neighbourhood. Qualitative approach has been used based on the agency model framework in order to elicit housing developers’ decision on their final property product. Housing developers have been interviewed and have been mainly analyzed descriptively. As a result, it is found guarded neighbourhood have been chosen due to developers’ interest and understandings towards the risks and rewards of guarded neighbourhood development process and market preferences. However, the security features and services of a guarded neighbourhood are provided for temporary use depending on residents’ ability to sustain them. It shows that housing developers actually have short-term interest on the type of gated and guarded housing they built as long as they can achieve their economic interest once the physical development is completed despite the economic burden that the residents have to bear afterwards.
Copyright (c) 2026 Journal of Advanced Research in Applied Sciences and Engineering Technology
Multilayer Perceptron Network of ECG Peaks for Cardiac Abnormality Detection
The inception of artificial neural networks (ANNs) was predicated on computational adaptations of human biology, specifically the fundamental principles underlying neurons. The feasibility of utilising ANN for diverse problem domains has been extensively investigated, with a particular emphasis on the domain of biomedical engineering. Applications of ANN are commonly employed in the fields of medicine and education for decision-making purposes. The ANNs employed in the present investigation were trained to identify cardiac anomalies by utilising a diverse set of reference data. The input parameters utilised for cardiac difficulties are commonly known as reference parameters, specifically pertaining to the amplitude and duration of the electrocardiogram (ECG) signal. The ECG complex is composed of three distinct components: the P peak, the QRS wave, and the T peak. The artificial neural network is provided with six input parameters, which are obtained by measuring the amplitude and length of each P peak, QRS wave, and T peak. The present study utilises a multilayer perceptron (MLP) as the structure for the ANN. This study examines the impact of the Tansig and Purelin activation functions on the structure of the MLP. All other networks were not as good as the MLP network, which got the best performance of 96.32% by using the BayR training method and the Tansig activation function.
Fabrication Biowaste-Derived Carbon Quantum Dot/Calcium Oxide for Photodegradation of Methylene Blue under Visible Light
In response to the significant environmental risks posed by the discharge of synthetic dyes into water bodies from the textile industry in Malaysia, this study presents a sustainable solution utilizing orange peel waste-derived carbon quantum dot (CQD) and calcium oxide (CaO) for the photodegradation of methylene blue (MB) under visible light. This approach not only addresses environmental contamination but also promotes the reuse of biowastes, aligning with circular economy principles and environmental sustainability. This research includes the CQD/CaO nanocomposites sourced from biomass wastes using a microwave-hydrothermal method, investigation on the dyes photodegradation efficiency under visible light, focusing on catalyst loadings and initial dye concentrations. Additionally, the kinetic studies and type of heterojunction were explored as well. Characterizations involve a range of techniques, including Fourier-transform infrared spectroscopy (FTIR), photoluminescence (PL) spectroscopy, and Diffuse reflectance ultraviolet-visible spectroscopy (DR UV-Vis). The CQD/CaO heterojunction significantly enhances photocatalytic efficiency by promoting charge separation and visible light absorption. PL analysis showed that the nanocomposite exhibits much lower PL intensity than pure CQD, indicating reduced electron-hole recombination. FTIR results confirm an increased surface hydrophilicity and the introduction of new hydroxyl groups, while UV-Vis analysis demonstrats a narrowed band gap and broader light absorption in the CQD/CaO nanocomposite. Together, these properties enable more efficient photodegradation of MB under visible-light illumination. Under optimized reaction conditions, specifically with a catalyst loading of 0.5 g/L, initial MB concentration of 15 ppm, at pH 7 and in the presence of 10 mL 5 mM H2O2, the CQD/CaO photocatalyst achieved a photodegradation efficiency of 72.61% within 3 hours of visible-light irradiation. The findings are expected to provide insights into the feasibility of deploying biowaste-derived materials for effective environmental remediation, offering a dual benefit of pollution reduction and waste management.
Synthesis of Bio Epoxide from Sunflower Oil by In Situ Epoxidation
Depletion of non-renewable resources throughout the years remains the hot topic on the table. One of the solutions available is vegetable oils whereas, in general have similar properties or better than petroleum in terms of viscosity, toxicity, flash point, evaporative loss and biodegradable sources.
Unlock the Recent Prediction Models using Clinical Variables for Diabetic Retinopathy
Diabetic retinopathy (DR) poses a major challenge to clinician and public health personnel due to the potential complication of vision loss especially among young adults. Thus, many studies came out with artificial intelligence prediction models for diabetic retinopathy to enhance the management of potential complication. The aims of this systematic review are to identify published prediction models using clinical variables for diabetic retinopathy and to compare their accuracy and quality. A systematic search was conducted in PubMed, Scopus, ScienceDirect, ProQuest, Web of Science databases. Studies were included if the model was applicable in type I or type II diabetes mellitus and the outcome was diabetic retinopathy. The methods of Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model risk Of Bias Assessment Tool (PROBAST) were used as a guide. Ten studies since 2019 were identified and included here. Results of each model were compared in terms of area under ROC curve (AUC), sensitivity, specificity and others. This review provides an insight about the existing DR prediction models and to foresee the future prospects. Future work includes combination of prediction models using clinical variables with fundus images of the patients to predict the area possible for development of diabetic retinopathy.
Seasonal Distribution of Thunderstorm Activity in the Southern Region of Peninsular Malaysia
This paper comprehensively analyses the thunderstorm activity in southern Peninsular Malaysia from 2011 to 2018 utilizing lightning data from the Lightning Detection Network System (LDNS) managed by TNB-Research (TNBR). Employing statistical analysis with Microsoft Excel and Python 3.9, this study aims to evaluate and characterise the regional CG lightning, considering geographical, temporal and climatic factors and their relation to seasonal variations. Negative cloud-to-ground (-CG) lightning dominates at 98.00 %, with only 2.00 % positive cloud-to-ground (+CG) lightning. Monthly trends exhibit a double-peak characteristic, with primary and secondary maxima in April and October, respectively, coinciding with monsoon transitions. The first inter-monsoon (IM1) emerges as the most active period, with +CG lightning contributing 28.40 % of total CG lightning compared to -CG lightning at 71.60 %. This study emphasizes critical implications, including polarity imbalance, double-peak distribution, and seasonality. The significant disparity between +CG and -CG lightning necessitates further investigation into charge separation mechanisms within the clouds, particularly during IM1. The presence of double-peak feature in the monthly variation underscores the significant influence of monsoon dynamics on lightning activity initiation. The dominance of IM1 in total CG lightning and the high percentage of +CG lightning emphasize the need for enhanced lightning protection measures and preparedness during critical months, improving lightning risk assessment, public safety and infrastructure resilience. Future studies should focus on employing advanced meteorological models to explain the charge separation and polarity variations.
Comparison of Naive Bayes Multinomial Algorithm and Decision Tree on Tweet Emotion Classification
Social media has become a place where everyone can express their feelings and thoughts without limitations. One of the most widely used social media platforms is Twitter. Twitter has 238 million active users and provides access to search for information through specific tweets. Therefore, Twitter can be used as a source of information to analyze a person's emotions based on their writings/tweets. In analyzing the feelings of a tweet, a method is needed to classify tweets into appropriate emotion classes. The classification of tweet emotions aims to group tweets into predetermined emotion classes such as anger, joy, fear, love, and sadness. The algorithms used to build machine learning models for emotion classification are Multinomial Naive Bayes and Decision Trees. This research aims to determine which classification algorithm, Multinomial Naive Bayes or Decision Tree, is better by comparing the accuracy values of these classification algorithms. This study applies feature extraction using Bag of Words, Bigram and the SMOTE method. The research results show that the Multinomial Naive Bayes classification model, which involves feature extraction using Bag of Words and the SMOTE method, has the highest accuracy value of 67.15%.
Key Factors Influencing the Purchase Intention of Green Residential Buildings among Homebuyers in Sarawak
Green residential buildings (GRBs) have been widely introduced as one of the most effective methods to cope with the issue of global warming. However, the development of GRBs in Malaysia is in an imbalance state whereby none of it has been developed in Sarawak. This might be due to many homebuyers are reluctant to pay the higher cost for GRBs. Thus, this paper aims to uncover the key factors influencing the purchase intention of GRBs. A questionnaire survey was employed to collect data from 200 homebuyers followed by statistical data analysis using the Statistical Package for the Social Sciences (SPSS) software. The majority of Sarawak homebuyers perceive environmental attitude and value perception and trustworthiness as the most important key factors to their GRBs purchase decision compared to perceived financial risk. These findings can assist related authorities on the readiness and expectation of Sarawak homebuyers towards the future GRBs development in Sarawak. Future studies are suggested to expand the sampling size and investigate other determinants that may affect GRBs purchase intention.
The Level of Building Information Modelling (BIM) for Facilities Management Areas in Malaysia: Preliminary Research Results
Building Information Modelling (BIM) is emerging as a prominent and captivating subject within the construction sector. The discussion on the advancement of BIM and its significant effects on building projects and organisations has garnered primary focus within academic and professional circles. Facilities Management (FM) is the sector that derives an immense advantage from Building Information Modelling (BIM). However, it remains the least advanced in terms of implementation. The perception of BIM for the FM sector in Malaysia has varied since FM has begun to embrace the implementation of BIM nationwide. The aim is to provide the initial findings of the current level of BIM implementation for FM application areas in Malaysia. Each quantitative and qualitative study was used at different phases in the research design. Ten FM practitioners underwent qualitative data collection and analysis. Afterwards, a survey questionnaire was conducted among 36 FM practitioners in Malaysia. The research findings reveal that BIM application for FM in Malaysia is in its early stages, and 19 FM applications are areas for BIM use. The most relevant current levels of BIM adoption in FM areas are locating building components, maintenance and operation, non-capital construction feasibility and planning studies, performance measurement and data management.
Vehicle Microsleep Detection System Using Heart Rate Monitoring System
The occurrence of sleep in a few seconds called microsleep while driving is very likely to cause traffic accidents. Currently, the microsleep monitoring system on vehicles is taken lightly. The absence of an alarm system causes the driver to be unaware of microsleep detection. In addition, existing monitoring systems have low accuracy for determining microsleep conditions. Therefore, a microsleep detection system with a heart rate input based on a photoplethysmography (PPG) sensor was designed, an alarm system on Android smartphone was used and a PPG system capable of determining the state of the driver was designed. This study uses Arduino software to process heart rate data with a peak detection algorithm method, Internet of Things (IoT) is used to trigger alarm system on a smartphone. Study results show that the PPG system can detect and act according to the state of the driver and trigger an alarm system on the smartphone when microsleep is detected. A comparison of heart rate readings on PPG system sensors with commercial sensors is taken by calculating the Pearson’s correlation coefficient using 25 samples which shows r = 0.93709 and r = 0.98734 for smartwatch and pulse oximeter sensor respectively which is close to the linear correlation coefficient, r= 1. 25 samples were taken to determine the range of normal (79 - 63 BPM), drowsy (62 - 50 BPM) and microsleep (49 BPM and below). In conclusion, the use of the PPG system should be combined with existing sensors such as facial image sensors to determine the state of microsleep for higher accuracy.
Risk Assessment Fuzzy-FMEA for the Prevention and Control of COVID-19 at Sarawak Longhouses
The COVID-19 outbreak causes great concern due to the high rates of infection and the large number of deaths worldwide. This paper presents a risk assessment Fuzzy-FMEA for the prevention and control of COVID-19 at Sarawak longhouses. The paper also provides a comprehensive review study on the transmission potentials, effects, and causes of COVID-19, which emphasize Artificial Intelligence (AI), Multi-Criteria Decision Making (MCDM), integrated approaches, and mathematical programming with Failure Mode and Effect Analysis (FMEA). The spreading of COVID-19 can be controlled and prevented by implementing the FMEA method by considering each failure mode's severity, occurrence, and detection rating via the Risk Priority Number (RPN) value. However, FMEA alone cannot provide a precise risk evaluation as the generated RPN might be unreliable in real-life applications. Recent research shows that the limitation of conventional FMEA can be tackled by aggregating it with other approaches. In conclusion, FMEA with a combination of fuzzy methods is a great integration in order to conduct a risk assessment to prevent and control infectious diseases, which in this paper is focused on COVID-19 incidences.
Relationship Light Absorption-Based Sensor for Detecting Oil Pollution
Oil pollution can destroy nature, aquatic life and crops. A simple approach for detecting oil pollution was developed using photodiode, microcontroller and light-emitting diode (LED). Our system was developed based on light absorption where the intensity of light that passed through a slick of oil was measured to detect the presence of oil in water. The analog value (intensity of light) from photodiode was converted to resistance value for further data processing. Experimental setup was conducted by varying distances (6 cm to 9 cm) from light source to photodiode, varying volumes (50 ml to 200 ml) and varying exposure time (1-6 hours). As the oil volumes increased, the average resistance values decreased. The system can be tested in irrigation canals, lakes or reservoirs used in agriculture. The system has the potential to assist farmers to detect oil pollution as a preliminary precaution before harming the plants.
Infusing work-based experience through Project-Based Learning (POPBL) in Water Courses
This research aims to develop mobile robot-based navigation with PSO. The system will be based on ROS environment to identify the PSO performance in mobile robot navigation. Further analysis on the mobile robot navigation with PSO is discussed in the literature review section of this paper. The aim for this paper is for the mobile robot to solve the path planning problem of mobile. The basic robot’s navigation of a robot requires many data or resources to process the output path of the navigation. To achieve this, LiDAR sensor is used to map the environment using SLAM functionality that is available on the mobile robot. A distance sensor will also be used for the mobile robot to avoid obstacles during the navigation process. However, it is found out that errors in terms of the sensor measurement may occur between the computer and real time performance. Thus, PSO is used to overcome this problem. To implement the PSO to the robot, basic research and review need to be done. All the results of this project had been collected in figures in the result section, including the differences before and after PSO implementation to the system. Further analysis and discussion were made on the system’s performance.
Parameter Estimation on Simultaneous Linear Functional Relationship of Melaka Wind Data with Error Terms
This study extends the parameter estimation from a bivariate linear functional relationship (LFRM) to a simultaneous LFRM designed for multiple linear variables, employing the maximum likelihood estimation (MLE) method. A simulation study was conducted to examine the bias in parameter estimation. The significance of the simultaneous LFRM lies in its ability to explore relationships among multiple linear variables, with particular emphasis on considering error terms for all variables. The study reveals that the estimated parameters exhibit minimal bias. The applicability of this proposed simultaneous model is illustrated using environmental data such as wind speed, temperature, and humidity from Malacca throughout the southwest monsoon for 128 days in 2020.
Eye-DR: Mobile Application for Diabetic Retinopathy Screening
The development of smartphone-based healthcare applications especially in medical diagnosis has become more appreciated as the pandemic COVID-19 happened. Diabetic Retinopathy (DR) is a condition that affects the small blood vessels in the retina as a result of prolonged impact of diabetes which may lead to complete loss of vision as it is the predominant cause of visual impairment among working-age individuals [1]. The evolution of portable ophthalmoscopes allows for smartphone cameras to be used as a medium for retinal imaging, especially in rural areas where healthcare services are limited. Therefore, utilizing the development of portable ophthalmoscopes has given the rural area of East Malaysia more opportunities to enhance healthcare services to reach more patients in the outskirts of Malaysia. Hence, the Eye-DR mobile application is designed to offer a third opinion to medical officers on DR diagnosis. The research is focused on the development of the user interface (UI) and user experience (UX) of a smartphone-based application for DR screening that will utilize a deep learning model. It is essential to identify the UI/UX building process of the smartphone-based app for medical disease diagnosis in order to develop a mobile app that can diagnose DR for any given retinal image. The study will also revolve around the design process of building a mobile application using Flutter and utilizing cloud computing (Google Firebase) for the model to work while reducing the application storage memory on the phone.
Compressive Strength of Cement Treated Base of Runway
Material base course on highway or runway pavement that stabilized using cement is well known as Cement Treated Base (CTB). For this kind of material it should be tested when the specimen is tested at seventh day for unconfined compressive strength (UCS). Here, it carries out testing at 7th, 14th, 21th, and 28th day respectively. Furthermore it will be got a curve with mean of normalization to compressive strength at 28th day.
Contractors’ Perspective on the Maturity Level of Green Building Practises in Sarawak Construction Industry
Globalization with rapid construction activities resulted in releasing carbon emissions that reaching to an alarming rate. Such high emission alerted different stakeholders to look into sustainable practices and green building is one of the initiatives. Malaysian government have been promoting green building practices with the support from Construction Industry Development Board and Green Building Council for green rating tools. Despite the efforts from various parties, the maturity level of green practices in Malaysia is still not high comparing to the developed countries. This study aims to investigate the maturity level of green building practices and factors influencing such practices in the Sarawak construction industry from contractors’ perspective. Semi-structured interviews with six contractors that are ever involved with green building projects were conducted. Content analysis were employed to analyse the collected data. The findings revealed that the interviewees aware on the existence and benefits of green building practices but not taking initiatives to involve. Hence, the results indicated that the maturity level of green building practices in Sarawak construction industry is still at the low level. The results showed that the key factors that affecting the maturity level are cost and awareness. This study could serve as a basis and provide greater insights into the adoption of green building practices in the Sarawak construction industry.
Chlorophyll Intensity Forecasting in Diverse Plant Species: Advancing with Enhanced Convolutional Neural Networks
Chlorophyll pigment plays a crucial role in photosynthesis by absorbing and harnessing light energy, ultimately supporting the plant's overall health and growth by providing essential nutrients. A convenient assessment of chlorophyll content is essential in smart management agriculture. It is very important to measure chlorophyll intensity in a leaf accurately as it can indicate the plant health status in terms of its growth, photosynthetic capacity, and nutritional stress. Several attempts have been made to implements computer vision to enhance the precision agriculture techniques However, recent literatures about chlorophyll intensity prediction of various plant species using non-destructive methods are limited and yet existing methods for chlorophyII intensity forecasting are time-consuming. Hence, a rapid and straightforward convolutional neural network (CNN) algorithm was proposed to predict chlorophyll intensity of various plant species based on leaf reflectance spectra. The datasets were taken from ANGERS Leaf Optical Properties Database (2003). The proposed model consists of Hybrid CNN as a feature extractor and Support Vector Regression (SVR) network as a predictor. Hybrid CNN was designed by modifying the architectures of AlexNet and PNet. The performance of Hybrid CNN with SVR (CNN-SVR) was also compared with AlexNet, PNet, and SVR. Hybrid CNN exhibits superior performance when compared to established models, including AlexNet, PNet, and standalone SVR. Notably, the mean square error values for training and testing datasets stand at 0.1558 and 1.149, respectively. This substantiates the model's efficacy in predicting chlorophyll intensity accurately, underscoring its potential utility in advancing precision agriculture techniques.
Defense Energy-Efficient Lora Mesh Network Topologies in Disaster Operations
Amidst the changing realm of radio communication and IoT frameworks, the formulation of propagation models aligns with emerging IoT communication protocols for optimal performance. This study delves into the judicious arrangement of LoRa gateways and nodes to facilitate effective communication in the context of disaster operations. The complexities confronting emergency management, notably the Malaysia Civil Defence Force (MCDF), are heightened by the escalating intricacy of disasters and emergencies. The continuous amelioration of search and rescue operations, evacuation procedures, emergency medical services, and the provisioning of essential amenities is imperative. Consequently, a tangible solution materializes in the form of a prototype device integrating a comprehensive LoRa radio module. Functioning within the 433MHz and 868MHz frequency bands, this energy-efficient, long-range transceiver streamlines efficient communication. The proposed offline communication network incorporates primary LoRa modules and an array of LoRa submodules, providing Wi-Fi access points and facilitating communication through Wi-Fi-enabled smart devices. This setup empowers survivors to relay vital details to rescue teams via LoRa communication, thereby expediting essential actions. Suggestions for bolstering security measures for mobile devices operating in ad hoc mode and a meticulous examination of gateway-free solutions in LoRa mesh networks for practical disaster scenarios are put forth. Future work endeavours aim to augment the efficacy of disaster management initiatives reliant on LoRa technology.
Emotion Recognition Using Galvanic Skin Response (GSR) Signal
Physiological signals play a vital role in emotion recognition as they are not controllable and are of immediate response type. The primary purpose of this work is to acquire skin conductance of GSR by performing GSR signal classification for emotion recognition using various classifiers. Machine learning applications penetrate more spheres of everyday life. Recent studies show promising results in analyzing other physiological signals using machine learning to access emotional states. Commonly, specific emotions are invoked by playing compelling videos or sounds. However, there is no canonical way for emotional state interpretation. The primary materials are GSR data signals collected from ASCERTAIN database and MATLAB software. The affective arousal seven-point emotional scale was obtained from the classified GSR signals implemented using machine learning algorithms. Features and class labels can be imported into the Classification Learner application in MATLAB software to train and test various classifiers. A comparison of GSR signal classification of the different classifiers has shown that the highest accuracy was achieved using k-nearest neighbours (KNN) and Ensemble classifiers with 97.9% emotion detection. The advantage of this work shows the importance of features and class label selection in emotion recognition tasks. Moreover, the dataset obtained must be suitable for machine learning algorithms. Acquired results may help select proper GSR signals with emotional labels for further dataset pre-processing and feature extraction.
Energy Trading of Renewable Energy Resources by using Blockchain Technologies
This article explores the utilisation of blockchain technologies to improve renewable energy, having an emphasis on Sarawak. In Sarawak, solar power presents a viable option for generating sustainable electricity. This article seeks to demonstrate the capability of blockchain technologies to offer transparency, effectiveness and verifiability in energy transactions. The study explores the execution of an energy trading smart contract on the Sepolia Network, facilitating the involvement of energy buyers and sellers in trade on the Ethereum blockchain explorer Etherscan through a user interface. The smart contract oversees the trade process, ensuring that both parties comply with the set quantity and price of energy. The study emphasises the significance of gas pricing and MetaMask in guaranteeing the prompt and reliable implementation of the energy trading smart contract. The experimental results derived from the energy trading smart contract are demonstrated, accompanied by an elaborate exposition of each function and its significance inside the smart contract architecture. In conclusion, the article showcases the efficiency, clarity, and security necessary for successful energy trading in the Sarawak context.
Impact of Seismic Design on Steel Reinforcement and Cost of Structural Works for Medium Rise Apartment
The Sabah region in East Malaysia has been officially classified as a region with active local seismic faults. The region of Ranau and Kundasang saw a significant seismic event on June 5th, 2015, characterised by a magnitude of Mw 6.1. This earthquake resulted in the unfortunate loss of 18 lives and damaged 61 structures. The damages can be attributed to the structural deficiencies that rendered them incapable of withstanding seismic forces. Implementing seismic design is necessary for new buildings to guarantee the general public's safety. Nevertheless, this practice possesses advantages and disadvantages, notably concerning financial implications. The present study aims to investigate the influence of seismic considerations on the expenses associated with steel reinforcement and structural work. This will be achieved using the Malaysian National Annex to Eurocode 8 as a reference. The present study used an 8-story residential building as a representative model. This study examined the two soil types, C and E, in the Sabah region. Various levels of seismicity have been simulated by considering the reference peak ground acceleration, αgR, within the range of 0.04g to 0.16g. The findings indicate that incorporating earthquake considerations into the design process led to an average increase in the utilisation of steel reinforcement by approximately 3% to 110% for beams, columns, and lift cores. Nevertheless, the extent of the rise in expenses related to structural work due to seismic design varied between 1.0% and 18.0%, depending on factors such as soil type and seismic activity levels, compared to the model that did not incorporate seismic considerations. Therefore, using seismic design principles in constructing new buildings merits careful consideration, as it can enhance their resilience and mitigate the financial burden associated with earthquake-related repairs and losses.
CMOS Based ESD Protection Circuit for Low Voltage
Electrostatic discharge (ESD) is a common phenomenon, poses a risk to devices and circuits due to their high energy transfer. Catastrophic or latent failures in ICs can result from ESD damage, with latent damage potentially causing malfunctions or premature failure. To safeguard against ESD, protection methods and schemes, such as CMOS -based approaches, are implemented to divert and minimize high currents and voltages, especially as IC technology advances to smaller scales. This project proposed a protection scheme based on 45 nm CMOS technology which was done in Cadence EDA tools. CMOS structures and clamping diode with the implementation of inverter were utilized to reduce peak discharge current and mitigate ESD damage through fast rise times. The Human Body Model (HBM) which is equivalent to 2 kV was used as a testbench for the protection circuit. The first peak current (31.169 mA) of proposed design has results in 97.66 % reduction with the rise time of 55.58 fs, fall time 2.188 ps, and a peak discharge current period of 174.66 fs. The total power dissipation for the DUT is 29.15 mW with circuit layout of 16.81 nm2. In summary, the proposed ESD protection circuit can be implemented in the low voltage application.
Dual Orifice Outlet Configurations in Backward Bent Duct Buoy (BBDB) for Enhanced Hydrodynamic Performance
This research explored the Backward Bent Duct Buoy (BBDB), a type of Wave Energy Converter (WEC), to examine the relationship between the location of dual-orifice air column and the primary conversion efficiency. The BBDB type of WEC is promising due to its simple design and relatively strong performance, yet limited studies have investigated it, particularly concerning the orifice design, in comparison to other WEC types. The study aimed to investigate the impact of varying the location of the dual orifice air column of the BBDB device on its primary conversion efficiency. This was achieved by designing and fabricating three top panels each for horizontally and vertically aligned dual orifices with varying distance, tested in a 3D wave tank. The results indicated that the top panel with Configuration 2.3 exhibited the highest efficiency at 0.106%, followed by top panels with Configuration 1.3 (0.088%), Configuration 2.1 (0.085%), and Configuration 2.2 (0.082%). The findings suggest that top panels with vertically aligned dual orifices have higher efficiency compared to those with horizontally aligned dual orifices.
A Proposed Framework to Determine the Reliability and Spare Parts Replacement for Medical Devices
The lack of malfunction of medical equipment has the potential to significantly impact the medical industry and pose a threat to the well-being of billions of individuals globally. Subsequently, maintenance services are executed to ascertain that the equipment is functioning following the standards set by the manufacturer and upheld at the desired level of quality. Downtime resulting from equipment breakdowns can significantly impact the operational efficiency of equipment, leading to decreased output, heightened operating expenses, and disruptions in customer services. The main objective of this conceptual framework is to explore on the spare parts replacement and their impact on the maintenance function of the health system. The proposed approach uses the Failure Mode and Effects Analysis (FMEA) method to identify potential failures effectively. It will describe how obtained scores are used to set up guidelines for appropriate spare parts replacement for improved maintenance strategies. To forecast the demand for spare parts for medical devices, this paper proposes a semi-supervised learning algorithm to solve the problem. Through the use of data-driven insights and predictive analytics, machine learning algorithms can enhance decision-making processes about the timing and selection of spare parts replacement.
The Solution of Overlapping Claimed Areas Issue During Process of Issuing Native Customary Rights (NCR) Land Titles
This article is related to the Native Customary Rights (NCR) lands in Sarawak which focuses on overlapping claimed areas while issuing NCR land titles. The purpose is to identify the factors of overlapping claimed areas issues during the process of land measurement before issuing the NCR land titles and to examine the solutions to the issues of overlapping claimed areas during the process of land measurement. This article has applied the qualitative approach based on the land administrators' and land surveyors' experiences in solving the overlapping claimed areas issue during the land measurement process. The findings were obtained by conducting semistructured interviews in focus group discussions with the Land and Survey Department in Bintulu. Analysis of the study found that native area lands and native customary lands are frequently involved in the overlapping claimed areas, where four factors are causing the issues as analysed in the literature review and current factors contributed by the land administrators. The focal point of the solutions to the overlapping claimed areas issues is the role of administrators in communication and negotiation with the native people during the dialogue session in disseminating the knowledge of NCR so that the native people can recognize their boundaries accurately. The parties involved must promptly rectify all weaknesses in carrying out the settlement action. It needs to be improved by making improvements that can further strengthen the existing settlement actions more effectively.
Exploring the Significance Elements of Collaborative Partnership in Successful Private Finance Initiative (PFI) Projects: A Comprehensive Review towards Sustainable Development
The construction industry has significantly improved, especially in construction development delivery. The Private Finance Initiative (PFI) project is where private companies obtain federal government funds to complete public facilities. Private Finance Initiative (PFI) in the Malaysian construction sector under the Ninth Malaysian Plan (9MP) has encouraged Malaysia to start implementing different project procurement approaches to complete public infrastructure projects. These project procurement approaches have evolved from the concession, privatisation, public-private partnerships (PPP), and finally, the PFI to ease the burden on the government in financing infrastructure projects. Collaborative partnerships (CP) play an essential role in building industry growth, especially in PFI projects, because they involve stakeholders from the public and private sectors working together in their processes. This paper explores the significant elements of collaborative partnership in successful Private Finance Initiative (PFI) projects and their implications for sustainable development. The study reviews existing literature to identify the critical elements of CP, and the analysis focuses on how these collaborative partnerships align with and promote the sustainable development goals encompassing the sustainable pillars; environmental, social and economic dimensions. The outcome of this paper will highlight the importance of strategic focus on the activities, good working relationships, effective communication, mutual respect and equal opportunities, and ethical conduct and acknowledging honours as essential elements for successful collaborative partnerships in PFI projects. Additionally, this study will benefit the construction industry in moving towards sustainable development by integrating environmentally friendly practices and enhancing the collaborative partnerships in PFI Projects while advancing sustainable development objectives.
Performance of StormPav Green Pavement as Detention Pond Stormwater Management Infrastructure
Implementation of green pavement practices can significantly improve the rainfall-runoff responses. StormPav, a system consisting of a micro-detention pond covered with precast honeycomb as surface and base structure layer is proposed to serve as an alternative green pavement application. The main focus of this study is to investigate the hydrological benefits of the proposed system. Detention storage capacity of the system was constructed under two different conditions, (i) various void capacities at road pavement thickness of 450 mm and (ii) 70% void capacity with different pavement thickness. The system can provide significant peak runoff reduction, resulting from the detention storage which is able to store the 10 to 100-year major storm events within duration of 20 to 30 minutes without overflow. In other words, such a condition allows the system to cope with the urban major storms which normally have a time of concentration of 10 to 15 minutes.
The Key Factors for Uncertainty on BIM Software usage for the Interior Design Industry
Building Information Modeling (BIM) can contribute numerous benefits to the construction industry, especially for the interior design industry. The purpose of this study is to explore the barriers to software usage in BIM technology and identify the key factors of uncertainty for practitioners while BIM is implemented in the interior design industry in Malaysia. To investigate the research objectives, the mixed method was adopted, and a structured questionnaire was designed. A total of sixty-three respondents were involved in answering questionnaires. Experts among interior design firms determined in Klang Valley, Malaysia, were filtered through the registered Lembaga Arkitek Malaysia (LAM) to be certified as professional respondents. The results of the questionnaires showed that the barriers are a lack of skilled BIM operators, not being interested in BIM use, licensing issues, interoperability between software, and certain projects that require BIM. The findings from this study will help develop a framework to enhance the readiness to use software in BIM technology among the interior design industry in Malaysia, which is considering implementing BIM and enabling the development of a new paradigm in the BIM process. This research reflects the future of increasing the efficiency and productivity of the interior design industry in the construction industry in the Malaysian context.
The Effects of Training Intervention on Knowledge Level of Occupational Risk Assessment Among Engineering Students in a Public University in Selangor
Knowledge on occupational safety is crucial in the engineering field. In addition to reducing workplace risks, solid understanding on the basics of occupational safety and how it corresponds to health in the specific direction of Occupational Safety and Health (OSH) will develop engineers with improved ability to ensure safety and health performance. Current curriculum for engineering requires OSH-specific training to be incorporated in body of knowledge. This study aims to assess the effects of OSH risk assessment training on knowledge level among final year engineering students in a local university in Malaysia. Method: This was a one-group pre-test and post-test quasi experimental design conducted among 70 civil, agricultural and mechanical engineering students in a public university in Malaysia. An OSH risk assessment training module based on Hazard Identification, Risk Assessment and Risk Control (HIRARC) guideline was developed and validated as the intervention tool and a 20-item questionnaire was employed as assessment tool disseminated at pre- and post-test stage to evaluate knowledge level. The intervention was delivered in classroom settings and participants answered a self-administered questionnaire at pre- and post-delivery of the training. Data collected was entered into statistical program and was analysed according to objectives. Results: Majority were males (54%), less than 23 years old (29%) and had cumulative grade of 3.0-3.5 (67%). The average (median ± interquartile range) knowledge scores at pre- and post- test stage were 10 (9-11) to 14 (13-15). There was a significant (p<0.05) increase in knowledge level at the post-test stage. Implications: The HIRARC training module developed has a potential to increase knowledge on risk assessment among engineering students. Knowledge on risk assessment will enable engineers to eliminate hazards in the conceptual or designing phase of engineering work, thus reducing probability of accidents and ill-health from occurring in workplace.
Materials Management Concern on Construction Projects
The success of a construction project's execution hinges on efficient materials management. Delays, cost overruns, poor quality, and conflicting views can all result from ineffective materials management. By presenting alternative approaches to material management in building projects, the study aims to enhance its efficacy. To gain insights into the challenges faced in managing materials in construction projects, the research focused on success factors and potential solutions. Data were collected through literature reviews and surveys conducted among construction players operating under organizations graded G7 in Johor Bahru. The analysis utilized the Relative Importance Index (RII) to evaluate the significance of various variables, ranking them based on their perceived relevance or influence. Findings indicated that the most significant challenge in material management was design changes during construction. Effective material planning and the selection of trusted suppliers emerged as critical success factors for successful materials management. Additionally, the study identified proper on-site construction workers' training and the use of technology for monitoring and record-keeping as viable solutions. Construction industry experts can leverage these insights to manage resources more effectively, allocate resources wisely, and improve project outcomes. By comprehending and addressing these challenges, construction stakeholders can enhance project efficiency, reduce expenses, and ensure on-time project completion.
Smartphone Retinal Imaging: Addressing Technical and Clinical Challenges
Smartphone-based retinal imaging is a method introduced to address the limitations of conventional methods of retinal imaging. An example of a conventional retinal imaging method is the desktop mydriatic and non-mydriatic fundus cameras which are known to be bulky, expensive, and highly dependent on trained professional users. For screening purposes specifically for low- and middle- income populations located at geographically remote areas, the smartphone-based retinal imaging method which are known to be portable, cost-effective, and easier-to-use is the most suitable alternative. This would help to increase the rate of patients getting early diseases diagnosis from the rural areas. However, smartphone-based retinal imaging still has significant limitations that need to be tackled for future improvement. Thus, this paper aims to address the technical and clinical challenges of smartphone-based retinal imaging based on existing studies. Besides this, this paper also reviewed existing smartphone-based retinal imaging devices and adapters where the current trend these devices and adapters are identified and discussed. Based on the research, smartphone-based retinal imaging method is still incapable of competing against reference standards from conventional methods based on the repeatability in obtaining clinically adequate image quality shown in the studies reviewed. Besides this, the current state of smartphone-based retinal imaging method has limited applicability due to several reasons such as the small size of field of view, highly dependent on dilation, and limited smartphone compatibility. Hence, more research into the optimization of smartphone-based retinal imaging method is necessary to improve its current state, in realizing its full potential.