Universiti Tenaga Nasional

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Natural fibers are getting attention from researchers and academician to utilize in polymer composites due to their ecofriendly nature and sustainability. The aim of this review article is to provide a comprehensive review of the foremost appropriate as well as widely used natural fiber reinforced polymer composites (NFPCs) and their applications. In addition, it presents summary of various surface treatments applied to natural fibers and their effect on NFPCs properties. The properties of NFPCs vary with fiber type and fiber source as well as fiber structure. The effects of various chemical treatments on the mechanical and thermal properties of natural fibers reinforcements thermosetting and thermoplastics composites were studied. A number of drawbacks of NFPCs like higher water absorption, inferior fire resistance, and lower mechanical properties limited its applications. Impacts of chemical treatment on the water absorption, tribology, viscoelastic behavior, relaxation behavior, energy absorption flames retardancy, and biodegradability properties of NFPCs were also highlighted. The applications of NFPCs in automobile and construction industry and other applications are demonstrated. It concluded that chemical treatment of the natural fiber improved adhesion between the fiber surface and the polymer matrix which ultimately enhanced physicomechanical and thermochemical properties of the NFPCs.

A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.

Purpose Aims to investigate the extent of the effectiveness of monitoring functions of board of directors, audit committee and concentrated ownership in reducing earnings management among 97 firms listed on the Main Board of Bursa Malaysia over the period 2002‐2003. Design/methodology/approach The current study employs the cross‐sectional modified version of Jones, where abnormal working capital accruals are used as proxy for earnings management. Findings The study reveals that earnings management is positively related to the size of the board of directors. This supports the view that larger boards appear to be ineffective in their oversight duties relative to smaller boards. A possible explanation for the insignificant relationship between other corporate governance mechanisms (independence of board and audit committee) and earnings management is that the board of directors is seen as ineffective in discharging their monitoring duties due to management dominance over board matters. The apparent reason for this phenomenon is attributed to the board of directors' relative lack of knowledge in company's affairs. The study also found that ethnicity (race) has no effect in mitigating earnings management, possibly due to the more individualistic behaviour of the Bumiputra directors. The modernisation of Malaysia and also the increase in Bumiputra ownership of national wealth may have caused the Malays to be more individualistic, similar to their Chinese counterpart. Originality/value Since, there are relatively few studies conducted in this area specifically among Malaysian firms, this study will broaden the scope by providing empirical evidence of the relationship between various corporate governance characteristics, cultural factors and earnings management.

Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery's behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.

End-of-life (EOL) solar panels may become a source of hazardous waste although there are enormous benefits globally from the growth in solar power generation. Global installed PV capacity reached around 400 GW at the end of 2017 and is expected to rise further to 4500 GW by 2050. Considering an average panel lifetime of 25 years, the worldwide solar PV waste is anticipated to reach between 4%-14% of total generation capacity by 2030 and rise to over 80% (around 78 million tonnes) by 2050. Therefore, the disposal of PV panels will become a pertinent environmental issue in the next decades. Eventually, there will be great scopes to carefully investigate on the disposal and recycling of PV panels EOL. The EU has pioneered PV electronic waste regulations including PV-specific collection, recovery and recycling targets. The EU Waste of Electrical and Electronic Equipment (WEEE) Directive entails all producers supplying PV panels to the EU market to finance the costs of collecting and recycling EOL PV panels in Europe. Lessons can be learned from the involvement of the EU in forming its regulatory framework to assist other countries develop locally apposite approaches. This review focused on the current status of solar panel waste recycling, recycling technology, environmental protection, waste management, recycling policies and the economic aspects of recycling. It also provided recommendations for future improvements in technology and policy making. At present, PV recycling management in many countries envisages to extend the duties of the manufacturers of PV materials to encompass their eventual disposal or reuse. However, further improvements in the economic viability, practicality, high recovery rate and environmental performance of the PV industry with respect to recycling its products are indispensable.

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Carbon nanotubes (CNTs) are allotropes of carbon with a nanostructure that can have a length‐to‐diameter ratio greater than 1,000,000. Techniques have been developed to produce nanotubes in sizeable quantities, including arc discharge, laser ablation, and chemical vapor deposition. Developments in the past few years have illustrated the potentially revolutionizing impact of nanomaterials, especially in biomedical imaging, drug delivery, biosensing, and the design of functional nanocomposites. Methods to effectively interface proteins with nanomaterials for realizing these applications continue to evolve. The high surface‐to‐volume ratio offered by nanoparticles resulted in the concentration of the immobilized entity being considerably higher than that afforded by other materials. There has also been an increasing interest in understanding the influence of nanomaterials on the structure and function of proteins. Various immobilization methods have been developed, and in particular, specific attachment of enzymes on carbon nanotubes has been an important focus of attention. With the growing attention paid to cascade enzymatic reaction, it is possible that multienzyme coimmobilization would be one of the next goals in the future. In this paper, we focus on advances in methodology for enzyme immobilization on carbon nanotubes.

A microgrid (MG) is a local entity that consists of distributed energy resources (DERs) to achieve local power reliability and sustainable energy utilization. The MG concept or renewable energy technologies integrated with energy storage systems (ESS) have gained increasing interest and popularity because it can store energy at off-peak hours and supply energy at peak hours. However, existing ESS technology faces challenges in storing energy due to various issues, such as charging/discharging, safety, reliability, size, cost, life cycle, and overall management. Thus, an advanced ESS is required with regard to capacity, protection, control interface, energy management, and characteristics to enhance the performance of ESS in MG applications. This paper comprehensively reviews the types of ESS technologies, ESS structures along with their configurations, classifications, features, energy conversion, and evaluation process. Moreover, details on the advantages and disadvantages of ESS in MG applications have been analyzed based on the process of energy formations, material selection, power transfer mechanism, capacity, efficiency, and cycle period. Existing reviews critically demonstrate the current technologies for ESS in MG applications. However, the optimum management of ESSs for efficient MG operation remains a challenge in modern power system networks. This review also highlights the key factors, issues, and challenges with possible recommendations for the further development of ESS in future MG applications. All the highlighted insights of this review significantly contribute to the increasing effort toward the development of a cost-effective and efficient ESS model with a prolonged life cycle for sustainable MG implementation.

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

The development of rapid and reliable processes for the synthesis of nanosized materials is of great importance in the field of nanotechnology. Synthesis of silver nanoparticles using microorganism have been reported, but the process is rather slow. In this paper, we describe a novel combinatorial synthesis approach which is rapid, simple and “green” for the synthesis of metallic nanostructures of noble metals such as silver (Ag), by using a combination of culture supernatanant of Bacillus subtilis and microwave (MW) irradiation in water in absence of a surfactant or soft template. It was found that exposure of culture supernatanant of Bacillus subtilis and microwave irradiation to silver ion lead to the formation of silver nanoparticles. The silver nanoparticles were in the range of 5‐60 nm in dimension. The nanoparticles were examined using UV‐Visible Spectroscopy, and Transmission Electron Microscopy (TEM) analyses. The formation of nanoparticles by this method is extremely rapid, requires no toxic chemicals and the nanoparticles are stable for several months. The main conclusion is that the bio‐reduction method to produce nanoparticles is a good alternative to the electrochemical methods.

Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models’ performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm.

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Internet shopping is a phenomena that is growing rapidly nowadays. A peep into the exponential growth of the main players in this industry indicates there is still a large reservoir of market potential for e-commerce. The conveniency of online shopping rendering it an emerging trend among consumers, especially the Gen Y. The prevalence of online shopping has raised the interest of the retailers to focus on this area. Therefore, this study was to determine the relationship between subjective norm, perceived usefulness and online shopping behavior while mediated by purchase intention. University students aged between 18 and 34 that currently pursuing their studies in University Malaysia Perlis were selected as the subject of analysis. 662 out of 800 sets of questionnaires distributed were valid for coding, analyzing and testing the hypothesis. Collected data were then analyzed using SPSS version 18.0 and AMOS version 16.0. Structural Equation Modeling to examine the model fits and hypothesis testing. The conclusion can be depicted that subjective norm and perceived usefulness significant positively influence online purchase intention but subjective norm insignificant influence shopping behavior in a negative way. It is interesting to note that perceived usefulness also insignificantly influence online shopping behavior. Finding also revealed that purchase intention significant positively influence online shopping behavior. For future research, sample from working adults and other variables that related to online shopping were to be included to minimise sampling bias.

In recent years, increasing interest has been shown in targeting energy efficiency as a roadmap for carbon mitigation, limiting energy use, improving buildings’ energy performance, and reducing energy consumption for achieving sustainable buildings. This article presents a systematic review to provide the best practices in this area and identify the challenges, motivations, recommendations, and pathways for future work. Discussing the methodological aspects gives insights for future researchers. This research used papers published on three scientific and reliable databases—Web of Science, ScienceDirect, and IEEE Xplore-from 2014 to May 23, 2021. The selected papers reached N = 134 based on inclusion and exclusion criteria and divided into review papers, proceeding conference, and articles. The review articles (N = 16/134) give an overall view on improving energy efficiency to achieve sustainability in buildings by using green building rating systems, developing and implementing policies, technology utilization, adopting techniques, and applying strategies. The conferences (N = 33/134) and articles (N = 85/134) focus more on details of different aspects of improving energy efficiency by reducing environmental, economic, social, and other impacts. A few articles proposed multiple-criteria decision-making methods to solve energy efficiency gaps for promoting sustainability in buildings. Achieving energy efficiency toward sustainable buildings is a hot topic in the sustainable development area. The outcomes from this paper will provide a valuable reference to stakeholders, governments, and decision-makers and give suggestions from the selected past studies. This review will provide motivation and attract future research endeavors in the field.

This paper describes the design and performance of a 6-kW, full-bridge, bidirectional isolated dc-dc converter using a 20-kHz transformer for a 53.2-V, 2-kWh lithium-ion (Li-ion) battery energy storage system. The dc voltage at the high-voltage side is controlled from 305 to 355 V, as the battery voltage at the low-voltage side (LVS) varies from 50 to 59 V. The maximal efficiency of the dc-dc converter is measured to be 96.0% during battery charging, and 96.9% during battery discharging. Moreover, this paper analyzes the effect of unavoidable dc-bias currents on the magnetic-flux saturation of the transformer. Finally, it provides the dc-dc converter loss breakdown with more focus on the LVS converter.

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%.

Hydroxyapatite (HA) has been used clinically for many years. It has good biocompatibility in bone contact as its chemical composition is similar to that of bone material. Porous HA ceramics have found enormous use in biomedical applications including bone tissue regeneration, cell proliferation, and drug delivery. In bone tissue engineering it has been applied as filling material for bone defects and augmentation, artificial bone graft material, and prosthesis revision surgery. Its high surface area leads to excellent osteoconductivity and resorbability providing fast bone ingrowth. Porous HA can be produced by a number of methods including conversion of natural bones, ceramic foaming technique, polymeric sponge method, gel casting of foams, starch consolidation, microwave processing, slip casting, and electrophoretic deposition technique. Some of these methods have been combined to fabricate porous HA with improved properties. These combination methods have yielded some promising results. This paper discusses briefly fundamental aspects of porous HA for artificial bone applications as well as various techniques used to prepare porous HA. Some of our recent results on development of porous HA will be presented as well.

The high demand for plastic and polymeric materials which keeps rising every year makes them important industries, for which sustainability is a crucial aspect to be taken into account. Therefore, it becomes a requirement to makes it a clean and eco-friendly industry. Cellulose creates an excellent opportunity to minimize the effect of non-degradable materials by using it as a filler for either a synthesis matrix or a natural starch matrix. It is the primary substance in the walls of plant cells, helping plants to remain stiff and upright, and can be found in plant sources, agriculture waste, animals, and bacterial pellicle. In this review, we discussed the recent research development and studies in the field of biocomposites that focused on the techniques of extracting micro- and nanocellulose, treatment and modification of cellulose, classification, and applications of cellulose. In addition, this review paper looked inward on how the reinforcement of micro- and nanocellulose can yield a material with improved performance. This article featured the performances, limitations, and possible areas of improvement to fit into the broader range of engineering applications.