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Showing papers by "Universiti Teknologi Malaysia published in 2020"


Journal ArticleDOI
TL;DR: An evaluation on how the future EV development, such as connected vehicles, autonomous driving, and shared mobility, would affect EV grid integration as well as the development of the power grid moves toward future energy Internet is presented.
Abstract: Transportation electrification is one of the main research areas for the past decade. Electric vehicles (EVs) are taking over the market share of conventional internal combustion engine vehicles. The increasing popularity of EVs results in higher number of charging stations, which have significant effects on the electricity grid. Different charging strat2egies, as well as grid integration methods, are being developed to minimize the adverse effects of EV charging and to strengthen the benefits of EV grid integration. In this paper, a comprehensive review of the current situation of the EV market, standards, charging infrastructure, and the impact of EV charging on the grid is presented. The paper introduces the current EV status, and provides a comprehensive review on important international EV charging and grid interconnection standards. Different infrastructure configurations in terms of control and communication architectures for EV charging are studied and evaluated. The electric power market is studied by considering the participation roles of EV aggregators and individual EV owners, and different optimization and game based algorithms for EV grid integration management are reviewed. The paper specially presents an evaluation on how the future EV development, such as connected vehicles, autonomous driving, and shared mobility, would affect EV grid integration as well as the development of the power grid moves toward future energy Internet and how EVs would affect and benefit the development of the future energy Internet. Finally, the challenges and suggestions for the future development of the EV charging and grid integration infrastructure are evaluated and summarized.

417 citations


Journal ArticleDOI
TL;DR: This review discussed about the green biosynthesis of magnetite nanoparticles (Fe3O4-NPs) and the biomedical applications, which mainly focus on the targeted anticancer drug delivery, and many researches showed the promising results of Fe3O 4-Nps in treating cancer cells via in vitro study.

274 citations


Journal ArticleDOI
TL;DR: The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rates, specificity, and accuracy for the training and validation datasets.

256 citations


Journal ArticleDOI
TL;DR: In this paper, the authors provide insights into recent approaches of various types of materials and methods applied in oily wastewater treatment for hydrophilic membranes and the methods used to reduce the fouling issues within the membrane for oil/water emulsion separation.

254 citations


Journal ArticleDOI
TL;DR: The present review thoroughly summarized the classification, synthesis route of Fe 2O3 with different morphologies, and several modifications of Fe2O3 for improved photocatalytic performance, including the incorporation with supporting materials, formation of heterojunction with other semiconductors, as well as the fabrication of Z-scheme.

245 citations


Journal ArticleDOI
TL;DR: The lockdown of anthropogenic activities due to COVID-19 has led to a notable decrease in AOD over SEA and in the pollution outflow over the oceanic regions, while a significant decrease in tropospheric NO2 was observed over areas not affected by seasonal biomass burning.

232 citations


Journal ArticleDOI
TL;DR: This review is an effort to provide a recent update into the diversity of genes in Acidobacteria useful for characterization, understanding ecological roles, and future biotechnological perspectives.
Abstract: Acidobacteria represents an underrepresented soil bacterial phylum whose members are pervasive and copiously distributed across nearly all ecosystems. Acidobacterial sequences are abundant in soils and represent a significant fraction of soil microbial community. Being recalcitrant and difficult-to-cultivate under laboratory conditions, holistic, polyphasic approaches are required to study these refractive bacteria extensively. Acidobacteria possesses an inventory of genes involved in diverse metabolic pathways, as evidenced by their pan-genomic profiles. Because of their preponderance and ubiquity in the soil, speculations have been made regarding their dynamic roles in vital ecological processes viz., regulation of biogeochemical cycles, decomposition of biopolymers, exopolysaccharide secretion, and plant growth promotion. These bacteria are expected to have genes that might help in survival and competitive colonization in the rhizosphere, leading to the establishment of beneficial relationships with plants. Exploration of these genetic attributes and more in-depth insights into the belowground mechanics and dynamics would lead to a better understanding of the functions and ecological significance of this enigmatic phylum in the soil-plant environment. This review is an effort to provide a recent update into the diversity of genes in Acidobacteria useful for characterization, understanding ecological roles, and future biotechnological perspectives.

229 citations


Journal ArticleDOI
TL;DR: This review summarizes the utilization of different surface functional groups, such as oxygen-containing, nitrogen- containing, and sulphur-containing functionalized graphene oxide composites in the adsorption of cationic and oxyanionic heavy metals.

226 citations



Journal ArticleDOI
TL;DR: This review focuses on recent developments of green synthesized AuNPs and discusses their numerous biomedical applications, and sources of green materials with successful examples and other key parameters that determine the functionalities of AuNPS are discussed.
Abstract: Gold nanoparticles (AuNPs) are extensively studied nanoparticles (NPs) and are known to have profound applications in medicine. There are various methods to synthesize AuNPs which are generally categorized into two main types: chemical and physical synthesis. Continuous efforts have been devoted to search for other more environmental-friendly and economical large-scale methods, such as environmentally friendly biological methods known as green synthesis. Green synthesis is especially important to minimize the harmful chemical and toxic by-products during the conventional synthesis of AuNPs. Green materials such as plants, fungi, microorganisms, enzymes and biopolymers are currently used to synthesize various NPs. Biosynthesized AuNPs are generally safer for use in biomedical applications since they come from natural materials themselves. Multiple surface functionalities of AuNPs allow them to be more robust and flexible when combined with different biological assemblies or modifications for enhanced applications. This review focuses on recent developments of green synthesized AuNPs and discusses their numerous biomedical applications. Sources of green materials with successful examples and other key parameters that determine the functionalities of AuNPs are also discussed in this review.

202 citations


Journal ArticleDOI
TL;DR: In this paper, the influence of Green Human Resource Management (HRM) practices (green competence building practices, green motivation enhancing practices, and green employee involvement practices) on the organisational citizenship behaviour towards the environment (OCBE) of academic staff and its impact on the environmental performance is assessed.

Journal ArticleDOI
TL;DR: In this article, the authors investigated the factors influencing the adoption of green innovation, and its potential effects on the performance of the hotel industry, and determined the two factors of environmental and economic performance were determined to have the strongest influence, affecting green innovation procedures positively and significantly.

Journal ArticleDOI
TL;DR: This review highlights the therapeutic approaches of using wound dressings functionalized with silver nanoparticles and their potential role in revolutionizing wound healing and the physiology of the skin and wounds is discussed to place the use of Ag-NPs in wound care into perspective.
Abstract: Infections are the main reason why most people die from burns and diabetic wounds. The clinical challenge for treating wound infections through traditional antibiotics has been growing steadily and has now reached a critical status requiring a paradigm shift for improved chronic wound care. The US Centers for Disease Control have predicted more deaths from antimicrobial-resistant bacteria than from all types of cancers combined by 2050. Thus, the development of new wound dressing materials that do not rely on antibiotics is of paramount importance. Currently, incorporating nanoparticles into scaffolds represents a new concept of 'nanoparticle dressing' which has gained considerable attention for wound healing. Silver nanoparticles (Ag-NPs) have been categorized as metal-based nanoparticles and are intriguing materials for wound healing because of their excellent antimicrobial properties. Ag-NPs embedded in wound dressing polymers promote wound healing and control microorganism growth. However, there have been several recent disadvantages of using Ag-NPs to fight infections, such as bacterial resistance. This review highlights the therapeutic approaches of using wound dressings functionalized with Ag-NPs and their potential role in revolutionizing wound healing. Moreover, the physiology of the skin and wounds is discussed to place the use of Ag-NPs in wound care into perspective.

Journal ArticleDOI
TL;DR: In this article, the authors used a meta-analysis to synthesize the effects of destination image from 87 studies and found that overall and affective images have the greatest impact on tourist intention, followed by cognitive image.

Journal ArticleDOI
TL;DR: The potential of adsorbent derived from coffee waste in textile wastewater treatment is revealed and surface chemistry modification is proven as an effective strategy to enhance the performance of biowaste-derived adsorbents.
Abstract: Adsorption of Reactive Black 5 and Congo Red from aqueous solution by coffee waste modified with polyethylenimine was investigated. The removal percentages of both dyes increased with amount of polyethyleneimine in the modified adsorbent. Characterization revealed that polyethyleneimine modification improved the adsorbent surface chemistry, while slight improvement of adsorbent textural properties was also observed. The adsorbent’s excellent performance was demonstrated by high removal percentages towards the anionic dyes in most experimental runs. The modelling result showed that anionic dyes adsorption occurred via monolayer adsorption, and chemisorption was the rate-controlling step. The adsorbent possesses higher maximum adsorption capacity towards Reactive Black 5 (77.52 mg/g) than Congo Red (34.36 mg/g), due to the higher number of functional groups in Reactive Black 5 that interact with the adsorbent. This study reveals the potential of adsorbent derived from coffee waste in textile wastewater treatment. Furthermore, surface chemistry modification is proven as an effective strategy to enhance the performance of biowaste-derived adsorbents.

Journal ArticleDOI
TL;DR: In this article, a review of the challenges of municipal solid waste management, summarizing the health significance of MSW management, explaining the opportunities and requirements of energy recovery from MSW through waste-to-energy (WtE) technologies, explaining several WtE technologies in detail, and discussing the current status of WTE technology in India.

Journal ArticleDOI
TL;DR: In this paper, a review of recent developments in binary semiconductor materials and their application for photocatalytic water splitting toward hydrogen production are systematically discoursed, and the role of sacrificial reagents for efficient photocatalysis through reforming and hole-scavenger are elaborated.

Journal ArticleDOI
TL;DR: The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time.
Abstract: Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

Journal ArticleDOI
TL;DR: In this article, the effectiveness of low impact development (LID) in the mitigation of urban flood is analyzed to identify their limitations and further research on the success of these techniques in urban flood mitigation planning is also recommended.

Journal ArticleDOI
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
Abstract: These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications such as Natural Language Processing (NLP) and image processing, more efforts have been made by the researchers to exploit deep learning methods for improving the performance of RS. However, despite the several research works on deep learning based RS, very few secondary studies were conducted in the field. Therefore, this study aims to provide a systematic literature review (SLR) of deep learning based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the field. This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications. The paper particularly adopts an SLR approach based on the standard guidelines of the SLR designed by Kitchemen-ham which uses selection method and provides detail analysis of the research publications. Several publications were gathered and after inclusion/exclusion criteria and the quality assessment, the selected papers were finally used for the review. The results of the review indicated that autoencoder (AE) models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs) models. Also, the results showed that Movie Lenses is the most popularly used datasets for the deep learning-based RS evaluation followed by the Amazon review datasets. Based on the results, the movie and e-commerce have been indicated as the most common domains for RS and that precision and Root Mean Squared Error are the most commonly used metrics for evaluating the performance of the deep leaning based RSs.

Journal ArticleDOI
TL;DR: In this article, the authors extensively review the dyes and chemicals utilized in the textile industry focusing on the traditional treatment methods for their removal from industrial wastewaters, and a critical analysis on Internet of Things based management systems for their remote monitoring and control of the water quality is reported.

Journal ArticleDOI
TL;DR: Data analysis of 171 Iranian small and medium manufacturing firms revealed that complexity, uncertainty and insecurity, trialability, observability, top management support, organizational readiness, and external support affect significantly on BDA adoption, and the results enable BDA service providers to attract and diffuse BDA in small to medium-sized enterprises.

Journal ArticleDOI
TL;DR: In this article, stable hybrid nanofluids were produced by dispersing graphene nanoplatelets (GnPs) and titanium dioxide (TiO2) in a mixture of distilled water and ethylene glycol (DW/EG) using a two-step method.

Journal ArticleDOI
TL;DR: This review describes the choices of support materials and cross-linkers together with several mechanisms that influence the performance, stabilization and hyperactivation of immobilized enzymes.
Abstract: The primary means of immobilizing enzymes are to boost the enzyme productivity and operational stability, alongside facilitating the reuse of enzymes. Notwithstanding the aforementioned benefits, enzyme immobilization promotes high catalytic activity and stability, convenient handling of enzymes, in addition to their facile separation from reaction mixtures without contaminating the products. This review describes the choices of support materials and cross-linkers together with several mechanisms that influence the performance, stabilization and hyperactivation of immobilized enzymes. Altering enzyme properties often changes the enzyme structure due to random modifications in the behavior, which in some cases can be positive or negative. Future strategy to develop new generations of immobilized enzymes should capitalize on the rapid advances of genetic manipulation, organic chemistry, computational chemistry and bioinformatics, reactor and reaction design. Upcoming efforts to improve enzymes as industrial biocatalysts must consider their development for increased selective promiscuity suitable for multiple biotransformations, either independently or as catalytic cascade processes thereby enhance the cost-effectiveness of the processes.

Journal ArticleDOI
13 May 2020
TL;DR: This review primarily analyzes recent developments in fuel cells technologies and up-to-date modeling for PEMFCs, SOFCs and DMFCs.
Abstract: Energy storage and conversion is a very important link between the steps of energy production and energy consumption. Traditional fossil fuels are a natural and unsustainable energy storage medium with limited reserves and notorious pollution problems, therefore demanding a better choice to store and utilize the green and renewable energies in the future. Energy and environmental problems require a clean and efficient way of using the fuels. Fuel cell functions to efficiently convert oxidant and chemical energy accumulated in the fuel directly into DC electric, with the by-products of heat and water. Fuel cells, which are known as effective electrochemical converters, and electricity generation technology has gained attention due to the need for clean energy, the limitation of fossil fuel resources and the capability of a fuel cell to generate electricity without involving any moving mechanical part. The fuel cell technologies that received high interest for commercialization are polymer electrolyte membrane fuel cells (PEMFCs), solid oxide fuel cells (SOFCs), and direct methanol fuel cells (DMFCs). The optimum efficiency for the fuel cell is not bound by the principle of Carnot cycle compared to other traditional power machines that are generally based on thermal cycles such as gas turbines, steam turbines and internal combustion engines. However, the fuel cell applications have been restrained by the high cost needed to commercialize them. Researchers currently focus on the discovery of different materials and manufacturing methods to enhance fuel cell performance and simplify components of fuel cells. Fuel cell systems’ designs are utilized to reduce the costs of the membrane and improve cell efficiency, durability and reliability, allowing them to compete with the traditional combustion engine. In this review, we primarily analyze recent developments in fuel cells technologies and up-to-date modeling for PEMFCs, SOFCs and DMFCs.

Journal ArticleDOI
TL;DR: This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions, and summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts.
Abstract: A significant growth in solar photovoltaic (PV) installation has observed during the last decade in standalone and grid-connected power generation systems. The solar PV system has a non-linear output characteristic because of weather intermittency, which tends to have a substantial effect on overall PV system output. Hence, to optimize the output of a PV system, different maximum power point tracking (MPPT) techniques have been used. But, the confusion lies while selecting an appropriate MPPT, as every method has its own merits and demerits. Therefore, a proper review of these techniques is essential. A “ Google Scholar ” survey of the last five years (2015-2020) was conducted. It has found that overall seventy-one review articles are published on different MPPT techniques; out of those seventy-one, only four are on uniform solar irradiance, seven on non-uniform and none on hybrid optimization MPPT techniques. Most of them have discussed the limited number of MPPT techniques, and none of them has discussed the online and offline under uniform and hybrid MPPT techniques under non-uniform solar irradiance conditions all together in one. Unfortunately, very few attempts have made in this regard. Therefore, a comprehensive review paper on this topic is need of time, in which almost all the well-known MPPT techniques should be encapsulated in one paper. This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions. It summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts. This research will provide a valuable pathway to researchers, energy engineers, and strategists for future research and implementation in the field of maximum power point tracking optimization.

Journal ArticleDOI
TL;DR: The proposed model fits to real data from Ghana in the time window 12th March 2020 to 7th May 2020, with the aid of python programming language using the least-squares method and formulated based on the sensitivity analysis.
Abstract: COVID-19 potentially threatens the lives and livelihood of people all over the world. The disease is presently a major health concern in Ghana and the rest of the world. Although, human to human transmission dynamics has been established, not much research is done on the dynamics of the virus in the environment and the role human play by releasing the virus into the environment. Therefore, investigating the human-environment-human by use of mathematical analysis and optimal control theory is relatively necessary. The dynamics of COVID-19 for this study is segregated into compartments as: Susceptible (S), Exposed (E), Asymptomatic (A), Symptomatic (I), Recovered (R) and the Virus in the environment/surfaces (V). The basic reproduction number R 0 without controls is computed. The application of Lyapunov’s function is used to analyse the global stability of the proposed model. We fit the model to real data from Ghana in the time window 12th March 2020 to 7th May 2020, with the aid of python programming language using the least-squares method. The average basic reproduction number without controls, R 0 a , is approximately 2.68. An optimal control is formulated based on the sensitivity analysis. Numerical simulation of the model is also done to verify the analytic results. The admissible control set such as: effective testing and quarantine when boarders are opened, the usage of masks and face shields through media education, cleaning of surfaces with home-based detergents, practising proper cough etiquette and fumigating commercial areas; health centers is simulated in MATLAB. We used forward-backward sweep Runge-Kutta scheme which gave interesting results in the main text, for example, the cost-effectiveness analysis shows that, Strategy 4 (safety measures adopted by the asymptomatic and symptomatic individuals such as practicing proper coughing etiquette by maintaining a distance, covering coughs and sneezes with disposable tissues or clothing and washing of hands after coughing or sneezing) is the most cost-effective strategy among all the six control intervention strategies under consideration.

Journal ArticleDOI
TL;DR: An almost complete MB removal could be attained within 4 h, higher than that of the TiO2 alone (30%) under the same conditions, and subsequent biological treatments are unnecessary for completing biodegradation of remaining oxidation by-products in the wastewater effluents.

Journal ArticleDOI
TL;DR: The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher.
Abstract: Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multi-objective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher.

Journal ArticleDOI
TL;DR: Comparisons of the power and effectiveness of five machine learning, benchmark algorithms in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran suggest the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions is recommended.
Abstract: Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naive Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.