Tun Hussein Onn University of Malaysia

Digital fabrication technology, also referred to as 3D printing or additive manufacturing, creates physical objects from a geometrical representation by successive addition of materials. 3D printing technology is a fast-emerging technology. Nowadays, 3D Printing is widely used in the world. 3D printing technology increasingly used for the mass customization, production of any types of open source designs in the field of agriculture, in healthcare, automotive industry, locomotive industry and aviation industries. 3D printing technology can print an object layer by layer deposition of material directly from a computer aided design (CAD) model. This paper presents the overview of the types of 3D printing technologies, the application of 3D printing technology and lastly, the materials used for 3D printing technology in manufacturing industry.

Microalgae can be used as a source of alternative food, animal feed, biofuel, fertilizer, cosmetics, nutraceuticals and for pharmaceutical purposes. The extraction of organic constituents from microalgae cultivated in the different nutrient compositions is influenced by microalgal growth rates, biomass yield and nutritional content in terms of lipid and fatty acid production. In this context, nutrient composition plays an important role in microalgae cultivation, and depletion and excessive sources of this nutrient might affect the quality of biomass. Investigation on the role of nitrogen and phosphorus, which are crucial for the growth of algae, has been addressed. However, there are challenges for enhancing nutrient utilization efficiently for large scale microalgae cultivation. Hence, this study aims to highlight the level of nitrogen and phosphorus required for microalgae cultivation and focuses on the benefits of nitrogen and phosphorus for increasing biomass productivity of microalgae for improved lipid and fatty acid quantities. Furthermore, the suitable extraction methods that can be used to utilize lipid and fatty acids from microalgae for biofuel have also been reviewed.

Waste materials from natural sources are important resources for extraction and recovery of valuable compounds. Transformation of these waste materials into valuable materials requires specific techniques and approaches. Hydroxyapatite (HAp) is a biomaterial that can be extracted from natural wastes. HAp has been widely used in biomedical applications owing to its excellent bioactivity, high biocompatibility, and excellent osteoconduction characteristics. Thus, HAp is gaining prominence for applications as orthopaedic implants and dental materials. This review summarizes some of the recent methods for extraction of HAp from natural sources including mammalian, aquatic or marine sources, shell sources, plants and algae, and from mineral sources. The extraction methods used to obtain hydroxyapatite are also described. The effect of extraction process and natural waste source on the critical properties of the HAp such as Ca/P ratio, crystallinity and phase assemblage, particle sizes, and morphology are discussed herein.

Scholars believe that the newly introduced Industry 5.0 has the potential to move beyond the profit-centered productivity of Industry 4.0 and to promote sustainable development goals such as human-centricity, socio-environmental sustainability, and resilience. However, little has been done to understand how this ill-defined phenomenon may deliver its indented sustainability values despite these speculative promises. To address this knowledge gap, the present study developed a strategy roadmap that explains the mechanism by which Industry 5.0 delivers its intended sustainable development functions. The study first developed and introduced the Industry 5.0 reference model that describes the technical and functional properties of this phenomenon. The study further conducted a content-centric synthesis of the literature and identified the sustainable development functions of Industry 5.0. Next, the interpretive structural modeling (ISM) technique was employed to identify the sequential relationships among the functions and construct the Industry 5.0-enabled model of sustainable development. The ISM involved collecting the opinions of 11 Industry 5.0 experts through expert panel meetings. Results revealed that Industry 5.0 delivers sustainable development values through 16 functions. Circular intelligent products, employee technical assistance, intelligent automation, open sustainable innovation, renewable integration, and supply chain adaptability are examples of the functions identified. These functions are highly interrelated and should be developed in a specific order so that the synergies and complementarities among them would maximize the sustainable development value gains. The roadmap to Industry 5.0-driven sustainability developed in this study is expected to provide a better understanding of ways Industry 5.0 can contribute to sustainable development, explaining how the development of its functions should be managed to maximize their synergies and contribution to the intended sustainability values. The study also highlights important avenues for future research, emphasizing the potential enablers of Industry 5.0 development, such as Government 5.0 or Corporate Governance 5.0.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

Leadership stains affect the follower’s performances regarding innovative work behavior, and a gap is found in leadership research in higher education, specifically in Pakistan. The basic purpose of this research is to point out the effect of leadership styles on innovative work behavior under the mediating and moderating roles of organizational culture and organizational citizenship behavior among the Head of the Departments (HODs) in higher education institutions (HEIs). A survey method has been carried out to collect data from 160 respondents to, further, verify how leadership styles of academic leaders affect employees’ performances in universities. The statistical study exposes a substantial positive effect of leadership styles on innovative work behaviors of employees highlighting mediating and moderating effects of organizational culture and OCB on such a relationship. This study carries various implications for prior research in both theoretical and practical fields, and its scope may also be enlarged, geographically or institutionally, to another context. This research uncovers the relationship of leadership styles and innovative work behavior in academic research, which has been ignored before in higher education of Pakistan.

This paper presents a survey on the state-of-the-art of timber-concrete composite research in the past and recent years. The most important literature references were carefully selected and reviewed to provide an overview and some depth in the development of this construction technique. After highlighting the advantages of the composite system, the standards and design methods currently available are presented. An extensive description of the connection systems developed around the world is also provided. The experimental and numerical investigations performed on connections and beams in both the short- and long-term (at collapse and under sustained load, respectively) are discussed at length in the paper. Other aspects covered are prefabrication, the influence of concrete properties, fatigue tests, fire resistance, vibrations, and acoustics.

Higher order thinking skills is an important aspect in teaching and learning especially at higher education institutions. Thinking skills practices are part of the generic skills that should be infused in all technical subjects. Students with higher order thinking skills are able to learn, improve their performance and reduce their weaknesses. Hence, the purpose of this research was to identify the level of Marzano Higher Order Thinking Skills among technical education students in the Faculty of Technical Education (FPTek), Universiti Tun Hussein Onn Malaysia. A total of 158 students of FPTek were randomly selected as sample. A set of questionnaires adapted from Marzano Rubrics for Specific Task or Situations (1993) was used as research instrument. This is a quantitative research and the gathered data was analyzed using Statistical Package for Social Science (SPSS) software. The findings indicated that students perceived they have moderate level for investigation, experimental inquiry, comparing, deducing, constructing support, inducing and invention. However, decision making, problem solving, error analyzing, abstracting, analyzing perspectives and classifying are at low level. The Eta analysis indicated that there is a very low positive relationship between the level of Marzano Higher Order Thinking Skills and gender, academic achivement as well as socio economic status. Besides that, the findings also showed that there is no statistically significant difference in gender, academic achivement and socio economic status on the level of Marzano Higher Order Thinking Skills. However, there is significant difference in socio economic status on the level of decision making.

This tutorial review considers defect chemistry of TiO2 and its solid solutions as well as defect-related properties associated with solar-to-chemical energy conversion, such as Fermi level, bandgap, charge transport and surface active sites. Defect disorder is discussed in terms of defect reactions and the related charge compensation. Defect equilibria are used in derivation of defect diagrams showing the effect of oxygen activity and temperature on the concentration of both ionic and electronic defects. These defect diagrams may be used for imposition of desired semiconducting properties that are needed to maximize the performance of TiO2-based photoelectrodes for the generation of solar hydrogen fuel using photo electrochemical cells (PECs) and photocatalysts for water purification. The performance of the TiO2-based semiconductors is considered in terms of the key performance-related properties (KPPs) that are defect related. It is shown that defect engineering may be applied for optimization of the KPPs in order to achieve optimum performance.

One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

Nanoparticles are defined as ultrafine particles sized between 1 and 100 nanometres in diameter. In recent decades, there has been wide scientific research on the various uses of nanoparticles in construction, electronics, manufacturing, cosmetics, and medicine. The advantages of using nanoparticles in construction are immense, promising extraordinary physical and chemical properties for modified construction materials. Among the many different types of nanoparticles, titanium dioxide, carbon nanotubes, silica, copper, clay, and aluminium oxide are the most widely used nanoparticles in the construction sector. The promise of nanoparticles as observed in construction is reflected in other adoptive industries, driving the growth in demand and production quantity at an exorbitant rate. The objective of this study was to analyse the use of nanoparticles within the construction industry to exemplify the benefits of nanoparticle applications and to address the short-term and long-term effects of nanoparticles on the environment and human health within the microcosm of industry so that the findings may be generalised. The benefits of nanoparticle utilisation are demonstrated through specific applications in common materials, particularly in normal concrete, asphalt concrete, bricks, timber, and steel. In addition, the paper addresses the potential benefits and safety barriers for using nanomaterials, with consideration given to key areas of knowledge associated with exposure to nanoparticles that may have implications for health and environmental safety. The field of nanotechnology is considered rather young compared to established industries, thus limiting the time for research and risk analysis. Nevertheless, it is pertinent that research and regulation precede the widespread adoption of potentially harmful particles to mitigate undue risk.

The next generation (NG) optical technologies will unveil certain unique features, namely ultra-high data rate, broadband multiple services, scalable bandwidth, and flexible communications for manifold end-users. Among the optical technologies, free space optical (FSO) technology is a key element to achieve free space data transmission according to the requirements of the future technologies, which is due to its cost effective, easy deployment, high bandwidth enabler, and high secured. In this article, we give the overview of the recent progress on FSO technology and the factors that will lead the technology towards ubiquitous application. As part of the review, we provided fundamental concepts across all types of FSO system, including system architecture comprising of single beam and multiple beams. The review is further expanded into the investigation of rain and haze effects toward FSO signal propagation. The final objective that we cover is the scalability of an FSO network via the implementations of hybrid multi-beam FSO system with wavelength division multiplexing (WDM) technology.

The sustainability of petroleum-based fuel supply has gained broad attention from the global community due to the increase of usage in various sectors, depletion of petroleum resources, and uncertain around crude oil market prices. Additionally, environmental problems have also arisen from the increasing emissions of harmful pollutants and greenhouse gases. Therefore, the use of clean energy sources including biodiesel is crucial. Biodiesel is mainly produced from unlimited natural resources through a transesterification process. It presents various advantages over petro-diesel; for instance, it is non-toxic, biodegradable, and contains less air pollutant per net energy produced with low sulphur and aromatic content, apart from being safe. Considering the importance of this topic, this paper focuses on the use of palm oil, its by-products, and mill effluent for biodiesel production. Palm oil is known as an excellent raw material because biodiesel has similar properties to the regular petro-diesel. Due to the debate on the usage of palm oil as food versus fuel, extensive studies have been conducted to utilise its by-products and mill effluent as raw materials. This paper also discusses the properties of biodiesel, the difference between palm-biodiesel and other biodiesel sources, and the feasibility of using palm oil as a primary source for future alternative and sustainable energy sources.

This paper discussed how the applying of Rasch Model in validity and reliability of research instruments. Three sets of research instruments were developed in this study. The Felder-Solomon Index of Learning Styles (ILS) is essential to find out the learning style abilities of learners. Students’ Perception in Cognitive Dimension (SPCD) was developed to identify student perception toward their cognitive abilities, and Students’ Cognitive Mastery Achievement Test (CMAT) is used to measure student mastery in a particular subject. The study aims to produce empirical evidence of validity and reliability using the Rasch Model. A small survey was conducted on 28 vocational college students enrolled in the Building Construction course. The ILS consists of four constructs, whereas the SPCD and CMAT validate based on three constructs. The value of reliability was based on Cronbach alpha with appropriate values range. The construct validity was analyzed based on the Rasch model with infit and outfit mean square (MNSQ) value. Three experts in the building construction subject examined the content validity of SPCD and CMAT. Assessor agreement can be calculated as percent-agreement. Percent-agreement statistics can be calculated and explained easily. In summary, Rasch Model is suitable to apply in instrument validation process because the concept of item response theory.

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Abstract A scalable approach for the mass production of chemically modified graphene has yet to be developed, which holds the key to the large‐scale production of stable graphene colloids for optical electronics, energy conversion, and storage materials, catalysis, sensors, composites, etc. Here, a facile approach to fabricating covalently modified graphene and its polymer nanocomposites is presented. The method involves: i) employing a common furnace, rather than a furnace installed with a quartz tube and operated in inert gas as required in previous studies, to treat a commercial graphite intercalation compound with thermal shocking and ultrasonication and fabricate graphene platelets (GnPs) with a thickness of 2.51 ± 0.39 nm that contain only 7 at% oxygen; ii) grafting these GnPs with a commercial, long‐chain surfactant, which is able to create molecular entanglement with polymer matrixes by taking advantage of the reactions between the epoxide groups of the platelets and the end amine groups of the surfactant, to produce chemically modified graphene platelets ( m ‐ GnPs); and iii) solution‐mixing m ‐GnPs with a commonly used polymer to fabricate nanocomposites. These m ‐GnPs are well dispersed in a polymer with highly improved mechanical properties and a low percolation threshold of electrical conductivity at 0.25 vol%. This novel approach could lead to the future scalable production of graphene and its nanocomposites.

Emerging pollutants (EPs), also known as micropollutants, have been a major issue for the global population in recent years as a result of the potential threats they bring to the environment and human health. Pharmaceuticals and personal care products (PPCPs), antibiotics, and hormones that are used in great demand for health and cosmetic purposes have rapidly culminated in the emergence of environmental pollutants. EPs impact the environment in a variety of ways. EPs originate from animal or human sources, either directly discharged into waterbodies or slowly leached via soils. As a result, water quality will deteriorate, drinking water sources will be contaminated, and health issues will arise. Since drinking water treatment plants rely on water resources, the prevalence of this contamination in aquatic environments, particularly surface water, is a severe problem. The review looks into several related issues on EPs in water environment, including methods in removing EPs. Despite its benefits and downsides, the EPs treatment processes comprise several approaches such as physico-chemical, biological, and advanced oxidation processes. Nonetheless, one of the membrane-based filtration methods, ultrafiltration, is considered as one of the technologies that promises the best micropollutant removal in water. With interesting properties including a moderate operating manner and great selectivity, this treatment approach is more popular than conventional ones. This study presents a comprehensive summary of EP’s existence in the environment, its toxicological consequences on health, and potential removal and treatment strategies.

Inflammation is the main key role in developing chronic diseases including cancer, cardiovascular diseases, diabetes, arthritis, and neurodegenerative diseases which possess a huge challenge for treatment. With massively compelling evidence of the role played by nutritional modulation in preventing inflammation-related diseases, there is a growing interest into the search for natural functional foods with therapeutic and preventive actions. Honey, a nutritional healthy product, is produced mainly by two types of bees: honeybee and stingless bee. Since both types of honey possess distinctive phenolic and flavonoid compounds, there is recently an intensive interest in their biological and clinical actions against inflammation-mediated chronic diseases. This review shed the light specifically on the bioavailability and bioaccessibility of honey polyphenols and highlight their roles in targeting inflammatory pathways in gastrointestinal tract disorders, edema, cancer, metabolic and cardiovascular diseases and gut microbiota.

Abstract MXene is a recently emerged multifaceted two-dimensional (2D) material that is made up of surface-modified carbide, providing its flexibility and variable composition. They consist of layers of early transition metals (M), interleaved with n layers of carbon or nitrogen (denoted as X) and terminated with surface functional groups (denoted as T x /T z ) with a general formula of M n+1 X n T x , where n = 1–3. In general, MXenes possess an exclusive combination of properties, which include, high electrical conductivity, good mechanical stability, and excellent optical properties. MXenes also exhibit good biological properties, with high surface area for drug loading/delivery, good hydrophilicity for biocompatibility, and other electronic-related properties for computed tomography (CT) scans and magnetic resonance imaging (MRI). Due to the attractive physicochemical and biocompatibility properties, the novel 2D materials have enticed an uprising research interest for application in biomedicine and biotechnology. Although some potential applications of MXenes in biomedicine have been explored recently, the types of MXene applied in the perspective of biomedical engineering and biomedicine are limited to a few, titanium carbide and tantalum carbide families of MXenes. This review paper aims to provide an overview of the structural organization of MXenes, different top-down and bottom-up approaches for synthesis of MXenes, whether they are fluorine-based or fluorine-free etching methods to produce biocompatible MXenes. MXenes can be further modified to enhance the biodegradability and reduce the cytotoxicity of the material for biosensing, cancer theranostics, drug delivery and bio-imaging applications. The antimicrobial activity of MXene and the mechanism of MXenes in damaging the cell membrane were also discussed. Some challenges for in vivo applications, pitfalls, and future outlooks for the deployment of MXene in biomedical devices were demystified. Overall, this review puts into perspective the current advancements and prospects of MXenes in realizing this 2D nanomaterial as a versatile biological tool.

Purpose This study investigates the impact of Industry 4.0 technologies on green innovation performance. In this relationship, the mediating role of green innovation behavior is also studied. Moreover, open innovation is tested as a mediator between Industry 4.0 technologies and green innovation behavior. Design/methodology/approach A quantitative research method is adopted in which a structured questionnaire was used to collect data from 217 manufacturing firms of Malaysia. After collecting data, the partial least squares-structural equation modeling (PLS-SEM) technique is applied to analyze data and test the hypothesis of study. Findings It is found that Industry 4.0 positively impacts open innovation which leads to green innovation behavior. Also, the former lays positive impact on green innovation behavior which leads to improve green innovation performance. Research limitations/implications The authors conclude that Industry 4.0 technologies can play an important role to improve green innovation performance of Malaysian manufacturing firms by managing open innovation for green innovation behavior which further improves the green innovation performance. In this context, it is recommended that strategists and policymakers should undertake the role of open innovation and Industry 4.0 technologies to promote environment-friendly innovations and to promote the green behavior in companies. The authors suggest hereby that firms should be given incentives to adopt and utilize Industry 4.0 technologies and collaborative innovation interactions – as they foster a climate for sustainable green innovations (which is also a key component to achieve competitive advantage) and a growing concern nowadays. Practical implications First of all the research contributes to achieving the broader of United Nations to promote sustainable innovation through green innovations. Moreover, the companies can also incorporate the findings and insights of this study while devising their policies to foster green innovations. Originality/value This research has done the novel contribution by bridging the gap between open innovation approach and sustainability fields while promoting green innovations in small and medium enterprises (SMEs). These two research fields are rarely studied in previous studies by focusing open innovation particularly. Hence, the authors suggest researchers to undertake these fields to further enhance the level of scholarship between innovation management and sustainability. Also, the authors recommend considering technological orientation and technological absorptive capacity of firms to improve green innovations. The current study has investigated the SMEs perspective in general irrespective to their sectoral differences, thus, for future researchers the authors suggest investigating the sector-wise comparison, i.e. electrical and electronics sector, chemical sector, etc.; or service and manufacturing sector differences.