Universiti Teknologi Petronas

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional "live" learning in classrooms and "virtual" learning through online-based educational technologies.

In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed. First, a brief overview of the field of emerging memory technologies is provided. The material properties, resistance switching mechanism, and electrical characteristics of RRAM are discussed. Also, various issues such as endurance, retention, uniformity, and the effect of operating temperature and random telegraph noise (RTN) are elaborated. A discussion on multilevel cell (MLC) storage capability of RRAM, which is attractive for achieving increased storage density and low cost is presented. Different operation schemes to achieve reliable MLC operation along with their physical mechanisms have been provided. In addition, an elaborate description of switching methodologies and current voltage relationships for various popular RRAM models is covered in this work. The prospective applications of RRAM to various fields such as security, neuromorphic computing, and non-volatile logic systems are addressed briefly. The present review article concludes with the discussion on the challenges and future prospects of the RRAM.

Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Recent research has given a better understanding of the requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of populations with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. It constitutes typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.

Graphene oxide (GO) and reduced graphene oxide (RGO) are known to have superior properties for various applications. This work compares the properties of GO and RGO with graphite. GO was prepared by using Improved Hummer’s method whereas the produced GO was subjected to chemical reduction with the use of hydrazine hydrate. Graphite, GO and RGO had different morphologies, quality, functionalized groups, UV-Vis absorption peaks and crystallinity. With the removal of oxygen-containing functional group during reduction for RGO, the quality of samples was decreased due to higher intensity of D band than G band was seen in Raman results. In addition, platelet-like surface can be observed on the surface of graphite as compared to GO and RGO where wrinkled and layered flakes, and crumpled thin sheets were observed on GO and RGO surface respectively. Fourier Transform Infra-Red (FTIR) analysis showed the presence of abundant oxygen-containing functional groups in GO as compared to RGO and graphite. The characteristic peaks at 26.62°, 9.03° and 24.10° for graphite, GO and RGO, respectively, can be detected from X-Ray diffraction (XRD). Furthermore, the reduction also caused red shift at 279nm from 238nm, as obtained from ultraviolet visible (UV-Vis) analysis. The results proved that GO was successfully oxidized from graphite whereas RGO was effectively reduced from GO.

The richness of high-value bio-compounds derived from microalgae has made microalgae a promising and sustainable source of useful product. The present work starts with a review on the usage of open pond and photobioreactor in culturing various microalgae strains, followed by an in-depth evaluation on the common harvesting techniques used to collect microalgae from culture medium. The harvesting methods discussed include filtration, centrifugation, flocculation, and flotation. Additionally, the advanced extraction technologies using ionic liquids as extractive solvents applied to extract high-value bio-compounds such as lipids, carbohydrates, proteins, and other bioactive compounds from microalgae biomass are summarized and discussed. However, more work needs to be done to fully utilize the potential of microalgae biomass for the application in large-scale production of biofuels, food additives, and nutritive supplements.

A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.

Over the last twenty years, remarkable advances have taken place in the research and application of ultra-high performance concrete (UHPC), which exhibits excellent rheological behaviors that include workability, self-placing and self-densifying properties, improved in mechanical and durability performance with very high compressive strength, and non-brittleness behavior. It is the ‘future’ material with the potential to be a viable solution for improving the sustainability of buildings and other infrastructure components. This paper will give an overview of UHPC focusing on its fundamental introduction, design, applications and challenges. After several decades of development, a wide range of commercial UHPC formulations have been developed worldwide to cover an increasing number of applications and the rising demand of quality construction materials. UHPC has several advantages over conventional concrete but the use of it is limited due to the high cost and limited design codes. This paper also aims to help designers, engineers, architects, and infrastructure owners to expand the awareness of UHPC for better acceptance.

Over the last couple of decades, the advancement in Microelectromechanical System (MEMS) devices is highly demanded for integrating the economically miniaturized sensors with fabricating technology. A sensor is a system that detects and responds to multiple physical inputs and converting them into analogue or digital forms. The sensor transforms these variations into a form which can be utilized as a marker to monitor the device variable. MEMS exhibits excellent feasibility in miniaturization sensors due to its small dimension, low power consumption, superior performance, and, batch-fabrication. This article presents the recent developments in standard actuation and sensing mechanisms that can serve MEMS-based devices, which is expected to revolutionize almost many product categories in the current era. The featured principles of actuating, sensing mechanisms and real-life applications have also been discussed. Proper understanding of the actuating and sensing mechanisms for the MEMS-based devices can play a vital role in effective selection for novel and complex application design.

Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequential data with long-term dependencies. Despite its popularity, the challenge of effectively initializing and optimizing RNN-LSTM models persists, often hindering their performance and accuracy. This study presents a systematic literature review (SLR) using an in-depth four-step approach based on the PRISMA methodology, incorporating peer-reviewed articles spanning 2018-2023. It aims to address how weight initialization and optimization techniques can bolster RNN-LSTM performance. This SLR offers a detailed overview across various applications and domains, and stands out by comprehensively analyzing modeling techniques, datasets, evaluation metrics, and programming languages associated with RNN-LSTM networks. The findings of this SLR provide a roadmap for researchers and practitioners to enhance RNN-LSTM networks and achieve superior results.

Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition.

Rainfall-induced slope failure is a common geotechnical problem in the tropics where residual soils are abundant. Although the significance of rainwater infiltration in causing landslides is widely recognized, there have been different conclusions as to the relative roles of antecedent rainfall to landslides. The relative importance of soil properties, rainfall intensity, initial water table location and slope geometry in inducing instability of a homogenous soil slope under different rainfall was investigated through a series of parametric studies. Soil properties and rainfall intensity were found to be the primary factors controlling the instability of slopes due to rainfall, while the initial water table location and slope geometry only played a secondary role. The results from the parametric studies also indicated that for a given rainfall duration, there was a threshold rainfall intensity which would produce the global minimum factor of safety. Attempts have also been made to relate the findings from this study to those observed in the field by other researchers. Results of this parametric study clearly indicated that the significance of antecedent rainfall depends on soil permeability.

Global issues such as environmental problems and food security are currently of concern to all of us. Circular bioeconomy is a promising approach towards resolving these global issues. The production of bioenergy and biomaterials can sustain the energy-environment nexus as well as substitute the devoid of petroleum as the production feedstock, thereby contributing to a cleaner and low carbon environment. In addition, assimilation of waste into bioprocesses for the production of useful products and metabolites lead towards a sustainable circular bioeconomy. This review aims to highlight the waste biorefinery as a sustainable bio-based circular economy, and, therefore, promoting a greener environment. Several case studies on the bioprocesses utilising waste for biopolymers and bio-lipids production as well as bioprocesses incorporated with wastewater treatment are well discussed. The strategy of waste biorefinery integrated with circular bioeconomy in the perspectives of unravelling the global issues can help to tackle carbon management and greenhouse gas emissions. A waste biorefinery-circular bioeconomy strategy represents a low carbon economy by reducing greenhouse gases footprint, and holds great prospects for a sustainable and greener world.

The rising world population and its corresponding energy demands pose a considerable burden on natural energy sources. The exploitation of fossil fuels at such an alarming rate blurs the goals of sustainable development and controlling global warming as pledged during the Paris Agreement. Due to the detrimental effects of exhausts from conventional diesel fuel on the environment, biodiesel has earned significant importance during the last decade. Biodiesel is produced from different feedstocks such as neem oil, palm oil, waste frying oil, vegetable oil, animal fat, microbial oil, etc. These feedstocks react with acidic, alkaline, enzymic, homogeneous, heterogeneous, and hybrid Deep Eutectic Solvents (DES) catalysts, along with monohydric alcohol via transesterification reaction. The flexibility in its feedstock and the type of catalysts used, production cost, biodegradable and renewable nature makes it a promising alternative fuel than conventional diesel. The selection of apt feedstock and catalyst is the challenging task and governing factor of economic biodiesel production. Green solvents such as DES have high thermal stability and low volatility and can address the economic and green production issues significantly as compared to conventional alkali and acid catalysts. This review bridges the gap between the selection of feedstock and optimal catalyst for the respective feedstock. The exploration of DES fills the gap by attributing to 3Rs (i.e., recyclability, recovery, and reusability). This review highlights the contemporary trends and prospects in the selection of the feedstocks, synthesis routes, and catalysts for the transesterification reactions for biodiesel production.

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

Graphene has emerged as the most popular topic in the active research field since graphene's discovery in 2004 by Andrei Geim and Kostya Novoselov.

Large-scale, self-organizing wireless sensor and mesh network deployments are being driven by recent technological developments such as The Internet of Things (IoT), Smart Grids and Smart Environment applications. Efficient use of the limited energy resources of wireless sensor network (WSN) nodes is critically important to support these advances, and application of topology control methods will have a profound impact on energy efficiency and hence battery lifetime. In this survey, we focus on the energy efficiency issue and present a comprehensive study of topology control techniques for extending the lifetime of battery powered WSNs. First, we review the significant topology control algorithms to provide insights into how energy efficiency is achieved by design. Further, these algorithms are classified according to the energy conservation approach they adopt, and evaluated by the trade-offs they offer to aid designers in selecting a technique that best suits their applications. Since the concept of "network lifetime" is widely used for assessing the algorithms' performance, we highlight various definitions of the term and discuss their merits and drawbacks. Recently, there has been growing interest in algorithms for non-planar topologies such as deployments in underwater environments or multi-level buildings. For this reason, we also include a detailed discussion of topology control algorithms that work efficiently in three dimensions. Based on the outcomes of our review, we identify a number of open research issues for achieving energy efficiency through topology control.

A range of numerous corrosion inhibitors, viz. organic molecules with hetero atoms and π-electron clouds, inorganic salts and plant excerpts likewise their corresponding phytoconstituents were reported with success for metals in different corrosive media. Various literature reviews related to corrosion inhibitors have been reported by many authors based on their application, classification, and inhibition mechanism. A short view of all these reviews is summarized in this manuscript. Various aspects of corrosion inhibitors as well as their recent trends and advancement are also discussed.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

The drinking water quality was investigated in suspected parts of Perak state, Malaysia, to ensure the continuous supply of clean and safe drinking water for the public health protection. In this regard, a detailed physical and chemical analysis of drinking water samples was carried out in different residential and commercial areas of the state. A number of parameters such as pH, turbidity, conductivity, total suspended solids (TSS), total dissolved solids (TDS), and heavy metals such as Cu, Zn, Mg, Fe, Cd, Pb, Cr, As, Hg, and Sn were analysed for each water sample collected during winter and summer periods. The obtained values of each parameter were compared with the standard values set by the World Health Organization (WHO) and local standards such as National Drinking Water Quality Standard (NDWQS). The values of each parameter were found to be within the safe limits set by the WHO and NDWQS. Overall, the water from all the locations was found to be safe as drinking water. However, it is also important to investigate other potential water contaminations such as chemicals and microbial and radiological materials for a longer period of time, including human body fluids, in order to assess the overall water quality of Perak state.

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.