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African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.

Blockchain technology has become significant for financial sectors, especially digital payment systems, offering enhanced security, transparency, and efficiency. However, there is limited research on the factors influencing user trust in and acceptance of blockchain adoption in digital payment systems. This systematic review provides insight into the key factors impacting consumers’ perceptions and behaviours towards embracing blockchain technology. A total of 1859 studies were collected, with 48 meeting the criteria for comprehensive analysis. The results showed that security, privacy, transparency, and regulation are the most significant factors influencing trust for blockchain adoption. The most influential factors identified in the Unified Theory of Acceptance and Use of Technology (UTAUT) model include performance expectancy, effort expectancy, social influence, and facilitating conditions. Incorporating a trust and acceptance model could be a viable approach to tackling obstacles and ensuring the successful integration of blockchain technology into digital payment systems. Understanding these factors is crucial for creating a favourable atmosphere for adopting blockchain technology in digital payments. User-perspective research on blockchain adoption in digital payment systems is still insufficient, and this aspect still requires further investigation. Blockchain adoption in digital payment systems has not been sufficiently conducted from the user’s perspective, and there is a scope for it to be carried out. This review aims to shed light on the factors of trust in and acceptance of blockchain adoption in digital payment systems so that the full potential of blockchain technology can be realised. Understanding these factors and their intricate connections is imperative in fostering a conducive environment for the widespread acceptance of blockchain technology in digital payments.

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Bio-waste valorization of Mango Seed (MS) husk to Activated Carbon (AC) was conducted via carbonization followed by chemical activation using KOH for sustainable supercapacitor material. Various physicochemical techniques were used to characterise both MS husk and AC, while electrochemical techniques were used to assess the energy storage capability. Carbonized MS husk was activated at four different temperatures 800, 900, 1000, and 1100 °C. Optimal activation temperature was found to be 1100 °C resulting in a hierarchical porous structure, possessing the highest specific surface area 1943 m2 g−1 and average pore volume 0.397 cm3 g−1, the critical parameters for their energy storage performance. In a three-electrode configuration, the MS-AC sample synthesized at 1100 °C (MS-AC 1100) exhibited a maximum specific capacitance of 135 F g−1 at 5 mA cm−2 with the energy density of 19 Wh kg−1 at the power density of 1077 W kg−1, which is higher than the commercially available AC. The MS-AC 1100 delivered 97% of the capacitance when increasing the current densities from 2 to 10 mA cm−2. The capacitance retention is 100% at 5 mA cm−2 with 1000 cycles. Moreover, Machine Learning (ML) models are used to identify the non-linear pattern for predicting the AC capacitance using various input parameters. Out of the four ML models considered in this work, the Multi-Layer Perceptron (MLP) model outperformed the other models to better correlate the predicted data and the experimental measurements. This study demonstrates the economic perspective of repurposing MS husk bio-waste to a low-cost super-capacitor electrode material and developing high-performance carbon materials to aid the integration of renewable energy into electricity systems.

Porous carbon derived from grape marc (GM) was synthesized via carbonization and chemical activation processes. Extrinsic nitrogen (N)-dopant in GM, activated by KOH, could render its potential use in supercapacitors effective. The effects of chemical activators such as potassium hydroxide (KOH) and zinc chloride (ZnCl2) were studied to compare their activating power toward the development of pore-forming mechanisms in a carbon electrode, making them beneficial for energy storage. GM carbon impregnated with KOH for activation (KAC), along with urea as the N-dopant (KACurea), exhibited better morphology, hierarchical pore structure, and larger surface area (1356 m2 g−1) than the GM carbon activated by ZnCl2 (ZnAC). Moreover, density functional theory (DFT) investigations showed that the presence of N-dopant on a graphite surface enhances the chemisorption of O adsorbates due to the enhanced charge-transfer mechanism. KACurea was tested in three aqueous electrolytes with different ions (LiOH, NaOH, and NaClO4), which delivered higher specific capacitance, with the NaOH electrolyte exhibiting 139 F g−1 at a 2 mA current rate. The NaOH with the alkaline cation Na+ offered the best capacitance among the electrolytes studied. A multilayer perceptron (MLP) model was employed to describe the effects of synthesis conditions and physicochemical and electrochemical parameters to predict the capacitance and power outputs. The proposed MLP showed higher accuracy, with an R2 of 0.98 for capacitance prediction.

In this paper, a model for controlling paddy drying by incorporating accumulated temperature is introduced and defined. Drying experiments using freshly harvested paddy were conducted at different levels of drying air parameters including temperature (T = 27°C, 31°C, 35°C, 39°C, and 43°C), relative humidity (RH = 45%, 50%, 55%, 60%, and 65%), initial moisture content (M0 = 17%, 19%, 21%, 23%, and 25%) and airspeed (V = 0.4, 0.5, 0.6, 0.7, and 0.8 m s−1). When T = 31°C, RH = 60%, M0 = 19%, and V = 0.5 ms−1, the crack additional percentage reached a minimum value of 0.508%, with the average precipitation rate at 0.719%·h. Further, an accumulated temperature and quality chart was provided in this paper. The x-coordinate of this chart is temperature, and the initial moisture content represents the y-coordinate. It covers the drying conditions of the actual dryer well and has a wide range of applicability for real-world environment. The model developed in this study not only provides a scientific reference for precise drying and intelligent control of paddy but also guide the actual drying operation.

Abstract Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia.

EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns.

The success of using any types of technology in education depends on a large extent of the acceptance of information technology (IT) by students. Therefore, understanding the factors influencing the acceptance of electronic book (e-book) is essential for decision-makers and those interested in the e-book industry. Based on an extended technology acceptance model (TAM), this paper examines the impact of some factors on the students’ behavioural intention (BI) toward adoption of the e-book in mathematics and statistics. This paper also investigates the effect of gender differences on the relationship between the factors affecting the acceptance of e-book. A self-administered survey was used to collect data from 392 mathematics and statistics undergraduate students. The research model has shown that the factors related to the social factor and users’ characteristics are the critical factors that affect the acceptance of the e-book. The results also indicated that perceived usefulness (PU), perceived ease of use (PEOU) and students’ attitude (AU) have strongly affected students’ BI. Self-efficacy (SE) has a significant impact on PEOU while social influence (SI) has a significant influence on students’ AU. Moreover, the results confirmed that most of the TAM constructs were significant in both models (males and females), where there are no differences between males and females; however, only PEOU has been affected by the gender moderator. The results showed that the impact of the factor of SI on females was more than males. On the other hand, female students were more confident in the use of the e-book than males. In general, the female students’ model was more powerful in explaining the variance than males’ model.

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

Effective system development to support higher education institutions is important. There are two steps in developing a good system and they are system architecture analysis and design. The first task of this research is to design the use case diagrams, system overview and the system architecture. The second is to evaluate the system architecture of a proposed Student Relationship Management (SRM) system using Internet of Things (IoT) to collect digital footprint of higher education institutions. The outcome of this research include the system architecture of the proposed student relationship management system (SRMS)-IoT consists of six main parts: 1) service stations, 2) system identification, 3) system integration API, 4) SRM internal system, 5) report analytic and 6) web server and database server. Evaluation of the results shows an overall appropriateness at a very high level: the overall appropriateness of usability result was also at a very high level, which shows that this research is appropriate to be used as a guideline for further system development to support student services, and to promote learning and analysis of student behavior in higher education institutions. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to investigate the factors that may impact on the acceptance of the e-book amongst mathematics and statistics (MAS) students at higher education institutions in Libya. Design/methodology/approach - A theoretical model has been developed based on the technology acceptance model (TAM). The factors divided into three groups: intrinsic variables represent the first group of factors that are related to the user or the potential users itself, such as Self-Efficacy and Resistance to Change. Second, extrinsic variables include all the factors that reflect the characteristics of the e-book and the infrastructure for universities, which are Mobility (M), Facilities (F) and Library Service Quality (LS). TAM constructs from Perceived Ease Of Use (PEOU), Perceived Usefulness (PU), Attitude (AU) and Behaviour Intention (BI) represent the last group. Data relating to the constructs were collected through a self-administered survey in 2014. The developed model was measured and tested by structural equation modelling using AMOS (22.0). Findings - The result confirmed that the structural model fits the data well. According to the results, TAM constructs have a significant impact on the acceptance of e-book among MAS students at universities in Libya. In addition, the intrinsic variables play the important role to predict students' BI. Although the extrinsic variables were less important, Facilities was the strong factor in this group. Originality/value - The adoption of e-book amongst higher education students in Libya is still quite low. Hence, identifying the factors that may contribute to e-book adoption is crucial. The contribution of this paper is to determine the factors that effect on the acceptance of e-book amongst MAS students at higher education institutions in Libya. [ABSTRACT FROM AUTHOR]

EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns [ABSTRACT FROM AUTHOR]

Several studies and experiments have been conducted in recent years to examine the value and the advantage of using the Gesture-Based Learning System (GBLS). The investigation of the influence of the GBLS mode on the learning outcomes is still scarce. Most previous studies did not address more than one category of learning outcomes (cognitive, affective outcomes, etc.) at the same time when used to understand the impact of GBLS. Moreover, none of these studies considered the difference in students' characteristics such as learning styles and spatial abilities. Therefore, a comprehensive empirical research on the impact of the GBLS mode on learning outcomes is needed. The purpose of this paper is to fill in the gap and to investigate the effectiveness of the GBLS mode on learning using Technology Mediated Learning (TML) models. This study revealed that the GBLS mode has greater positive impact on students' learning outcomes (cognitive and affective outcomes) when compared with other two learning modes that are classified as Computer Simulation Software Learning (CSSL) mode and conventional learning mode. In addition, this study also found that the GBLS mode is capable of serving all students with different learning styles and spatial ability levels. The results of this study revealed that the GBLS mode outperformed the existing learning methods by providing a unique learning experience that considers the differences between students. The results have also shown that the Kinect user interface can create an interactive and an enjoyable learning experience. [ABSTRACT FROM AUTHOR]

E-learning in higher education has changed the way teaching and learning are conducted, and this has also created new learning opportunities for both on and off campus education. To implement e-learning in higher education institutions successfully, many factors need to be examined. The scope of the subject of e-learning discussed in this paper is limited to internet-based learning, which includes networked learning, distance learning, and online learning. The main purpose of this paper is to review past research in the domain of e-learning in order to identify the Critical Success Factors (CSF) that can affect the successful implementation of e-learning in higher education. This paper presented eight important CSFs commonly found in the review, which are educational technology, computing experience, attitude, social influence, curriculum development, language, teaching and learning styles and demography of the students. [ABSTRACT FROM AUTHOR]