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This scoping review summarises the literature on resilience interventions that use interactive technology. Resilience refers to a person’s mental ability to adaptively deal with challenging situati...

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.

This scoping review examines studies using eye tracking technology to monitor learning and performance in virtual or mixed reality settings. The aim of this review is to describe the variou...

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

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.

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.

Despite evidence promoting simulation platforms as effective modes of learning, virtual simulation is underutilised in Australian Initial Teacher Education (ITE) programs, and as such, casts doubt on the sustainability of next generation simulation platforms. As a case example, we present findings from a systematic review of the literature on the benefits and limitations of Second Life as one of the most widely adopted simulation platforms used in ITE. We reveal salient issues from the literature on Second Life in teacher education that may provide key insight into the sustainability of future simulation platforms in ITE. Grossman's (2009) Pedagogies of practice are used to frame the discussion and offer insight into the opportunities and challenges for educators facilitated by simulation platforms. We recommend broad-based research to capture the transformative pedagogical qualities of simulation platforms and call for an exploration into education policies that both hinder and help the adaptation of next generation simulation platforms within initial teacher education.

PurposeThe 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/approachA 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).FindingsThe 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/valueThe 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.

The effectiveness of Gesture-Based Learning System (GBLS) has been reported in some recent studies. However, not many of those studies have investigated on how GBLS mode influences the learning outcomes. The aim of this study therefore focuses on investigating how GBLS mode impacts the learning outcomes. The findings of this study revealed that GBLS's features positively affect the students' intrinsic motivation. Consequently, the increase in the intrinsic motivation leads to improving the learning outcomes; this study also showed that GBLS's features indirectly influence the learning outcomes via intrinsic motivation. In other words, this study found that the GBLS's features (interactivity and multimodality) create an instructional learning environment that positively influences the students' intrinsic motivation. The increase of the students' positive intrinsic motivation led to enhancing the learning achievements of students.

Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.

The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data.

Recently, extensive use of digital music has led to an increase in songs in online music applications and personal music libraries. In large music libraries, songs which are not listened to regularly, most probably will go unnoticed. There are many ways of browsing songs in an online music library. In the field of Music Information Retrieval (MIR), some type of visual forms such as colour, avatar, mood picture and album cover to visualise music, have been introduced. However, there is no research focusing explicitly on textures. To create new method of browsing music, we proposed a framework to visualise music mood using visual texture. In order to determine how well people can interact with the visual texture to browse songs in music library, usability testing was conducted. In this paper, we will present the results of the usability testing.

Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient problems. While LSTM and other memory networks address this problem, they are not capable of handling long sequences (50 or more data points long sequence patterns). Language modelling requiring learning from longer sequences are affected by the need for more information in memory. This paper introduces Long Term Memory network (LTM), which can tackle the exploding and vanishing gradient problems and handles long sequences without forgetting. LTM is designed to scale data in the memory and gives a higher weight to the input in the sequence. LTM avoid overfitting by scaling the cell state after achieving the optimal results. The LTM is tested on Penn treebank dataset, and Text8 dataset and LTM achieves test perplexities of 83 and 82 respectively. 650 LTM cells achieved a test perplexity of 67 for Penn treebank, and 600 cells achieved a test perplexity of 77 for Text8. LTM achieves state of the art results by only using ten hidden LTM cells for both datasets.