A great number of universities worldwide are having their education interrupted, partially or fully, by the spread of the novel coronavirus (COVID-19). Consequently, an increasing number of universities have taken the steps necessary to transform their teaching, including laboratory workshops into an online or blended mode of delivery. Irrespective of the measures taken, universities must continue to maintain their high academic standards and provide a high-quality student experience as required for delivery of learning outcomes associated with each degree programme. This has created a challenge across the higher education landscape, where academics had to switch to remote teaching and different approaches to achieving laboratory delivery. As a result, students have not been receiving face-to-face teaching, and access to laboratory facilities has been limited or nearly impossible. This paper reviews numerous approaches taken by universities to deliver teaching and laboratory practices remotely, in consideration of the COVID-19 pandemic, whilst also considering the potential impacts on the student learning experience. This review is primarily focused on the fields of engineering, science and technology, based on published literature including books, reviewing web-based provision of selected universities, institutional and national policy documents.

Globally, the number of COVID-19 cases continues to rise daily despite strict measures being adopted by many countries. Consequently, universities closed down to minimise the face-to-face contacts, and the majority of the universities are now conducting degree programmes through online delivery. Remote online delivery and assessment are novel experiences for many universities, which presents many challenges, particularly when safeguarding academic integrity. For example, invigilated assessments, often considered as more secure, are not an option given the current situation and detecting any cheating would be significantly challenging. This paper reviews assessment security in the digital domain and critically evaluates the practices from different universities in safeguarding academic integrity, including associated challenges.

Personal values play a significant role when adopting learning approaches by individuals during their studies. Particularly in higher education, these values significantly influence the character that individuals play within their learning community and ultimately influence their academic achievements. The purpose of this paper is to investigate personal values in their choice of learning approaches and, subsequently, how it impacts one’s academic achievements. It also investigates the importance of developing an individual’s personal values as a part of their wider studies, while aligning these with graduate attributes and balancing them with knowledge and skills, to produce successful graduates in a society.

Scintillator based coded-aperture imaging has proven to be effective when applied for X- and gamma-ray detection. Adaptation of the same method for neutron imaging has resulted in a number of propitious systems, which could be potentially employed for neutron detection in security and nuclear decommissioning applications. Recently developed scintillator based coded-aperture imagers reveal that localisation of neutron sources using this technique may be feasible, since pulse shape discrimination algorithms implemented in the digital domain can reliably separate gamma-rays from fast neutron interactions occurring within an organic scintillator. Moreover, recent advancements in the development of solid organic scintillators make them a viable solution for nuclear decommissioning applications as they present less hazardous characteristics than currently dominating liquid scintillation detectors. In this paper existing applications of coded-apertures for radiation detection are critically reviewed, highlighting potential improvements for coded-aperture based neutron source localisation. Further, the suitability of coded-apertures for neutron imaging in nuclear decommissioning is also assessed using Monte-Carlo modelling

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications.