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Embedding sustainability into pedagogical approaches is a key priority in higher education. Equipping students with knowledge, understanding, and skills, and developing the next generation of innovators and leaders, can potentially provide the change needed and create a real impact in the journey to a sustainable future. Advancement in Science, Technology, Engineering, and Mathematics (STEM), and corresponding industries can be seen as vital for the success of meeting a majority of the sustainable development goals, and hence the approaches taken to embed sustainability in learning and teaching in STEM higher education programmes can be considered significant in many ways. This paper is based on published literature over the last two decades and a semi-structured interview with 12 university academics from a developing country. Hence, the paper reviews approaches taken to embed sustainability in learning, teaching and assessments in STEM programmes. It also aims to investigate the actions taken by universities to integrate sustainability in STEM education and the remedies taken to minimise the impact of the pandemic on the effectiveness of the learning pedagogies used to integrate sustainability concepts.

Coaching and mentoring has been regarded as one of the key learning techniques in the modern learning environment in the recent past. With the swift shift in the higher education sector to a more digitally driven learning environment, limited research has evaluated the impact of coaching and mentoring on student engagement in online learning. This study aimed to evaluate the impact of coaching and mentoring on enhancing student engagement in the higher education sector. The study applied a combination of quantitative and qualitative methods of data collection, where the primary data were collected from 120 respondents via a questionnaire and 20 in-depth interviews, covering undergraduates through a simple random sampling technique. The data analysis showed that the mentor's ability to facilitate learning and encourage mentees to be focused on their goals were the most significant factors that affected student engagement in online learning. In total, 43% of the respondents extremely agreed that they are willing to recommend their colleagues to receive the service of a mentor to help them reach their optimum potential in an online learning environment. The findings of the study recommend that the higher educational institutes should administer a sound mentoring process that meets the ethical backgrounds to consistently support the continuous improvement of the students in an online learning environment to enhance their engagement in learning activities.

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.

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.

The final year design project is one of the most important components of any undergraduate engineering program. Fair and efficient project allocation procedures can be vital in ensuring a great student experience and exceptional learning out of these projects, which then could contribute in shaping students' future prospects. In this paper, we review a wide range of project allocation strategies used in various universities at undergraduate levels. We then focus on the project allocations in transnational education (TNE) contexts, which inherit additional allocation challenges. We highlight these challenges and provide recommendations to solve them. We present and compare project allocation strategies adopted at two of the largest TNE programs in China. We also present the factors that influence the project allocations, particularly regarding TNE provisions. Finally, we describe the challenges associated with the project allocations in the TNE scenario, along with proposing some feasible solutions to address these challenges.

Assessment and feedback (A&F) are two major components of any educational program and must be properly in place to ensure student learning and quality of experience. However, these important components come under severe challenges of meeting student expectations in the large class size context. When the program delivery relates to a transnational educational (TNE) scenario, the additional constraints on staff-student physical interaction, regional time differences and cultural background gaps introduce additional challenges: Conducting proper assessments and provide timely and constructive feedback to the students. In this paper, the authors propose a novel assessment and feedback framework which exploits having a large student number as a positive factor by introducing staff-student partnership to implement efficient assessment and feedback strategies. Authors propose to use students for peer-review, assessment design, evaluation rubric design and tutorial-based feedback. The students also take part in preparing feedback clusters based on which the instructor provides pseudo-personalised video feedback. Through feedback clusters, authors introduce the trade-off between individual feedback and generic feedback. The results of the study are particularly promising in terms of student satisfaction and learning enhancement.

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics. [ABSTRACT FROM AUTHOR]

An automated device is designed to measure the density of a liquid material using hydrostatic pressure method. A low cost pressure sensor is calibrated and used to get highly accurate readings. The calibration is done by measuring the pressure values vs. the generated voltage signal. The calibration has been challenging due to the low accuracy of the sensor but proved to be highly effective in applications. The interface is developed using a microcontroller, motor drives, analog to digital converters and sensors. The device is designed to get several readings automatically by changing the positions of the device/liquid column heights to increase the accuracy. Also the device can be programmed to measure the real time density of a liquid continuously. The readings were analyzed and averaged by a software developed in python language. The instruments accuracy was tested against 3 liquid types, water, coconut oil, kerosene oil, and showed a low error (0.007%, 0.001%, and 0.002% respectively) compared to the readings of a standard Pycnometer. The low error percentages confirm the accuracy of the device and the effectiveness of the sensor calibrations. [ABSTRACT FROM AUTHOR]

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. [ABSTRACT FROM AUTHOR]

Collaboration between staff and students for improved curriculum design is presented as highly beneficial in many contexts. In particular, Education for Sustainable Development (ESD) is seen as facilitating co-design and collaboration. However, students tend not to be actively involved in curriculum change, especially in whole programme design or review. Furthermore, few studies consider partnership with students in the context of ESD inclusion, which was the focus for this collaboration. The aim of this study was to explore staff and student perceptions of participating in a collaborative approach to the review and development of the undergraduate law curriculum in a university in the UK. A developmental evaluation using mixed methods was used to study the expectations and experiences of students and staff through a process of curriculum review and change to incorporate ESD. Our findings demonstrate the rewards of a collaborative process incorporating diverse perspectives. However, they also highlight barriers for students and staff, including perceptions of risk associated with student involvement in the process, and we offer reflections on navigating these potential risks. [ABSTRACT FROM AUTHOR]

In recent times, higher education has seen a growing concern regarding the utilisation of artificial intelligence, especially with the emergence of ChatGPT. This technology can generate written content and respond to queries at a level that is nearly indistinguishable from a human writer. This feature has drawn substantial interest from students in higher education and has led to concern that students will use ChatGPT's capabilities to cheat on written formative and summative assessments. In this paper, we will review the usage of ChatGPT in higher education assessments and investigate why students want to cheat using artificial intelligence capabilities. It also offers a critical perspective on the challenges associated with detecting ChatGPTgenerated content and its impact on academic integrity. We also consider whether artificial intelligence provides more opportunities for academics to focus on assessing higherorder thinking and strategies. [ABSTRACT FROM AUTHOR]

Contract cheating has become increasingly an issue as universities adapt to online and hybrid teaching, learning, and assessments. Due to the COVID-19 pandemic, higher education institutions began to administer examinations more frequently online, and it led to the emergence of websites and commercial service providers who offer contract cheating services globally. In this paper, we examine the key elements that lead students to turn to contract cheating as well as the elements that deter the students from engaging in such unethical behaviour. We also investigate how assessment design can encourage authentic learning, although assessment design alone cannot eliminate contract cheating. The effects of contract cheating on academic standards and quality assurance are also examined. Mainly the study results show that the act of contract cheating is a result of interrelated internal and external factors in an individual. Although a number of measures, including authentic evaluations and digital tools, have been implemented to discourage students from cheating, no strategy is strong enough to control the issue permanently. Hence, academic integrity is still not assured, highlighting the necessity of a global movement to solve the problem. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 K i , 0.7741 K p , and 0.1850 K d . The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network. [ABSTRACT FROM AUTHOR]

The opportunity to develop graduate attributes beyond disciplinary expertise is an essential component of any degree programme, and students should develop these during their time within higher education. Graduate attributes can be considered as high-level skills and qualities beyond technical knowledge, which enable higher education institutions to produce more employable graduates. However, there are significant challenges in developing such skills and qualities, where remote teaching and learning complicate this further. Online teaching does not wholly derail the opportunities for students to achieve existing graduate attributes—in contrast, it also creates opportunities for innovation and shapes graduates in preparation to be the next-generation workforce. In this paper, we aim to study and synthesise existing knowledge on the effect of online hybrid teaching on graduate attributes. A systematic literature survey was completed revealing that educational institutions continue using online or hybrid instructional modes, affecting graduate attributes positively and negatively. Therefore, to effectively develop graduate attributes, there is a requirement for fundamental changes in instructional strategies, teaching–learning behaviours, upgrading of facilities and curriculum adaptations. We review graduate attributes in the higher education literature, including the impacts resulting from remote teaching and learning. The paper also identifies the general challenges to developing graduate attributes, as well as more specific challenges as a result of hybrid and online teaching and learning. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on the global higher education system, where many universities have adapted to online and hybrid teaching and learning. They continue with some activities on campus, particularly laboratory-based teaching, but some content is delivered remotely. Significant adjustment to traditional face-to-face student engagement activities is crucial for the success of online and hybrid teaching and learning. This paper investigates the student engagement and experience in these environments. Engaged students are more likely to reach their full potential academically, and this paper identifies the areas for enhancement to student engagement activities. A survey was conducted (in Sri Lanka) to identify students' perceptions of engaging in activities during online and hybrid delivery. The results of the study illustrate a significant student engagement in learning whereas a pessimistic perception towards the transition to a completely online setting. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE). [ABSTRACT FROM AUTHOR]

The transmission line is an integral part of the electrical power system; however, a fault has a negative impact on the system, like blackout, power loss, financial losses, and socio-economic impact. This fault occurs due to ageing conductors, lightning stroke, switching surge and human interference. We reviewed the protection scheme implemented in the Nigerian transmission network, which has challenges relating to the environment's terrain and a long-distance transmission line of about 20,000 km. The different approach of fault classification, detection and location was analyzed and critically summarized. This review paper proposes a hybrid artificial neural network (ANN) and distance protection scheme that can automatically identify, locate, isolate, predict, correct faults, and real-time monitor and control the entire network. It can also detect the shortest possible trip time of 0.02 s and 0.03 s of line current and fault losses, respectively, during fault to avert damage on the line. However, this method has its challenges, such as the volume of data generated from load flow analysis, training time, and the total distance covered by the network. However, these can be averted by segmenting the entire network for easy evaluation and monitoring to achieve set goals. [ABSTRACT FROM AUTHOR]

The global higher education landscape is significantly impacted as a result of the COVID-19 pandemic and the majority of the universities now follow an online or hybrid mode of delivery. This presents substantial challenges for universities, particularly to conduct examinations, as traditionally most exams were conducted physically on campus. During the first wave of the pandemic, many universities had no option and were forced to move online in a very short period of time, causing universities also to conduct exams online without transforming pedagogy and the structure/s of closed-book exams. Inevitably, in non-proctored and unregulated examinations, this allowed room for students to collaborate and share material during online exams without being noticed by an invigilator as in the case of physical exams. Online exams, also leave room for students to find information online which made preventing plagiarism a significant challenge. This paper investigates the practices used in both closed-book and open-book exams and identifies the challenges associated with the transition to online exams. It also identifies potential ways forward for future online exams, while minimizing opportunities for students to collaborate, plagiarise and use online material. The findings of this study reveal that online examinations affect teachers and students differently: while teachers have mixed feelings about online exams, students are anxious about their grades and the technical hassle they experience in online exams. While viva has emerged as a popular form of alternative assessment, students still feel the need of returning to physical exams. None of the teachers who participated in this study discussed a psychosocial approach to education and exams in this pandemic. We conclude this study on the note that there is a need for the collaboration of social scientists, psychologists, psychosocial specialists, educationists, and humanities scholars/humanists for better educational policy and pedagogical practices during the pandemic. [ABSTRACT FROM AUTHOR]

Summary: The feasibility of utilizing CdSe/ZnS quantum dots (QDs) in liquid scintillator radioluminescent nuclear batteries to improve battery performance was studied. The peak position of the radioluminescence emission spectra of liquid scintillator can be regulated by controlling the QD components. This method is suitable for obtaining a satisfactory spectral matching between fluorescence materials and photovoltaic devices to increase the output performance of the battery. In the experiment, CdSe/ZnS QDs were introduced into Emulsifier‐Safe liquid scintillator, and the output properties of radioluminescent nuclear batteries were investigated via X‐ray. Results indicate that the battery with 15 mg CdSe/ZnS QDs generated the best electric power under different tube voltages. To analyze the X‐ray radioluminescence effects of the liquid scintillator, the radioluminescence spectra of the Emulsifier‐Safe with and without CdSe/ZnS QDs were measured and compared. The spectral matching degree between the Emulsifier‐Safe with different concentrations of CdSe/ZnS QDs and the GaAs device was also analyzed by considering luminescence utilization in batteries. This framework can serve as a guide for the development of a radioluminescent material system for long‐lasting, high‐performance power supplies. [ABSTRACT FROM AUTHOR]

The United Kingdom (UK) has a significant legacy of nuclear installations to be decommissioned over the next 100 years and a thorough characterisation is required prior to the development of a detailed decommissioning plan. Alpha radiation detection is notoriously time consuming and difficult to carry out due to the short range of alpha particles in air. Long-range detection of alpha particles is therefore highly desirable and this has been attempted through the detection of secondary effects from alpha radiation, most notably the air-radioluminescence caused by ionisation. This paper evaluates alpha induced air radioluminescence detectors developed to date and looks at their potential to develop a stand-off, alpha radiation detector which can be used in the nuclear decommissioning field in daylight conditions to detect alpha contaminated materials. [ABSTRACT FROM AUTHOR]

Existing remote radioactive contamination depth estimation methods for buried radioactive wastes are either limited to less than 2 cm or are based on empirical models that require foreknowledge of the maximum penetrable depth of the contamination. These severely limits their usefulness in some real life subsurface contamination scenarios. Therefore, this work presents a novel remote depth estimation method that is based on an approximate three-dimensional linear attenuation model that exploits the benefits of using multiple measurements obtained from the surface of the material in which the contamination is buried using a radiation detector. Simulation results showed that the proposed method is able to detect the depth of caesium-137 and cobalt-60 contamination buried up to 40 cm in both sand and concrete. Furthermore, results from experiments show that the method is able to detect the depth of caesium-137 contamination buried up to 12 cm in sand. The lower maximum depth recorded in the experiment is due to limitations in the detector and the low activity of the caesium-137 source used. Nevertheless, both results demonstrate the superior capability of the proposed method compared to existing methods. [ABSTRACT FROM AUTHOR]

In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source of the alpha emissions. This paper details experiments carried out to detect this fluorescence, with the focus on photons in the ultraviolet C (UVC) wavelength range (180-280 nm). A detector, UVTron R9533 (Hamamatsu, 325-6, Sunayama-cho, Naka-ku, Hamamatsu City, Shizuoka Pref., 430-8587, Japan), designed to detect the UVC emissions from flames for fire alarm purposes, was tested in various gas atmospheres with a 210Po alpha source to determine if this could provide an avenue for stand-off alpha detection. The results of the experiments show that this detector is capable of detecting alpha-induced air fluorescence in normal indoor lighting conditions, as the interference from daylight and artificial lighting is less influential on this detection system which operates below the UVA and UVB wavelength ranges (280-315 nm and 315-380 nm respectively). Assuming a standard 1/r2 drop off in signal, the limit of detection in this configuration can be calculated to be approximately 240 mm, well beyond the range of alpha-particles in air, which indicates that this approach could have potential for stand-off alpha detection. The gas atmospheres tested produced an increase in the detector count, with xenon having the greatest effect with a measured 52% increase in the detector response in comparison to the detector response in an air atmosphere. This type of alpha detection system could be operated at a distance, where it would potentially provide a more cost effective, safer, and faster solution in comparison with traditional alpha detection methods to detect and characterise alpha contamination in nuclear decommissioning and security applications. [ABSTRACT FROM AUTHOR]

Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations. [ABSTRACT FROM AUTHOR]

Reliable communication is the backbone of advanced metering infrastructure (AMI). Within the AMI, the neighbourhood area network (NAN) transports a multitude of traffic, each with unique requirements. In order to deliver an acceptable level of reliability and latency, the underlying network, such as the wireless mesh network (WMN), must provide or guarantee the quality-of-service (QoS) level required by the respective application traffic. Existing WMN routing protocols, such as optimised link state routing (OLSR), typically utilise a single metric and do not consider the requirements of individual traffic; hence, packets are delivered on a best-effort basis. This paper presents a QoS-aware WMN routing technique that employs multiple metrics in OLSR optimal path selection for AMI applications. The problems arising from this approach are non deterministic polynomial time (NP)-complete in nature, which were solved through the combined use of the analytical hierarchy process (AHP) algorithm and pruning techniques. For smart meters transmitting Internet Protocol (IP) packets of varying sizes at different intervals, the proposed technique considers the constraints of NAN and the applications' traffic characteristics. The technique was developed by combining multiple OLSR path selection metrics with the AHP algorithm in ns-2. Compared with the conventional link metric in OLSR, the results show improvements of about 23% and 45% in latency and Packet Delivery Ratio (PDR), respectively, in a 25-node grid NAN. [ABSTRACT FROM AUTHOR]

Purpose: Having been overlooked for many years, research is now starting to take into account the directional distribution of neutron workplace fields. Existing neutron dosimetry instrumentation does not account for this directional distribution, resulting in conservative estimates of dose in neutron workplace fields (by around a factor of 2, although this is heavily dependent on the type of field). This conservatism could influence epidemiological studies on the health effects of radiation exposure. This paper reports on the development of an instrument which can estimate the effective dose of a neutron field, accounting for both the direction and the energy distribution. Methods: A 6Li-loaded scintillator was used to perform neutron assays at a number of locations in a 20 × 20 × 17.5 cm³ water phantom. The variation in thermal and fast neutron response to different energies and field directions was exploited. The modeled response of the instrument to various neutron fields was used to train an artificial neural network (ANN) to learn the effective dose and ambient dose equivalent of these fields. All experimental data published in this work were measured at the National Physical Laboratory (UK). Results: Experimental results were obtained for a number of radionuclide source based neutron fields to test the performance of the system. The results of experimental neutron assays at 25 locations in a water phantom were fed into the trained ANN. A correlation between neutron counting rates in the phantom and neutron fluence rates was experimentally found to provide dose rate estimates. A radionuclide source behind shadow cone was used to create a more complex field in terms of energy and direction. For all fields, the resulting estimates of effective dose rate were within 45% or better of their calculated values, regardless of energy distribution or direction for measurement times greater than 25 min. Conclusions: This work presents a novel, real-time, approach to workplace neutron dosimetry. It is believed that in the research presented in this paper, for the first time, a single instrument has been able to estimate effective dose. [ABSTRACT FROM AUTHOR]

This paper proposes and analyses a new demand response technique for renewable energy regulation using smart hot water heaters that forecast water consumption at an individual dwelling level. Distributed thermal energy storage has many advantages, including high overall efficiency, use of existing infrastructure and a distributed nature. In addition, the use of a smart thermostatic controller enables the prediction of required water amounts and keeps temperatures at a level that minimises user discomfort while reacting to variations in the electricity network. Three cases are compared in this paper, normal operation, operation with demand response and operation following the proposed demand response mechanism that uses consumption forecasts. The results show that this technique can produce both up and down regulation, as well as increase water heater efficiency. When controlling water heaters without consumption forecast, the users experience discomfort in the form of hot water shortage, but after the full technique is applied, the shortage level drops to nearly the starting point. The amount of regulation power from a single dwelling is also discussed in this paper. [ABSTRACT FROM AUTHOR]

An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive) and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting. [ABSTRACT FROM AUTHOR]

The design, principle of operation and the results of measurements made with a four-channel organic scintillator system are described. The system comprises four detectors and a multiplexed analyzer for the real-time parallel processing of fast neutron events. The function of the real-time, digital multiple-channel pulse-shape discrimination analyzer is described together with the results of laboratory-based measurements with ^252\Cf, ^241\Am–Li and plutonium. The analyzer is based on a single-board solution with integrated high-voltage supplies and graphical user interface. It has been developed to meet the requirements of nuclear materials assay of relevance to safeguards and security. Data are presented for the real-time coincidence assay of plutonium in terms of doubles count rate versus mass. This includes an assessment of the limiting mass uncertainty for coincidence assay based on a 100 s measurement period and samples in the range 0–50 g. Measurements of count rate versus order of multiplicity for ^252\Cf and ^241\Am–Li and combinations of both are also presented. [ABSTRACT FROM AUTHOR]

As a result of recent advances in digital pulse-shape discrimination methods it has become possible to image mixed fields (radiation environments compromising both neutrons and gamma rays) relatively quickly with a single, organic liquid 'fast' scintillator within a heavy metal collimator. The use of a liquid scintillator has significant benefits over other techniques for imaging radiation environments as the acquired data can be analysed to provide separate information about the gamma and neutron emissions from a source (or sources) in a single scan. The imaging resolution achieved is fundamentally related to the detector efficiency and to the collimator geometry. In this paper the impact of using two different geometries of tungsten collimator are compared experimentally and three different materials considered in the Monte Carlo simulation, in order to determine the optimum set-up for mixed-field imaging. The measurements were performed in the low-scatter facility of the National Physical Laboratory, Teddington. [ABSTRACT FROM AUTHOR]

For decades, sustainability researchers have tenaciously insisted on transforming higher education institutions into more sustainable and inclusive campuses. Yet, as the 2030 agenda seems unlikely to be achieved, universities are struggling to meet the fourth Sustainable Development Goal (SDG 4) before the 2030 deadline. In addition, the post-COVID-19 era demands quality and inclusive education that entails care for students experiencing high stress levels. So far, most of the significant achievements are within the environmental or economic dimensions of sustainable development, but strengthening the social dimension is still one pending task. The importance of happiness to sustainability initiatives on campus, and beyond, deserves further research. To this end, this article offers insights into incorporating the sustainability–happiness nexus into sustainable universities to enhance the social dimension of sustainability. COVID-19 reminds sustainability academics and stakeholders that teaching technical and scientific knowledge is necessary to become more sustainable. Still, it is not sufficient to achieve the goals in the 2030 agenda. Providing inclusive and sustainable quality education will be reached when more sustainable universities consider happiness the ultimate goal of human development. [ABSTRACT FROM AUTHOR]

The in-situ characterisation of strontium-90 contamination of groundwater at nuclear decommissioning sites would represent a novel and cost-saving technology for the nuclear industry. However, beta particles are emitted over a continuous spectrum and it is difficult identify radionuclides due to the overlap of their spectra and the lack of characteristic features. This can be resolved by using predictive modelling to perform a maximum-likelihood estimation of the radionuclides present in a beta spectrum obtained with a semiconductor detector. This is achieved using a linear least squares linear regression and relating experimental data with simulated detector response data. In this case, by simulating a groundwater borehole scenario and the deployment of a cadmium telluride detector within it, it is demonstrated that it is possible to identify the presence of 90 Sr, 90 Y, 137 Cs and 235 U decay. It is determined that the optimal thickness of the CdTe detector for this technique is in the range of 0.1 to 1 mm. The influence of suspended solids in the groundwater is also investigated. The average and maximum concentrations of suspended particles found at Sellafield do not significantly deteriorate the results. It is found that applying the linear regression over two energy windows improves the estimate of 90 Sr activity in a mixed groundwater source. These results provide validation for the ability of in-situ detectors to determine the activity of 90 Sr in groundwater in a timely and cost-effective manner. [ABSTRACT FROM AUTHOR]

The global higher education sector has been greatly affected by the COVID-19 pandemic, and the mode of delivery has transformed into a blended learning mode of delivery or fully remote mode. Online delivery significantly demands reliable and stable internet access and technology, at both the lecturer's and students' ends. This paper investigates the challenges and barriers to accessibility of technologies used for remote delivery of learning and teaching. The paper also investigates key digital skills students need to help them develop and enhance their technology literacy. A survey was also conducted among 555 university undergraduate students to identify their choice of device to connect to remote learning during the transition to online learning. It was revealed that students used laptops and smartphones considerably and least relied on desktop computers. The results indicate the significance of a device's portability, built-in network hardware and cost. Further, it identifies the impacts of accessibility of educational technologies on students' learning experience. [ABSTRACT FROM AUTHOR]

Many higher educational institutes are forced to the online mode of lecture delivery without fully understanding students' perspectives of remote study during COVID-19, and the resource limitations of students to engage successfully in remote learning. The present study aimed to understand this gap and the students' perspective. We collected and analysed the devices, which used by the students to connect to remote learning in a developing country. It was found that the majority of students are dependent on the laptop-smartphone combination to engage in remote learning, particularly the students in rural areas. The results highlighted the importance of smartphones in the online learning experience, considering their affordability, relatively long battery life, inbuilt internet capabilities, and portability. Although students indicated their willingness for remote learning, they clearly recognised the need for face-to-face teaching return to avoid some of the challenges and disadvantages they face as a part of remote learning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]