Equity in Admissions Policies of Undergraduate Students in Post Democracy in Selected South African Universities

This paper investigates the policy pathways that inform and regulate student selection and admission at three selected universities in South Africa, namely the University of the Witwatersrand, the University of Cape Town and the University of KwaZulu-Natal. We argue that these universities have progressed a long way in addressing the race problem in their enrolment strategies. However, their main target group remains students from rich or affluent communities, to the exclusion of potentially good students from marginalised groups, particularly those from under-resourced township and rural schools. As a result, their main challenge in the context of formal access to higher education in South Africa has largely shifted from a race problem to one of social class. This is due to an overemphasis on narrow conceptions of merit that cannot be reconciled with equity and social justice concerns. The paper suggests that current notions of merit warrant reconceptualization in order to embrace these missing dimensions. While there is plenty of evidence that most institutions agree on the need to embrace a particular form of affirmative action to address current social imbalances, given the fierce contestation of redress policies within the South African higher education sector, they find it difficult to develop and implement adequate admission strategies in practice

‘Did Anglians dream of electric screens?’ A history of broadcasting in Norfolk and East Anglia from 1923-1960

This thesis re-examines broadcasting history in Britain through the lens of the experience in Norfolk and East Anglia rather than via the nation state as has usually been the case in prior academic investigations. Using a combination of archival sources, secondary literature and selected extracts from original oral history interviews it aims to introduce a greater level of nuance into the historiography of broadcasting in Britain. These archival sources include the BBC Written Archives, the ITA Archives, Hansard and the archive of the most popular newspaper in Norfolk – the Eastern Daily Press. The first half of the thesis concentrates on the BBC’s policies towards the region in respect of both wireless and television broadcasting before the outbreak of war and in the immediate aftermath of the war’s end, highlighting the short and long term legacies of these policies and the reaction of the press and public in the area. The second half of the thesis includes a discussion of the opening of the regions first permanent television transmitter in 1955, a detailed and original analysis of the applications for the East Anglian ITV programme station contract in 1958 and an analysis of the arrival of both Anglia Television and a BBC Television local news bulletin during 1959. Utilising the results of this investigation it becomes possible to assess the extent to which the history of broadcasting in East Anglia both fits into, but also deviates from, the accepted historical timeline of British broadcasting, particularly in relation to supposedly pivotal events such as the 1953 Coronation and the launch of ITV in 1955. It also raises questions about how this new knowledge might change existing theoretical understandings of the relationship between broadcasting and society, specifically with respect to the idea of television and public service/the public sphere

Challenges in Developing a Real-time Bee-counting Radar

MDPI Sensors journal paper published on 01/06/2023: "Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data."Open Access. Funded by the Knowledge Econ- omy Skills Scholarships (KESS 2, Ref: BUK2E001) Welsh European Funding Office (WEFO): c81133

Early prediction of bumblebee flight task using machine learning

This work demonstrates the development of a neural network algorithm able to determine the function of a bee's flight within six measurements (≈18 s with current radar technology) of its relative position on leaving a nest. Engineering advancements have created technology to track individual insects, unlocking research possibilities to investigate how bumblebees react to their environment in more detail. This includes how they discover and make use of resources. The development of an intelligent algorithm would allow for the automated monitoring of resource use and nest health. An imbalance of bee flight tasks may indicate a shortage of resources or over-reliance on a plant that may soon stop flowering. Recent developments using drones to track insects can benefit from an intelligent target acquisition system given limited drone battery life. Such knowledge will also benefit the tracking itself by allowing for customised flight parameters to match target flight patterns. Data captured by these tracking techniques are taxing to parse manually using human expertise. Artificial intelligence can produce meaningful knowledge faster with equal precision. In this work, a comparison between a neural network (NN), random forest (RF), and support vector machine (SVM) is provided to distinguish the best model for the task by comparing cross entropy loss and accuracy across the dataset, showing improved results as time goes on. In situations where the radar lost sight of the target, a purpose-built filter was created to mitigate signal losses. The generated model provides results with a peak accuracy of 92%. This model, combined with the filter, create an opportunity to monitor the number of bees leaving the nest for each flight task with smaller, cheaper, and stationary receiver solutions with shorter ranges by removing the need to track a bee for its entire flight to ascertain its errand

Appropriateness of antibiotic prescribing in the Emergency Department

Background Antibiotics are some of the most commonly prescribed drugs in the Emergency Department (ED) and yet data describing the overall appropriateness of antibiotic prescribing in the ED is scarce. Objectives To describe the appropriateness of antibiotic prescribing in the ED. Methods A retrospective, observational study of current practice. All patients who presented to the ED during the study period and were prescribed at least one antibiotic were included. Specialists from Infectious Disease, Microbiology and Emergency Medicine and a Senior Pharmacist assessed antibiotic appropriateness against evidence-based guidelines. Results A total of 1019 (13.6%) of patient presentations involved the prescription of at least one antibiotic. Of these, 640 (62.8%) antibiotic prescriptions were assessed as appropriate, 333 (32.7%) were assessed as inappropriate and 46 (4.5%) were deemed to be not assessable. Adults were more likely to receive an inappropriate antibiotic prescription than children (36.9% versus 22.9%; difference 14.1%, 95% CI 7.2%–21.0%). Patients who met quick Sepsis-related Organ Failure Assessment (qSOFA) criteria were more likely to be prescribed inappropriate antibiotics (56.7% versus 36.1%; difference 20.5%, 95% CI, 2.4%–38.7%). There was no difference in the incidence of appropriate antibiotic prescribing based on patient gender, disposition (admitted/discharged), reason for antibiotic administration (treatment/prophylaxis) or time of shift (day/night). Conclusions Inappropriate administration of antibiotics can lead to unnecessary adverse events, treatment failure and antimicrobial resistance. With over one in three antibiotic prescriptions in the ED being assessed as inappropriate, there is a pressing need to develop initiatives to improve antibiotic prescribing to prevent antibiotic-associated patient and community harms.No Full Tex

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental science, from which we can develop new insights into materials synthesis for both robot and human chemists

A spectrum of physics-informed Gaussian processes for regression in engineering

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context

Interaction of nitrogen dioxide with human plasma Antioxidant depletion and oxidative damage

AbstractNitrogen dioxide (NO*2) is often present in inhaled air and may be generated in vivo from nitric oxide. Exposure of human blood plasma to NO*2 caused rapid losses of ascorbic acid, uric acid and protein thiol groups, as well as lipid peroxidation and depletions of α-tocopherol, bilirubin and ubiquinol-10. No increase in protein carbonyls was detected. Supplementation of plasma with ascorbate decreased the rates of lipid peroxidation. α-tocopherol depletion and loss of uric acid. Uric acid supplementation decreased rates of lipid peroxidation but not the loss of α-tecopherol. We conclude that ascorbic acid, protein -SH groups, uric acid and α-tocopherol may be important agents protecting against NO*2 in vivo. If these antioxidants are depleted, peroxidation of lipids occurs and might contribute to the toxicity of NO*2

The role of natural gas in setting electricity prices in Europe

The EU energy and climate policy revolves around enhancing energy security and affordability, while reducing the environmental impacts of energy use. The European energy transition has been at the centre of debate following the post-pandemic surge in power prices in 2021 and the energy crisis following the 2022 Russia-Ukraine war. Understanding the extent to which electricity prices depend on fossil fuel prices (specifically natural gas) is key to guiding the future of energy policy in Europe. To this end, we quantify the role of fossil-fuelled vs. low-carbon electricity generation in setting wholesale electricity prices in each EU-27 country plus Great Britain (GB) and Norway during 2015-2021. We apply econometric analysis and use sub/hourly power system data to estimate the marginal share of each electricity generation type. The results show that fossil fuel-based power plants set electricity prices in Europe at approximately 58% of the time (natural gas 39%) while generating only 34% of electricity (natural gas 18%) a year. The energy transition has made natural gas the main electricity price setter in Europe, with gas determining electricity prices for more than 80% of the hours in 2021 in several countries such as Belgium, GB, Greece, Italy, and the Netherlands. Hence, Europe’s electricity markets are highly exposed to the geopolitical risk of gas supply and natural gas price volatility, and the economic risk of currency exchange