Is renewable engery a suitable investment choice for South African non-bank institutional investors?

The research set out to examine the investment & economic suitability of Renewable Energy ("RE") assets for South African institutional investors. Data was collected through a series of structured and semi structured interviews and further triangulated and cross-checked through a thorough literature review of available policy documentation and academic literature. The limitations concerning this study have much to do with the nascent nature of the renewable energy program and therefore the lack of availability of hard economic and financial historical data. Further there is very little academic literature on renewable energy investing pertaining to a South African context. To mitigate some of the risks presented by the aforementioned limitations, interviewees were mainly subject-matter experts on the issue of RE investing and therefore provided key insights through a series of structured and semi-structured interviews. Within a South Africa specific context, there is very little academic material dealing with RE or infrastructure finance and investment. The implications of this study are therefore crucial in helping set the basis for the development of future theories around this and related topics. Interview discussions and review of other material revealed key themes, which allowed the researcher to discern some key findings: Firstly, there's a cautious but emerging consensus that the economic and financial features of RE assets make them suitable (and even attractive) for consideration in asset class allocation decisions. Further and related: the merging view was that RE assets could offer the benefit of both reducing risk and increasing expected returns within a given portfolio. A key related sub-theme and finding was the need to establish a common set of nomenclature, which would describe and ultimately help benchmark the economic and financial features of RE assets – the ability to benchmark financial and economic data being a key aspect of the asset allocation framework. Secondly data collected indicated that there is strong institutional support for government's energy policy and how it has been implemented to date. Thirdly, in working out the suitability of RE assets investors tend to default to comparable proxies such as bonds, equities, REITS. The emerging theme coming out of the data is that RE assets are likely to resemble fixed income assets in their financial and economic characteristics. Lastly, for all the emerging consensus in support of the government's RE policy, many investors seem to hedge their optimism and remain generally unsure and in some instances sceptical of the overall sustainability of the program, citing the fact that there are still too many unknowns regarding RE assets and their respective futures. This research therefore has some useful practical applications for institutional investors, hopefully further demystifying a sector that could be a lynchpin of the South African economy for some time to come

Health and safety practices at government mortuaries in Gauteng Province

M.Tech. (Environmental Health)Abstract: Hospital mortuaries are responsible for the receipt and storage of deceased people. The duties of a mortuary attendant or porter include the collection of a corpse from wards, signing acknowledgement slips, loading corpses onto trolleys, register corpse, allocate storage, maintain and clean mortuary equipment. This exposes them to a variety of health and safety risks, which include physical, chemical, ergonomics, biological and psychosocial hazards/stressors. The aim of this cross-sectional study was to assess the knowledge of occupational health and safety practices among government mortuary workers in Gauteng province. The target population was the mortuary workers in government hospitals, and convenient sampling was used to select such workers. Ethical clearance was obtained prior the commencement of the study from the University of Johannesburg Ethics Committee and Gauteng Department of Health (GDoH). A pilot study was conducted prior to the study. Data was collected using structured questionnaires and observational checklist. Data was analysed using Statistical Package for the Social Science (SPSS) version 25 software..

Detection of cosmic structures using the bispectrum phase. II. First results from application to cosmic reionization using the Hydrogen Epoch of Reionization Array

Characterizing the epoch of reionization (EoR) at $z\gtrsim 6$ via the redshifted 21 cm line of neutral Hydrogen (HI) is critical to modern astrophysics and cosmology, and thus a key science goal of many current and planned low-frequency radio telescopes. The primary challenge to detecting this signal is the overwhelmingly bright foreground emission at these frequencies, placing stringent requirements on the knowledge of the instruments and inaccuracies in analyses. Results from these experiments have largely been limited not by thermal sensitivity but by systematics, particularly caused by the inability to calibrate the instrument to high accuracy. The interferometric bispectrum phase is immune to antenna-based calibration and errors therein, and presents an independent alternative to detect the EoR HI fluctuations while largely avoiding calibration systematics. Here, we provide a demonstration of this technique on a subset of data from the Hydrogen Epoch of Reionization Array (HERA) to place approximate constraints on the IGM brightness temperature. From this limited data, at $z=7.7$ we infer "$1\sigma$" upper limits on the IGM brightness temperature to be $\le 316$ "pseudo" mK at $\kappa_\parallel=0.33\,\textrm{"pseudo"}\,h$ Mpc$^{-1}$ (data-limited) and $\le 1000$ "pseudo" mK at $\kappa_\parallel=0.875\,\textrm{"pseudo"}\,h$ Mpc$^{-1}$ (noise-limited). The "pseudo" units denote only an approximate and not an exact correspondence to the actual distance scales and brightness temperatures. By propagating models in parallel to the data analysis, we confirm that the dynamic range required to separate the cosmic HI signal from the foregrounds is similar to that in standard approaches, and the power spectrum of the bispectrum phase is still data-limited (at $\gtrsim 10^6$ dynamic range) indicating scope for further improvement in sensitivity as the array build-out continues

Detection of cosmic structures using the bispectrum phase. II. First results from application to cosmic reionization using the Hydrogen Epoch of Reionization Array

Characterizing the epoch of reionization (EoR) at $z\gtrsim 6$ via the redshifted 21 cm line of neutral Hydrogen (HI) is critical to modern astrophysics and cosmology, and thus a key science goal of many current and planned low-frequency radio telescopes. The primary challenge to detecting this signal is the overwhelmingly bright foreground emission at these frequencies, placing stringent requirements on the knowledge of the instruments and inaccuracies in analyses. Results from these experiments have largely been limited not by thermal sensitivity but by systematics, particularly caused by the inability to calibrate the instrument to high accuracy. The interferometric bispectrum phase is immune to antenna-based calibration and errors therein, and presents an independent alternative to detect the EoR HI fluctuations while largely avoiding calibration systematics. Here, we provide a demonstration of this technique on a subset of data from the Hydrogen Epoch of Reionization Array (HERA) to place approximate constraints on the IGM brightness temperature. From this limited data, at $z=7.7$ we infer "$1\sigma$" upper limits on the IGM brightness temperature to be $\le 316$ "pseudo" mK at $\kappa_\parallel=0.33\,\textrm{"pseudo"}\,h$ Mpc$^{-1}$ (data-limited) and $\le 1000$ "pseudo" mK at $\kappa_\parallel=0.875\,\textrm{"pseudo"}\,h$ Mpc$^{-1}$ (noise-limited). The "pseudo" units denote only an approximate and not an exact correspondence to the actual distance scales and brightness temperatures. By propagating models in parallel to the data analysis, we confirm that the dynamic range required to separate the cosmic HI signal from the foregrounds is similar to that in standard approaches, and the power spectrum of the bispectrum phase is still data-limited (at $\gtrsim 10^6$ dynamic range) indicating scope for further improvement in sensitivity as the array build-out continues

Measuring HERA's Primary Beam in Situ: Methodology and First Results

The central challenge in 21 cm cosmology is isolating the cosmological signal from bright foregrounds. Many separation techniques rely on the accurate knowledge of the sky and the instrumental response, including the antenna primary beam. For drift-scan telescopes, such as the Hydrogen Epoch of Reionization Array (HERA), that do not move, primary beam characterization is particularly challenging because standard beam-calibration routines do not apply (Cornwell et al.) and current techniques require accurate source catalogs at the telescope resolution. We present an extension of the method from Pober et al. where they use beam symmetries to create a network of overlapping source tracks that break the degeneracy between source flux density and beam response and allow their simultaneous estimation. We fit the beam response of our instrument using early HERA observations and find that our results agree well with electromagnetic simulations down to a -20 dB level in power relative to peak gain for sources with high signal-to-noise ratio. In addition, we construct a source catalog with 90 sources down to a flux density of 1.4 Jy at 151 MHz.The central challenge in 21 cm cosmology is isolating the cosmological signal from bright foregrounds. Many separation techniques rely on the accurate knowledge of the sky and the instrumental response, including the antenna primary beam. For drift-scan telescopes, such as the Hydrogen Epoch of Reionization Array (HERA), that do not move, primary beam characterization is particularly challenging because standard beam-calibration routines do not apply (Cornwell et al.) and current techniques require accurate source catalogs at the telescope resolution. We present an extension of the method from Pober et al. where they use beam symmetries to create a network of overlapping source tracks that break the degeneracy between source flux density and beam response and allow their simultaneous estimation. We fit the beam response of our instrument using early HERA observations and find that our results agree well with electromagnetic simulations down to a -20 dB level in power relative to peak gain for sources with high signal-to-noise ratio. In addition, we construct a source catalog with 90 sources down to a flux density of 1.4 Jy at 151 MHz

Optimizing Sparse RFI Prediction using Deep Learning

Radio Frequency Interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array grow larger in number of receivers. To address this, we present a Deep Fully Convolutional Neural Network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known "ground truth" dataset for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6$\times 10^{5}$ HERA time-ordered 1024 channeled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and Non-RFI. The inclusion of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and $F_{2}$ score of 0.75 as applied to our HERA-67 observations.Comment: 11 pages, 7 figure

Optimizing sparse RFI prediction using deep learning

Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known \u2018ground truth\u2019 data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6 7 105 HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time\u2013frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations

Automated Detection of Antenna Malfunctions in Large-N Interferometers: A case study With the Hydrogen Epoch of Reionization Array

We present a framework for identifying and flagging malfunctioning antennas in large radio interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study. Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets