3 research outputs found
Self-training artificial neural networks for risk reduction in nuclear power operations
No full textThe risk reduction potential of the class of artificial neural networks based on the Barto-Sutton architecture is established. The risk associated with nuclear power operations is characterized by sequences of discrete events, such as technical specification violation. The Barto-Sutton architecture has the capability to synthesize precursors to these events, and to synthesize mitigative control policies. To establish the risk reduction potential of the network, network control of a complex reactor control task was demonstrated. The task exemplifies the structure of risk in modern nuclear power plant operation.Made available in DSpace on 2011-05-07T11:59:43Z (GMT). No. of bitstreams: 2
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Previous issue date: 1992Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:35:16Z
Item is restricted indefinitely.Restriction data tranferred 2014-07-01T11:13:51-05:00
Original Data
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The Effect of Innate and Discretionary Components of Earnings Quality Properties on Stock Return Volatility in South Africa
No full textPrevious studies reported that the quality of financial reports, measured by earnings quality properties, is associated with changes in stock return volatility (SRV). However, the properties of earnings quality in previous studies have been examined without separating them into their innate and discretionary components. This study examined the effect of innate and discretionary components of earnings quality properties on the SRV of companies listed in the Johannesburg Stock Exchange (JSE) in South Africa (SA). Using Multilevel linear regression to analyse a sample of 800 firm-year observations, obtained from 80 non-financial companies for the period 2009-2018, the study found that the innate component of each earnings quality property has a greater impact on the SRV (measured by idiosyncratic volatility) than the discretionary component. These findings imply that, in SA, the stock return volatility is mostly driven by the earnings quality properties that emanate from the operating environment and the business model of the companies. The findings may assist investors in the factors to consider when assessing the risks of their investments. In addition, the findings could be useful to regulators in SA in the review or formulation of policies that may make the business environment more stable as these policies influence the operating environment of the companies
Diatom species of the Australian sector of the Southern Ocean
No full textProgress Code: completedStatement: See the word document in the download file for further information.The collection aims to showcase the range of Southern Ocean diatom species found in the major hydrological provinces of the Australian Sector of the Southern Ocean along the 140 degrees E. The collection includes specimens collected in the Sub-Antarctic Zone (SAZ), Polar Frontal Zone (PFZ) and Antarctic Zone (AZ). <br/><br/>Samples were collected with McLane Parflux time series sediment traps placed at several depths in the SAZ (47 degrees S site), PFZ (54 degrees S site) and AZ and (61 degrees S site) during the decade 1997-2007. The shortest sampling intervals were eight days and corresponded with the austral summer and autumn, whereas the longest interval was 60 days and corresponded with austral winter. Split aliquots were obtained for taxonomic analysis via scanning electron microscopy (SEM). For improved taxonomic imaging, samples were treated with hydrochloric acid and hydrogen peroxide to remove carbonates and organic matter, respectively. A micropipette was used to transfer the suspension of selected samples to a round-glass cover slip following standard decantation method outlined by Barcena and Abrantes (1998). Samples were air-dried and coated with gold for SEM analysis. SEM analysis was carried out using a JEOL 6480LV scanning electron microscope.<br/><br/>Taxonomy <br/><br/>Diatoms include all algae from the Class Bacillariophyceae and follow the standardised taxonomy of World Register of Marine Species (WoRMS).<br/><br/>Order Asterolamprales<br/><br/>Family Asterolampraceae<br/>Asteromphalus hookeri Ehrenberg <br/>Asteromphalus hyalinus Karsten <br/><br/>Order Achnanthales<br/><br/>Family Cocconeidaceae<br/>Cocconeis sp. <br/><br/>Order Bacillariales<br/><br/>Family Bacillariaceae<br/>Fragilariopsis curta (Van Heurck) Hustedt <br/>Fragilariopsis cylindrus (Grunow) Krieger <br/>Fragilariopsis kerguelensis (O'Meara) Hustedt <br/>Fragilariopsis pseudonana (Hasle) Hasle <br/>Fragilariopsis rhombica (O'Meara) Hustedt <br/>Fragilariopsis separanda Hustedt <br/>Nitzschia bicapitata Cleve<br/>Nitzschia kolaczeckii Grunow<br/>Nitzschia sicula (Castracane) Husted var. bicuneata (Grunow) Hasle <br/>Nitzschia sicula (Castracane) Husted var. rostrata Hustedt <br/>Pseudo-nitzschia heimii Manguin <br/>Pseudo-nitzschia lineola (Cleve) Hasle <br/>Pseudo-nitzschia turgiduloides Hasle <br/><br/>Order Chaetocerotanae incertae sedis<br/><br/>Family Chaetoceraceae<br/>Chaetoceros aequatorialis var. antarcticus Cleve <br/>Chaetoceros atlanticus Cleve <br/>Chaetoceros dichaeta Ehrenberg <br/>Chaetoceros peruvianus Brightwell <br/>Chaetoceros sp.<br/><br/>Order Corethrales<br/><br/>Family Corethraceae<br/>Corethron spp. <br/><br/>Order Coscinodiscales<br/><br/>Family Coscinodiscaceae<br/>Stellarima stellaris (Roper) Hasle et Sims<br/><br/>Family Hemidiscaceae<br/>Actinocyclus sp.<br/>Azpeitia tabularis (Grunow) Fryxell et Sims <br/>Hemidiscus cuneiformis Wallich<br/>Roperia tesselata (Roper) Grunow <br/><br/>Order Hemiaulales<br/><br/>Family Hemiaulaceae<br/>Eucampia antarctica (Castracane) Mangin<br/><br/>Order Naviculales<br/><br/>Family Plagiotropidaceae<br/>Tropidoneis group <br/><br/>Family Naviculaceae<br/>Navicula directa (Smith) Ralfs <br/><br/>Family Pleurosigmataceae<br/>Pleurosigma sp. <br/><br/>Order Rhizosoleniales<br/><br/>Family Rhizosoleniaceae<br/>Dactyliosolen antarcticus Castracane<br/>Rhizosolenia antennata f. semispina Sundstrom <br/>Rhizosolenia antennata (Ehrenberg) Brown f. antennata<br/>Rhizosolenia cf. costata Gersonde<br/>Rhizosolenia polydactyla Castracane f. polydactyla<br/>Rhizosolenia simplex Karsten<br/>Proboscia alata (Brightwell) Sundstrom<br/>Proboscia inermis (Castracane) Jordan Ligowski<br/><br/>Order Thalassiosirales<br/><br/>Family Thalassiosiraceae<br/>Porosira pseudodenticulata (Hustedt) Jouse <br/>Thalassiosira ferelineata Hasle et Fryxell<br/>Thalassiosira gracilis (Karsten) Hustedt <br/>Thalassiosira lentiginosa (Janisch) Fryxell <br/>Thalassiosira oestrupii (Ostenfeld) Hasle var. oestrupii Fryxell et Hasle<br/>Thalassiosira oliveriana (O'Meara) Makarova et Nikolaev <br/>Thalassiosira tumida (Janisch) Hasle<br/> <br/>Order Thalassionematales<br/><br/>Family Thalassionemataceae<br/>Thalassionema nitzschioides var. lanceolatum Grunow<br/>Thalassiothrix antarctica Schimper ex Karsten <br/><br/><br/>Data available: 73 SEM images of the most abundant diatom species found at the three sampling sites. <br/><br/>Samples were collected by several sediment traps placed at different depths in the Subantarctic Zone (47 degrees S site), Polar Frontal Zone (54 degrees S site) and Antarctic Zone (61 degrees S site) during the decade 1997-2007. The collection site and date for each species image can be found in Table 1 (see the word document in the download file)
