Benchmarking a sustainable energy engineering undergraduate degree against curriculum frameworks and pedagogy standards from industry and academia

There is an urgent need for educational institutions to produce graduates with appropriate skills to meet the growing global demand for professionals in the sustainable energy industry. For universities to stay at the forefront of meeting this global demand from industry, universities need to ensure their curricula and pedagogies stay relevant. The use of benchmarking is a key means of achieving this and ensuring any gap between university curricula and the practical needs of industry is minimized. The aim of this paper is to present an approach to benchmarking a sustainable energy engineering undergraduate degree with respect to curriculum frameworks recommended by industry and pedagogy standards required and recommended by academia and education research. The method uses the Murdoch University renewable energy engineering degree major as a case study. The results show that the learning outcomes of the renewable energy engineering units, in general, align well with the recommended learning outcomes for a complete sustainable energy degree, as prescribed by the Australian Government Office for Learning and Teaching. In addition, assessment task and marking criteria for the capstone unit of the major were at Australian Universities’ standard. A similar approach to benchmarking can be adopted by developers of new or existing sustainable energy engineering degrees in order to align with curriculum frameworks and pedagogy standards required by industry and academic peers

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Développement de modèles de queues et d'invariance d'échelle pour l'estimation régionale des débits d'étiage.

L’article propose une méthodologie d’analyse régionale des débits d’étiage; cette approche repose sur la combinaison de deux procédures. (i) La méthode d’invariance d’échelle simple des séries régionales de débits d’étiage, basée sur l’analyse de la relation moments–surfaces ou de la relation quantiles–surfaces; en d’autres termes cette méthode explique la variabilité spatiale des processus d’étiage par leur indexation sur une série de paramètres d’échelle, essentiellement la taille des bassins de drainage. Cette procédure a comme rôle principal, la délimitation des régions homogènes, qui caractérise la première condition d’une estimation régionale. (ii) L’analyse de fréquence des débits minimums avec l’approche par modèles de queue conditionnels, qui consiste à ajuster une distribution de probabilité aux données qui sont inférieures ou égales à un certain seuil de censure. En fait, cette procédure accorde plus de poids à la partie inférieure de la distribution en définissant a priori un niveau de censure des données principalement un seuil ui, appelé niveau plafond « ceiling value ». Cette méthode vérifie la deuxième condition d’estimation régionale, à savoir la détermination du modèle d’estimation régional. Cette méthodologie a été appliquée à l’analyse des caractéristiques des débits d’étiage de 187 stations hydrométriques réparties dans la province de Québec. Les résultats ont montré que les moments non centraux d’ordre un à six suivent une invariance d’échelle simple par rapport à la superficie du bassin versant. Suite à ce résultat, une analyse des résidus des six moments a permis de déterminer les bassins hydrologiquements semblables. Sur la base du résultat de l’analyse des résidus du sixième moment nous avons scindé la province en deux régions homogènes. Le comportement des débits minimums des différentes régions a montré qu’eux aussi suivaient une invariance d’échelle simple. Abstract This paper proposes a methodology for the regional analysis of drought flows. This approach lies on the combination of two procedures. (i) The simple scale invariance method for regional series of drought flows, based on the analysis of the relation moments-surfaces or the relation quantiles-surfaces, explains the spatial variability of drought processes by their indexation on a series of scale parameters, essentially the size of the drainage basin. This procedure principally aims at delimitating homogeneous regions, and this characterizes the first condition of a regional estimation. (ii) Frequency analysis of minimum flows, with the approach using tail conditional models, consists in adjusting a probability distribution to values that are smaller than or equal to some given threshold. As a fact, this procedure gives some more weight to the lower part of the distribution by defining a priori a level of censure, mainly a threshold ui called ceiling value. This procedure establishes the second condition of a regional estimation, that is, the determination of a regional estimation model. This methodology has been applied to analyze drought flow characteristics of 187 hydrometric stations scattered in the province of Quebec. Results have shown that the first to the sixth order non-central moments follow a simple scale invariance with respect to the area of the watershed. As a result, analysis of the residuals of these six moments has allowed classifying hydrologically similar watersheds. On the basis of the results from the analysis on the residuals of the sixth moment, the province has been divided into two homogeneous regions. The behaviour of the minimum flows from these different regions has shown that they, too, followed a simple scale invariance.[Journal translation

Treatment of Zeroes in Tail Modeling of Low Flows.

The presence of zeroes in a record of low flows usually has been interpreted as being indicative of a stream that goes dry during some periods. However, in some instances, zeroes may appear in a gauging record simply because the actual discharge was below a measurement threshold. For example, in some regions it is sometimes true that the lower limit of a stream gauge is above the true stream bottom, and any (censored) discharges not registered by the gauges are recorded as zeroes. This paper presents a comparison of alternative methods for treatment of zeroes in low-flow estimation procedures when tail models are employed for modeling purposes. Simulation experiments indicate that a Weibull tail model, as fitted using a likelihood-based method for inclusion of censored data, is the best estimator of the 10-year low-flow quantile when zeroes in a data set have arisen as a consequence of censoring. When zeroes in a data set exist because the stream is an ephemeral one, a lognormal mixed tail model fitted using maximum likelihood is the best performer. When one is not sure whether zeroes in a data set are real, it is recommended that the 10-year low-flow quantile be estimated by treating the zeroes as censored values and performing the estimation using the Weibull tail model fitted using maximum likelihood