scholarly journals Estimating lightning NOx production over South Africa

2021 ◽  
Vol 117 (9/10) ◽  
Author(s):  
Bathobile Maseko ◽  
Gregor Feig ◽  
Roelof Burger
Keyword(s):  
South Africa ◽  
Detection Efficiency ◽  
Model Performance ◽  
Tropospheric Chemistry ◽  
Anthropogenic Sources ◽  
Data Set ◽  
Lightning Strikes ◽  
Improve Model ◽  
The Usa ◽  
The Many

Nitrogen oxides (NOx = NO + NO2) are toxic air pollutants and play a significant role in tropospheric chemistry. Global NOx hotspots are the industrialised regions of the USA, Europe, Middle East, East Asia and eastern parts of South Africa. Lightning is one of the many natural and anthropogenic sources of NOx to the troposphere. It plays a role in the formation of particulate matter and tropospheric ozone, which are both linked to harmful health and climate effects. The discourse on NOx over the southern African continent has mainly focused on anthropogenic sources. However, lightning is known to be a main source of tropospheric NOx globally. It is therefore important to understand its contribution to the national and global NOx budget. Data from the South African Lightning Detection Network were used to approximate the influence of lightning on the NOx load over the country, and to develop a gridded data set of lightning-produced NOx (LNOx) emissions for the period 2008 2015. The Network monitors cloud-toground lightning strikes; and theoretically has a detection efficiency of 90% and a location accuracy of 0.5 km. An emission factor of 11.5 kg NO2/flash was employed to calculate the LNOx budget of ~270 kt NO2/year. The calculated LNOx was 14% of the total NOx emission estimates published in the EDGAR v4.2 data set for the year 2008. The LNOx emission inventory will improve model performance and prediction, and enhance the understanding of the contribution of lightning to ambient NO2.

Epistemic Ruptures in South African Standpoint Knowledge-Making: Academic Feminism and the #FeesMustFall Movement

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Desiree Lewis ◽  
Cheryl Margaret Hendricks
Keyword(s):  
South Africa ◽  
South African ◽  
Political Thought ◽  
Economic Restructuring ◽  
Student Protests ◽  
Political Processes ◽  
Academic Feminism ◽  
The Many ◽  
Radical Knowledge ◽  
Intellectual Activism

Alongside the many structural and political processes generated by the #FeesMustFall student protests between 2015 and 2016 were narratives and discourses about revitalising the transformation of universities throughout South Africa. It was the very notion of “transformation,” diluted by neo-liberal macro-economic restructuring from the late 1990s, that students jettisoned as they increasingly embraced the importance of “decolonisation.” By exploring some of the key debates and interventions driven by the #FeesMustFall movement, we consider how earlier trajectories of feminist knowledge-making resonate with these. The article also reflects on how aspects of intellectual activism within the student protests can deepen and push back the frontiers of contemporary South African academic feminism. In so doing, it explores how radical knowledge-making at, and about, universities, has contributed to radical political thought in South Africa.

The “Green Revolution”, Ethics and Engineering in the USA

1992 ◽  
Vol 25 (9) ◽  
pp. 235-243 ◽  
Author(s):  
W. F. Garber ◽  
D. R. Anderson
Keyword(s):  
Green Revolution ◽  
Final Analysis ◽  
Clean Water ◽  
Ethical Decisions ◽  
Societal Needs ◽  
Economic Methods ◽  
The Usa ◽  
Scientific Technical ◽  
The Many ◽  
Technical Organizations

Ethical behavior applied to any activity within our society is, in the final analysis, the responsibility of each of the individuals involved in that activity. The “Green Revolution”, which erupted in the U.S.A. resulted in conditions which presented difficult ethical decisions to individuals and organizations working on ecological/environmental questions. The problems posed are best observed in an examination of the enforcement of the U.S.A. Clean Water Act where construction workers, the media, regulators, lawyers, politicians, environmentalists, treatment facility operators, scientists, engineers, academics and scientific/technical organizations all substantially benefited. Unfortunately this legislation does not require ecological or net environmental improvement. It requires equitable distribution of the costs of compliance throughout the nation. This has encouraged nonscientific standards and criteria, and a narrow focus, which have in turn resulted in both nonresponsible environmental results, and costs such that other important ecological/societal needs cannot be funded. All societies, whether developing or industrialized, must conserve their resources by utilizing scientific/economic methods to attack clean water and similar problems if they are to really improve their ecology/environment. Since this procedure is minimally used in the U.S.A., what should or can be the ethical positions of the many individuals and groups now benefiting by the present flawed system?

scholarly journals Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

2021 ◽  
Vol 7 ◽  
pp. 237802312110244
Author(s):  
Katrin Auspurg ◽  
Josef Brüderl
Keyword(s):  
Social Sciences ◽  
Causal Reasoning ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Skin Tone ◽  
Data Set ◽  
The Social ◽  
The Many ◽  
Definition Of

In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

Economics of respiratory disease in dairy replacement heifers

2020 ◽  
Vol 21 (2) ◽  
pp. 143-148
Author(s):  
Michael W. Overton
Keyword(s):  
Respiratory Disease ◽  
Milk Production ◽  
Incident Case ◽  
Data Set ◽  
Dairy Heifers ◽  
The Usa ◽  
History Of ◽  
On Farm ◽  
The Impact ◽  
Selective Culling

AbstractBovine respiratory disease (BRD) is a frequent disease concern in dairy cattle and is most commonly diagnosed in young dairy heifers. The impact of BRD is highly variable, depending on the accuracy and completeness of detection, effectiveness of treatment, and on-farm culling practices. Consequences include decreased rate of weight gain, a higher culling risk either as heifers or as cows, delayed age at first service, delayed age at first calving, and in some cases, lower future milk production. In this data set of 104,100 dairy replacement heifers from across the USA, 36.6% had one or more cases diagnosed within the first 120 days of age with the highest risk of new cases occurring prior to weaning. Comparison of the raising cost for heifers with BRD and those without a recorded history of BRD resulted in an estimated cost per incident case occurring in the first 120 days of age of $252 or $282, depending upon whether anticipated future milk production differences were considered or not. Current market conditions contributed to a cost estimate that is significantly higher than previously published estimates, driven in part by the losses associated with selective culling of a subset of heifers that experienced BRD.

scholarly journals Boundaries of solidarity: a meta-ethnography of mask use during past epidemics to inform SARS-CoV-2 suppression

2021 ◽  
Vol 6 (1) ◽  
pp. e004068
Author(s):  
Po Man Tsang ◽  
Audrey Prost
Keyword(s):  
South Africa ◽  
Risk Perception ◽  
Healthcare Access ◽  
Individual Responsibility ◽  
Trust In Government ◽  
Political Leaders ◽  
Collective Levels ◽  
The Social ◽  
Social Meanings ◽  
The Usa

BackgroundMany countries aiming to suppress SARS-CoV-2 recommend the use of face masks by the general public. The social meanings attached to masks may influence their use, but remain underinvestigated.MethodsWe systematically searched eight databases for studies containing qualitative data on public mask use during past epidemics, and used meta-ethnography to explore their social meanings. We compared key concepts within and across studies, then jointly wrote a critical synthesis.ResultsWe found nine studies from China (n=5), Japan (n=1), Mexico (n=1), South Africa (n=1) and the USA (n=1). All studies describing routine mask use during epidemics were from East Asia. Participants identified masks as symbols of solidarity, civic responsibility and an allegiance to science. This effect was amplified by heightened risk perception (eg, during SARS in 2003), and by seeing masks on political leaders and in outdoor public spaces. Masks also acted as containment devices to manage threats to identity at personal and collective levels. In China and Japan, public and corporate campaigns framed routine mask use as individual responsibility for disease prevention in return for state- or corporate-sponsored healthcare access. In most studies, mask use waned as risk perception fell. In contexts where masks were mostly worn by patients with specific diseases (eg, for patients with tuberculosis in South Africa), or when trust in government was low (eg, during H1N1 in Mexico), participants described masks as stigmatising, uncomfortable or oppressive.ConclusionFace masks can take on positive social meanings linked to solidarity and altruism during epidemics. Unfortunately, these positive meanings can fail to take hold when risk perception falls, rules are seen as complex or unfair, and trust in government is low. At such times, ensuring continued use is likely to require additional efforts to promote locally appropriate positive social meanings, simplifying rules for use and ensuring fair enforcement.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Hospital acquired pressure injury prediction in surgical critical care patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Performance ◽  
Routine Care ◽  
Predictor Variables ◽  
Classification Algorithms ◽  
Data Set ◽  
Icu Patients ◽  
Pressure Injury ◽  
Surgical Icu ◽  
Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Peanut Web Blotch: I. Cultural Characteristics and Identity of Causal Fungus1

1984 ◽  
Vol 11 (2) ◽  
pp. 109-114 ◽  
Author(s):  
R. A. Taber ◽  
R. E. Pettit ◽  
G. L. Philley
Keyword(s):  
South Africa ◽  
Cultural Studies ◽  
Mycelial Growth ◽  
Fungal Species ◽  
Optimum Temperature ◽  
Cultural Characteristics ◽  
Foliar Disease ◽  
The Usa

Abstract A foliar disease of peanuts, previously unreported in the USA, was found in Texas in 1972. The pathogen was identified as a species of Ascochyta. Further cultural studies have revealed this fungus to be Phoma arachidicola Marasas, Pauer, and Boerema. Pycnidia form profusely at 20 C and 25 C. Pycnidiospores are borne on short pycnidiosphores and are predominantly one-celled in culture. Spores produced in pycnidia on infected leaflets become 1 septate. Large 1-septate spores, as well as an occasional 2-septate spore, may form in culture. Optimum temperature for mycelial growth in 20 C; little or no growth occurs at 5 C or above 30 C. The teleomorphic state develops in the field on fallen leaflets and can be induced to form in the laboratory on sterilized peanut leaflets between 15 and 20 C. Cultures derived from single ascospores form pseudothecia. Pycnidiospores, ascospores, and chlamydospores are all infective units. Because this fungus produces hyaline ascospores and pseudoparaphyses, it has been transferred to the genus Didymella as Didymella arachidicola (Choch.) comb. nov. Comparisons with 15 isolates causing web blotch of peanut in the USA, Argentina, and South Africa indicate that web blotch symptoms are produced by the same fungal species.

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