Insiders: Louisiana journalists Sallie Rhett Roman, Helen Grey Gilkison, Iris Turner Kelso

Sallie Rhett Roman, Helen Grey Gilkison and Iris Turner Kelso were three women journalists in Louisiana, active in consecutive time periods from 1891 to 1996. Their work brings up five particular questions. First, Why did these women start working and how did they negotiate public employment? Second, how did they balance the relationship between work and home since they did find employment outside of the home? Third, how did they fit into their contemporary image of women and journalists? Fourth, how did they use written language to portray a particular voice to the reader for a particular purpose? Fifth, did they choose to cover specifically male or female topics in their articles? Answering these questions reveals that these three women challenged traditional roles for women in different ways. Sallie Rhett Roman, wrote from 1891 to 1909, had to negotiate much more strict societal norms for women and portrayed herself as a male writer to her audience. Helen Grey Gilkison, active from the late 1920s to the late 1940s, did not mask her gender but encouraged the idea that she was a member of the political social club with political access. Iris Turner Kelso, working after World War II, portrayed herself as having access to politicians and political events, but only as a way to suggest that she could provide the reader the “straight scoop.” Each woman in her own way created an image of herself as an insider in the political process. By relying on the image of “insider,” these women did not overtly challenge the political or social system but rather supported it. Kelso was the only one who criticized those in politics and even she did not promote significant change in the political systems of New Orleans or the state of Louisiana

The Perfect Storm in Higher Education

Higher education has always faced challenges, but what happens when colleges and universities are facing a ‘perfect storm?’ One of the victims of a pandemic, rising tuition costs, and less funding could be the traditional classroom or worse still a dramatic decrease in student enrollment. In this paper, we explore some of the elements that could make it more difficult to fulfill the American dream of attending a university for the campus life and what might lie in the future for students post COVID-19

On the quality and value of probabilistic forecasts of wind generation

International audienceWhile most of the current forecasting methods provide single estimates of future wind generation, some methods now allow one to have probabilistic predictions of wind power. They are often given in the form of prediction intervals or quantile forecasts. Such forecasts, since they include the uncertainty information, can be seen as optimal for the management or trading of wind generation. This paper explores the differences and relations between the quality (i.e. statistical performance) and the operational value of these forecasts. An application is presented on the use of probabilistic predictions for bidding in a European electricity market. The benefits of a probabilistic view of wind power forecasting are clearly demonstrated

Ten Things Faculty Should Know Before Stepping Into Administration

Every year professors leave the ranks of the faculty and become members of the administration.  Yes, even jaded faculty sometimes become members of the administration and have to pursue charges and challenges previously unrecognized or unfathomed.  It is to these individuals the authors have prepared a list of ten items they feel all potential college and university administrators should be aware of before making the commitment. These items should not be construed to be a roadblock, but rather advance notice that the life of an administrator is very different from that of faculty member

Data mining for wind power forecasting

International audienceShort-term forecasting of wind energy production up to 2-3 days ahead is recognized as a major contribution for reliable large-scale wind power integration. Increasing the value of wind generation through the improvement of prediction systems performance is recognised as one of the priorities in wind energy research needs for the coming years. This paper aims to evaluate Data Mining type of models for wind power forecasting. Models that are examined include neural networks, support vector machines, the recently proposed regression trees approach, and others. Evaluation results are presented for several real wind farms

Uncertainty estimation of wind power forecasts: Comparison of Probabilistic Modelling Approaches

International audienceShort-term wind power forecasting tools providing “single-valued” (spot) predictions are nowadays widely used. However, end-users may require to have additional information on the uncertainty associated to the future wind power production for performing more efficiently functions such as reserves estimation, unit commitment, trading in electricity markets, a.o. Several models for on-line uncertainty estimation have been proposed in the literature and new products from numerical weather prediction systems (ensemble predictions) have recently become available, which has increased the modelling possibilities. In order to provide efficient on-line uncertainty estimation, choices have to be made on which model and modelling architecture should be preferred. Towards this goal we proposes to classify different approaches and modelling architectures for probabilistic wind power forecasting. Then, a comparison is carried out on representatives models using real data from several wind farms

Probabilistic short-term wind power forecasting based on kernel density estimators

International audienceShort-term wind power forecasting tools have been developed for some time. The majority of such tools usually provide single-valued (spot) predictions. Such predictions are however often not adequate when the aim is decision-making under uncertainty. In that case there is a clear requirement by end-users to have additional information on the uncertainty of the predictions for performing efficiently functions such as reserves estimation, unit commitment, trading in electricity markets, a.o. In this paper, we propose a method for producing the complete predictive probability density function (PDF) for each time step of the prediction horizon based on the kernel density estimation technique. The performance of the proposed approach is demonstrated using real data from several wind farms. Comparisons to state-of-the-art methods from both outside and inside the wind power forecasting community are presented illustrating the performances of the proposed method

Success in the Online Classroom: Lessons Learned

In the early 2000s, we embarked on research to study online education. At the time, online courses offered by traditional institutions was in its’ infancy. Through our research, we learned that increasing students’ intrinsic motivation could lead to more successful learning environments. Today’s online learning environments are afforded many more technological advances that were not available 20 years ago. In addition, the Covid19 Pandemic has forced the creation online learning environment. Therefore, we believe that revisiting the elements that lead to successful online learning is timely and necessary. Through this research, we affirm that technological advancements have led to more meaningful ways to enhance online learning environments