ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE

Covid-19 has spread across the world, and several vaccines have been developed to counter its surge. To identify the correct sentiments that are associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets that are associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BERT and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models – specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative, and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task. © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License.

The Optimization of COVID-19 Detection Based on DesNet Model

In recent years, with the pandemic of COVID-19, how to identify the positive cases of COVID-19 accurately and rapidly from patients has become the key to block the spread of the epidemic and assist clinical diagnosis. In this paper, a COVID-19 detection model was constructed for the purpose to identify the positive cases from patients with other lung diseases as well as the normal using the chest X-ray images. The basic structure of the detection system is a CNN model based on DesNet with some optimization algorithms and the accuracy has reached 94.2%. We also applied three multi-sample data augmentation methods: SMOTE, mixup and CutMix to the model to analyze their performance. By applying these methods, the model finally reached 97.9% on test set and showed a good generalization on other datasets, which could reach over 80% without extra training. The results show that using transfer learning and some muli-sample data augmentation methods can significantly improve the accuracy and overcome overfitting problem of fewshot learning, while others may not be so effective. © 2023 IEEE.

Digital Exclusion and COVID-19: Does Communication Technology Correlate with Wellbeing?

To explore the implications of digital use on the wellbeing of older people during the pandemic. 33 adults aged 70 and above responded to an online and phone survey, a communication technology usage questionnaire, and the Personal Wellbeing Index-Adult (PWI-A). A Spearman test determined the correlation between frequency of communication technology usage and wellbeing. A significant medium correlation (r=.488, p=.004) was found between frequent digital communication usage and a higher average score on the PWI-A. Significant correlations were found between frequent technology use and health satisfaction (r=.377, p=.03), a sense of personal security (r=.404, p=.02), and a sense of future security (r=.597, p≤.001). Of all the communication platforms, video calls and emails yielded the most significant positive correlations with personal wellbeing. Results suggest that frequent users of communication technology felt greater levels of wellbeing and life satisfaction during the pandemic than non-frequent users.

Dynamic correlations of renewable-energy companies: Evidence from a multilayer network model

Against the background of seeking to achieve carbon neutrality, relationships among renewable-energy companies around the world have become multiple and complex. In this work, the Pearson, Kendall, tail, and partial correlation coefficients were applied to 51 global companies - including solar and wind firms, independent power plants, and utilities - to explore the linear, nonlinear, extreme-risk, and direct relations between them. Sample data from 7 August 2015 to 6 August 2021 were considered, and three sub-periods were extracted from these sample data by analysis of the evolution of multiple correlations combined with event analysis. A four-layer correlation network model was then constructed. The main results are as follows. (1) The multiple relations among the selected firms underwent dramatic changes during two external shocks (the China-US trade war and the COVID-19 pandemic). (2) The extreme-risk network layer verified that the trade war mainly affected the relationships among companies in the solar industries of China and the US. (3) During the COVID-19 pandemic period, the linear and direct relationships among wind firms from Canada, Spain, and Germany were significantly increased. In this sub-period, edge-weight distributions of the four different layers were heterogeneous and varied from power-law features to Gaussian distributions. (4) During all the sub-periods, most companies had similar numbers of neighbors, while the numbers of neighbors of a few companies varied greatly in the four different layers. These findings provide a useful reference for stakeholders and may help them understand the connectedness and evolution of global renewable-energy markets. © 2023 Author(s).

How Did the COVID-19 Crisis Affect the Efficiency of European Intraday Electricity Markets?

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models is more accurate than others. The focus is on the German intraday market for electricity. We want to find out whether the COVID-19 crisis has an influence on the price development. This paper includes a comprehensive review between Germany, France and Norway (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyse the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions (i) we reviewed the market data linked to COVID-19. We expected a higher volatility in the lockdowns than before and therefore decrease in efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer the question (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis we use data visualization and statistical models as well as sample and out-of-sample data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Proposed Quantitative Model for the Computation and Analysis of Waning Immunity for COVID-19 Virus among Human Population

Waning Immunity is an important and relevant concept during these days as the COVID-19 pandemic is expected to become endemic in the coming months. By definition, Waning Immunity is the loss of protective antibodies over time and hence necessitates booster shots at regular intervals of time. This quantitative study is on proposition of a model for computing a newly defined metric called Waning Immunity Index (WII). The model takes into account the three group of people namely, susceptible, infected and recovered individuals from the COVID-19 infections. The required data can be collected from the Kaggle repository that contains information on infections, recovery, vaccination and booster doses given on the human population while considering a geographical location. The proposed model and its implementation have thrown light on the spread, control and effect of COVID-19 virus. Results of the proposed model and the measurement can help health officials to seamlessly plan the duration of booster doses administered on vaccinated population. A sample data has been prepared for testing the model and the application of the proposed metrics. Based on the results, it is found that vulnerability of the Waning Immunity increases steeply at some duration and gradually steadies in time. © 2022 IEEE.

Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.