The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.

Due to the increase of the amount of electrical and electronical equipment waste (e-waste), the understanding of individual consumers’ main decision triggers represents a key point in increasing the quantity of recycled e-waste. A series of studies from the literature have shown a positive relationship between the consumers’ attitude, awareness, self-efficacy, social norms, and their e-waste recycling intention, as well as the positive influence between the intention and the manifested behavior. Additional to these determinants, in the present study, the influence of social media was analyzed along with the actions taken by the government and nongovernmental organizations, with the purpose to include and to capture, as much as possible, a high amount of determinants in the e-waste recycling process. Nevertheless, the demographic or socio-economic variables, such as age, gender, income, education, number of family members, etc., have shown over time to have a contribution to predicting the consumers’ pro-recycling behavior. As on one side, in the research literature, the opinions related to which of the demographic or socio-economic factors can have an impact on the recycling behavior have been divided and, on another side, a series of researchers believe that the discrepancies in the findings of different studies can be due to culture in various countries, in this paper we conducted such an analysis with reference to the Romania’s case. The results have shown that the demographic variables, such as age and gender, can have a contribution to predicting residents’ pro-e-waste recycling behavior. Based on these findings, the policymakers can gain a better understanding of the e-waste recycling phenomenon and on its main triggers, with results in creating better policies for sustaining a proper e-waste managing system.

The novel coronavirus (SARS-CoV-2) has imposed the need for a series of social distancing restrictions worldwide to mitigate the scourge of the COVID-19 pandemic. This applies to many domains, including airplane boarding and seat assignments. As airlines are considering their passengers’ safety during the pandemic, boarding methods should be evaluated both in terms of social distancing norms and the resulting efficiency for the airlines. The present paper analyzes the impact of a series of restrictions that have been imposed or mooted worldwide on the boarding methods used by the airlines, featuring the use of jet-bridges and one-door boarding. To compare the efficacy of classical airplane boarding methods with respect to new social distancing norms, five metrics were used to evaluate their performance. One metric is the time to complete the boarding of the airplane. The other four metrics concern passenger health and reflect the potential exposure to the virus from other passengers through the air and surfaces (e.g., headrests and luggage) touched by passengers. We use the simulation platform in NetLogo to test six common boarding methods under various conditions. The back-to-front by row boarding method results in the longest time to complete boarding but has the advantage of providing the lowest health risk for two metrics. Those two metrics are based on passengers potentially infecting those passengers previously seated in the rows they traverse. Interestingly, those two risks are reduced for most boarding methods when the social distance between adjacent passengers advancing down the aisle is increased, thus indicating an unanticipated benefit stemming from this form of social distancing. The modified reverse pyramid by half zone method provides the shortest time to the completing boarding of the airplane and—along with the WilMA boarding method—provides the lowest health risk stemming from potential infection resulting from seat interferences. Airlines have the difficult task of making tradeoffs between economic productivity and the resulting impact on various health risks.

The occurrence of the novel coronavirus has changed a series of aspects related to people’s everyday life, the negative effects being felt all around the world. In this context, the production of a vaccine in a short period of time has been of great importance. On the other hand, obtaining a vaccine in such a short time has increased vaccine hesitancy and has activated anti-vaccination speeches. In this context, the aim of the paper is to analyze the dynamics of public opinion on Twitter in the first month after the start of the vaccination process in the UK, with a focus on COVID-19 vaccine hesitancy messages. For this purpose, a dataset containing 5,030,866 tweets in English was collected from Twitter between 8 December 2020–7 January 2021. A stance analysis was conducted after comparing several classical machine learning and deep learning algorithms. The tweets associated to COVID-19 vaccination hesitancy were examined in connection with the major events in the analyzed period, while the main discussion topics were determined using hashtags, n-grams and latent Dirichlet allocation. The results of the study can help the interested parties better address the COVID-19 vaccine hesitancy concerns.

Research background: Over the last ten years, a substantial amount of scholarly research has delved into energy communities (ECs) from diverse viewpoints. These ECs are extremely important in setting the pathway to a clean energy transition. Purpose of the article: Our objective is to glean valuable insights from publications indexed in the Web of Science (WoS) database to deepen our comprehension of ECs and their academic discourse. Methods: Data analytics, factorial analysis, and more complex natural language processing (NLP) techniques such as latent Dirichlet allocation (LDA) are implemented to extract valuable insights from over 1000 WoS publications relevant in the EC field. The primary contribution of this study lies in furnishing details regarding the key contributors to the EC scholarly landscape, including authors, their affiliations, universities, and countries of origin. Additionally, we aim to elucidate the prevalent keywords and thematic approaches employed in their research endeavors. Findings & value added: Considering the extracted dataset, an annual growth rate of 21.15% has been recorded, highlighting the research community’s interest in the field of ECs. Furthermore, three topics are optimally obtained. Overall, a coherence score of 0.44 suggests that the LDA model performs adequately in terms of topic interpretation. Topic 1 relates to community-based energy initiatives. Topic 2, featuring terms like “grid,” “study” and “EU” alongside “energy” and “community,” suggests a focus on energy systems. Topic 3 includes terms such as “generation,” “analysis” and “consumption,” indicating a topic that is centered around the technical or analytical aspects of energy production and usage. This study underscores how the alignment between state laws and EU directives in supporting ECs can serve as a model for other regions. The findings suggest that similar policy frameworks could be effectively adapted to different national contexts, providing valuable insights for countries looking to enhance their renewable energy strategies.

The airplane turnaround time costs money to the airline companies and, over the years, it has been determined that the best way to reduce it is by using efficient boarding strategies. Many boarding strategies have been proposed but a consensus as to which is the best method has not been reached yet. The aim of this paper is to gather and test all these strategies considering the same initial conditions in order to help airline companies identify the best boarding strategy. Minimizing the costs not only contributes to airlines’ sustainability and long-term performance, but also influences their ticketing policy, and has an impact on their customers’ choices. Moreover, airports can benefit from the fact that the airline companies are optimizing their boarding strategies as they can extend the number of services offered to more airlines during one day without investing in new infrastructure. Thus, this paper considers 24 boarding strategies and builds a configurable agent-based model using NetLogo 6.0.2 to perform different simulations. The results are analysed using grey systems theory considering the aircraft dimensions, aircraft occupancy and whether or not passengers are carrying hand luggage that may need storage.