This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe. An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans. Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.

Cyber-physical energy systems (CPES) describe a specialization of the cyber-physical system concept, in which energy systems are transformed into intelligent energy networks. These systems provide the basis for the realization of smart microgrids and smart grids. In the last decade, numerous research projects have intensively explored the fundamentals and modeling of CPES and validated them in pilot projects. In the meantime, more and more CPES solutions have been appearing on the market and the battle for the most suitable standards has begun. This paper gives an overview of the currently available standards for CPES sensor technologies and assesses the suitability for implementation. In two case studies in the application area of operational energy management in German companies, a sensor retrofitting is described—once with proprietary technology and once using the standards Long Range (LoRa) Wide Area Network and OPC Unified Architecture (OPC UA). As a result, the shortcomings of the standards for their use in CPES are shown and discussed. OPC UA, which was originally developed for the manufacturing industry, turns out to be to be a suitable standard for a wide range of CPES implementations.

Our paper tackles the development of media reporting during the COVID-19 pandemic, focusing on the January - November 2020 time span, in France, Germany, Romania, Spain, Switzerland, and the United Kingdom. We aim to make media reporting transparent on two dimensions: the coverage of COVID-19-related topics and the negativity of the COVID-19 media reporting. To achieve this goal, we analysed a large news dataset with 841,415 pieces of news—including 202,608 COVID-19 media reports—on an LSTM neural network. The news sentiment data and the corresponding coverage are set in relation to the WHO data on COVID-19 and to Google Trends. This compares the reality, that is WHO data, the perceived and reported reality, that is news data, and the actions based on the perceived and the actual reality, that is Google Trends. The results show that media reporting on COVID-19 is unprecedented in terms of coverage and negativity. Furthermore, the study quantifies how far media reporting detached from the facts after the first wave of COVID-19 and how an Infodemic spread across Europe.

REVIEW consumers' behaviour and their orientation towards e-commerce during the pandemic, our study might set starting points in designing entrepreneurial strategies for survival or development during disruptive times (e.g. health crises).