Cities are multifunctional by definition, and an increasingly significant function is the tourist function. City tourism is one of the most dynamically developing forms of tourism. Tourists' decisions regarding choosing a destination are influenced by a number of factors determining the subjective assessment of the tourist attractiveness of a given city, and one of them may be the state of air pollution, as it can have a negative impact on the health of both city dwellers and tourists. This article is an attempt to determine whether potential tourists consider information about the level of a city's air quality in the assessment of its tourist attractiveness and the impact of this information on their travel decisions. The article presents the results of surveys conducted among a group of 509 respondents from Poland. On this basis, an assessment was made of the extent to which information on the condition of air quality in a given city is relevant for persons planning a tourist trip. In the conducted research, decisions regarding both business and private trips were evaluated. In addition, information on factors that could increase the respondents' interest in the condition of air quality in the city of the intended trip (e.g., trip with children, trip length) was collected. Due to the fact that tourism is a significant source of income for many cities, the research results presented in the article may be of significant importance for entities creating the urban tourist product and responsible for its management. The article also draws attention to the fact that reducing pollution in cities can contribute to increases in their tourist attractiveness.
An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination (R2) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515.
The concept of a sharing economy, as part of a wider collaborative economy concept, is among the most important economic and technological trends that will influence socioeconomic development in the future. Interest in using the opportunities offered by sharing platforms is increasing; hence, the subject is a current and important issue. Confidence in technology, service providers and application providers is a key issue when making decisions about using such solutions. The aim of the paper is to examine the level of trust in sharing economy business models considering two groups of factors, trust in people and in technology, among several demographic groups. The paper has an empirical character and the results are provided on the basis of a survey conducted in Szczecin, Poland, with 403 respondents who are current and potential users of sharing platforms. The obtained results show that platform management requires more attention focused on building mutual trust networks among participants rather than strengthening the confidence in using the technology.Sustainability 2019, 11, 6838 2 of 26 supply side. This paper presents the results of empirical research, which, together with the results obtained by other researchers, can enrich the current state of knowledge.The aim of the paper is to examine the level of trust in sharing economy business models considering two groups of factors, trust in people and in technology, among several demographic groups. Although there are papers concerning the impact of social referrals and technological enablers on trust in sharing platforms [8], our research differs in factors that characterize each of these groups. The paper has an empirical character and the results are provided based on a survey conducted by the authors. The analytical part of the study is based on answers given by 403 respondents from Szczecin, Poland. The statistical analysis of the results was conducted with the use of basic statistics measures, as well as Cronbach's alpha measure, nonparametric Kolmogorov-Smirnoff test, Kruskal-Wallis test, centre of gravity (CoG), histograms, box plots, and radar charts.The paper is divided as follows: First we introduce the idea of the sharing economy as a business model that can act for sustainability, then we provide a literature review on trust in the sharing economy. After that, we explain our research procedure and methods, discuss the main findings and provide managerial implications for sharing platforms.
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