As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.

Sustainable, improved rescue operations contribute to both the safety of traffic participants and the quality of transportation services. From this perspective, we considered the need to determine to what extent the target audience, consisting of owners and/or users of road vehicles, is prepared to purchase and use an eCall In-Vehicle System (eCall IVS) for passenger vehicles, using the Technology Acceptance Model (TAM) as a research framework. To confirm the hypotheses of our research, we resorted to structural equation modelling (SEM), based on the analysis of variance (PLS—partial least squares), and we were able to validate all the causal relationships of the conceptual model, with significant path coefficients at a chosen significance threshold of p < 0.01. Analyzing the obtained results achieved in the field of eCall IVS, one can state that the integration of these approaches with the TAM research framework gives a much more complete picture of a driver’s acceptance of using an aftermarket eCall IVS, which can also serve as a starting point in aftermarket vehicle implementation and future studies.

Ensuring a higher degree of road safety contributes both to the quality of transport services and to the level of the costs involved in rescue operations. The initiation points of managing a rescue operation are situational awareness on accident detection and the appropriate assessment of the required resources. The eCall in Vehicle System (eCall IVS) for passenger vehicles aims to minimize rescue team arrival times to accident sites, and meet assignment needs in the rescue chain. Implementation costs and benefits for the eCall IVS have been investigated, providing implications for rescue operations management. The findings show that the benefits of eCall IVS implementation outweigh the costs, and savings achieved in the rescue operations are obtained by shortening the time to reach the place of the accident and by efficient intervention through a more accurate allocation of the necessary resources, due to timely and relevant information.

Increasing road traffic safety is a requirement for the growth and development of economies and society in general, and any endeavour contributing to an increase in the safety of road traffic participants must be evaluated and even encouraged, as there is no measurement for the value of human life. An important technological improvement that contributes to road traffic safety by reducing response time in case of an accident is the eCall In Vehicle System, which provides real-time data on-site, even without human intervention. The aim of the present study is to approach the introduction of the eCall In Vehicle System from a cost-benefit standpoint of equipping aftermarket passenger vehicles with this technology. To this end, we propose an analytic research approach in the form of a conclusive study on statistical data, in order to support decisions regarding future implementation.

The public interest towards renewable and clean energy is an important part of shaping consumer behavior and policy towards these topics. A “big data” approach towards assessing the public interest for various topics consists in using freely available search frequency data for the Google search engine, through the Google Trends service. The search data frequency can be used to assess public opinion for a variety of topics, such as medicine, climate change and environmental concerns, finance, and economics etc. A study of the public interest towards wind energy topics is reported here. Six Google search keywords (“Wind power”, “Wind energy”, “Offshore wind”, “Wind farm”, “Wind turbine” and “Wind generator”) were investigated in the 2004–2020-time range. All keywords except “Offshore wind” show a steady decrease from a 2008 – 2010 maximum up to 2015, followed by a period limited change in the 2015 – 2020 range. The interest towards offshore wind topics follows a similar trend but increases in frequency starting from 2015 and reaches a maximum in 2018. Overall, the Google Trends data show a decrease of public interests towards most wind energy topics, with the exception of “Offshore wind”, for English speaking users, in the 2004–2020-time range.