Multivariate Pharma Technology Transfer Analysis: Civilization Diseases and COVID-19 Perspective.

The importance of studying civilization diseases manifests itself in the impact of changing lifestyles, on the number of deaths and causes of death. Technology transfer plays an important role in the prevention and treatment of these diseases. Through this, it is possible to transfer new treatments and diagnostics to clinics and hospitals more quickly and effectively, which leads to better healthcare for patients. Technology transfer can also aid in the development of new drugs and therapies that can be effective in the treatment of civilization diseases. The paper aims to evaluate the technology transfer process in the field of civilization diseases, using COVID-19 as an example of a pandemic that requires quick development and transfer of technology. To achieve the assumed goal, we propose a multivariate synthetic ratio in the field of civilization diseases (SMTT-Synthetic Measure of Technology Transfer) to analyze data from the Global Data database. We used sub-measures like SMTT_value (Synthetic Measure of Technology Transfer_value) and SMTT_quantity (Synthetic Measure of Technology Transfer_quantity) to measure technology transfer and put the data into a graph. Our analysis focuses on 14 diseases over a period of 10 years (2012-2021) and includes nine forms of technology transfer, allowing us to create a tool for analysing the process in multiple dimensions. Our results show that COVID-19 is similar in terms of technology transfer to diseases such as diabetes, cardiovascular diseases, neurodegenerative diseases, and breast cancer, even though data for COVID-19 is available for only 2 years.

Analysis of Sustainable Transport Systems in Service of Selected SEA-EU Consortium Countries' Airports-A Pilot Case Study of Passenger Choices for Gdansk Airport.

The article presents the results of a pilot study, namely a passenger survey on travel choices regarding commuting to the airport in one chosen location (Gdansk, Poland). The study aimed at establishing which factors which influenced their travel time, assessment of travel time, choosing more or less sustainable transport mode, and also single-mode or multimodal travel. Research results show that choice of the means of transport influences travel time, that the highest travel times are generated by bus and car travel and that assessing the travel time as acceptable or not depends on travel time. However, the longer the travel time, the more likely was the passenger to accept it. What is more, it appeared that a few factors influence choosing a more sustainable transport mode: the purpose of the trip, the start of the trip to the airport, place of living, and job situation.

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

The Ideas of Sustainable and Green Marketing Based on the Internet of Everything—The Case of the Dairy Industry

The use of advanced computer technologies has dramatically changed marketing. Concepts such as smart, sustainable, and green marketing have emerged in the last 20 years. One of these new technologies is the Internet of Things (IoT), which has led to the development of the activities and performances of industries in various dimensions. For the various objects, such as people, processes, and data, involved in marketing activities, the Internet of Everything (IoE) as an evolved IoT is a possible future scenario. Some sectors pretend to be the first to implement this, and the more they rely on dynamic, unstable customer needs, the better a solution the IoE is for them. Therefore, this paper presents a clear vision of smart, sustainable marketing based on the IoE in one of the fast-moving consumer goods (FMCG) industries, the dairy industry. Key factors are identified to help readers understand this concept better. The expert interview makes it possible to draw a picture of the factors that have helped successfully implement the IoE in the dairy sector.

Lockdowned: Everyday mobility changes in response to COVID-19.

Background: This paper looks into the impact of the recent COVID-19 epidemic on the daily mobility of people. Existing research into the epidemic travel patterns points at transport as a channel for disease spreading with especially long-distance travel in the centre of interest. We adopt a different approach looking into the effects that epidemic has on the transport system and specifically in relation to short-distance daily mobility activities. We go beyond simple travel avoidance behaviours and look into factors influencing change in travel times and in modal split under epidemic. This leads to the research problems we posit in this paper. We look into the overall reduction of daily travel and into the factors impacting peoples' decisions to refrain from daily traveling. This paper focuses on modes affected and explores differences between various societal groups. Methods: We use a CATI survey with a representative sample size of 1069 respondents from Poland. The survey was carried out between March, 24th and April, 6th2020, with a start date one week after the Polish government introduced administrative measures aimed at slowing down the COVID-19 epidemic. For data analysis, we propose using the GLM (general linear model), allowing us to include all the qualitative and quantitative variables which depict our sample. Results: We observe significant drops in travel times under epidemic conditions. Those drops are similar regardless of the age group and gender. The time decrease depended on the purpose of travels, means of transport, traveller's household size, fear of coronavirus, main occupation, and change in it caused by the epidemic. The more the respondent was afraid of coronavirus, the more she or he shortened the travel time.

Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy

All countries have suffered from the COVID-19 crisis;the pandemic has adversely impacted all sectors. In this study, we examine the transport sector with a specific focus on the problem of commuting mode choice and propose a new decision-making approach for the alternative modes after synthesizing expert opinions. As a methodology, a customized model of the recently developed best–worst method (BWM) is used to evaluate mobility choice alternatives. The survey reflects citizens’ opinions toward mobility choices in two Italian cities, Palermo and Catania, before and during the pandemic. BWM is a useful tool for examining mobility choice in big cities. The adopted model is easy to apply and capable of providing effective solutions for sustainable mode choice. The urban context is analyzed considering the importance of transport choices, evaluating the variation of resilience to the changing opinions of users.