In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO₂ emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas.

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R²) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R² and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R² values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

The aim of this research is to enhance desirability optimization models to create a global healthcare competitiveness index (GHCI) covering 53 countries with gross domestic product per capita (GDP PC) of over $10,000. The GHCI is defined as an index that reveals the progress and quality of the healthcare systems in countries providing their patients with easier access opportunities to healthcare services within the scope of this work. Methods of statistical analysis have been adopted together with optimization models and techniques in this research. The optimum and feasible values of the factors considered influential on objective functions have been determined as the basis of healthcare expenditure (HE) and GHCI in those relevant countries. Those released optimum outcomes are displayed between 0.64 and 0.66 in terms of desirability value. The GHCI values of those aforementioned countries range from 0 to 6. The computed average of the GHCI values of those countries is estimated as 2.4758. Finally, GHCI values of 53 countries have been calculated to set the current basis of desirability optimization models. These findings will be deemed as the basic essence of those prospective theories to be established for the future researches to constitute a new index to measure the competitiveness of healthcare systems in various countries all over the world.

In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients who are not urgent, or who may be treated as outpatients in ESs. By applying discrete-event simulation on a 1/24 (daily) and 7/24 (weekly) basis, and by employing ANs, it was determined that the number of patients that were treated increased by 26.71% on a 1/24 basis, and by 15.13% on a 7/24 basis. The waiting time that was spent from the admission to the ES until the treatment time decreased by 38.67% on a 1/24 basis and 53.66% on a 24/7 basis. Similarly, the length of stay was reduced from 82.46 min to 53.97 min in the ES. Among the findings, it was observed that the efficiency rate of the resources was balanced by the employment of ANs, although it was not possible to obtain sufficient efficiency from the resources used in the ESs prior to the present study.

Recent developments in global financial markets revealed that cryptocurrencies experienced rapid growth due to the popularity of blockchain technology and its evolving position in the digital finance industry. The rise of cryptocurrencies led economists to question generally accepted financial practices. Particularly the interaction between two different types of financial markets arose as a hot research topic to discover specific relationships and differences between major cryptocurrencies and fiat currencies. Therefore, this article aims to examine analyze by attaching importance to the Bitcoin to investigate significant linkages and analyze critical direct and indirect connections. In this research, Bitcoin—which is known as the most prominent cryptocurrency on the market—and 50 different conventional currencies are taken into consideration by applying cross-correlation, HT (hierarchical tree), and MST (minimum spanning tree) methods. The results of this work can be utilized by academicians and economists for further research related to the subject.

The purpose of this research was to establish the urban competitiveness index (UCI) by using the statistical optimization method for the econophysics approach. With this technique, economic data regarding urban areas and the factors affecting UCI have been determined. The research covers 30 urban centres located in 15 countries worldwide. Urban centres with the gross domestic product per capita of $10,000 or more were taken into consideration. The significant levels of the factors were determined with the statistical optimization method, and optimum values were calculated with the developed optimization models. Re‐index values were calculated and compared with the results of PricewaterhouseCoopers and World Economic Forum. According to the results, the high UCI value of these locations depends not only on economic data but also on high values of social factors. Thus, those locations are becoming the centre of attraction for investments and capital with increasing competitiveness.