• Energy Research

Only recently, smart cities are taking shape, thanks to the rapid development of Internet of Things (IoT), cloud computing, and other similar technologies. Given the high demands placed on advanced technologies such as autonomous driving, cloud data services, and high-precision sensors, smart cities are creating an intelligent transportation environment conducive to the introduction of autonomous vehicles (AVs). In this context, the use of AVs in transportation is also considered a form of transportation innovation. As a result, AVs are considered more favorable to people interested in new technologies because they appear to be technologically superior. Their association with the most up-to-date technology can serve as a symbol for those who wish to demonstrate their interest in new technologies through their appearance. The positive image of technological innovation projected by AVs may influence their acceptance among technology enthusiasts to a significant degree. In this context, this study investigates the effects of perceived advantage, perceived risk, and perceived safety on the intention to use autonomous vehicles. For this purpose, data were collected from vehicle users living in Turkey by survey method. Secondly, factor analyses and regression analyses were performed with the data set obtained from 611 participants. As a result of the analyses, it has been determined that the perceived advantage and perceived security increase the intention to use autonomous vehicles. In contrast, the perceived risk reduces this intention to use. According to these results, recommendations were made to the companies about the level of acceptance of this technology by the users to assess their investments in autonomous vehicles better.

Only recently, smart cities are taking shape, thanks to the rapid development of Internet of Things (IoT), cloud computing, and other similar technologies. Given the high demands placed on advanced technologies such as autonomous driving, cloud data services, and high-precision sensors, smart cities are creating an intelligent transportation environment conducive to the introduction of autonomous vehicles (AVs). In this context, the use of AVs in transportation is also considered a form of transportation innovation. As a result, AVs are considered more favorable to people interested in new technologies because they appear to be technologically superior. Their association with the most up-to-date technology can serve as a symbol for those who wish to demonstrate their interest in new technologies through their appearance. The positive image of technological innovation projected by AVs may influence their acceptance among technology enthusiasts to a significant degree. In this context, this study investigates the effects of perceived advantage, perceived risk, and perceived safety on the intention to use autonomous vehicles. For this purpose, data were collected from vehicle users living in Turkey by survey method. Secondly, factor analyses and regression analyses were performed with the data set obtained from 611 participants. As a result of the analyses, it has been determined that the perceived advantage and perceived security increase the intention to use autonomous vehicles. In contrast, the perceived risk reduces this intention to use. According to these results, recommendations were made to the companies about the level of acceptance of this technology by the users to assess their investments in autonomous vehicles better.

The world is changing faster than ever before. Continuous changes are also affecting the higher education sector. The number of programs on offer is growing, attraction strategies are changing, and the branding and positioning of higher education institutions are gaining significance. The growing influence of social networks on personal choice cannot be ignored. Of the world’s population, 57% uses social networks and spends an average of 2.5 h a day on them. The most popular social network, Facebook, has up to 2.9 billion active users every month. Therefore, the questions arise as to which factors influence one or another consumer choice, how social networks contribute to brand awareness, and what impact brand has on the higher education sector. After systematic and comparative analysis of concepts published in the scientific literature, the analysis of brand, brand promotion concepts, and factors that increase brand awareness is performed. This study seeks to determine whether and to what extent individual factors influencing student motivation and social networking influence the distinctiveness of a higher education institution brand and how factors influencing student motivation and social networking affect the distinctiveness of higher education institutions’ brands in general. The results of this study can help higher education institutions to develop their own plans, strategies, and good practices. Research methods: systematic and comparative analysis of concepts and methods published in the scientific literature, mathematical and statistical methods, statistical processing, and expert survey.

The world is changing faster than ever before. Continuous changes are also affecting the higher education sector. The number of programs on offer is growing, attraction strategies are changing, and the branding and positioning of higher education institutions are gaining significance. The growing influence of social networks on personal choice cannot be ignored. Of the world’s population, 57% uses social networks and spends an average of 2.5 h a day on them. The most popular social network, Facebook, has up to 2.9 billion active users every month. Therefore, the questions arise as to which factors influence one or another consumer choice, how social networks contribute to brand awareness, and what impact brand has on the higher education sector. After systematic and comparative analysis of concepts published in the scientific literature, the analysis of brand, brand promotion concepts, and factors that increase brand awareness is performed. This study seeks to determine whether and to what extent individual factors influencing student motivation and social networking influence the distinctiveness of a higher education institution brand and how factors influencing student motivation and social networking affect the distinctiveness of higher education institutions’ brands in general. The results of this study can help higher education institutions to develop their own plans, strategies, and good practices. Research methods: systematic and comparative analysis of concepts and methods published in the scientific literature, mathematical and statistical methods, statistical processing, and expert survey.

Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

Personal security is one of the many factors that must be assessed comprehensively when planning an urban competitiveness strategy. The aim of this article is to assess the competitive position of the social environment of cities in terms of security with regard to time and other competing cities. Having conducted a systemic and comparative analysis of the concepts published in the scientific literature, we analysed the concepts of sustainable cities and the social environment, reviewed the particularities of urban competitiveness, conducted research into the methods of assessing urban competitiveness, and presented an integrated assessment model (MDK) of social environment competitiveness in terms of security in the Baltic capitals. The following research methodology was used: systemic and comparative analysis of concepts and methods published in the scientific literature, statistical processing and multicriteria assessment methods. The results of the study can be used as a tool to determine the competitive position of a city in terms of time and other competing cities, a tool to identify factors that strengthen or weaken the sustainable competitiveness of cities, a tool to justify strategic decisions of cities, and a tool to determine the effectiveness of the strategic decisions taken.