There are numerous risks associated with high-rise buildings, which not only affect stakeholders during the design and construction phase but also impact the occupants and the surrounding environment during the post-occupancy phase. While previous studies examined the risks of high-rise building construction, less attention has been paid to the diverse impacts of high-rise buildings on their occupants. To fill this gap, this study applied a mixed-method approach (both quantitative and qualitative) to identify and prioritize their most significant social impacts. First, the possible social impacts of these buildings were identified via a literature review. The interrelationships among the identified factors were then determined by drawing on the opinions of relevant experts. Next, through the quantitative phase, the high-rise residential buildings of District 22 of Tehran were considered as a case study, and according to the opinions of 230 chosen residents, the level of influence of factors on one another was determined. The DEMATEL approach was employed subsequently to analyze the data and identify the most important and influential factors. Finally, through the qualitative phase, in-depth interviews were conducted with residents to explain and validate the results. The most significant and influential impacts identified by this study were anti-social behavior, lack of social cohesion, and lack of social contact with neighbors. This study assists designers and policymakers to adopt strategies that could mitigate the identified impacts and improve occupants’ social wellbeing more efficiently.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Objective. This article is devoted to the analysis of the concept of an agro-industrial cluster and the development of recommendations for improving competitiveness in the agro-industrial complex. Materials and methods. The research methodology is based on the collection and analysis of initial data and information. The article uses a systematic approach to the study of socio-economic processes and phenomena. The study is based on modern theories of competition, innovation, as well as the modern paradigm of the cluster development of the economy. Results. The conducted research makes a certain contribution to the development of theoretical science. A model of the structure of a regional industrial agricultural cluster is proposed. Practical recommendations can be used by economic and state authorities, heads of agricultural organizations, peasant (farm) enterprises, LPH. Conclusion. It is concluded that the characteristic features of the cluster and the effects arising in it increase the competitiveness of both individual members of the cluster and the cluster as a whole, and the competitiveness of the region in which the cluster is located, which, in turn, leads to an increase in the competitiveness of the national economy.

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies