The sheer complexity of the factors influencing decision-making has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. On the other hand, agricultural products need proper planning and decision-making, like any country’s economic pillars. This is while the segmentation of customers and the analysis of their behavior in the manufacturing and distribution industries are of particular importance due to the targeted marketing activities and effective communication with customers. Customer segmentation is done using data mining techniques based on the variables of purchase volume, repeat purchase, and purchase value. This article deals with the grouping of agricultural product customers. Based on this, the K-means clustering method is used based on the Davies–Bouldin index. The results show that grouping customers into three clusters can increase their purchase value and customer lifespan.
Muslim scholars have defined ethics as enduring traits and characteristics in the individual that cause actions appropriate to those traits to be issued spontaneously without the need for human thought and reflection. Islamic ethics state the rightness or wrongness of these attributes within the framework of Islamic concepts, while the concepts of Islamic work ethics deal with the functioning of the framework of Islamic concepts in the form of human work activities in various organisations. Furthermore, work ethics can be effective in the organisation when it can shape the culture of the organisation. Research shows that Islamic work ethics have a significant relationship with various individual, professional and organisational factors. The purpose of this study is to investigate the relationship between Islamic work ethics and organisational culture. The statistical population of this research consists of 1500 Muslim staff of 30 service organisations (financial, educational, medical and hotel organisations) in Moscow, Russia, in 2021, of which 306 people have been selected as statistical samples using Krejcie and Morgan’s sample size table. Data analyses were performed by statistical software, Statistical Package for the Social Sciences (SPSS). The results of this study confirm the significant and positive relationship between Islamic work ethics and organisational culture among the Muslim Russian staff (β = 0.53; T = −8.65).Contribution: This study examines the relationship between Islamic work ethics and organisational culture in Russia and has expanded the results of previous studies conducted in other contexts.
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.
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