This study analyzes the interaction between legitimacy, innovation, uncertainty, and electric vehicle (EV) purchase intention in Spain, Portugal, Italy, and Greece. Using partial least squares structural equation modeling (PLS-SEM) and data from 2016 to 2023, the relationships between these key variables are assessed. The results show that legitimacy has a positive impact on purchase intention, while innovation influences legitimacy but does not directly affect purchase intention. Uncertainty moderates these relationships in complex ways. The findings suggest that enhancing the perception of legitimacy is crucial to increase EV purchase intention, and strategies promoting innovation and managing uncertainty can improve market acceptance.

This study investigates the relationships among entrepreneurship, technical competency, and business performance, focusing on CEOs in the beauty service industry in the Busan area. A total of 215 survey responses were collected, with 213 valid responses selected for final analysis after excluding 2 unsuitable responses. The key findings of the study are as follows: First, entrepreneurship was found to partially influence technical competency. Second, technical competency was found to influence business performance. Third, entrepreneurship was found to partially influence business performance. Fourth, technical competency was found to partially mediate the relationship between entrepreneurship and business performance. Based on these results, the study systematically analyzes and explains the causal relationships among the entrepreneurship of CEOs in the beauty service industry, their technical competency, and business performance. It also seeks to provide useful reference materials for strengthening the innovation and competitiveness of CEOs in the beauty service industry and establishing a theoretical foundation for future research in related fields.

Onion (Allium cepa L.) is one of the important vegetables in Egypt. The study was conducted in the vegetable field to study the effect of different rates of phosphorus fertilizers and foliar application of Nano-Boron, Chitosan, and Naphthalene Acidic Acid (NAA) on growth and seed productivity of Onion plant (Allium cepa L., cv. Giza 6 Mohassan). The experiments were carried out in a split-plot design with three replicates. The main plot contains 3 rates of phosphorus treatments (30, 45 and 60 kg P₂O₅/feddan), Subplot includes foliar application of Nano-Boron, Nano-Chitosan and Naphthalene Acidic Acid (NAA) at a concentration of 50 ppm for each and sprayed at three times (50, 65 and 80 days after transplanting). Increasing the phosphorus fertilizers rate to 60 kg P₂O₅/fed significantly affects the growth and seed production of the Onion plant. Foliar application of nano-boron at 50 ppm concentration gave maximum values of onion seed yield in both seasons. Results stated that the correlation between yield and yield contributing characters over two years was highly significant. It could be recommended that P application at a rate of 60 kg P₂O₅ and sprayed onion plants at 50 ppm nano-boron three times (at 50, 65, and 80 days from transplanting) gave the highest seed yield of onion plants. Moreover, the maximum increments of inflorescence diameter (94.4%) were recorded to nano-boron foliar spray (60 p × nB) compared to the other treatments in both seasons.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.