The agronomic and oenological behavior of the Pinot noir grape variety was studied in relation to different rootstocks on the Agroscope estate in Leytron (VS): 3309 C, 5 BB, Fercal, 41 BMGt, Riparia Gloire, 420 AMGt, 101-14 MGt and 161-49 C. Rootstock primarily influenced vigor, speed of vine establishment, and mineral nutrition of the graft. Riparia Gloire, 41 BMGt, 420 AMGt and 161-49 C rootstocks were less vigorous and, for the last three, induced a lower nitrogen and potassium supply leading to the production of slightly more acidic wines. The less vigorous rootstocks and 101-14 MGt were slightly more sensitive to water stress.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

The main purpose of this study is to investigate the effect of environmental transformational leadership on organizational citizenship behavior through the mediating role of perceived meaningful work in Tehran District 22 Municipality. The study population in this study is the employees of the municipal district of District 22 of Tehran. The number is about 400 people, and the sample size was obtained according to Cochran’s 196 formula. The research method in the present study is applied in terms of purpose and descriptive in terms of implementation method. The Kolmogorov-Smirnov test was used to test the normality of the data, which proved with 95% confidence that the variables had an abnormal distribution. Therefore, due to the abnormality of the data distribution, Pls software was used to analyze the data. The results showed that environmental transformational leadership has an effect on organizational citizenship behaviors.

The goal of this study is to examine how external prestige (PEP) affects workplace deviations, which are mediated by job satisfaction. The study’s sample consisted of 310 respondents who work in the hospitality industry in Nigeria, and data was collected using the purposive sampling method. Structural Equation Model (SEM) tests were performed. According to the study’s findings, job satisfaction is positively influenced by PEP, but it has a negative impact on deviant conduct in the workplace. It is clear that job satisfaction plays a detrimental role in mediating the harmful impacts of perceived external status on deviant behavior at work.

The study focuses on the employees’ behavioral intentions towards the usage of disruptive technology in the industry. The digital technology application in consumer, retail, and hospitality, education and training, financial services, the health sector, infrastructure, government, and airports. The study objectives were to explore the possible adoption of innovation and creativity changes and their acceptance by the employees in the organization. To identify the variables impacting behavioral intention and analyze how these variables relate to perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. A structured questionnaire was used to collect data from 335 respondents, who were selected based on their relevance to the study objectives. The questionnaires were distributed through the Google Forms application, and the data were collected and analyzed periodically. The findings of the study provide valuable insights into the behavioral intention towards disruptive technologies in Kuala Lumpur and Putrajaya locations in Malaysia and highlight the significance of factors such as perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. The research contributes to the existing body of knowledge on Industry 4.0 by providing empirical evidence and practical implications for organizations seeking to leverage disruptive technologies in their operations management.