Hate speech in higher education institutions is a pressing issue that threatens democratic values and social cohesion. This research explores student perspectives on hate speech within the university setting, examining its forms, causes, and impacts on democratic principles such as freedom of expression and inclusivity. This research is extended to determine the debates and theories elaborated from different perspectives qualitative and quantitative analysis of data collected from 108 participants at Higher Education in Kosovo. From the communication standpoint, analyzing hate speech in the media and social media is key to understanding the type of message used, its emitter, how the message rallies supporters, and how they interpret message. The findings highlight the need for proactive policies and educational interventions to mitigate Research on hate speech in higher education in Kosovo is crucial for fostering social cohesion and inclusivity in its diverse society. Hate speech undermines the academic environment, negatively affecting students' mental health, learning outcomes, and overall well-being, necessitating efforts to create safer educational spaces. The study aligns with Kosovo's aspirations for European integration, emphasizing adherence to human rights and anti-discrimination principles. Despite the issue's significance, there is a lack of empirical data on hate speech in Kosovo's higher education, making this research vital for evidence-based policymaking. With a youth-centric focus, the study aims to educate and empower young people as future leaders to embrace respect and inclusivity. By addressing hate speech's local challenges and global relevance, the research supports institutional reforms and offers valuable insights for post-conflict and multicultural societies. Hate speech while fostering a culture of mutual respect and democratic engagement.

Consumers waste significant amounts of food. Food waste presents a substantial problem for the environment, society and economy. Addressing the food waste challenge is crucial for fostering sustainable behavior and achieving the Sustainability Development Goal 12.3 agenda. Norms are a significant determinant in motivating consumers to prevent food waste and could be activated by other factors. Religiosity has the potential to influence norms related to food waste behavior. This study investigated how religiosity affects the intentions of consumers to minimize food waste. The interplay of religiosity, personal norms, subjective norms, and intention to avoid food waste was examined by the extended norm activation model. Data were obtained from Muslim consumers in Indonesia. Structural equation modeling evaluation showed that religiosity positively affects the intention to prevent food waste. The intention to avoid food waste is more closely associated with personal norms compared to subjective norms. Personal norms mediate the religiosity and food waste reduction intention relationship. Consumer awareness activates personal norms by making them feel accountable for food waste’s negative impact. These findings provide insights to stakeholders in developing policies to mitigate the food waste issue.

The aim of this research is to determine the incidence of socioeconomic variables in migration flows from the main countries of origin that form part of the international South-North migration corridor, such as Mexico, China, India, and the Philippines, during the 1990–2022 period. The independent variables considered are GDP per capita, unemployment, poverty, higher education, and public health, while the dependent variable is migration flows. An econometric panel data model is implemented. The tests conducted indicate that all variables have an integration order of I (1) and exhibit long-term equilibrium. The econometric models used, Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS), reveal that unemployment and poverty had the strongest influence on migration flows. In both models, within this international migration corridor, GDP per capita, higher education, and health follow in order of importance.

The purpose of this study is to analyze how the entrepreneurial mindset, social context, and entrepreneurial ambitions of university students in the United Arab Emirates (UAE) have progressed over time in terms of starting their businesses. The research aims to investigate the evolution of the entrepreneurship mindset, considering the implementation of educational and governmental policies over the past decade to promote entrepreneurship among UAE university graduates. To collect primary data and evaluate the impact of the studied variables on the dependent variable “entrepreneurial ambitions,” a self-created questionnaire was used. The results reveal a positive correlation between personal context variables and entrepreneurial ambitions, as well as between personality traits and entrepreneurial ambitions. Furthermore, the study demonstrates the constructive effect of education, government policies, and capital availability on fostering entrepreneurial ambitions in the UAE.

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.