This study aimed to examine the compliance of post-disaster emergency assembly areas with their planning criteria in the Battalgazi district of Malatya province. This district is one of the settlements that was most affected by the two big earthquakes that occurred in Türkiye on 6 February 2023. The emergency assembly areas were evaluated qualitatively based on the criterion of “appropriateness”, with the sub-variables of “usability”, “accessibility”, and “safety”. They were also evaluated quantitatively based on the criterion of “adequacy” with the sub-variable “per capita m²”. There are a total of 103 neighborhoods in the district. However, there are only eight emergency assembly areas in total within its boundaries. According to the results of this study, only 7.5% of the current population of the district resides within 500 m of the emergency assembly areas. The fact that four emergency assembly areas (Hürriyet Park, Şehit Kemal Özalper High School, the Community Garden, Battalgazi Municipality) are situated next to each other and there are emergency assembly areas in only six of the 103 neighborhoods within the municipal boundaries shows that were significant problems in the decisions made regarding their locations. In addition, it was determined that there were disadvantages in terms of accessibility and usability within the criterion of appropriateness, while there were some positive aspects in terms of safety. When examined with regard to the criterion of adequacy, it was determined that the emergency assembly areas at Mişmiş Park, the Community Garden, Battalgazi Municipality, and Şehit Kemal Özalper High School were most adequate, while the emergency assembly areas at Hürriyet Park, Fırat Neighborhood Mukhtar, Nevzat Er Park, and 100 Yıl İmam Hatip Secondary School were least adequate.

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.

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 purpose of the study was to examine the role of personalization in motivating senior citizens to use AI driven fitness apps. Vroom’s expectancy theory of motivation was applied to examine the motivation of senior citizens. The responses from participants were collected through structured interviews. The participants belonged to South Asian origin belonging to India, Bangladesh, Nepal and Bhutan. The authors adopted a content analysis approach where the gathered interview responses were coded in the context of elements of Vroom’s theory. The findings of the study indicated that a highly personalized approach in the context of motivation, expectancy, instrumentality and valence will motivate senior citizens to use AI based fitness apps. The study contributes to the personalization of AI fitness apps for senior citizens.

The Indonesian government is currently carrying out massive infrastructure development, with a budget exceeding 10. Risk mapping based on good risk management is crucial for stakeholders in organizing construction projects. Projects financed by government, whether solicited or unsolicited schemes, should also include risk mapping to add value and foster partnerships. Therefore, this study aimed to develop a risk management model for solicited and unsolicited projects, focusing on the collaborative management system among stakeholders in government-financed projects. Risk review was conducted from various stakeholders’ perspectives, examining the impacts and potential losses to manage uncertainty and reduce losses for relevant parties. Furthermore, qualitative analysis was conducted using Focus Group Discussion (FGD) and in-depth interviews. The results showed that partnering-based risk management with risk sharing in solicited and unsolicited projects had similarities with Integrated Project Delivery (IPD). This approach provided benefits and value by developing various innovations in the project life cycle.

Recognizing the importance of competition analysis in telecommunications markets is essential to improve conditions for users and companies. Several indices in the literature assess competition in these markets, mainly through company concentration. Artificial Intelligence (AI) emerges as an effective solution to process large volumes of data and manually detect patterns that are difficult to identify. This article presents an AI model based on the LINDA indicator to predict whether oligopolies exist. The objective is to offer a valuable tool for analysts and professionals in the sector. The model uses the traffic produced, the reported revenues, and the number of users as input variables. As output parameters of the model, the LINDA index is obtained according to the information reported by the operators, the prediction using Long-Short Term Memory (LSTM) for the input variables, and finally, the prediction of the LINDA index according to the prediction obtained by the LSTM model. The obtained Mean Absolute Percentage Error (MAPE) levels indicate that the proposed strategy can be an effective tool for forecasting the dynamic fluctuations of the communications market.