To achieve greater sustainability of the traffic system, the trend of traffic accidents in road traffic was analysed. Injuries from traffic accidents are among the leading factors in the suffering of people around the world. Injuries from road traffic accidents are predicted to be the third leading factor contributing to human deaths. Road traffic accidents have decreased in most countries during the last decade because of the Decade of Action for Road Safety 2011–2020. The main reasons behind the reduction of traffic accidents are improvements in the construction of vehicles and roads, the training and education of drivers, and advances in medical technology and medical care. The primary objective of this paper is to investigate the pattern in the time series of traffic accidents in the city of Belgrade. Time series have been analysed using exploratory data analysis to describe and understand the data, the method of regression and the Box–Jenkins seasonal autoregressive integrated moving average model (SARIMA). The study found that the time series has a pronounced seasonal character. The model presented in the paper has a mean absolute percentage error (MAPE) of 5.22% and can be seen as an indicator that the prognosis is acceptably accurate. The forecasting, in the context of number of a traffic accidents, may be a strategy to achieve different goals such as traffic safety campaigns, traffic safety strategies and action plans to achieve the objectives defined in traffic safety strategies.

The rapid advancement of artificial intelligence (AI) and natural language processing has led to the development of increasingly sophisticated and versatile language models. AI models are capable of generating new data based on patterns and structures learned from existing data, and they can create multimedia content. These generative AI models rely on unsupervised machine learning techniques and neural networks to analyze, understand, and generate content that closely resembles human-generated output. Among these, ChatGPT, an AI chatbot developed by OpenAI, has emerged as a powerful tool with diverse applications across various fields. In recent years, the scientific and academic communities have devoted significant attention to researching and developing the ChatGPT chatbot. As of November 2024, over 212,000 articles have been published on ChatGPT across national and international journals and conferences, according to Google Scholar. The growing development of the ChatGPT chatbot is poised to significantly reshape the education sector and raises concerns within the academic community regarding how future generations should be taught. For instance, this technology can be used to efficiently write articles or essays in seconds, potentially reducing the need for human involvement. This paper provides a literature review on the potential applications of the ChatGPT chatbot in education, as well as the key challenges and risks associated with its use. The integration of chatbots like ChatGPT and other AI models in education offers numerous opportunities to enhance the learning experience for students and support teachers. However, to fully harness their educational potential, it is crucial to approach the use of these models with caution, critically assessing their limitations and potential biases.

Traffic accidents on the roads are one of the most common causes of human death and human injuries in modern societies. Progress in collecting data about subject of research has introduced changes in various fields of science and engineering. The field of transport engineering tracks progress and trends in other areas. One of the main tasks of smart cities' projects is the analysis of large amounts of data collected from the participants in road traffic. These data are used for the purposes of traffic planning, traffic regulation, traffic congestion management and for increasing public safety. Traffic accident databases are the most important source of data for analyzing and defining preventive actions. A large amount of data about traffic accidents are collected, but they are available for analysis to a few institutions and people. Concept of open data represents a new concept that has been started to apply in the Republic of Serbia. The paper presents a partial analysis of open data for traffic accidents in the city of Belgrade in 2016.

Micro: bit is a new learning device that makes it easy to learn programming and electronics. This device enables the development of logical thinking, problem solving skills and digital literacy in students in general. Using the micro: bit enables relatively easy programming in different programming environments on different computers and mobile phones. Additional functionalities of the micro: bit are achieved by extending it with special elements. The research was conducted in a primary school in the Republic of Serbia. The pupils were divided into a control and an experimental group, with the students in the experimental group using the micro: bit device. The results of the work show that the use of the micro: bit in class has a positive effect on the students' performance in the subject "Technique and Technology" for the lesson "Managing processes and things remotely using information and communication technologies". In addition, the students' thoughts about the use of the micro: bit in class were revealed.

Wireless sensor networks with specific hardware configuration and possibilities for their network lifetime improvement are examined in this paper. Nodes in the network are equipped with solar cells. Storing solar energy in batteries is a very cost-effective and long lasting solution for sensor nodes that are difficult to reach for regular maintenance. Solar power provides more power for more frequent data acquisition and greater communication distance. The downside of this approach is battery lifetime, not a single charge lifetime but the total overall number of times battery can be recharged before it fails. Here we propose a design of the whole sensor node configuration and, most importantly, hardware and software improvements of sensor nodes for increasing battery life and thus network lifetime. A survey of relevant literature is provided, as well as the analysis of experimental data from referenced authors. Some experiments have been done to discuss the testing methods of other researchers. Good result