Wildlife–vehicle collisions (WVCs) represent a growing safety, ecological, and economic challenge, with direct consequences for human lives, material damage, and biodiversity conservation. In the Federation of Bosnia and Herzegovina, systematic analyses that link traffic accidents caused by collisions with wildlife are lacking. This research identifies high-risk locations of WVCs and applies geospatial analysis to the main roads of the Federation of Bosnia and Herzegovina. The analysis is based on official police reports documenting 14,169 traffic accidents between 2021 and 2023, of which 104 cases (0.73%) were classified as animal-related. Although species were not specified in the reports, these accidents predominantly occurred in areas where wildlife crossings are expected, and thus are treated as potential wildlife–vehicle collisions. The results indicate a concentration of WVCs in nine municipalities, with eight critical road segments identified on main roads. Additional analyses explored the relationship between collisions, road infrastructure (bridges, tunnels), and ecological features of habitats (Emerald Network, Natura 2000, Red List of FBiH, IUCN). Based on the findings, it can be concluded that spatially targeted prevention is essential, with priority given to infrastructural measures (wildlife overpasses, fencing, signage) and strategic measures (improved databases, continuous monitoring, and integration into spatial planning). The obtained results provide a foundation for policies that simultaneously enhance traffic safety and contribute to the protection of wildlife populations.

Connected and autonomous vehicles can potentially increase traffic safety by using various information and communication technologies (ICT). Data collected using technologies such as the Internet of Things (IoT) enables better traffic safety based on specific safety indicators. Modeling these indicators implies considering traditional traffic components such as driver-vehicle-road-environment. Eventually, if expressed in a suitable aggregate manner, traffic safety indicators can be presented and displayed to drivers to increase their attention and influence them to make decisions to avoid and mitigate traffic incidents. Existing driving risk assessment models usually consider a limited set of indicators related to individual drivers and their psycho-physical abilities which are important for participation in traffic. Data collected using IoT infrastructure alongside distributed computing and cloud technologies enables an expanded set of traffic safety indicators and a better assessment of driving risk. In this study, the common driver-vehicle-road-environment traffic safety indicators were considered and extended with the same indicators collected from neighboring drivers, weather conditions, surrounding awareness, and driver behavior data. We propose a novel architectural framework to provide dynamic driving risk assessment based on data collected using IoT technologies. The architectural framework provides a foundation for efficient data transmission between multiple sources and their processing, thus enabling the prediction of personal driving risk indicators.

Recently, the necessity of video testing at the point of reception has become a challenge for video distributors. This paper presents a new system framework for managing the quality of video degradation detection. The system is based on objective video quality assessment metrics and unsupervised machine learning techniques that use the dimensionality reduction of time series. It was demonstrated that it is possible to detect anomalies in the video during video streaming in soft real time. In addition, the model discovers degradations based on the visible correlation between adjacent images in the video sequence regardless the quick or slow change of a scene in the sequence. With additional hardware manipulations on the equipment on the user side, the proposed solution can be used in practical implementations where the need for monitoring possible degradations during video streaming exists.

Software processes consist of a complex set of activities required to deliver software products within predicted quality, costs, and deadlines. To accomplish such goals, a software organization needs a quality and mature software process as a prerequisite for success. Adopting Software Capability Maturity Model Integration (CMMI) represents a well-known path in the pursuit of mature software processes. However, its implementation is a subject of a permanent effort that implies different approaches and methods, and often leads to unsuccessful or limited success, though. This is especially emphasized in small software companies given the dynamic environment influenced by different factors, including insufficient resources, changes in technology, and staff turnover. In this paper, a case study of a small software company implementing software process improvement is presented. In a tailored approach to process improvement, a specific method using the balanced scorecard as input into the IDEAL model has been designed, enabling a narrow link between business goals and specific improvement goals. The results show that the software process and selected performance indicators were improved, and suggest the potential of the proposed approach in small organizations.

The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.

Urban mobility is one of the most significant factors in the successful development and sustainable future of large cities. The increasing demand for fast, safe, and eco-friendly transportation services is a trend in modern society. These requirements pose the challenge of finding corresponding solutions for efficient mobility of people in urban areas. However, many problems are caused by the increased traffic in cities, leading to high congestion, negative impacts on the environment, rising security challenges, etc. Therefore, the research community and other stakeholders have increased their focus on finding solutions for these issues. The Internet of Things (IoT) has enabled the development of efficient and cost-effective solutions to enhance urban mobility. Enabling IoT technologies has become a significant driver for smart mobility concept development. The continuous development of IoT has led to various applications focused on urban mobility improvement. This paper presents some IoT possibilities and potentials for developing solutions for smart urban mobility.

Software process improvement implies a set of complex and systematic activities of software engineering. It requires theory and models established in management, technical and social sciences. The improvement is based on the assumption that the organization if it owns mature and capable processes, would be able to deliver quality software on time and in line with predicted costs. The maturity models are initially aimed for implementation in enterprise software organizations, government organizations and within the military industry. Their complexity and the size make them difficult to use in small software organizations and companies. In such organizations the interest for use and the efforts to make an efficient and effective organization is always presented, though. In this paper, the basic and derived capability maturity models are described and cases from their implementation are analyzed, along with assessment of results of such projects in business practices. The problem of the software process improvement in small organizations is described, extracting the risks and recommendations for its enhancement. These recommendations are provided in order to set up a foundation for implementation of these models in a specific managerial and organizational environment characterized by small organizations.

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.