The existing data indicates a steady decrease in the grey wolf (Canis lupus) population of Bosnia and Herzegovina (BiH), but despite this there remains no official protective legislation in place for the species. In an attempt to address the issue of protective legislation, we initiated monitoring projects on the grey wolf in BiH with the support of the Rufford Foundation. The aim of these projects was to obtain data on the presence, activity, abundance, and behavior of wolves, while continuously expanding the area of monitoring. Monitoring has been conducted via camera trapping since 2015. Cameras were set up at several localities, at one of which a confrontation between a wolf and European brown bear (Ursus arctos) was recorded. Since these two apex predators have recolonized common regions and habitats across Europe, resource competition and the possibility of inter-specific conflict is more likely. These conflicts may jeopardize the continued existence and future expansion of populations of both bears and wolves in these recolonized habitats. Accordingly, it is very important to study the nature of their coexistence, and the resulting data is ultimately essential for helping to create or resume conservation management plans for both species. Moreover, these data can help highlight areas for data collection and monitoring, thus providing important baseline information for survey planning.

Shortly after the first publication on the new disease called Coronavirus Disease 2019 (Covid-19), studies on the causal consequences of this disease began to emerge, initially focusing only on transmission methods, and later on its consequences analyzed in terms of gender, age, and the presence of comorbidities. The aim of our research is to determine which comorbidities have the greatest negative impact on the worsening of the disease, namely which comorbidities indicate a predisposition to severe Covid-19, and to understand the gender and age representation of participants and comorbidities. The results of our study show that the dominant gender is male at 54.4% and the age of 65 and older. The most common comorbidities are arterial hypertension, diabetes mellitus, and cardiovascular diseases. The dominant group is recovered participants aged 65 and older, with comorbidities most frequently present in this group. The highest correlation between patients with different severity of the disease was found with cardiovascular diseases, while the coefficient is slightly lower for the relationship between patients with different disease severity and urinary system diseases and hypertension. According to the regression analysis results, we showed that urinary system diseases have the greatest negative impact on the worsening of Covid-19, with the tested coefficient b being statistically significant as it is 0.030 < 0.05. An increase in cardiovascular diseases affects the worsening of Covid-19, with the tested coefficient b being statistically significant as it is 0.030 < 0.05. When it comes to arterial hypertension, it has a small impact on the worsening of Covid-19, but its tested coefficient b is not statistically significant as it is 0.169 > 0.05. The same applies to diabetes mellitus, which also has a small impact on the worsening of Covid-19, but its tested coefficient b is not statistically significant as it is 0.336 > 0.05. Our study has shown that comorbidities such as urinary system diseases and cardiovascular diseases tend to have a negative impact on Covid-19, leading to a poor outcome resulting in death, while diabetes mellitus and hypertension have an impact but without statistical significance.

Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi) media researchers. They call this area of study “Quality of Experience” (QoE). In this paper, we present a theoretical model based on review of previous QoE’s models. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them; therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. This novel top-down model serves as a practical guide for structuring QoE experiments, ensuring the incorporation of influential factors in a confirmatory manner.

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

Requirements elicitation has since long been recognized as critical to the success of requirements engineering, hence also to the success of systems engineering. Achieving sufficient scope and quality in the requirements elicitation process poses a great challenge, given the limited slices of budget and time available for this relatively sizeable activity. Among all predominant requirements elicitation techniques and approaches, operational scenarios development has a special standing and character. The set of operational scenarios is acknowledged as a constituent deliverable in the requirements engineering process, serving many purposes. Hence, ensuring success in the development of operational scenarios constitutes a consequential area of research. In this paper we present the results from an industrial survey on experienced and presumptive success factors in the development of operational scenarios. The survey was done using a strength-based approach, involving engineers and managers in two organizations developing cyber-physical systems in the transportation and construction equipment businesses. Our results suggest that operational scenarios reusability and a collaborative operational scenarios development environment are two prime areas for success. Our study provides two contributions. First, we provide an account of success factors in the view of practitioners. This is fundamental knowledge, since a successful deployment of any state-of-the-art approach and technology in a systems engineering organization needs to take the views of the practitioners into consideration. Second, the study adds input to the body of knowledge on requirements elicitation, and can thereby help generate suggestions on direction for future work by researchers and developers.

Introduction: Social support is not a one-way relationship but is based on the connections people have with other people, groups, and the wider community. This study aimed to assess the perception of social support by people in the third age and to investigate the correlation of social support with the sociodemographic characteristics of the respondents. Methods: A quantitative cross-sectional study was conducted with 147 elderly people who actively use the services of the Center for Health Promotion and Improvement “Generacija” in Sarajevo. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to assess social perceptions. Results: The results show a weak negative relationship between age and the total scale (r = −0.199, p = 0.05), with older people having lower scores on the total scale. A significant relationship was found between the subscale other factors and age (r = −0.202, p = 0.05). The evaluation of the performance of daily activities correlates weakly with the evaluation of the friend’s subscale (r = 0.186, p = 0.05). The friend’s subscale correlates significantly with the quality of social life (r = 0.227, p = 0.05). The subjective assessment of the quality of social life after arriving at the center showed a correlation with the overall scale score (r = 0.182, p = 0.05) and especially with the friend subscale (r = 0.219, p = 0.05), with the increase in social life and the subscales examined in both cases. Conclusion: Users of the “Generacija” center rate social support on the MSPSS with high scores, with users receiving the most support from family. The sociodemographic characteristics of the respondents have an impact on the perception of social support by the users of the Center for Health Promotion and Improvement “Generacija,” more specifically; they were statistically significantly influenced by age, the way of performing daily activities, the quality of social life and the quality of social life after arrival at the Center.

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4% - 90% in critical network conditions when compared to state-of-the-art.

Introduction: Previous studies have found that, in addition to the general factors for the occurrence of pain and reduced mobility of the cervical spine, the use of electronic devices promotes these, the excessive use of which can also lead to the occurrence of depressive symptoms in students. The aim of this study was to determine the mobility limitation of the cervical spine in students with reported neck pain, to determine the degree of disability and depression due to neck pain, to determine the correlation of mobility limitation of the cervical spine with the degree of disability and depression of students, and to determine the correlation of the degree of disability with the degree of depression. Methods: The research was conducted as a cross-sectional study from May to July 2021 at the University of Zenica in four faculties. The study used the General Questionnaire and two standardized questionnaires to assess disability due to neck pain (Index of Disability due to Neck Pain) and the degree of depression (patient health questionnaire). Results: A total of 147 students with reported neck pain participated in the study. A limitation of mobility was found in 30.6% of the students in flexion, 25.2% in rotation, 23.8% in lateral flexion, and 20.4% on extension. Mild disability due to neck pain was found in 58.5% of students, moderate in 23.8%, and severe in 2.7%. 45.6% of the students had mild depression, 18.4% had moderate depression, and 5.4% had severe depression.Conclusion: Restricted flexion and rotation are more common than restricted lateral flexion and extension of the cervical spine. About half of the students who reported neck pain had a mild degree of disability and mild depression. A strong positive correlation was found between the degree of disability and depression in students with neck pain.

Graphical abstract

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study’s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.

Clusters of neurons generate electrical signals which propagate in all directions through brain tissue, skull, and scalp of different conductivity. Measuring these signals with electroencephalography (EEG) sensors placed on the scalp results in noisy data. This can have severe impact on estimation, such as, source localization and temporal response functions (TRFs). We hypothesize that some of the noise is due to a Wiener-structured signal propagation with both linear and nonlinear components. We have developed a simple nonlinearity detection and compensation method for EEG data analysis and utilize a model for estimating source-level (SL) TRFs for evaluation. Our results indicate that the nonlinearity compensation method produce more precise and synchronized SL TRFs compared to the original EEG data.

Detecting failures early in cloud-based software systems is highly significant as it can reduce operational costs, enhance service reliability, and improve user experience. Many existing approaches include anomaly detection in metrics or a blend of metric and log features. However, such approaches tend to be very complex and hardly explainable, and consequently non-trivial for implementation and evaluation in industrial contexts. In collaboration with a case company and their cloud-based system in the domain of PIM (Product Information Management), we propose and implement autonomous monitors for proactive monitoring across multiple services of distributed software architecture, fused with anomaly detection in performance metrics and log analysis using GPT-3. We demonstrated that operations engineers tend to be more efficient by having access to interpretable alert notifications based on detected anomalies that contain information about implications and potential root causes. Additionally, proposed autonomous monitors turned out to be beneficial for the timely identification and revision of potential issues before they propagate and cause severe consequences.