Introduction Serological detection of SARS-CoV-2-specific immunoglobulins G (IgG) and M (IgM) antibodies is becoming increasingly important in the management of the COVID-19 pandemic. Methods We report the first results of COVID-19 serological testing in Bosnia and Herzegovina on 2841 samples collected and analysed in 2 medical institutions in Sarajevo. Antibody detection was performed using commercially available kits. Results In the first cohort, 43 IgM-positive/IgG-negative and 16 IgM-positive/IgG-positive individuals were detected, corresponding to 3.41% of participants having developed antibodies. In the second cohort, 4.28% participants were found to be IgM-negative/IgG-positive. Conclusions Our results suggest the need for population-wide serological surveying in Bosnia and Herzegovina.

Authors analyse and conceptually problematise specific phenomena of ‘two schools under one roof’ in Bosnia and Herzegovina. They argue that education in Bosnia and Herzegovina has been routinely exposed to various, contradicting demands and pressures, which result in, among other contradictions, ‘two schools under one roof’, which presents one of the world’s phenomenon within education. The authors are eager to present this specific education issue to the global public and provide some answers on various consequences, which appeared in this contemporary segregation form in Bosnia and Herzegovina. One of the significant objectives is to underline the concept in which education should be a human practice of cognition that is not determined by ideological currents. Furthermore, authors using several sociological and political science aspects regarding education, in general, will investigate and enlighten this specific phenomenon of segregation that is unique not only in the local but in the global context as well. The main objective of this article will be to present viable solutions on how ‘two schools under one roof’ can be altered or even abolished.

In this paper, based on the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, a matrix of Threats, Opportunities, Weaknesses and Strengths (TOWS) was formed. It represents possible business strategies of the transport company. To choose the right plan, a model based on the integration of Fuzzy PIvot Pairwise RElative Criteria Importance Assessment (fuzzy PIPRECIA), Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) methods, has been formed. A case study was conducted in the transport company from Bosnia and Herzegovina which provides services on the domestic and the European Union market for 20 years and belongs to a group of small and medium enterprises (SMEs). The SWOT analysis in this transport company was the basis for forming the TOWS matrix, which represents a set of possible business strategies. These strategies are the basis for developing five basic alternatives. The transport company should choose the best one of them for future business. The research focuses on forming a model for choosing the best strategy by which the transport company seeks to improve its business. Decision-making (DM) is not a straightforward sequence of operations, so the harmonization of methods as well as the verification of their results, are essential in the research. This model is applicable in SMEs that make these and similar decisions. Using this model, companies can adjust their business policies to the results of the model and achieve better business results. This research is the first that allows the use of such a model in making strategic decisions.

The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.

This paper presents the health chatbot application created on the Chatfuel platform. This application allows people to interact with the health chatbot in the same way as they do with other people. The health chatbot identifies their symptoms through a series of queries and guides them to decide whether or not to go to doctor. Such application can be of great benefit to people who are not sure whether their symptoms are transients or require a response to a doctor for detailed tests. It also offers advice to users on minor illnesses, and in that way, encourages people to take appropriate measures to stay healthy, which is a great example of promoting a healthy life. For the purpose of this research, an end-user survey was created and conducted with aim to collect the users’ opinion regarding the acceptance and usage motivation of health chatbot. The results showed good acceptance and usage motivation of health chatbot.

Contact centers are an operationally complex element of a company and play a major role in the experience of its customers. By offering relevant and quick responses to questions and prompt problem solving, a company can achieve a better customer experience. Contact centers generate huge amounts of very useful data, which are often underused, misused, or even not used at all. Our research aims to apply data research techniques to the problem of creating customer profiles in the contact center. Customer profiling mechanisms should provide an explicit set of information about the observed customer's preferences, interests, and behavior patterns. Based on the attributes contained in the customer profile, the system makes decisions in terms of choosing the right contact center agent by anticipating the needs of the observed customer. In our paper, the customer profile is based on the extraction of his properties from log information about used services, behavior patterns, and other general characteristics of each customer. The purpose of our research is to determine which attributes are the most relevant for creating a customer profile and how to evaluate them.

The paper presents recommendations for a responsive user interface (UI) implementation. Google guidelines for UI implementation - Material Design, are briefly presented and compared against the Nielsen’s design principles. We have identified limitations in preserving interaction design principles while implementing Material Design Guidelines. With objective to achieve flexible and responsive layouts respective improvement recommendations are discussed. Sample design case is presented, illustrated by screens before and after the implementation of the recommendations on Android devices with different resolutions.

The aim of this research is to implement Computer Vision technologies on existing published concept proposed by the same author in previous researches "Collaborative and Non-Collaborative Dynamic Path Prediction for Mobile Agents Collision Detection with Dynamic Obstacles". Author proposed usage of Computer Vision technologies in order to increase independency of single robotic units in the swarm. This new method and algorithm is based on analysis of behavior of human objects and its implementation in form of functional method and algorithm which can be used in mobile robotics. In prior research papers, several new terms are proposed and explained such as Metamorphous Hyperspace, Relevant predicted collision time, Coefficient of agility etc. The method implements human behavior in mobile robotics in a way it allows full decentralization of collision detection and ensures many other advantages starting from minimizing network traffic to simplifying inclusion of additional agents in relevant workspace. Algorithm requires a negligible amount of resources allowing mobile agents to exploit more resources for additional tasks. This method and algorithm can be implemented in all kinds of vehicles: ground, naval or airborne objects. Experimental model using Computer Vision technology OpenCV library is implemented and experimental result are described in this paper.

This article presents proportional navigation(PN) and its few variants used in modern tactical missile guidance. This article develops 6-DOF mathematical model and an autopilot for PN guided missile. Full Simulink simulation and animation of PN navigation in three dimensions is shown and discussed.

This research analyzes prediction of student’s success in related courses on Universities. Syllabus of the Faculty of Information Technologies of University "Dzemal Bijedic" in Mostar contains linked courses which are conditioned by each-other. These courses which are pre-requisite to others are in some cases on the same academic years and some of them are in following year. In this research authors proposed regression analysis of student’s success dependency on two courses on first year of study. Correlational analysis indicated existence of moderate correlation. Regression analysis showed that the proposed model indicates weak determination of correlation between subjects. One variant of regression equation is rejected since independent variable was not significant. Regression equation y = ax is accepted.

This paper examines the Internet of Things (IoT) as a critical area of interest to regulatory authorities. Although the IoT technologies are driven by market forces, without facilitation from the regulatory authority IoT rollout would be challenging. IoT is a major trend with enormous possibilities, potential advantages, and side effects. Since regulatory authorities have a key role in customer protection, fostering innovation and growth, the outdated or nonexistent regulatory framework for the IoT could be one of the barriers for the IoT long term growth and avoiding the side effects could be difficult to achieve.

This paper investigates the influence of electric vehicle charging station variations for the cases with and without supplementary renewable sources integration, concentrating on symmetry and voltage stability of the network. The study was performed on a realistic low voltage network using is the load flow analysis in DIgSILENT Power Factory software and P-V method. The analysis is based on defined variations for analysis of the baseline variation and electric vehicles with and no additional source as the PV system. It was demonstrated that the complementary operation of EVs and PV can, if planned properly, improve the power system voltage quality parameters.

The network model is a powerful tool in the study of localization-delocalization transitions and has been used to describe a variety of topological systems without crystalline symmetry. Here, the authors show that network models can also realize topological phases protected by point-group symmetries. The latter lead to the formation of a higher-order topological phase characterized by midgap modes present at the corners of the system.

This paper proposes power generation forecasting for photovoltaic power plants by using Adaptive Neuro-Fuzzy Inference Systems library in MATLAB and considering meteorological factors. Renewable energy sources (RES) introduce compensation instability problems in the grid hence forecasting methods are considered. Especially important for grid operators is a day ahead forecasting as it can reduce negative imbalance price. Means of ensuring the balance reliability of the power system in terms of RES integration are presented. The installation of charging stations for electric vehicles or use of hydrogen technologies and modern storage systems can provide grid balance. In addition, decreasing the deviation of the current (real) value from the predicted value of power generation is a way to compensate for power unbalance.