Abstract Microstructure of austenitic stainless steel is primarily monophasic, i.e. austenitic. However, precipitation of the δ-ferrite in the austenite matrix is possible depending on the chemical composition of steel. δ-Ferrite is stable on room temperature but it transforms into σ-phase, carbides and austenite during heat treatment. In this work, the results of analysis of influence of temperature and time on decomposition of δ-ferrite are presented. Magnetic induction method, microstructure and hardness analyses were used for testing the degree of decomposition of the δ-ferrite. Analysis of results showed that increase in temperature and time increases the degree of decomposition of δ-ferrite.

This paper investigates the relationship of information communication technology (ICT) and virtual reality (VR), and tourism, or specifically its interrelations and links to tourism sustainability. As a consumer technology, VR is still a relatively new concept, although it has been researched and used in the tourism industry for marketing purposes. The aim is to understand the different aspects of VR and ICTs and potentially link them to sustainability and perspectives on mass tourism, as well as to the potential future developments related to the ability of ICT and tourism to meet the tourists’ needs to a greater extent in the future. By use of the systematic mapping methodology, the insights into these concepts and their relations to each other are provided. The study reveals the evolution and links between the investigated concepts, the existing challenges and solutions, and the remaining gaps. The present findings indicate that VR as a trend in the tourism industry still needs significant work and improvement until it is ready to fully immerse itself into the tourism sector and especially involve itself into the issues concerning tourism and the potential of sustainability concept within the industry. Many of the concerns and conflicts still exist, but the potential of its right implementation is enormous.

Neural networks are important method of machine learning that can be used to predict air quality with high accuracy. Using NARX-SP neural network type, several neural network models are developed to predict concentration of air pollutants in Sarajevo for two prediction cases, for 24th and 48th hour ahead, with different combinations of inputs and outputs. The data used in this paper contain hourly values of meteorological parameters (air humidity, pressure and temperature, wind speed and direction) and concentrations of SO2, PM10, NO2, O3 and CO from 2016 to 2018. Optimal models are selected for both prediction cases. It is concluded that the optimal models have very good performances and can be used to predict concentration of pollutants in Sarajevo with great accuracy and contribute to improve quality of life. By adequate application of optimal models, concentration of air pollutants can be predicted for each hour over the next 48 hours.

Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.

Noninvasive contactless methods for electric power line monitoring based on magnetic field measurement have become an interesting topic for researchers and the electric power industry since introduction of the Smart Grid concept. By measuring and analyzing magnetic field originating from currents in power line conductors, it is possible to detect faults in the network. In medium voltage distribution networks, where a variety of different pole geometries are present, different criteria for fault detection have to be employed for each geometry, which complicates detection and influences accuracy. This paper proposes a novel approach for fault detection in medium voltage distribution networks which is based on processing of signals measured by low cost contactless magnetic field sensors. In order to create a generalized method for fault detection, a sequence of mathematical transformations of the measured magnetic field components is applied. A novel geometric transformation which eliminates influence of pole geometry is introduced, providing signals from which steady-state symmetrical components of the rotating magnetic field are computed. Those components are used as general fault detection criteria. The proposed approach is confirmed to be applicable for different fault types by a set of experiments on three-phase overhead power line model scaled to laboratory conditions.

Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling -action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent. The performance of different agents when probed this way reveals that they learn to encode factual, and seemingly compositional, information about objects, properties and spatial relations from their physical environment. Our approach is intuitive, i.e. humans can easily interpret responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach. By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives.

Over the course of the last decade, the subfield of artificial intelligence, called deep learning, becomes the main technology that provides breakthroughs in the computer vision area. Likewise, deep learning algorithms made a major impact in the automated driving domain. This research aims to apply and evaluate the performance of two pre-trained deep learning algorithms in order to recognize different street objects. Both RCNN, as well as YOLO algorithms, are used to recognize bikes, cars and pedestrians using the public GRAZ-02 dataset composed of 1476 raw images of street objects. Accuracy greater than 90% is achieved in recognizing all considered objects. The fine-tuning and training of both algorithms is established using databases named ImageNet and COCO, and afterwards, trained models are tried on the test data.

Introduction: Health education is a process of acquiring knowledge and skills in order to improve the health of the individual and the community. It is considered the most effective, most economical and most rational aspect of health care and health culture. Aim To provide data on the effectiveness of printed health-educational materials. Methods This is a quantitative, applied, descriptive-analytical study. According to the type of research, it presents a public health evaluation manipulative study with triple testing. The research was conducted in elementary schools in the Zenica-Doboj Canton. The total number of students participating in the research is divided into groups: examined, control group. The research consisted of four phases. The research tool is a modified questionnaire The Health Behavior in School-aged Children (HBSC) with 38 questions, 8 modules. Results The total number of respondents was 120. The method of distribution of health-educational posters shows a lower but still present statistical significance (p<0.05) in relation to the acquired knowledge and a change in attitudes between the conducted surveys at different time points. There is no statistically significant change (p>0.05) in the level of knowledge and attitudes using leaflets between conducted surveys at three different times. In the control group without education, there was a low statistical significance (p<0.05) in terms of changing the level of knowledge and attitudes. Conclusion The distribution of health-educational posters is recommended in situations where it is necessary to reach a wide audience for a long period of time, if the site of the poster is protected. According to this study, there is no evidence that the leaflet distribution method should be used when it comes to the promotion of healthy lifestyles among healthy children. Alternative methods and ways of health education need to be identified.

Non‐invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill‐posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAxADD) that provides non‐parametric and orientationally invariant estimates of the whole distribution.

Cocoa shell is a by-product of the chocolate industry that is rich in dietary fiber and bioactive components. In this research, the influence of high voltage electric discharge (HVED) treatment on chemical and physical characteristics of the cocoa shell, i.e., the effects of applied time and frequencies on grinding ability, water binding capacity (WBC), dietary fibers and tannin content was investigated. HVED had a significant influence on the chemical and physical properties of cocoa shell, all of which could be linked to changes in fiber properties. Along with the fiber content, grinding ability and water binding capacity were increased. These properties have already been linked to fiber content and soluble/insoluble fiber ratio. However, this research implies that change in fiber properties could be linked to tannin formation via complexation of other polyphenolic components. Additional research is needed to verify this effect and to establish mechanisms of tannin formation induced by HVED and its influence on fiber quantification.