Nowadays, high voltage circuit breaker (CB) simulations are mostly based on Computational Fluid Dynamics (CFD) models. Such simulations require significant computer resources. An alternative approach is to use enthalpy flow models, which do not use space discretization of the interrupter unit chambers and valves. Gas flow is calculated based on state of gas in adjacent chambers and valve settings. However, the valve shape has significant influence on the effective flow cross section between chambers. Therefore, in order to ensure correct simulation results, it is necessary to determine the correct values of discharge coefficients for all valves in the interrupter unit of a circuit breaker. In this paper, the discharge coefficients were determined by combining a series of CFD and enthalpy flow simulations for each valve in the interrupter unit. After that, discharge coefficients are used as input for further simulations based on the enthalpy flow model. This way, benefits from both models are combined: more precise gas flow calculation and faster simulations. The proposed novel approach is validated in a high-power laboratory by pressure measurements on a 420 kV 63 kA self-blast circuit breaker.

Background: Smoking is one of the most significant modifiable exosomes risk factors for rheumatoid arthritis (RA) (1). Studies suggest that 25-30% of people with RA in Denmark smoke (2). This is almost twice as many as in the background population in Denmark. People with RA have a significant increased risk of severe comorbidity including cardiovascular disease. In addition, there are indications that smokers with RA have a poorer effect of the medical inflammatory treatment compared to non-smokers, and consequently more difficult to achieve remission of the disease activity (3). Tobacco addiction is complex and can be a challenge in smoking cessation. In addition to physiological dependence, habits and social and environmental factors may influence addiction. Tobacco smoking is associated with an addiction to nicotine and it is unexplored how this addiction appears in people with RA. Objectives: The aim of this study was to examine from the patient’s perspective how tobacco addiction appears in people with rheumatoid arthritis. Methods: We conducted a qualitative study based on a hermeneutics approach. People with RA who previously had participated in a randomized controlled study (4) about smoking cessation conducted at the Center for Rheumatology and Spine Diseases at Rigshospitalet, Denmark were recruited for semi-structured interviews. Results: In total, 12 people with RA (50% female) were included in the study. The median age was 62 years and median RA disease duration was 12 years. The degree of physical dependence measured by Fagerstroms Test for Nicotine dependence (FTND) was on average: 4.9 (score: 0-10, 0=nonphysical dependence). Three categories of how tobacco addiction appeared emerged during the analysis: 1) It develops into ingrown habits referring to the fact that smoking already in adolescence contributes to the development of specific physical, mental and social smoking behavior. Not all individuals considered themselves addicted to nicotine as they did not necessarily connect the nicotine to the ingrown habits. 2) The body craves for nicotine referring to nicotine proved calming, while a lacking or insufficient dose caused withdrawal symptoms. Furthermore, smoking became a habit where a craving for smoking occurred in certain situations. 3) Ambivalence – for and against referring to the physical dependence and smoking habits making a smoking cessation difficult. Dependency to nicotine and challenges to quit smoking led to a feeling of ambivalence and a lack of control. Conclusion: Tobacco addiction appeared as a physical dependence and a habit, which, during a smoking cessation, led to ambivalent feelings. Therefore, based on this study, there is still a need for health professionals to talk to patients about smoking. But also, a need to articulate the complexity of addiction in order to support for smoking cessations. Information should be strengthened in the clinical practice in relation to nicotine’s implication in tobacco addiction as well as the consequences of tobacco smoking for individuals with RA. References: [1]Scott DL, Wolfe F, Huizinga TW. Lancet. 2010 ### [2]Loppenthin K, Esbensen BA, Jennum P, Ostergaard M, Tolver A, Thomsen T, et al. Clin Rheumatol. 2015. ### [3]Roelsgaard IK, Ikdahl E, Rollefstad S, Wibetoe G, Esbensen BA, Kitas GD, et al. Rheumatology (Oxford). 2019. ### [4]Roelsgaard IK, Thomsen T, Ostergaard M, Christensen R, Hetland ML, Jacobsen S, et al. Trials. 2017;18(1):570.### Disclosure of Interests: None declared

Abstract This paper presents a novel iris recognition system based on machine learning methods. The motivation behind this research resides in the interrelatedness of biometric systems and stylometry, as shown in our previous research. The main goal of the proposed model is to reach virtually perfect classification accuracy, eliminate false acceptance rates, and cancel the possibility of recreating an iris image from a generated template. To achieve this, we omit Gabor wavelets and other filter banks typically employed in iris recognition systems based on the pioneering work of John Daugman. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting a normalized iris image into a one-dimensional set of fixed-length codes, which then undergoes stylometric feature extraction. The extracted features are further used for classification. A new recognition method is developed using the CASIA iris database, and its generalizability is demonstrated on the MMU and IITD iris databases separately, and also on their unification with the CASIA database, by applying oversampling before and during the cross-validation procedure. The experimental evaluation shows that the system performs as intended. In addition, the computational costs are significantly decreased with respect to traditional systems, which in turn reduces the overall complexity of the recognition system, making it suitable for use in practical applications.

Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for many roads such features are unavailable. We propose a fully automated approach, RasterNet, for estimating free-flow speed without the need for explicit geometric features. RasterNet is a neural network that fuses large-scale overhead imagery and aerial LiDAR point clouds using a geospatially consistent raster structure. To support training and evaluation, we introduce a novel dataset combining free-flow speeds of road segments, overhead imagery, and LiDAR point clouds across the state of Kentucky. Our method achieves state-of-the-art results on a benchmark dataset.

Highlights • Aerosol-jet printing is used to fabricate microfluidic devices with customised geometries, including steps and slopes.• A rapid prototyping method for producing bespoke molds for microfluidic devices with precise channel functionalization.• The functionalization capability is demonstrated by rendering a section of a microfluidic channel hydrophilic using PVA.

Abstract Assets such as gold, silver, government bonds are widely considered good hedges against adverse movements in the stock market. At times, market participants move between these markets in order to hedge against any immediate risks. The shifts from one market to another likely create a dynamic relationship between the stock market and other assets. We investigate short-run and long-run causality between the industry returns and the prices of the four assets: gold, silver, oil, and 10-year Treasury bonds. Using symmetric and asymmetric Granger causality, we try to identify different industries within the S&P500 that are caused by movement in the prices of these four assets. Although we were able to find short-run and long-run bidirectional causality between the four asset prices and share returns in many industries, our findings are industry-specific. We discover overwhelming and robust evidence of symmetric and asymmetric causality between 10-Year Treasury yield and returns of almost all 18 industries we considered.

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.