Background: Due to the COVID-19 pandemic, global authorities have imposed rules of social distancing that directly influence overall physical activity in populations. The aim of this study was to evaluate the trends of changes in physical-activity levels (PALs) in adolescents and factors that may be associated with PALs among the studied boys and girls. Methods: Participants in this prospective study comprised 388 adolescents (126 females; mean age: 16.4 ± 1.9 years) from southern Croatia who were tested at a baseline (before the imposed rules of social distancing) and at a follow-up measurement (three weeks after the initiation). Baseline testing included anthropometric variables, variables of fitness status (done at the beginning of the school year), and PALs. At the follow-up, participants were tested on PALs. PALs were evaluated over an online platform using the Physical Activity Questionnaire for Adolescents. Results: A significant decrease of PALs was evidenced for the total sample (t-test = 3.46, p < 0.001), which was primarily influenced by a significant decrease of PALs in boys (t-test = 5.15, p < 0.001). The fitness status (jumping capacity, abdominal strength, aerobic endurance, and anaerobic endurance) was systematically positively correlated with PALs at the baseline and follow-up among boys and girls, with the most evident association between aerobic and anaerobic endurance capacities and PALs. Correlations between anthropometric and fitness variables with changes in physical activity (e.g., the difference between baseline and follow-up PALs) were negligible. Conclusions: Differences in PAL changes between genders were probably related to the fact that PALs among boys were mostly related to participation in organized sports. Correlations between baseline fitness status and PALs indicated the importance of overall physical literacy in preserving PALs in challenging circumstances, such as the COVID-19 pandemic observed here.

Abstract In the most developed countries the first estimations of Gross Domestic Product (GDP) are available 30 days after the end of the reference quarter. In this paper, possibilities of creating an econometric model for making short-term forecasts of GDP in B&H have been explored. The database consists of more than 100 daily, monthly and quarterly time series for the period 2006q1-2016q4. The aim of this study was to estimate and validate different factor models. Due to the length limit of the series, the factor analysis included 12 time series which had a correlation coefficient with a quarterly GDP at the absolute value greater than 0.8. The principal component analysis (PCA) and the orthogonal varimax rotation of the initial solution were applied. Three principal components are extracted from the set of the series, thus together accounting for 73.34% of the total variability of the given set of series. The final choice of the model for forecasting quarterly B&H GDP was selected based on a comparative analysis of the predictive efficiency of the analysed models for the in-sample period and for the out-of-sample period. The unbiasedness and efficiency of individual forecasts were tested using the Mincer-Zarnowitz regression, while a comparison of the accuracy of forecast of two models was tested by the Diebold-Mariano test. We have examined the justification of a combination of two forecasts using the Granger-Ramanathan regression. A factor model involving three factors has shown to be the most efficient factor model for forecasting quarterly B&H GDP.

LS-DYNA is a well-known multiphysics code with both explicit and implicit time stepping capabilities. Implicit simulations rely heavily on sparse matrix computations, in particular direct solvers, and are notoriously much harder to scale than explicit simulations. In this paper, we investigate the scalability challenges of the implicit structural mode of LS- DYNA. In particular, we focus on linear constraint analysis, sparse matrix reordering, symbolic factorization, and numerical factorization. Our problem of choice for this study is a thermomechanical simulation of jet engine models built by Rolls-Royce with up to 200 million degrees of freedom, or equations. The models are used for engine performance analysis and design optimization, in particular optimization of tip clearances in the compressor and turbine sections of the engine. We present results using as many as 131,072 cores on the Blue Waters Cray XE6/XK7 supercomputer at NCSA and the Titan Cray XK7 supercomputer at OLCF. Since the main focus is on general linear algebra problems, this work is of interest for all linear algebra practitioners, not only developers of implicit finite element codes.

Abstract Objectives Psychotic disorders have large treatment gap in low- and middle-income countries (LMICs) in South-Eastern Europe, where up to 45% of affected people do not receive care for their condition. This study will assess the implementation of a generic psychosocial intervention called DIALOG+ in mental health care services and its effectiveness at improving patients’ clinical and social outcomes. Methods This is a protocol for a multi-country, pragmatic, hybrid effectiveness–implementation, cluster-randomised, clinical trial. The trial aims to recruit 80 clinicians and 400 patients across 5 South-Eastern European LMICs: Bosnia and Herzegovina, Kosovo*, Montenegro, Republic of North Macedonia and Serbia. Clusters are clinicians working with patients with psychosis, and each clinician will deliver the intervention to five patients. After patient baseline assessments, clinicians will be randomly assigned to either the DIALOG+ intervention or treatment as usual, with an allocation ratio of 1:1. The intervention will be delivered six times over 12 months during routine clinical meetings. TThe primary outcome measure is the quality of life at 12 months [Manchester Short Assessment of Quality of Life (MANSA)]; the secondary outcomes include mental health symptoms [Brief Psychiatric Rating Scale (BPRS), Clinical Assessment Interview for Negative Symptoms (CAINS), Brief Symptom Inventory (BSI)], satisfaction with services [Client Satisfaction Questionnaire (CSQ-8)] and economic costs at 12 months [based on Client Service Receipt Inventory (CSRI), EQ-5D-5L and Recovering Quality of Life (ReQOL-10)]. The study will assess the intervention fidelity and the experience of clinicians and patients’ about implementing DIALOG+ in real-life mental health care settings. In the health economic assessment, the incremental cost-effectiveness ratio is calculated with effectiveness measured by quality-adjusted life year. Data will also be collected on sustainability and reach to inform guidelines for potentially scaling up and implementing the intervention widely. Conclusion: The study is expected to generate new scientific knowledge on the treatment of people with psychosis in health care systems with limited resources. The learning from LMICs could potentially help other countries to expand the access to care and alleviate the suffering of patients with psychosis and their families. Trial registration: ISRCTN 11913964

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

The HERe2Cure project, which involved a group of breast cancer experts, members of multidisciplinary tumor boards (MTB) from health-care institutions in Bosnia and Herzegovina, was initiated with the aim of defining an optimal approach to the diagnosis and treatment of HER2 positive breast cancer. After individual multidisciplinary consensus meetings were held in all oncology centers in Bosnia and Herzegovina, a final consensus meeting was held to reconcile the final conclusions discussed in individual meetings. Guidelines were adopted by consensus, based on the presentations and suggestions of experts, which were first discussed in a panel discussion and then agreed electronically between all the authors mentioned. The conclusions of the panel discussion represent the consensus of experts in the field of breast cancer diagnosis and treatment in Bosnia and Herzegovina. The objectives of the guidelines include the standardization, harmonization, and optimization of the procedures for the diagnosis, treatment, and monitoring of patients with HER2-positive breast cancer, all of which should lead to an improvement in the quality of health care of mentioned patients. The initial treatment plan for patients with HER2-positive breast cancer must be made by a MTB comprised of at least: A medical oncologist, a pathologist, a radiologist, a surgeon, and a radiation oncologist/radiotherapist.

Abstract The aim of this research is to segment foreign tourists to Sarajevo based on the frequency of visits in order to make a distinction between first-time and repeat foreign tourists. The purpose is to discover if repeat foreign tourists have more positive intention to revisit and recommend Sarajevo, if they have more positive attitude towards overall satisfaction with tourist destination and if they have more positive opinion about the general quality of this tourist destination offer than first-time foreign tourists. The study used a quantitative approach for research. The survey sample is a convenience sample of 250 foreign tourists who visited Sarajevo during the winter (from December 10, 2018 to January 31, 2019). To achieve scientific relevance, during the analysis and interpretation of the obtained data, descriptive statistics and Mann–Whitney U test were used. The results showed that there was no statistically significant difference, and that first-time and repeat foreign tourists had the same intention of recommending Sarajevo, had a positive attitude towards the overall satisfaction of the tourist destination and had the same opinion about the general quality of this tourist destination offer. The results also indicated that repeat foreign tourists had more positive intention to revisit Sarajevo.

In this short paper, we present goDASH, an infrastructure for headless streaming of HTTP adaptive streaming (HAS) video content, implemented in the language golang, an open-source programming language supported by Google. goDASH's main functionality is the ability to stream HAS content without decoding actual video (headless player). This results in low memory requirements and the ability to run multiple players in a large-scale-based evaluation setup. goDASH comes complete with numerous state-of-the-art HAS algorithms, and is fully written in the Google golang language, which simplifies the implementation of new adaptation algorithms and functions. goDASH supports two transportation protocols Transmission Control Protocol (TCP) and Quick UDP Internet Connections (QUIC). The QUIC protocol is a relatively new protocol with the promise of performance improvement over the widely used TCP. We believe that goDASH is the first emulation-based HAS player that supports QUIC. The main limitation in using QUIC protocol is the need for a security certificate setup on both ends (client and server) as QUIC demands an encrypted connection. This limitation is eased by providing our own testbed framework, known as goDASHbed. This framework uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. As part of Mininet, goDASH can be used in conjunction with other traffic generators.

The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bit-rate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their users' streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that SMASH guarantees a high QoE with consistent performance across a variety of streaming contexts.

In this paper, we show our novel teleoperation system that mediates proximity perception at the slave system as tactile information to the user. We have equipped a robot’s end-effector with a capacitive proximity sensor array. Based on the proximity information, tactile feedback is generated for the user via a tactile display. Thus, the user can feel some of an object’s features through his fingers, without the need for establishing contact between the slave system and the object. In our setting, the proximity sensing-based feedback complements the visual feedback provided by a workspace camera and a robot tool camera. Both the sensor array and the tactile display, have a spatial resolution of 4×4. To evaluate the impact, we conducted a user study covering scenarios with visual occlusion and distortion in pre-touch and pre-manipulation phases. The study revealed an improvement in the accuracy of positioning of the end-effector when the visual and the tactile feedback were both provided to the user. The study also showed high acceptance of the new modality by the users.