COVID-19 pandemic is plaguing the world and representing the most significant stress test for many national healthcare systems and services, since their foundation. The supply-chain disruption and the unprecedented request for intensive care unit (ICU) beds have created in Europe conditions typical of low-resources settings. This generated a remarkable race to find solutions for the prevention, treatment and management of this disease which is involving a large amount of people. Every day, new Do-It-Yourself (DIY) solutions regarding personal protective equipment and medical devices populate social media feeds. Many companies (e.g., automotive or textile) are converting their traditional production to manufacture the most needed equipment (e.g., respirators, face shields, ventilators etc.). In this chaotic scenario, policy makers, international and national standards bodies, along with the World Health Organization (WHO) and scientific societies are making a joint effort to increase global awareness and knowledge about the importance of respecting the relevant requirements to guarantee appropriate quality and safety for patients and healthcare workers. Nonetheless, ordinary procedures for testing and certification are currently questioned and empowered with fast-track pathways in order to speed-up the deployment of new solutions for COVID-19. This paper shares critical reflections on the current regulatory framework for the certification of personal protective equipment. We hope that these reflections may help readers in navigating the framework of regulations, norms and international standards relevant for key personal protective equipment, sharing a subset of tests that should be deemed essential even in a period of crisis.

The need for the exact overview of technology, which is the target for some purposes is often very crucial. Patent databases provide nowadays very suitable source of data and information which can be further analyzed. Global overview is thus possible to provide for any kind of technology. Smart Furniture is a very used term recently, which is related to current trends such as digitization, smart city or internet of things. Within these phenomena, Smart Furniture is used in different contexts, and so its concept is not clarified. The aim of the article is to show the technology analysis of Smart Furniture based on the patent data analysis and literature analysis by clustering and visualization. The definition of Smart Furniture was recently provided in literature based on previous research which was undertaken based on searching in scientific and patent databases. Thus, the term is defined by its technical properties and parameters. This definition is put into the context of actual trends of patents content with selected future trends. A patent analysis was undertaken between 20 October 2019 and 09 November 2019, while the Web of Science database was included, which was searched by keywords that included the phrase "Smart Furniture" and variants. Patent searching was performed in the PatentInspiration database. In total 31 articles from scientific database and 491 patent applications were examined against strict criteria containing meaningful definitions of Smart Furniture. Based on the analysis of key technologies and properties, clustering of results and their further analysis, it was found that the concept of smart furniture is specific to the following components: intelligent system, controller operated with user's data and energy sources, sensors and actuators.

We present a simple method to quickly explore C-spaces of robotic manipulators and thus facilitate path planning. The method is based on a novel geometrical structure called generalized bur. It is a star-like tree, rooted at a given point in free C-space, with an arbitrary number of guaranteed collision-free edges computed using distance information from the workspace and simple forward kinematics. Generalized bur captures large portions of free C-space, enabling accelerated exploration. The workspace is assumed to be decomposable into a finite set of (possibly overlapping) convex obstacles. When plugged in a suitable RRT-like planning algorithm, generalized burs enable significant performance improvements, while at the same time enabling exact collision-free paths.

Abstract This paper analyses the inefficiency of social services targeting in the Federation of Bosnia and Herzegovina (FB&H). Using official statistics microdata of the Household Budget Survey 2015, three models of social minimum in FB&H were constructed: extreme and general poverty, and the model with multidimensional poverty aspects. The analysis of features of poor household categories showed that the most vulnerable residents of FB&H are not beneficiaries of permanent financial assistance. The reason for such an inefficient targeting was recognized in the Federal Law on Principles of Social Care, Care for the War-Disabled Civilians and Care for Families with Children that stipulates that only persons and families that (cumulatively): are incapable for work, have insufficient income, and there are no family members who are legally obligated to support them. The results indicated a high inconsistency in the legal criteria for qualification, and also in the amounts of permanent social assistance among cantons. The Proxy Means Test (PMT) Model is offered as one of the possible solutions for the improvement of social services targeting in FB&H. Given the importance of efficiency of targeting in social services, the research results could be useful, for both, vulnerable segments of the society and federal and cantonal ministries of labour and social affairs, in the process of targeting the households qualified for social support programmes.

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.