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.

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.

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.

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 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 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.

Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

The reliability of the operations of the high-voltage circuit breaker is the key to the stable power system, so it’s fault diagnosis and condition assessment it is of great significance. Considering that high-voltage circuit breaker vibration fingerprints contain valuable information about its mechanical integrity and that the vibration measurements are non-invasive, this paper presents the algorithm for the analysis of residual life of a high-voltage circuit breaker. The algorithm is based on the variational mode decomposition (VMD) and Shannon information entropy mean (EM) in order to obtain indices that are used as an indicator of the circuit breaker structural deterioration.

Background Immune checkpoint inhibitors (ICIs) have significantly improved the outcome in metastatic cutaneous melanoma (CM). However, therapy response is limited to subgroups of patients and clinically useful predictive biomarkers are lacking. Methods To discover treatment-related systemic changes in plasma and potential biomarkers associated with treatment outcome, we analyzed serial plasma samples from 24 patients with metastatic CM, collected before and during ICI treatment, with mass-spectrometry-based global proteomics (high-resolution isoelectric focusing liquid chromatography–mass spectrometry (HiRIEF LC-MS/MS)) and targeted proteomics with proximity extension assays (PEAs). In addition, we analyzed plasma proteomes of 24 patients with metastatic CM treated with mitogen-activated protein kinase inhibitors (MAPKis), to pinpoint changes in protein plasma levels specific to the ICI treatment. To detect plasma proteins associated with treatment response, we performed stratified analyses in anti-programmed cell death protein 1 (anti-PD-1) responders and non-responders. In addition, we analyzed the association between protein plasma levels and progression-free survival (PFS) by Cox proportional hazards models. Results Unbiased HiRIEF LC-MS/MS-based proteomics showed plasma levels’ alterations related to anti-PD-1 treatment in 80 out of 1160 quantified proteins. Circulating PD-1 had the highest increase during anti-PD-1 treatment (log2-FC=2.03, p=0.0008) and in anti-PD-1 responders (log2-FC=2.09, p=0.005), but did not change in the MAPKis cohort. Targeted, antibody-based proteomics by PEA confirmed this observation. Anti-PD-1 responders had an increase in plasma proteins involved in T-cell response, neutrophil degranulation, inflammation, cell adhesion, and immune suppression. Furthermore, we discovered new associations between plasma proteins (eg, interleukin 6, interleukin 10, proline-rich acidic protein 1, desmocollin 3, C-C motif chemokine ligands 2, 3 and 4, vascular endothelial growth factor A) and PFS, which may serve as predictive biomarkers. Conclusions We detected an increase in circulating PD-1 during anti-PD-1 treatment, as well as diverse immune plasma proteomic signatures in anti-PD-1 responders. This study demonstrates the potential of plasma proteomics as a liquid biopsy method and in discovery of putative predictive biomarkers for anti-PD-1 treatment in metastatic CM.

A semi-automated and sensitive method was developed for simultaneous determination of the six most consumed artificial sweeteners (AS) in surface waters using thin-film solid-phase microextraction (TF-SPME) and high-performance liquid chromatography (HPLC). A triple quadrupole mass spectrometer and an electrospray ionization source (ESI-MS) run in negative ionization and multiple reaction monitoring modes were employed for instrumental analysis. The TF-SPME method was optimized for the extraction phase, sample pH, desorption solvent, extraction time, and desorption time. In-house-synthetized-hydrophilic-lipophilic balance weak anion exchange (HLB-WAX) particles imbedded within a polyacrylonitrile (PAN) binder were selected as the extraction phase for the thin-film coating due to their cost-effectiveness and enhanced sensitivity for artificial sweeteners. Suitable analytical parameters that include linearity (R2 > 0.9914), recovery > 80%, inter, and intra-reproducibility less than 18% were obtained for the AS compounds studied. The developed method estimated limits of detection (LODs) ranging from 0.004 to 0.038 ng mL-1 The SPME method was successfully applied for the determination of ultra-trace levels of AS in water samples collected from Grand River (Ontario, Canada), downstream of three municipal wastewater treatment plants (WWTPs). Concentrations ranging from 0.03 to 20.3 ng mL-1 were found for the AS compounds studied.

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.