Background: Coronary artery disease as a consequence of atherosclerosis is the most common cause of morbidity and mortality in type 2 Diabetes Mellitus (DM) patients. Homocysteine (HCY), as one of the risk factors, and uric acid (UA) as the most common antioxidant in serum have their roles in the processes of inflammation and atherogenesis, which underlie the pathogenesis of acute myocardial infarction (AMI). The effect of HCY in cardiovascular disease is thought to be manifested primarily through oxidative damage, implying a potential correlation between the HCY level and antioxidant status. Since the data related to the diagnostic significance of both HCY and UA in diabetic patients with AMI are conflicting, and so far not reported in Bosnian patients, this research aimed to examine the association of HCY and UA levels with glomerular filtration rate (eGFR) and explore the pathophysiological significance of these data in Bosnian diabetic patients with AMI. Methods: This prospective research included 52 DM type 2 patients diagnosed with AMI. Blood samples were taken on admission and used for biochemical analysis. Results of the biochemical analyses were statistically analysed. Results: Elevated HCY and UA levels were observed in diabetic patients. Females have higher HCY compared to males. A positive correlation was revealed between HCY and UA and was confirmed with different HCY levels in subgroups with different UA level. A negative correlation was observed between UA and HbA1c, as well as between both HCY and UA with eGFR. Conclusions: These results contribute to the clarification of the biochemical mechanisms characteristic in AMI patients with DM. According to these results, we believe that joint measurement of HCY and UA could enable a better assessment of the prognosis for this group of patients. This kind of assessment, as well as regression analysis, can identify high-risk patients at an earlier stage when appropriate interventions can influence a better outcome in such patients.

COVID-19 is a global pandemic affecting all aspects of life in all countries. We assessed COVID-19 knowledge and associated factors among dental academics in 26 countries. We invited dental academics to participate in a cross-sectional, multi-country, online survey from March to April 2020. The survey collected data on knowledge of COVID-19 regarding the mode of transmission, symptoms, diagnosis, treatment, protection, and dental treatment precautions as well as participants’ background variables. Multilevel linear models were used to assess the association between dental academics’ knowledge of COVID-19 and individual level (personal and professional) and country-level (number of COVID-19 cases/ million population) factors accounting for random variation among countries. Two thousand forty-five academics participated in the survey (response rate 14.3%, with 54.7% female and 67% younger than 46 years of age). The mean (SD) knowledge percent score was 73.2 (11.2) %, and the score of knowledge of symptoms was significantly lower than the score of knowledge of diagnostic methods (53.1 and 85.4%, P <  0.0001). Knowledge score was significantly higher among those living with a partner/spouse than among those living alone (regression coefficient (B) = 0.48); higher among those with PhD degrees than among those with Bachelor of Dental Science degrees (B = 0.48); higher among those seeing 21 to 30 patients daily than among those seeing no patients (B = 0.65); and higher among those from countries with a higher number of COVID-19 cases/million population (B = 0.0007). Dental academics had poorer knowledge of COVID-19 symptoms than of COVID-19 diagnostic methods. Living arrangements, academic degrees, patient load, and magnitude of the epidemic in the country were associated with COVD-19 knowledge among dental academics. Training of dental academics on COVID-19 can be designed using these findings to recruit those with the greatest need.

Naturalistic studies of human-robot interaction (HRI) in various domains of everyday life, including healthcare, domestic assistance, education, transportation, and other services, show that natural exchanges between people and robots often take place in the broader context of small groups, organizations, and communities. While initial research in HRI focused largely on evaluating the one-on-one interactions between humans and robots, the field is increasingly turning its attention to interactions involving multiple people and/or multiple robots. This talk is motivated by this 'group turn' in HRI, by theories and studies of group interaction in the social sciences, and by our own initial observations of group human-robot interaction (HRI) in public spaces. I will discuss how we can go beyond a dyadic understanding of HRI to incorporate group, organizational, and community goals and experiences into robot design. Group effects are well established in social psychology, and suggest that intergroup interactions are more aggressive and negative than ingroup interactions. Translating this to HRI means that interactions between humans and robots could be more negative if robots are seen as outgroup members, and more positive if robots are seen as ingroup members. Observations of open-ended interactions between people and robots also suggest that people interact with robots differently when they are in groups compared to when they are alone. Over the past five years, we have explored whether group effects from human interaction transfer to HRI in terms of people's reactions to multiple (as opposed to single) robots; characteristics of robot groups (such as synchrony or appearance) that may influence people's responses; tests of interventions that have been demonstrated to reduce prejudice or intergroup bias in humans (such as perspective taking); and tests of other theoretical predictions drawn from work on human intergroup behavior. While some of our findings confirmed theoretical expectations from social psychology, there were many exceptions. Many of our day-to-day small group interactions are in turn situated in various organizations (e.g. schools, hospitals) and even broader communities. The design and application of social robots should therefore incorporate an understanding of the social dynamics, goals, and other factors which are salient to the diverse actors in these broader contexts, and which affect HRI. Along with lab and field research on small group interactions, we also study how robots are designed, adopted, used, and perceived within organizational and community settings. This work suggests that the success of a particular robot is not tied only to the characteristics of the robot itself or the experiences of individual users, but depends on various organizational factors: work load and flow, available financial resources, the needs of and dynamics between diverse stakeholders, and community goals and values. This suggests that the design of robots for organizations and communities should take into consideration these broader factors, and involve community partners in the early stages of designing social robots that can address their shared needs. I will discuss several studies that use this approach.

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

The goal of the research was to monitor the quality of corn silage on farms in the period from 2017. to 2019th year, and to compare the quality of silage by years of research. The analysis of corn silage has been done at 20 farms in the municipality Kalesija. The following parameters of corn silage quality were determined: acidity (pH), crude protein (CP), crude fiber (SC) and moisture content (SV). The quality of corn silage varies much more in one year, by the farms, than by years of production, although the agro-climatic conditions for production were different in the years of production. The medium value of CP by years of research is in 2017 - 6.94 %, 2018 - 6.82 % and in 2019th was 6.31 %. The low level of protein indicates a bad choice of hybrids for sowing and storing silage at a later stage of corn development. The acidity (pH), the medium value by year of research is in 2017 - 3.81, in 2018 - 4.03 and in 2019 - 4.01. The acidity is in the limits of optimal values for corn silage. The medium value of SC by years of research is in 2017th 31.69 %, 2018 - 31.9 % and in 2019 - 33.99 %. The high content of cellulose is an indicator of storing corn silage in later stage of corn maturation. Moisture content, the medium value by years of the research is in 2017 - 68.48 %, 2018 - 68.75 % and in the 2019 - 68.43 %. The moisture content is within the optimum values for corn silage.

Abstract Data-driven models are rising as an auspicious method for the geometrical design of materials and structural systems. Nevertheless, existing data-driven models customarily address the optimization of structural designs rather than metamaterial designs. Metamaterials are emerging as promising materials exhibiting tailorable and unprecedented properties for a wide spectrum of applications. In this paper, we develop a deep learning (DL) model based on a convolutional neural network (CNN) that predicts optimal metamaterial designs. The developed DL model non-iteratively optimizes metamaterials for either maximizing the bulk modulus, maximizing the shear modulus, or minimizing the Poisson's ratio (including negative values). The data are generated by solving a large set of inverse homogenization boundary values problems, with randomly generated geometrical features from a specific distribution. Such s data-driven model can play a vital role in accelerating more computationally expensive design problems, such as multiscale metamaterial systems.

A reliable and precise model of the environment is of the highest importance for autonomous vehicles. Occupancy grids are a well-known approach for environment modeling and are a crucial part of multiple autonomous driving functionalities. The standard method is to use a single 2D occupancy grid to model the environment using nonground points. In this paper, we propose a decentralized occupancy grid filtering chain (pipeline) where a high-density 64-layer LiDAR provided the input to our pipeline. Our approach enables us to obtain detailed 2D and 3D models of the environment simultaneously. The pipeline was validated on different scenarios in both simulation and real world. The performance of the designed occupancy grid pipeline was evaluated by the proposed key performance indicators (KPIs) based on accuracy. The results have shown that the approach was able to extract free space information with a high degree of accuracy, while reducing the size of the unobserved area in the grid compared to the standard methods and achieving real-time performance.

BACKGROUND To determine the existence of the toothlessness within the patients in the area of Mostar. The aim is to determine the topography of toothlessness within the population of Mostar, according to Kennedy classification. The aim is to connect measures of socioeconomic status with the appearance of the toothlessness. To develop a model that includes a form of toothlessness and the socioeconomic status of the patients in Mostar. SUBJECTS AND METHODS The study was conducted at the Health Center in Mostar and the Regional Medical Center in Mostar. The research was cross-sectional study. It included 800 patients who regularlyoccurred to the dental ambulance because of the toothlessness and because of the prosthodontics treatment. The measurement was conducted by the dentist based on the anonymous research cardboard at the first examination of the patient. The dentist will determine the topography of the toothlessness according to Kennedy classification and the etiology of the toothlessness. RESULTS In the total sample of respondents, the toothlessness was significantly higher represented (P<0.001). The manifestation of thetoothlessness was significantly higher among temporary employees and the retirees (P<0.001). In the total sample, toothlessness affected the sociological status of a higher percentage of the respondents (P<0.001). CONCLUSION In our study, in a total sample of respondents, toothlessness was significantly higher represented (90% of respondents). The influence on the sociological status of the patient is most visible in the groups with the lower material status.

Dilatation and percutaneous coronary intervention in the presence of calcified lesions is particularly demanding and presents a challenge in the daily work of an interventional cardiologist. Coronary calcification is a marker of the progress of the atherosclerotic process. The existence of calcifying lesions predicts a poorer clinical outcome and is associated with increased mortality and the occurrence of postprocedural major adverse cardiovascular events (MACEs). A male patient who was 61 years old was admitted as a result of ST-elevation myocardial infarction (STEMI) complicated by cardiac arrest caused by in-stent thrombosis of a previously suboptimally expanded stent. The lesion did not respond to a dilation attempt with a noncompliant (NC) balloon; however, an optimal result was obtained with inflation from a super-high-pressure NC balloon (OPN NC) for ultra-high-pressure inflations. Resistant, calcified lesions require a careful and comprehensive approach. The OPN NC balloon has a place in the treatment of this type of lesion. An optimized therapeutic modality after the procedure is imperative to prevent a MACE.

BACKGROUND Chronic hepatitis C was until recently treated with a combined therapy of interferon and ribavirin. More recently, direct antiviral agents (DAA), are being introduced. They are more tolerable and have fewer side effects, with better treatment results. In the Federation of Bosnia and Herzegovina we have started using this new therapy, with a limited financial opportunity. Large numbers of patients with chronic hepatitis C are former or current addicts, some of them treat their addiction with methadone or buprenorphine. These patients often formerly have a depression disorder and during treatment of chronic hepatitis need supervision of a psychiatrist, due to one of the side effects of interferon being deterioration of depression. Using this research we wanted to valorize the depression disorder of our patients, to indicate the effects of interferon on depression deterioration and the need for a new therapy protocol. SUBJECTS AND METHODS Examinees were patients with chronic hepatitis C on interferon therapy, which we divided into three groups: those who were never addicts, then the group of patients who were earlier addicts and have a long abstinence and patients who treat their addiction with a replacement therapy of methadone or buprenorphine. All patients completed Beck's test, which determines the level of depression, before and after interferon therapy. RESULTS Patients who used to be addicts or were on replacement therapy had mild or moderate depression before interferon treatment in a large number. After interferon therapy, there was a statistically substantial increase of patients with serious depression, which was not noted before the therapy. CONCLUSION Interferon therapy deteriorates depression in patients with chronic hepatitis C and there should be a strive for new therapies with less side effects in treatment. No patients stopped therapy. That is a result of community work and supervision over patients from both hepatologists and psychiatrists.

Abstract Preliminary insights in the marketing literature indicate that flexibility is important in marketing and sales processes and interaction. However, to date, marketing and sales management literature lacks an understanding of what flexibility in marketing-sales interfaces looks like, its potential organizational consequences, and potential boundary conditions. Using data from interviews with marketing and sales managers, this study explores the nature, outcomes and facilitators of flexibility at the marketing-sales interface. This study conceptualizes marketing-sales interface flexibility (MSIF) as a process of flexible cross-functional resource exchange and finds that MSIF has positive organizational outcomes (both in terms of performance and relationship quality), that MSIF is essential for firms when dealing with exigencies in turbulent environments, and that the utility of MSIF is conditioned by the speed with which MSIF is implemented. The research contribution is twofold. At a theoretical level, the study defines the construct for the first time, revealing MSIF's conceptual composition for examination, and develops theory regarding MSIF's direct relationships with key business outcomes, as well as likely contingencies that shape its importance. At a practical level, the study's framework offers a tool that managers can use to help build organizational success through enhanced flexibility in their marketing-sales interfaces.

The long‐wavelength geoid is sensitive to Earth's mantle density structure as well as radial variations in mantle viscosity. We present a suite of inversions for the radial viscosity profile using whole‐mantle models that jointly constrain the variations in density, shear‐ and compressional‐wavespeeds using full‐spectrum tomography. We use a Bayesian approach to identify a collection of viscosity profiles compatible with the geoid, while enabling uncertainties to be quantified. Depending on tomographic model parameterization and data weighting, it is possible to obtain models with either positive‐ or negative‐buoyancy in the large low shear velocity provinces. We demonstrate that whole‐mantle density models in which density and VS variations are correlated imply an increase in viscosity below the transition zone, often near 1,000 km. Many solutions also contain a low‐viscosity channel below 650 km. Alternatively, models in which density is less‐correlated with VS—which better fit normal mode data—require a reduced viscosity region in the lower mantle. This feature appears in solutions because it reduces the sensitivity of the geoid to buoyancy variations in the lowermost mantle. The variability among the viscosity profiles obtained using different density models is indicative of the strong nonlinearities in modeling the geoid and the limited resolving power of the geoid kernels. We demonstrate that linearized analyses of model resolution do not adequately capture the posterior uncertainty on viscosity. Joint and iterative inversions of viscosity, wavespeeds, and density using seismic and geodynamic observations are required to reduce bias from prior assumptions on viscosity variation and scalings between material properties.

In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).