Every human brain is a special, unique, and impressive organ and it does not fail to fascinate us every time with its endless possibilities and adaptation. New technology, such as four-dimensional ultrasound diagnostic devices, has gave us a chance to take a peek into the most complex, incredibly well-organized, and spectacular architecture of formation of fetal brain. Neuroscientists have made incredible discoveries about different structures and regions of the brain, and many elements of brain cognitive functions. However, what remains a great mystery is the interaction of different parts of the brain, in other words, that we are not entirely sure how individual parts of the brain exchange data and how and to what extent each is important and contributes to different patterns of behavior, feelings, or memory. Scientific research toward mapping the brain connections is on the way. Assessing fetal behavior in utero, its motor and cognitive functions, is one of the major challenges in perinatal medicine. Fetal behavior reflects the maturation and integrity of the fetal central nervous system (CNS). Understanding the course and timing of fetal neurodevelopmental events in relation to the development of motor and sensory systems is crucial to determining how environmental influences can affect certain structures as well as functions. With the Kurjak’s antenatal neurodevelopmental test (KANET) it is possible, for the first time ever, to evaluate the neurological state of the fetus in real time and to differentiate normal, borderline, and abnormal fetal behavioral patterns. If the KANET score is normal, that is highly predictive of favorable neurodevelopment of the infant. On the other hand, if the KANET score is borderline or abnormal in a high-risk pregnancy, the child’s postnatal development may appear abnormal. Thorough postnatal prospective neurodevelopmental (shortand long-term) follow-up of these children is highly recommended.

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.

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.

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.

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.

Introduction Successful rehabilitation of the growing number of older citizens receiving healthcare services can lead to preservation of functional independence and improvement in quality of life. Adequate intake of dietary protein and physical training are key factors in counteracting the age-related decline in strength performance and physical function. However, during rehabilitation, many older people/persons have insufficient protein intake, and difficulties in performing exercise training with sufficient intensity and volume. The primary aim of this trial is to investigate if individualised physical exercise training programmes combined with increased protein intake (IPET+P) can improve measures on all International Classification of Functioning, Disability and Health levels, such as strength, gait speed and health-related quality of life, when compared with care as usual in municipality-based rehabilitation alone (usual care, UC) or care as usual in combination with increased protein intake (UC+P). Further, the trial investigates whether UC+P will potentiate more significant improvements in outcome measures than UC. Methods and analysis The trial is a three-armed multicentre, block-randomised controlled trial consisting of a 12-week intervention period with a 1-year follow-up. Citizens above 65 years referred to rehabilitation in the municipality without restricting comorbidities are eligible. Participants are randomised to either a UC group, a UC group with protein supplementation receiving 27.5 g protein/day (UC+P), or an IPET+P supplementation of 27.5 g protein/day. The Short Musculoskeletal Function Assessment questionnaire is the primary outcome. Ethics and dissemination Approvals from The Ethics Committee in Region Zealand, Denmark (SJ-758), and the General Data Protection Regulation at the University of Southern Denmark, Odense (10.330) have been obtained. Trial registration number NCT04091308

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA’s Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware.

High-sensitive cardiac Troponin T (hsTnT) is the most frequently used biomarker for the detection of cardiomyocyte injury. Severe aortic stenosis (AS) leads to an increased left ventricular load, with the potential of myocardial injury reflected by increased TnT levels. However, there is a lack of studies showing the prevalence and prognostic role of elevated hsTnT in patients with asymptomatic AS. To examine the association between the hsTnT levels and AS severity in asymptomatic AS patients. We hypothesized that patients with more severe AS will have elevated hsTnT levels and that hsTnT levels are associated with a higher risk for aortic valve events (AVE) and all-cause mortality (ACM). We performed a post-hoc analysis in 1739 asymptomatic patients with mild to moderate-severe AS, enrolled in the randomized, double-blinded SEAS-study (Simvastatin and Ezetimibe in Aortic Stenosis). All patients had available hsTnT blood samples measured at baseline (Year 0) and Year 1. We defined moderate to severe (mod-severe) AS as a transaortic maximal outflow velocity (Vmax)>3.5 m/s combined with aortic valve area (AVA)<1.0 cm2, otherwise non-severe AS. An hsTnT>14 ng/L was high according to assay (Roche, Elecsys Troponin T hs on cobas e 601). Linear multivariable regression model examined the association of hsTnT levels to clinical and echocardiographic variables. Cox multivariable regression model evaluated competing risks and hazard ratios (HR) of outcomes while adjusting for relevant variables, including a Framingham 10-years risk score of cardiovascular diseases. The competing risks were either ACM or AVE, i.e. the first of AVR, cardiovascular death and heart failure due to AS progression. At baseline, hsTnT was high in 26% (453/1739) patients; 25% (380/1529) in non-severe and 35% (73/210) in mod-severe AS. Relative TnT change over one year was 17% (mean 1.17, SD 1.01); 15% in non-severe vs. 32% in mod-severe AS, and neither associated to AS severity, hsTnT at baseline or lipid-lowering treatment. In multivariable linear regression analysis, there were significant correlations between hsTnT at baseline and age, male gender, creatinine, left ventricular mass index and BMI (all p<0.001, R-square=0.42), but not with AS severity. In multivariable Cox regression analyses, a high hsTnT at baseline was associated with AVE 1.61 [95% CI 1.29–1.99]. In contrast, hsTnT at baseline was not associated to all-cause mortality (see figure). In asymptomatic AS patients without severe AS, high-sensitive Troponin T is not associated with AS severity in cross-sectional analyses, and its levels do not change substantially during one year of follow-up. However, patients with hsTnT >14 ng/l had a sixty percent higher independent risk of subsequent aortic valve events. Multivariable Cox regression Type of funding source: Private company. Main funding source(s): Acknowledgements: Main sponsor (SEAS): MSD Singapore Company, LLC, partnership between Merck & Co. Inc. and Schering-Plough Corporation. Blood analysis sponsor: Roche

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).

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.

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.