Real-Time Robot Software Platform for Industrial Application

In this study, we present the requirements of a real-time robot software (SW) platform that can be used for industrial robots and examine whether various kinds of existing middleware satisfy them. Moreover, we propose a real-time robot SW platform that extends RTMIA to various industrial applications, which is implemented on Xenomai real-time operating system and Linux. The proposed SW platform utilizes the timer-interrupt based approach to keep strict period and the shared memory for convenient usage, on which the shared variable is designed and used. We verify the proposed platform by showing that the robot task and the Programmable Logic Controller (PLC) program are performing with interlocking each other on the presented platform

Toward an unbiased flow measurements in LHC pppp collisions

Long-range correlations for pairs of charged particles with two-particle angular correlations are studied in $pp$ at ${\sqrt{{\textit s}}}=13$~TeV with various Monte Carlo generators. The correlation functions are constructed as functions of relative azimuthal angle $\Delta\varphi$ and pseudorapidity separation $\Delta\eta$ for pairs of different particle species with the identified hadrons such as $\pi$, $K$, $p$, and $\Lambda$ in wide $\Delta\eta$ ranges. Fourier coefficients are extracted for the long-range correlations in several -multiplicity classes using a low-multiplicity template fit method. The method allows to subtract the enhanced away-side jet fragments in high-multiplicity with respect to low-multiplicity events. However, we found that due to a kinematic bias on jets and differing model implementation of flow and jet components, subtracting the non-flow contamination in small systems can bias the results. It is found that PYTHIA8 Default model where the presence of the collective flow is not expected but the bias results in very large flow. Also extracting flow signal from the EPOS4 and PYTHIA8 String Shoving models is not possible because of flow signal introduced in the low-multiplicity events. Only AMPT String Melting model among studied model calculations is free from this bias, and shows a mass ordering at low $p_{\mathrm{T}}$ and particle type grouping in the intermediate $p_{\mathrm{T}}$ range. This feature has first found in large systems but the mass ordering in small systems is different from what is observed in the large collision systems.Comment: 16 pages, 16 figure

Surface modification through oxide ALD to improve oxygen exchange rate on perovskite surface

Segregation and phase separation on perovskite oxide (ABO3) surface have been considered as a key detrimental factor to the performance of energy conversion devices such as solid oxide/electrolysis cells. Recently, the overcoat of less reducible cations has been suggested as a way to suppress the surface Sr segregation on Sr-containing perovskite oxides. However, the detailed requirements of the coating layer to sufficiently stabilize the perovskite surface hasn’t been systematically investigated yet. In this wok, we fabricate La0.6Sr0.4CoO3 (LSC) thin-film model electrode via pulse layer deposition and observe how the degree of Sr segregation varies with the type and thickness of the overcoat layer. Al2O3 and HfO2 with different thickness are coated on LSC via ALD, and the oxygen exchange rate of both bare and ALD-coated samples is measured via electrical conductivity relaxation. It is found that both Al2O3 and HfO2 layers suppress the Sr segregation only within a narrow thickness range, i.e., 1-2 nm for Al2O3 and 0.2 – 0.4 nm for HfO2, respectively. These observations are discussed with solubility and diffusivity of Al and Hf in the host oxide lattice, providing a critical guideline of a new surface modification method to stabilize the perovskite surface at high temperatures. Please click Additional Files below to see the full abstract

On-Board Traffic Prediction for Connected Vehicles: Implementation and Experiments on Highways

An on-board traffic prediction algorithm is pro- posed for connected vehicles traveling on highways. The pre- diction is based on data received from other connected vehicles ahead in the traffic stream, leveraging the fact that a vehicle will enter the traffic that other vehicles ahead have already met. Our method includes traffic state estimation with Kalman filter and prediction via traffic flow models describing the propagation of congestion waves. The end result is an individualized speed preview in real time up to about half a minute for the connected vehicle executing prediction. Most importantly, the traffic prediction was successfully implemented on board of a real vehicle and predictions were tested in real traffic with experiments involving connected human-driven vehicles

Gene-based copy number variation study reveals a microdeletion at 12q24 that influences height in the Korean population

AbstractHeight is a classic polygenic trait with high heritability (h2=0.8). Recent genome-wide association studies have revealed many independent loci associated with human height. In addition, although many studies have reported an association between copy number variation (CNV) and complex diseases, few have explored the relationship between CNV and height. Recent studies reported that single nucleotide polymorphisms (SNPs) are highly correlated with common CNVs, suggesting that it is warranted to survey CNVs to identify additional genetic factors affecting heritable traits such as height.This study tested the hypothesis that there would be CNV regions (CNVRs) associated with height nearby genes from the GWASs known to affect height. We identified regions containing >1% copy number deletion frequency from 3667 population-based cohort samples using the Illumina HumanOmni1-Quad BeadChip. Among the identified CNVRs, we selected 15 candidate regions that were located within 1Mb of 283 previously reported genes. To assess the effect of these CNVRs on height, statistical analyses were conducted with samples from a case group of 370 taller (upper 10%) individuals and a control group of 1828 individuals (lower 50%).We found that a newly identified 17.7kb deletion at chromosomal position 12q24.33, approximately 171.6kb downstream of GPR133, significantly correlated with height; this finding was validated using quantitative PCR. These results suggest that CNVs are potentially important in determining height and may contribute to height variation in human populations

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Identification and comparison of pandemic-to-symptom networks of South Korea and the United States

BackgroundThe Coronavirus (COVID-19) pandemic resulted in a dramatic increase in the prevalence of anxiety and depression globally. Although the impact on the mental health of young adults was especially strong, its underlying mechanisms remain elusive.Materials and methodsUsing a network approach, the present study investigated the putative pathways between pandemic-related factors and anxiety and depressive symptoms among young adults in South Korea and the U.S. Network analyses were conducted on cross-country data collected during the COVID-19 lockdown period (n = 1,036). Our model included depression symptoms (PHQ-9), generalized anxiety symptoms (GAD-7), and COVID-19-related factors (e.g., COVID-19-related traumatic stress, pandemic concerns, access to medical/mental health services).ResultsThe overall structure of pandemic-to-symptom networks of South Korea and the U.S. were found to be similar. In both countries, COVID-related stress and negative future anticipation (an anxiety symptom) were identified as bridging nodes between pandemic-related factors and psychological distress. In addition, worry-related symptoms (e.g., excessive worry, uncontrollable worry) were identified as key contributors in maintaining the overall pandemic-to-symptom network in both countries.ConclusionThe similar network structures and patterns observed in both countries imply that there may exist a stable relationship between the pandemic and internalizing symptoms above and beyond the sociocultural differences. The current findings provide new insights into the common potential pathway between the pandemic and internalizing symptoms in South Korea and in the U.S. and inform policymakers and mental health professionals of potential intervention targets to alleviate internalizing symptoms

A trial for the use of qigong in the treatment of pre and mild essential hypertension: a study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Hypertension is a risk factor for cardiovascular disease, and the prevalence of hypertension tends to increase with age. Current treatments for hypertension have side effects and poor adherence. Qigong has been studied as an alternative therapy for hypertension; however, the types of qigong used in those studies were diverse, and there have not been many well-designed randomized controlled trials.</p> <p>Our objectives are the following: 1) To evaluate the effects of qigong on blood pressure, health status and hormone levels for pre- or mild hypertension. 2) To test the methodological appropriateness of this clinical trial and calculate a sample size for future randomized trials.</p> <p>Methods</p> <p>Forty subjects with pre- or mild hypertension will be randomized to either the qigong exercise group or the non-treated group. Participants in the qigong group will conduct qigong exercises 5 times per week for 8 weeks, and participants in the non-treated group will maintain their current lifestyle, including diet and exercise. The use of antihypertensive medication is not permitted. The primary endpoint is a change in patient blood pressure. Secondary endpoints are patient health status (as measured by the SF-36 and the MYMOP2 questionnaires) and changes in hormone levels, including norepinephrine, epinephrine, and cortisol.</p> <p>Discussion</p> <p>This study will be the first randomized trial to investigate the effectiveness of qigong exercises for the treatment of pre- and mild hypertension. The results of this study will help to establish the optimal approach for the care of adults with pre- or mild hypertension.</p> <p>Trial registration</p> <p>Clinical Research Information Service KCT0000140</p

Incremental prognostic utility of coronary CT angiography for asymptomatic patients based upon extent and severity of coronary artery calcium: results from the COronary CT Angiography EvaluatioN For Clinical Outcomes InteRnational Multicenter (CONFIRM) Study

Aim Prior evidence observed no predictive utility of coronary CT angiography (CCTA) over the coronary artery calcium score (CACS) and the Framingham risk score (FRS), among asymptomatic individuals. Whether the prognostic value of CCTA differs for asymptomatic patients, when stratified by CACS severity, remains unknown. Methods and results From a 12-centre, 6-country observational registry, 3217 asymptomatic individuals without known coronary artery disease (CAD) underwent CACS and CCTA. Individuals were categorized by CACS as: 0-10, 11-100, 101-400, 401-1000, >1000. For CCTA analysis, the number of obstructive vessels—as defined by the per-patient presence of a ≥50% luminal stenosis—was used to grade the extent and severity of CAD. The incremental prognostic value of CCTA over and above FRS was measured by the likelihood ratio (LR) χ2, C-statistic, and continuous net reclassification improvement (NRI) for prediction, discrimination, and reclassification of all-cause mortality and non-fatal myocardial infarction. During a median follow-up of 24 months (25th-75th percentile, 17-30 months), there were 58 composite end-points. The incremental value of CCTA over FRS was demonstrated in individuals with CACS >100 (LRχ2, 25.34; increment in C-statistic, 0.24; NRI, 0.62, all P 0.05). For subgroups with CACS >100, the utility of CCTA for predicting the study end-point was evident among individuals whose CACS ranged from 101 to 400; the observed predictive benefit attenuated with increasing CACS. Conclusion Coronary CT angiography provides incremental prognostic utility for prediction of mortality and non-fatal myocardial infarction for asymptomatic individuals with moderately high CACS, but not for lower or higher CAC