• Stiles Sheridan posted an update 1 month, 3 weeks ago

    Our findings identified an epigenetic event associated with inhibition of adipocyte generation by MAOC, suggesting its potential as an efficient therapeutic compound to cure obesity and metabolic diseases.The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters.Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the differences in the movement patterns of various parts of the human, and cannot well connect the relationship between the different parts of the human skeleton. To capture the unique features of different parts of human skeleton data and the correlation of different parts, we propose two new graph convolution methods the whole graph convolution network (WGCN) and the part graph convolution network (PGCN). WGCN learns the whole scale skeleton spatiotemporal features according to the movement patterns and physical structure of the human skeleton. PGCN divides the human skeleton graph into several subgraphs to learn the part scale spatiotemporal features. Moreover, we propose an adaptive fusion module that combines the two features for multiple complementary adaptive fusion to obtain more effective skeleton features. By coupling these proposals, we build a whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.The article summarizes and compares data on the properties and biological activity of o-benzoic sulfimide and sulfanilamide compounds. Attention is given to the biochemical conditions under which o-benzoic sulfimide and sulfanilamides have similar activity groups. The results of the experimental and theoretical studies aimed at understanding the molecular organization and biological activity of folic acid and its homologous complexes are analyzed. A hypothesis about the possible mechanisms of the formation of such complexes with the participation of o-benzoic sulfimide is presented. The perspectives for the use of o-benzoic sulfimide and its homologues in biomedicine are evaluated.Positioning systems are used to determine position coordinates in navigation (air, land, and marine). Statistical analysis of their accuracy assumes that the position errors (latitude-δφ and longitude-δλ) are random and that their distributions are consistent with the normal distribution. However, in practice, these errors do not appear in a random way, since the position determination in navigation systems is done with an iterative method. It causes so-called “Position Random Walk”, similar to the term “Random Walk” known from statistics. It results in the empirical distribution of δφ and δλ being inconsistent with the normal distribution, even for samples of up to several thousand measurements. This phenomenon results in a significant overestimation of the accuracy of position determination calculated from such a short series of measurements, causing these tests to lose their representativeness. This paper attempts to determine the length of a measurement session (number of measurements) that is representatribution of δφ and δλ being inconsistent with the normal distribution. The size of the PRW depends on the nominal accuracy of position determination by the system. It was found that measurement sessions consisting of 1000 fixes (for the GPS system) overestimate the accuracy analysis results by 109.1% and cannot be considered representative. Furthermore, when analyzing the results of long measurement campaigns (GPS and DGPS), it was found that the representative length of the measurement session differs for each positioning system and should be determined for each of them individually.Discharging fireworks during the Chinese Lunar New Year celebrations is a deep-rooted custom in China. In this paper, we analyze the effect of this cultural activity on PM2.5 concentration using both ground observations and satellite data. learn more By combining remote sensing data, the problem of uneven spatial distribution of ground monitoring has been compensated, and the research time span has been expanded. The results show that the extensive firework displays on New Year’s Eve lead to a remarkable increase in nationwide PM2.5 concentration, which were 159~223% of the average level, indicating the instantaneous effect far exceeds that of any other factor over the whole year. However, the averaged PM2.5 concentrations of the celebration period were 0.99~16.32 μg/m3 lower compared to the average values of the corresponding pre-celebration period and post-celebration period, indicating the sustained effect is not very significant. The implementation of firework prohibition policies can greatly reduce the instantaneous PM2.5 increase, but no obvious air quality improvement is observed over the entire celebration period. Combining these findings and the cultural significance of this activity, we recommend that this custom is actively maintained, using new technologies and scientific governance programs to minimize the negative effects.