Guerreroskytte3896

In this work, the channel characterization in terms of large-scale propagation, small-scale propagation, statistical and interference analysis of Fifth-Generation (5G) Millimeter Wave (mmWave) bands for wireless networks for 28, 30 and 60 GHz is presented in both an outdoor urban complex scenario and an indoor scenario, in order to consider a multi-functional, large node-density 5G network operation. An in-house deterministic Three-Dimensional Ray-Launching (3D-RL) code has been used for that purpose, considering all the material properties of the obstacles within the scenario at the frequency under analysis, with the aid of purpose-specific implemented mmWave simulation modules. Different beamforming radiation patterns of the transmitter antenna have been considered, emulating a 5G system operation. Spatial interference analysis as well as time domain characteristics have been retrieved as a function of node location and configuration.Recent and unexpected food alerts about relatively high amounts of pyrrolizidine alkaloids in oregano samples have stressed the need to develop analytical strategies to ensure food safety in this type of foodstuff. Accordingly, this work presents the development of a miniaturized strategy based on the QuEChERS (quick, easy, cheap, effective, rugged and safe) method combined with ultrahigh liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) for the determination of 21 pyrrolizidine alkaloids suggested by the European Food Safety Authority to be monitored in food. The analytical method was properly validated, with overall average recoveries from 77 to 96% and relative standard deviations less then 13% (n = 9). The method proved to be a sustainable analytical strategy which meets green analytical chemistry principles as it showed good performance by using small amounts of sample (0.2 g), organic solvents (1000 µL), clean-up sorbents (175 mg) and partitioning salts (0.65 g). Its feasibility was verified through the analysis of 23 oregano samples. Of the samples analyzed, 100% were contaminated, with an average concentration of 1254 µg/kg. Lasiocarpine, lasiocarpine N-oxide, europine, europine N-oxide, senecivernine, senecionine, echimidine N-oxide, lycopsamine N-oxide and intermedine N-oxide were the alkaloids which significantly contributed to the contamination of the samples.B cell-depleting therapies have recently proven to be clinically highly successful in the treatment of multiple sclerosis (MS). This study aimed to determine the effects of the novel type II anti-human CD20 (huCD20) monoclonal antibody (mAb) obinutuzumab (OBZ) on spinal cord degeneration in a B cell-dependent mouse model of MS. Double transgenic huCD20xHIGR3 (CD20dbtg) mice, which express human CD20, were immunised with the myelin fusion protein MP4 to induce experimental autoimmune encephalomyelitis (EAE). Both light and electron microscopy were used to assess myelination and axonal pathology in mice treated with OBZ during chronic EAE. check details Furthermore, the effects of the already established murine anti-CD20 antibody 18B12 were assessed in C57BL/6 wild-type (wt) mice. In both models (18B12/wt and OBZ/CD20dbtg) anti-CD20 treatment significantly diminished the extent of spinal cord pathology. While 18B12 treatment mainly reduced the extent of axonal pathology, a significant decrease in demyelination and increase in remyelination were additionally observed in OBZ-treated mice. Hence, the data suggest that OBZ could have neuroprotective effects on the CNS, setting the drug apart from the currently available type I anti-CD20 antibodies.Background Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis.Weight is an important indicator of the growth and development of dairy cows. The traditional static weighing methods require considerable human and financial resources, and the existing dynamic weighing algorithms do not consider the influence of the cow motion state on the weight curve. In this paper, a dynamic weighing algorithm for cows based on a support vector machine (SVM) and empirical wavelet transform (EWT) is proposed for classification and analysis. First, the dynamic weight curve is obtained by using a weighing device placed along a cow travel corridor. Next, the data are preprocessed through valid signal acquisition, feature extraction, and normalization, and the results are divided into three active degrees during motion for low, medium, and high grade using the SVM algorithm. Finally, a mean filtering algorithm, the EWT algorithm, and a combined periodic continuation-EWT algorithm are used to obtain the dynamic weight values. Weight data were collected for 910 cows, and the experimental results displayed a classification accuracy of 98.6928%. The three algorithms were used to calculate the dynamic weight values for comparison with real values, and the average error rates were 0.1838%, 0.6724%, and 0.9462%. This method can be widely used at farms and expand the current knowledgebase regarding the dynamic weighing of cows.