Stimulation of processes of self-propagating high temperature synthesis in system Ti+Al at low temperatures by influence of [gamma]-quanta

In the present work, the influence of the irradiation with gamma-quanta 60Со upon the structural and phase state of the components of the mechanically activated powder composition of Ti+Al is investigated. The phase composition, structural parameters, and crystallinity are examined by means of X-ray diffractometry. It is found out that the irradiation with gamma-quanta changes the structure of the mechanically activated powder composition. The higher irradiation dose, the higher the structure crystallinity of both components with no change in phase state. At the same time, the parameters of Ti and Al crystal lattices approach to the initial parameters observed before the mechanical activation. The irradiation with gammaquanta leads to decrease of internal stresses in the mechanically activated powder composition while nanocrystallinity of the structure remains unchanged. Using of powder compositions exposed to the irradiation with gamma-quanta for the SH-synthesis helps to increase speed of the reaction, decrease the peak firing temperature and improve homogeneity, as well as the main phase of the produced material is TiAl

Increasing Availability of the International Normalized Ratio Control in Russia

Background: Warfarin is still, in some cases, the only medication to prevent thromboembolic complications. Warfarin intake imposes regular INR monitoring, which can be performed domiciliary. Currently, in the Russian market, there are two models of automatic portable blood coagulometers: CoaguChek XS (Germany) and qLabs ElectroMeter (China). The main problem of portable coagulometers is their high cost and high cost of operation, which the majority of patients cannot afford. To explore the demand for development of a Russian coagulometer with a more affordable price, a questionnaire survey was carried out among the patients who needed this device. Methods and Results: We surveyed 70 patients taking Warfarin, with 5 years duration paroxysmal, persistent/or stable AF of nonvalvular etiology, having >2 CHADS-VASc score for thrombembolia risk assessment and <3 HAS-BLED score for hemorrhage risk assessment. According to the survey results, 7 (10%) patients had portable coagulometers, including 3 persons with CoaguChek XS and 4 persons with Micropoint qLabs ElectroMeter. Among these patients, there were 4 persons who continued regular INR monitoring domiciliary, while 3 patients had financial difficulties in getting testing strips. At the same time, 14 (20%) patients were not aware of the possibility of domiciliary INR monitoring. As it turned out, those patients who received regular INR monitoring domiciliary with a portable coagulometer, or at their local polyclinics, had neither ischemic strokes nor hemorrhages within a period of five years. Conclusion: It is critical to develop and manufacture a domestic equivalent of a portable coagulometer and testing strips for household use at a more affordable price

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)<i>_x</i>(LiNbO₃)_{100−<i>x</i>} Nanocomposite Memristors

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements