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Ti-6.5Al-2Zr-1Mo-1V/TiB metal matrix composites with 3 wt.% of TiB2 were obtained using vacuum arc melting and spark plasma sintering methods and compared with an unreinforced Ti-6.5Al-2Zr-1Mo-1V alloy. The microstructures of the unreinforced Ti6.5Al-2Zr-1Mo-1V alloy in the as-cast and as-sintered conditions were quite typical and consisted of colonies of α-lamellae embedded in the β matrix. The microstructure of the as-cast Ti-6.5Al-2Zr-1Mo-1V/TiB composite composed of TiB fibers randomly distributed within the two-phase α/β matrix, while the as-sintered composite had a network-like microstructure, in which areas of the two-phase α/β matrix were delineated by walls of TiB fibers. At room temperature, the yield strength of the as-cast and as-sintered Ti-6.5Al-2Zr-1Mo-1V alloy were 800 and 915 MPa, respectively, with a plasticity of 18% in both conditions. The addition of TiB fibers contributed to a ~40 and 50% strength increment, with values of 1100 and 1370 MPa for the as-cast and as-sintered composites, respectively. In the as-sintered composite, the strengthening effect reduced at 400 °C and almost disappeared at elevated temperatures of 800–950 °C. The as-cast composite showed much higher strength during warm and hot deformation—at 800–950 °C, the yield strength of the as-cast composite was 50% higher compared to the Ti-6.5Al-2Zr-1Mo-1V unreinforced alloy. A higher rate and degree of globularization were established for the as-cast composite compared to the unreinforced alloy. For the as-sintered composite, a noticeably lower rate and degree of globularization was shown. During hot compression of the as-cast composite, TiB fibers reoriented towards the metal flow direction, while the network microstructure formed in the as-sintered composite transformed into clusters of borides unevenly distributed within the matrix. Based on the obtained results, the apparent activation energy of plastic deformation was calculated, and the operating deformation mechanisms were discussed both for the as-cast and as-sintered composites. The Arrhenius flow stress model and the dynamic material model were used to evaluate the deformation behavior of composites beyond the experimentally studied temperatures and strain rates.

Russia remains one of the areas most affected by HIV in Eastern Europe and Central Asia. The aim of this study was to analyze HIV infection indicators and study trends in Russia using data from the Federal Statistic Form No. 61 “Information about HIV infection”. HIV incidence, prevalence, HIV testing and mortality rates (from 2011 to 2022), and treatment success rates (from 2016 to 2022) were analyzed. These indicators were compared across different federal districts (FDs) of Russia. The findings revealed a significant downward trend in HIV incidence, while a significant upward trend was observed for HIV prevalence. The mortality rate has stabilized since 2018. The coverage of HIV testing and antiretroviral therapy increased over time. The number of people living with HIV-1 (PLWH) with a suppressed viral load in Russia as a whole varied between 72% and 77% during the years under observation. The Siberian and Ural federal districts recorded the highest HIV incidence, while the North Caucasian FD reported the lowest. An increase in HIV testing coverage was observed across all FDs. This comprehensive evaluation of HIV infection indicators within the regional context contributes to the timely implementation of measures aimed at preventing the spread of HIV.

The avidity index (AI) of IgG to the RBD of SARS-CoV-2 was determined for 71 patients with a mild (outpatient) course of COVID-19, including 39 primarily and 36 secondarily reinfected, and 92 patients with a severe (hospital) course of COVID-19, including 82 primarily and 10 secondarily infected. The AI was shown to correlate with the severity of repeated disease. In the group of outpatients with a mild course, the reinfected patients had significantly higher median AIs than those with primary infections (82.3% vs. 37.1%, p < 0.0001). At the same time, in patients with a severe course of COVID-19, reinfected patients still had low-avidity antibodies (median AI of 28.4% vs. 25% in the primarily infected, difference not significant, p > 0.05). This suggests that the presence of low-avidity IgG to RBD during reinfection is a negative prognostic factor, in which a patient’s risk of developing COVID-19 in a severe form is significantly increased. Thus, patients with IgG of low avidity (AI ≤ 40%) had an 89 ± 20.5% chance of a severe course of recurrent COVID-19, whereas the detection of high-avidity antibodies (AI ≥ 50%) gave a probability of 94 ± 7.9% for a mild course of recurrent disease (p < 0.05).

We study the bound state spectrum and the conditions for entering a supercritical regime in graphene with strong intrinsic and Rashba spin-orbit interactions within the topological insulator phase. Explicit results are provided for a disk-shaped potential well and for the Coulomb center problem.

Detecting activities of daily living (ADL) allows for rich inference about user behavior, which can be of use in the care of for example, elderly people, chronic diseases, and psychological conditions. This paper proposes a domain knowledge-based solution for detecting 24 different ADLs in the UJA dataset. The solution is inspired by a Finite State Machine and performs activity recognition unobtrusively using only binary sensors. Each day in the dataset is segmented into: morning, day, evening in order to facilitate the inference from the sensors. The model performs the ADL recognition in two steps. The first step is to detect the sequence of activities in a given event stream of binary sensors, and the second step is to assign a starting and ending times for each of detected activities. Our proposed model achieved an accuracy of 81.3% using only a very small amount of operations, making it an interesting approach for resource-constrained devices that are common in smart environments. It should be noted, however, that the model can end up in faulty states which could cause a series of mis-classifications before the model is returned to the true state.