Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of number of lookbacks of the beam measurements to study the prediction accuracy. Simulations show that at a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.Comment: 22 pages, 9 figures. Submitted to IEEE Transactions on Communication

Study of the nature of the dynamic coefficient of internal friction of grainmaterials

The article highlights the issues related to the study of physical and mechanical characteristics of bulk materials, namely internal friction coefficients in static and dynamic modes. An innovative device of the carousel type for determining the frictional characteristics of bulk materials is described, which allows to implement the tasks of practical determination of dynamic coefficients of internal friction. Presented the program, methodology and results of research on the practical study of the internal friction coefficient of typical bulk products of agricultural production in the range of linear velocities of displacement of layers from 0 to 2.79 m/s, the reliability of which is not lower than 0.878

Post-Little Ice Age Glacier Recession in the North-Chuya Ridge and Dynamics of the Bolshoi Maashei Glacier, Altai

The glacier recession of the North-Chuya ridge, Altai, after the maximum of the Little Ice Age (LIA) is estimated based on remote sensing and in situ studies of the Bolshoi Maashei glacier. The glacier area decreased from 304.9 ± 23.49 km2 at the LIA maximum to 140.24 ± 16.19 km2 in 2000 and 120.02 ± 16.19 km2 in 2021. The average equilibrium-line altitude (ELA) rise after the LIA was 207 m. The reduction of glaciers was caused by the warming trend, most rapid in the 1990s, and by the decrease in precipitation after the mid-1980s. The volume of glaciers decreased from approximately 16.5 km3 in the LIA maximum to 5.6–5.8 km3 by 2021. From the LIA maximum to 2022, the Bolshoi Maashei glacier decreased from 17.49 km2 to 6.25 km2, and the lower point rose from 2160 m to 2225 m. After the LIA, the glacial snout retreat was about 1 km. The fastest retreat of the glacier terminus was estimated in 2010–2022 as 14.0 m a−1 on average. The glacier mass balance index was calculated, with the results showing a strong negative trend from the mid-1980s until now. Strong melt rates caused the increase in the area of the Maashei lake, which could lead to the weakening of its dam, and prepared for its failure in 2012. The current climatic tendencies are unfavorable for the glaciers

Long-term safety and efficacy of benralizumab in patients with severe, uncontrolled asthma: 1-year results from the BORA phase 3 extension trial

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