О развитии цифровизации АПК Беларуси

Стратегической целью развития сельского хозяйства Беларуси на период до 2030 года является формирование конкурентоспособного на мировом рынке и экологически безопасного производства сельскохозяйственных продуктов, необходимых для поддержания достигнутого уровня продовольственной безопасности, обеспечения полноценного питания и здорового образа жизни населения при сохранении плодородия почв. The strategic goal of the development of agriculture of Belarus for the period until 2030 is to form a competitive in the world market and environmentally friendly production of agricultural products necessary to maintain the achieved food security level, ensure full food and a healthy lifestyle of the population while maintaining soil fertility

Numerical simulation of dynamics of brushless dc motors for aerospace and other applications. Volume 2: User's guide to computer EMA model

A description and user's guide of the computer program developed to simulate the dynamics of an electromechanical actuator for aerospace applications are presented. The effects of the stator phase currents on the permanent magnets of the rotor are examined. The voltage and current waveforms present in the power conditioner network during the motoring, regenerative braking, and plugging modes of operation are presented and discussed

Analytical modeling of the dynamics of brushless dc motors for aerospace applications: A conceptual framework

The modes of operation of the brushless d.c. machine and its corresponding characteristics (current flow, torque-position, etc.) are presented. The foundations and basic principles on which the preliminary numerical model is based, are discussed

Simultaneous measurement of N and O densities in plasma afterglow by means of NO titration

The paper presents an advanced method based on well established NO titration to determine the absolute densities of atoms and radicals in the afterglow plasma.Soucasne urceni konecntraci N a O v dohasinajicim plazmatu pomoci titrace NOSimultaneous measurement of N and O densities in plasma afterglow by means of NO titratio

Biologically inspired learning in a layered neural net

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong. It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possible, Hebbian and Anti-Hebbian learning. Hebbian learning should take place when the output is right, while there should be Anti-Hebbian learning when the output is wrong. For the Anti-Hebbian part of the learning rule a particular choice is made, which guarantees an adequate average neuronal activity without the need of introducing, by hand, control mechanisms like extremal dynamics. A network with realistic, i.e., non-zero threshold potentials is shown to perform its task of realizing the desired input-output relations best if it is sufficiently diluted, i.e. if only a relatively low fraction of all possible synaptic connections is realized

Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from optimal. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called \emph{decentralized stick-breaking policy representation} (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods

Changes in precipitation and river flow in northeast Turkey: associations with the North Atlantic Oscillation

This paper explores the relationships between the North Atlantic Oscillation (NAO) index and precipitation and river flow over northeast Turkey. Precipitation totals and maximum, mean and minimum river flow are analysed at the seasonal scale for 12 and 10 stations, respectively. Pearson’s and Mann-Kendall correlation tests are applied to assess relationships between the NAO index and precipitation and river flow metrics, and to detect trends in time-series. Autumn precipitation totals display significant increasing trends, especially for coastal stations, while inland stations show significant increasing trends for spring precipitation. Minimum and maximum river flow decreases significantly for spring and summer. This tendency implies varying conditions towards a drier regime. Seasonal precipitation patterns show a negative association with the NAO for December–January–February (DJF), March–April–May (MAM) and September–October–November (SON) for some stations. Positive associations between the NAO and winter-extended winter (December–March) river flows are detected for some stations in northeast Turkey

Laser control for the optimal evolution of pure quantum states

Starting from an initial pure quantum state, we present a strategy for reaching a target state corresponding to the extremum (maximum or minimum) of a given observable. We show that a sequence of pulses of moderate intensity, applied at times when the average of the observable reaches its local or global extremum, constitutes a strategy transferable to different control issues. Among them, post-pulse molecular alignment and orientation are presented as examples. The robustness of such strategies with respect to experimentally relevant parameters is also examined.Comment: 16 pages, 9 figure