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The simplest and most desirable method of traffic signal control is precalculated regulation, when the parameters of the traffic light object operation are calculated in advance and activated in accordance to a schedule. This work proposes a method of forming a signal plan that allows one to calculate the control programs and set the period of their activity. Preparation of initial data for the calculation includes the formation of a time series of daily traffic intensity with an interval of 15 minutes. When carrying out field studies, it is possible that part of the traffic intensity measurements is missing. To fill up the missing traffic intensity measurements, the spline interpolation method is used. The next step of the method is to calculate the daily set of signal plans. The work presents the interdependencies, which allow one to calculate the optimal durations of the control cycle and the permitting phase movement and to set the period of their activity. The present movement control systems have a limit on the number of control programs. To reduce the signal plans' number and to determine their activity period, the clusterization using the $k$-means method in the transport phase space is introduced In the new daily signal plan, the duration of the phases is determined by the coordinates of the received cluster centers, and the activity periods are set by the elements included in the cluster. Testing on a numerical illustration showed that, when the number of clusters is 10, the deviation of the optimal phase duration from the cluster centers does not exceed 2 seconds. To evaluate the effectiveness of the developed methodology, a real intersection with traffic light regulation was considered as an example. Based on field studies of traffic patterns and traffic demand, a microscopic model for the SUMO (Simulation of Urban Mobility) program was developed. The efficiency assessment is based on the transport losses estimated by the time spent on movement. Simulation modeling of the multiprogram control of traffic lights showed a 20% reduction in the delay time at the traffic light object in comparison with the single-program control. The proposed method allows automation of the process of calculating daily signal plans and setting the time of their activity.

Solution of Dynamic Light Scattering problem makes it possible to determine particle size distribution (PSD) from the spectrum of the intensity of scattered light. As a result of experiment, an intensity curve is obtained. The experimentally obtained spectrum of intensity is compared with the theoretically expected spectrum, which is the Lorentzian line. The main task is to determine on the basis of these data the relative concentrations of particles of each class presented in the solution. The article presents a method for constructing and using a neural network trained on synthetic data to determine PSD in a solution in the range of 1–500 nm. The neural network has a fully connected layer of 60 neurons with the RELU activation function at the output, a layer of 45 neurons and the same activation function, a dropout layer and 2 layers with 15 and 1 neurons (network output). The article describes how the network has been trained and tested on synthetic and experimental data. On the synthetic data, the standard deviation metric (rmse) gave a value of 1.3157 nm. Experimental data were obtained for particle sizes of 200 nm, 400 nm and a solution with representatives of both sizes. The results of the neural network and the classical linear methods are compared. The disadvantages of the classical methods are that it is difficult to determine the degree of regularization: too much regularization leads to the particle size distribution curves are much smoothed out, and weak regularization gives oscillating curves and low reliability of the results. The paper shows that the neural network gives a good prediction for particles with a large size. For small sizes, the prediction is worse, but the error quickly decreases as the particle size increases.

The work focuses on mathematical modeling of light influence mechanisms on macromolecular composition of microalgae batch culture. It is shown that even with a single limiting factor, the growth of microalgae is associated with a significant change in the biochemical composition of the biomass in any part of the batch curve. The well-known qualitative models of microalgae are based on concepts of enzymatic kinetics and do not take into account the possible change of the limiting factor during batch culture growth. Such models do not allow describing the dynamics of the relative content of biochemical components of cells. We proposed an alternative approach which is based on generally accepted two-stage photoautotrophic growth of microalgae. Microalgae biomass can be considered as the sum of two macromolecular components — structural and reserve. At the first stage, during photosynthesis a reserve part of biomass is formed, from which the biosynthesis of cell structures occurs at the second stage. Model also assumes the proportionality of all biomass structural components which greatly simplifies mathematical calculations and experimental data fitting. The proposed mathematical model is represented by a system of two differential equations describing the synthesis of reserve biomass compounds at the expense of light and biosynthesis of structural components from reserve ones. The model takes into account that a part of the reserve compounds is spent on replenishing the pool of macroergs. The rates of synthesis of structural and reserve forms of biomass are given by linear splines. Such approach allows us to mathematically describe the change in the limiting factor with an increase in the biomass of the enrichment culture of microalgae. It is shown that under light limitation conditions the batch curve must be divided into several areas: unlimited growth, low cell concentration and optically dense culture. The analytical solutions of the basic system of equations describing the dynamics of macromolecular biomass content made it possible to determine species-specific coefficients for various light conditions. The model was verified on the experimental data of biomass growth and dynamics of chlorophyll $a$ content of the red marine microalgae Pоrphуridium purpurеum batch culture.

The approach for the analysis of some large-scale biological systems, on the base of quasiequilibrium stages is proposed. The approach allows us to reduce the detailed large-scaled models and obtain the simplified model with an analytical solution. This makes it possible to reproduce the experimental curves with a good accuracy. This approach has been applied to a detailed model of the primary processes of photosynthesis in the reaction center of photosystem II. The resulting simplified model of photosystem II describes the experimental fluorescence induction curves for higher and lower plants, obtained under different light intensities. Derived relationships between variables and parameters of detailed and simplified models, allow us to use parameters of simplified model to describe the dynamics of various states of photosystem II detailed model.