All sciences. №8, 2023
International Scientific Journal

Authors: Aliyev Ibratjon Xatamovich, Maksudov Asatulla Urmanovich, Umaraliyev Nurmamat, Xakimov Murodjon Fozilovich, Abduraxmonov Sultonali Mukaramovich, Sayitov Shavkatjon Samiddinovich, Abdullayev Jamolitdin Solijonovich, Mavlyanov Aminjon, Jamoliddinov Javohir Iqboljonovich, Stultonov Shuxrat Davlatovich, Dadajonov Tulan

Editor-in-Chief Ibratjon Xatamovich Aliyev

Illustrator Ibratjon Xatamovich Aliyev

Illustrator Sultonali Mukaramovich Abduraxmonov

Illustrator Obbozjon Xatamovich Qo'ldashov

Cover design Ibratjon Xatamovich Aliyev

Cover design Ra'noxon Mukaramovna Aliyeva

Acting scientific supervisor Sultonali Mukaramovich Abduraxmonov

Economic manager Farruh Murodjonovich Sharofutdinov

Proofreader Gulnoza Muxtarovna Sobirova

Proofreader Abdurasul Abdusoliyevich Ergashev

© Ibratjon Xatamovich Aliyev, 2024

© Asatulla Urmanovich Maksudov, 2024

© Nurmamat Umaraliyev, 2024

© Murodjon Fozilovich Xakimov, 2024

© Sultonali Mukaramovich Abduraxmonov, 2024

© Shavkatjon Samiddinovich Sayitov, 2024

© Jamolitdin Solijonovich Abdullayev, 2024

© Aminjon Mavlyanov, 2024

© Javohir Iqboljonovich Jamoliddinov, 2024

© Shuxrat Davlatovich Stultonov, 2024

© Tulan Dadajonov, 2024

ISBN 978-5-0060-9088-0

Created with Ridero smart publishing system

PHYSICAL AND MATHEMATICAL SCIENCES

ON A BRIEF ANALYSIS AT A CERTAIN INTERVAL OF THE COLLATZ HYPOTHESIS

Aliyev Ibratjon Xatamovich

3rd year student of the Faculty of Mathematics and Computer Science of Fergana State University

Ferghana State University, Ferghana, Uzbekistan

Annotation. Modern research in the field of mathematics, including number theory, is developing quite actively, however, among a large number of very different mathematical models describing various natural phenomena, there are also those that are among the unsolved mathematical problems. Today we can refer to them the so-called Collatz hypothesis, the description of which is directed at the boundaries of this work.

Keywords: mathematics, research, physical and mathematical modeling, number theory, function.

Аннотация. Современные исследования в области математики, в том числе теории чисел развиваются достаточно активно, однако, среди большого количества самых различных математических моделей, описывающие различные явления природы существуют и те, которые находятся в ряду не решённых математических задач. К ним сегодня можно отнести так называемую гипотезу Коллатца, описанию на границах коих и направлена настоящая работа.

Ключевые слова: математика, исследование, физико-математическое моделирование, теория чисел, функция.

The Collatz hypothesis itself is one of the simplest unsolved problems known to date. It is a statement that let some natural number be taken and if it is not even, then it is multiplied by 3 and then one is added or, more precisely, the function 3x+1 is performed, if the number is even, then it is divided in half. Thus, it turns out the separated form of the function of the Collatz hypothesis (1).

Further, the result obtained in (1) may be repeated. So, the present model can be defined for the number 7, which is not even and the first function is executed, it turns out 22 is an even number. Now the second function is executed and 11 is obtained, etc. In general, this series looks like this (2).

Now you can choose another number, for example 9 (3), 8 (4) or 6 (5).

In all cases, one can observe the same pattern, that in the end a cycle of 4, 2, 1 is obtained, which will be repeated each time indefinitely. And the idea of the Collatz hypothesis is to prove that all natural numbers will lead to a real cycle. But it is noteworthy that the diagram of such a model has an interesting chaotic scheme with its maximum and minimum points. This scientific work is devoted to the analysis of changes in the graphs of the function of the Collatz hypothesis.

Initially, it is worth writing down the model of function (1) in general form (6).

So, you can substitute some numbers to get suitable values for even and non-even numbers (8—9), however, before the study it is worth noting that the exception is the number zero, which contains the only cycle that differs from the cycles of all natural numbers, consisting of 2 elements (7).

For the general series of the function, we get the representation (10).

So, initially it is worth paying attention to the analysis carried out using 110 stages of repeated operation, and at this interval the initial peaks are clearly visible on the graph of the analysis of natural numbers in the range from 1 to 10 (Graph 1).

Graph 1. Functions for the interval [1; 10] for 110 elements

In this case, it will be possible to observe that with increasing numbers, individual peaks can be observed, the number of which begins to increase each time, becoming chaotic. Some values can already take large indicators of the function at their beginning, reaching a small number of stages, each time coming to a repeated cycle more and more, as can be seen in the continuation of the right part of each of the functions. Further, the analysis of the graph continues in the next interval from 10 to 20, an increase in the height of the peaks of the function can be observed, although the density of the location of each of the functions also increases. This can be seen more clearly when considering the continuation of the function in the right part – against the background of cycles, where the correlation becomes more and more obvious (Graph 2).

Graph 2. Functions for the interval [10; 20] for 110 elements

While continuing the analysis, you can pay attention to an interesting approach in that after 20 functions change and the level of superposition of each one on the other begins to increase more and more each time, leading to the fact that already when analyzing the number from 17 to 27, the correlation level becomes maximum. This can also be clearly seen in Graph 3, where at least some difference is observed only at the beginning of the graphs, and already closer to an increase in the number of operations, all functions are increasingly combined, resulting in small increasing peaks at first, which seem to alternate in increasing and decreasing. Further, this trend increases by one large increase, followed by smaller, but still increasing peaks, coming to two maximum large peaks, ending only with the final peaks, again returning to the form of the cycle, which is more like a straight line against the general background. In this case, it is worth paying more attention to that. That the growth of the graph relative to the central peaks occurs more smoothly than the decline, which surprisingly describes examples of real physical phenomena quite well when presenting their graphs.

Graph 3. Functions for the interval [17; 27] for 110 elements

If we compare the values from 20 to 30, then we can see that the graph is preserved, but the level of coincidence of these graphs for 110 elements begins to decrease each time and what becomes even more noticeable when considering at the initial stages of the function, which was still noticeable in the previous graph, however, in this case this effect has intensified, although the overall completion of the graph has also been preserved, maintaining the same condition for approaching the level of reduction to the state of a straight line with fluctuations (Graph 4).

Graph 4. Functions for the interval [20; 30] for 110 elements

More significant changes, but at the same time a high level of coincidence is observed when considering the same large peaks of the graphs in the range from 30 to 40. At the same time, a decrease in correlation is observed at the moment of the initial state of the graph. However, another distinctive feature of the next level of the graph, unlike the previous one, is also the appearance of a straight line, the level of which is increasingly decreasing closer to an increase in the number of steps, the total number of elements of which continue to remain Graph. 5.

Graph 5. Functions for the interval [30; 40] for 110 elements

However, the tendency to preserve function matches is lost immediately in the next interval from 40 to 50 for the same 110 elements. In this case, the picture of the graph itself is already somewhat different. If we talk about its initial position, then indeed the differences in functions continue to increase somewhat, however, with increasing steps, we can observe a picture when the upper function begins to stand out, and other functions connect with other graphs forming an orange line. This time, the yellow upper function begins to increase distinctly, each time increasing by certain peaks, after which the graph falls off again, but quickly begins to gain growth again. This stage of growth is surprisingly quite interesting, because there is a double stage of doubling of peaks, after which the next small, but also doubled peak comes out, between each of which there is an increase, however, relatively small. Then this situation repeats again for the next maximum peaks, followed by a sharp and fairly rapid decline, after which the situation again reduces to a state of small increases until the overall picture decreases to standard small fluctuations in the cycle – a comparatively formed straight line. The second function in this case has a slightly different, more singular character due to the fact that correlation is observed for the first single peaks, followed by a faster decline at the end of Graph 6.

Graph 6. Functions for the interval [40; 50] for 110 elements

The picture described for the situation from 40 to 50 retains its specific role model for the subsequent graph for numbers from 50 to 60, which can be traced during its analysis, however, in this case, the role of the upper and lower maximum functions, of course, are already other values, which also carry a sharper increasing However, this, along with other things, can be traced during the analysis of the maximum and average initial peaks, after which there was a small drop, and after the maximum – a sharper one, as can be seen, with a large coincidence for the peaks on Graph 7.

Graph 7. Functions for the interval [50; 60] for 110 elements

Thus, in the future, graphs are presented for the intervals from 60 to 70, where, surprisingly, a sharp increase in correlation can be observed again, when part of the functions goes down as a separate line, and one single one acts as the only upper correlating one (Graph. 8). In the future, the graph begins to change again for the interval from 70 to 80 and the state described in the early interval for the interval from 40 to 50, it will be possible to observe an increase in the number of peaks at the beginning to two classes, and in the center of three large maximum peaks, where you can observe a situation where the main plan describes the main yellow function, correlation with which it increases for the red function at the third peak and with a small second central right peak, from where it is possible to trace the similarity of the pictures, but with a noticeable shift in Graph 9.

Graph 8. Functions for the interval [60; 70] for 110 elements

Graph 9. Functions for the interval [70; 80] for 110 elements

The continuation of the study allows us to observe the similarity of the interval from 17 to 27, from 20 to 30, from 60 to 70 and from 80 to 90, without small distinguishing features, as can be seen in Graph 10. And the situation for the interval from 90 to 100 is one of the most beautiful images, because here almost every graph is not like another, although most of them retain their definite trend, as can be seen in Graph 11. After that, the interval from 190 to 200 takes a more ordered, beautifully shaped form, where most of the functions take their general, uniform form, however, with a different level of bias with a decrease in the degree of correlation for each of them (Graph. 12).

This difference begins to decrease when analyzing Graph 13 for numbers from 290 to 300, where you can pay attention to an already more clearly verified and rather beautiful picture. This aspect is already beginning to change, leading to an increase in the degree of differing properties between functions from 390 to 400, as can be seen in Graph 14. The subsequent increase in the degree of gaps leads to the continuation of such a trend, which is clearly seen in a cardinal difference with the formation of a real house in the range from 490 to 500, so that even when most functions have already reached the final form, some functions begin to continue to increase forming massive peak forms (Graph. 15).

The continuation of the growth of the boundaries leads to a further increase, so the initial shape of the graph begins with a sharp increase, then decreases, and then continues at maximum peaks, which has never been repeated before, given that the graphs decline further and then sharply increase again to two peaks (Graph. 16). Further, the situation with a small difference continues at the moment from 690 to 700, while having a sharp shift of large peaks, having an elongation in the initial difference and the range of the initial small class of peaks (Graph. 17). And it would seem that the correlation situation can be increased, however, according to the graphs for values from 790 to 800, from 890 to 900, from 990 to 100, they retain the form of a house (Graph 18—20).

Graph 10. Functions for the interval [80; 90] for 110 elements

Graph 11. Functions for the interval [90; 100] for 110 elements

Graph 12. Functions for the interval [190; 200] for 110 elements

Graph 13. Functions for the interval [290; 300] for 110 elements

Graph 14. Functions for the interval [390; 400] for 110 elements

Graph 15. Functions for the interval [490; 500] for 110 elements

Graph 16. Functions for the interval [590; 600] for 110 elements

Graph 17. Functions for the interval [690; 700] for 110 elements

Graph 18. Functions for the interval [790; 800] for 110 elements

Graph 19. Functions for the interval [890; 900] for 110 elements

Schedule 20. Functions for the interval [990; 1000] for 110 elements

As a result of the analysis, it was possible to clearly see the change in the patterns of graphs for a variety of intervals when testing the Collatz hypothesis, each of which has its own importance, finding its application in a variety of fields. And today we can hope to find in the future the possibility of solving this problem in the face of proof of this hypothesis, or its refutation.

Used literature

1. Hayes Brian. The ups and downs of hailstone numbers // In the World of Science (Scientific American, Russian edition). – 1984. – No. 3. – pp. 102—107.

2. Stuart Ian. The greatest mathematical problems. – M.: Alpina non-fiction, 2015. – 460 p. – ISBN 978-5-91671-318-3.

3. Jeff Lagarias. The 3x+1 problem and its generalization // American Mathematical Monthly. – 1985. – Vol. 92 – P. 3—23.

4. Alfutova, N. B. Algebra and number theory. Collection of problems for mathematical schools / N. B. Alfutova, A.V. Ustinova. – M.: ICNMO, 2018. – 336 p.

5. Alpatova, N. B. Algebra and number theory: Collection of problems for mathematical schools / N. Alfutova B., And A. V. Ustinov. – M.: mtsnmo, 2009. – 336 c.

6. Arnold, V. I. the Theory of numbers by I. V. Arnold. – M.: Lenand, 2019. – 288 c.

7. Borevich, Z. I. number Theory / zi Borevich, I. R. Shafarevich. – M.: Lenand, 2019. – 504 c.

8. Boss, W. Lectures on mathematics: Theory of numbers / V. Boss. – M.: Lenand, 2014. – 224 p.

9. Boss, V. Lectures on Mathematics vol.14: Theory of numbers / V. Boss. – M.: CD Librocom, 2010. – 216 p.

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13. Weil, G. Algebraic theory of numbers / G. Weil. – M.: URSS, 2011. – 224 p.

14. Gankel, G. The theory of complex numerical systems, mainly ordinary imaginary numbers and Hamilton quaternions together with their geometric interpretation. Trans. from German / G. Gankel. – M.: Lenand, 2015. – 264 p.

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16. Egorov, V. V. Theory of numbers: A textbook / V. V. Egorov. – St. Petersburg: Lan, 2015. – 384 p.

17. Zolotarev, E. I. Theory of integral complex numbers with an application to integral calculus / E. I. Zolotarev. – M.: Lenand, 2016. – 216 p.

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ABOUT MODERN RESEARCH IN THE FIELD OF IMPROVING THE TECHNOLOGY OF ELECTRONIC TUNNELING

Aliyev Ibratjon Xatamovich

3rd year student of the Faculty of Mathematics and Computer Science of Fergana State University

Ferghana State University, Ferghana, Uzbekistan

Annotation. This article discusses the theoretical foundations and mathematical apparatus of a new method of transmitting information at high speeds, in contrast to the classical electromagnetic method, the method of using quantum entanglement and other similar recognized methods. The technological improvement of information transmission methods today really deserves attention, since they become a sufficient reason for a new revision of new achievements in this field. One of such technologies, currently developing mainly in a theoretical way, is the method of using the electronic tunnel effect. Now becoming more and more relevant.

Keywords: quantum tunneling effect, electrons, information transfer, theoretical foundations, physical and mathematical apparatus.

Аннотация. В настоящей статьи рассматриваются теоретические основы и математический аппарат нового метода передачи информации на больших скоростях, в отличие от классического электромагнитного метода, метода использования квантовой запутанности и прочих подобных признанных методом. Технологическое совершенствования методов передачи информации сегодня действительно заслуживает внимания, поскольку становятся достаточной причиной для нового пересмотра новых достижений в настоящей области. Одной из таких технологий, ныне развивающаяся в основном в теоретическом ключе является метод использования электронного туннельного эффекта. Ныне становящийся всё более актуальным.

Ключевые слова: квантовый туннельный эффект, электроны, передача информации, теоретические основы, физико-математический аппарат.

The phenomenon of the quantum tunneling effect is quite well-known and popular today. This effect itself is based on the fact that microparticles can overcome a certain potential barrier if its total energy, which remains unchanged and is not spent on overcoming the barrier, is less than the height of the barrier itself. Of course, such a phenomenon by definition could not occur on the scale of classical physics, at least because of its vivid contradiction, however, this effect itself is proven by numerous empirical results, since it underlies the most diverse phenomena of atomic, molecular physics, physics of the atomic nucleus and elementary particles, solid state and others.

For a better understanding of the present effect, we point out that let the definition of the kinetic energy of a particle be set initially according to (1), from which it can be seen that if the conditions of the quantum tunneling effect are met, it turns out that the momentum of such a particle satisfying the conditions set should become an imaginary quantity and it would seem that this could not be in reality, but together with this, the solution of the famous Schrodinger equation (2), where the potential energy of the particle is a constant, has a solution (3), from which the value for the momentum is derived as (4).

And although in this case the momentum becomes imaginary when the value of the potential barrier begins to exceed the total energy of the particle, as it was indicated. To understand the nature and causes of this phenomenon, you can resort to presenting a separate model with three potential barriers, for each of which your wave equations will be defined, after which the final expression will be derived, or you can use a more visual Heisenberg uncertainty relation. As can be seen from the first relation for inaccuracies of coordinates and momentum, with a more accurate determination of the coordinates of a particle, the accuracy of its momentum decreases, due to which we can talk about finding the magnitude of the momentum of particles in any suitable set of quantities at this time, which allows the particle to have a complex momentum value, which causes the tunnel effect. However, in this case, there will be a determination of the magnitude determining the probability of a particle passing through this barrier.

So the present transmission coefficient is determined according to the initial model, according to which let there be three potential barriers, the first and third of which have zero height, and the second is high enough to exceed the value of the total energy of the particle. In this case, during the approach of a particle to a potential barrier, the definition of its coordinate increases, due to which, according to the uncertainty ratio, the value of its momentum decreases, after which a certain number of its components can pass through the barrier, and a certain one can be reflected. It is the ratio of these two components that gives a certain definition of the probability current, where the probability current of the wave incident on the barrier acts as the numerator, and the probability current of the part of the wave passing through the barrier acts as the denominator. Also, the inverse value of this value is the reflection current, from where it is appropriate to determine their sum equal to one.

In addition, the value of these quantities, according to the laws of quantum mechanics, can be determined through a quasi-classical approximation, where the relations are determined according to (5).

In this case, we can say that if there is a particle with a certain energy value less than the value of the potential barrier, it also becomes possible to determine the probability with which this particle can pass through this potential barrier. So, for an electron with varying kinetic energy, to pass a potential barrier of 1 GeV, when its energy increases to this value, the probability function changes according to Graph 1.

In this case, it will be possible to visually observe how the probability of passage begins to change and already when the value becomes equal to the value of the potential barrier, even then it is no longer possible to talk about complete passage (6).

And although, on the one hand, the analysis of the present effect may be, in the original understanding, made to describe more well-known practical phenomena, but as it turned out, there are new methods according to which it is possible to transmit energy/information over an almost unlimited distance using this technology. The fact is that today it is possible to transfer to a particle a huge amount of energy up to tens of TeV, which is already equal to the value of a potential barrier consisting of 1,000 atoms standing in the way of the particle, that is, it can pass through a thousand atoms without expending energy with a probability of 64%, while initially giving a certain direction in space a real particle. And since the particle does not change its energy after passing the barrier, unless resources can be spent only as a means of overcoming probability, then we can talk about transferring the remaining amount of energy to a huge, cosmic distance.

So if the energy of 1 TeV becomes sufficient to overcome thousands of hydrogen atoms, with a diameter of 10—11 m, how can we say that this energy will be enough to overcome 10—8 m. It would seem that the distance is too small and the technology itself is not too cost – effective, but it is worth considering at least that such a method does not require the use of conductors and for transmitting, for example, energy to the ISS, the distance to which is estimated at a maximum point of 430 km, it is worth sending particles with energies of 4.3 * 1025 eV.

A value that becomes almost unrealistic given modern devices, but this definition is suitable if we take into account that the particle current will be measured in mA or MC, which can determine the charge through (7).

Where, from the available energy value, the velocity (8) can be calculated, but for a sufficient solution, it is necessary to initially decompose the resulting root with the transformation (9—10) into a Taylor series (11), from where it will be possible to obtain a percentage value.

Thus, it was possible to determine the approximation of the speed of light, which can be taken to be almost equal to the speed of light. And indicating that 1 micron is taken as the beam diameter, we can talk about the resulting charge value and the number of particles (12).

Therefore, we can say that it is possible to direct energy to a distance of 430 km in the amount of 4.3 * 1019 watts instantly, when the same value can be sent for 1.43 microseconds to the same distance, when acting with light radiation with the same power. And if, at such a relatively close distance, this method again does not seem to be cost-effective, then you can resort to the case when the distance is 1 light-year. Then it is worth resorting to a different definition.

Initially, it is worth pointing out that the density of matter in space is 3 * 10—28 kg/m3, which in turn is 2.9967* 1026 times less dense than the density of the estimated hydrogen, equal to 0.0899 kg/m3, from which we can say that with an already defined energy of 1025 eV, a particle can overcome in space as much the same distance or, by analogy, 1.288567 * 1029 km, which is 13,629,492,816,374.85 light-years, which is even more than the radius of the observable universe by 137,927.5 times. Therefore, in order to send energy to a distance of 1 light-year, it is sufficient to use the energy of a particle equal to 733.7 GeV at the available velocity in (13), it is possible to determine the magnitude of the charge (14).

Thus, it became possible to talk about the creation of a new method of transmitting energy over huge distances almost instantly, without spending several years on it, while the minimum value, of course, is equal to the value of the elementary charge charge, and hence the current (15), with a minimum energy for 1 light year of 733.7 GeV.

That is, it is possible to expend in a general sense, giving a particle only 2,762669 * 10—35 watts of energy, you can direct any amount of energy instantly, starting from this value to infinity to practically any distance from the planet instantly, without spending billions of years to overcome all obstacles with light or other radiation.

Used literature

1. Bondarev, B. V. Course of general physics. In 3 t. t. 2. Electromagnetism. Optics. Quantum physics: A textbook for bachelors / B. V. Bondarev. – M.: Yurayt, 2013. – 441 p.

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INFORMATION AND MEASUREMENT SYSTEM FOR THE STUDY OF EARTHQUAKE PRECURSORS

Asatulla Urmanovich Maksudov

Senior Researcher at the Institute of Physics and Technology of the Academy of Sciences of the Republic of Uzbekistan
Institute of Physics and Technology of the Academy of Sciences of the Republic of Uzbekistan asaduz50@rambler.ru
Nurmamat Umaraliev
Candidate of Technical Sciences, Associate Professor of the Department of “Electronics and Instrumentation” of the Faculty of Computer Design Systems of the Fergana Polytechnic Institute
Ferghana Polytechnic Institute, Ferghana, Uzbekistan
ORCHID-0000-0001-9822-8115

nurmuhammad@bk.ru

Annotation. Currently, earthquake forecasting is one of the most pressing problems. Seismic disasters caused by earthquakes not only cause great economic damage, but also lead to the death of many people.

Keywords: harbinger, earthquake forecast, neutron and charged particle fluxes, measurements, monitoring, information and measurement system.

Аннотация. В настоящее время прогноз землетрясений является одной из наиболее актуальных проблем. Сейсмические катастрофы, вызванные землетрясениями, не только наносят большой экономический ущерб, но и приводят к гибели многих людей.

Ключевые слова: предвестник, прогноз землетрясений, потоки нейтронов и заряженных частиц, измерения, мониторинг, информационно-измерительная система.

The purpose of the study

The study of new earthquake precursors to address issues of short- and medium-term forecasting of the prameters of upcoming earthquakes. To achieve this goal, this work uses an indirect method of measuring controlled parameters, constant monitoring of the studied values and statistical methods of data processing, including regression analysis methods.

An information-measuring system is proposed for studying the correlation between earthquakes and neutron and charged particle fluxes emanating from the Earth’s crust, as well as a method for calibrating this information-measuring system.

This work uses an experimental research method. An indirect method of measuring earthquake parameters, such as magnitude, hypocenter coordinates, is proposed.

The results of preliminary experiments are presented. Informative signs are described, the presence of which makes it possible to predict the parameters of upcoming earthquakes. The minimum number of implementations necessary for constructing regression models of earthquake parameters has been determined. A block diagram of the parts of the information and measurement system, through which preliminary experiments were carried out, is also given. The method of calibration of the entire information and measurement system is described.

The main conclusions. During the analysis of the results of preliminary experiments conducted at different times in the cities of Tashkent and Ferghana, an assumption arose about the existence of a correlation between the parameters of an earthquake and neutron and charged particle fluxes, i.e. neutron and charged particle fluxes carry informative signs about upcoming earthquakes in the near future. According to this assumption, they can be attributed to the precursors of earthquakes.

An information and measurement system is proposed that will allow in-depth study of the relationship between the parameters of the upcoming earthquake and the fluxes of neutrons and charged particles by continuously monitoring them and subsequent statistical processing of accumulated data.

The conditions for the prediction of all parameters of the upcoming earthquake, such as the hypocenter, magnitude and time of the upcoming earthquakes, are determined.

Introduction

Currently, earthquake forecasting is one of the most pressing problems. Seismic disasters caused by earthquakes not only cause great economic damage, but also lead to the death of many people.

The main difficulty in predicting earthquakes is that an earthquake model has not been developed. In practice, there is no reliable method and device that could predict its location, time, energy or intensity, which could meet the practical requirements for both accuracy and speed. However, these requirements are divided not only by the level of knowledge about earthquakes, but also into forecasts for specific purposes, long-term forecasts or short-term forecasts, depending on different types of practical goals. At the moment, the short-term forecast is relevant. This is the basis for a clear warning of an impending disaster and taking urgent measures to reduce earthquake damage. This article presents information about a device and an information system designed to measure the parameters of a physical process that can serve for short-term forecasting.

In the patent of Uzbekistan for a utility model [1], a measuring device for short-term forecasting is proposed. In this device, charged particles and neutron fluxes coming from underground, acting on scintillation detectors, generate optical impulses, which are converted into electrical impulses using photoelectronic multipliers attached to scintillation detectors (Fig. 1). Thus, this device allows you to measure the flow of neutrons and charged particles coming from underground. Despite the insignificance of the magnitude of the flows, the nature of the change is likely to carry information about the main root cause of their occurrence. Continuous monitoring of the flow values will allow for a correlation analysis between them and between earthquake parameters. On the basis of correlation analysis, accurate methods for predicting an approaching earthquake can be created.

Fig.1. Device for measuring neutron fluxes and charged particles: a-axonometric view; b-front view in the section; coal α = 450; 1— central scintillation detectors; 2-FEU-84; 3-direction detectors, 4-FEU-125; 5-carbon absorbers; 6-neutron counters.

Such correlation analysis requires the results of very large-scale measurement work. That is, it is necessary to constantly monitor the values of the streams received by the device and record them in the appropriate information base. In addition, to study the dependence of these currents on the distance, at least three devices are necessary and they must be placed in the form of triangles at a distance of at least 200 km from each other in seismically active zones.

The earthquake parameters – magnitude, time, coordinates of the hypocenter, charged particles and neutron fluxes from the devices proposed for prediction should be simultaneously recorded in one information database in chronological order.

In addition, the parameters of all earthquakes that occur during the monitoring of neutron fluxes and charged particles, such as – time, magnitude and coordinates of the hypocenter – should be included in the information base in chronological order.

To build an adequate regression model for predicting the parameters of the upcoming earthquake, the number of experiments should be no less than the number of corresponding sensors of the complex. For example, to build a regression model for predicting hypocenter parameters, based on the number of direction sensors, the number of experiments should be at least 24, and for magnitude prediction, the number of experiments should be at least 3.

This process is the “learning” mode of the proposed measurement and information system. Having collected a sufficient amount of statistical data in this mode, it will be possible to build a mathematical model predicting the parameters of the upcoming earthquake using regression analysis of measurement results. The proposed measurement and information system will then become a complex predicting the parameters of a possible earthquake. Like any SMART system, this system always works in the “forecast-correction” mode, while the accuracy of forecasting earthquake parameters increases.

The proposed measurement and information system is based on modern means of information transmission and information and communication technologies. Below is a block diagram of one channel of the measuring device of the measuring and information system:

Fig. 2. Block diagram of the transmission of information from the measuring device

Here:

• Device to be monitored – the measuring device shown in Figure1.

• UART- universal asynchronous receiver/transmitter – universal asynchronous receiver – transmitter;

• Microcontroller – microcontroller with analog-to-digital converter;

• SPI – serial information transfer interface;

• EtherSield – Ethernet module;

• Hardware – a set of programmable measuring devices.

Fig. 3. Block diagram of the system

Here:

• Hardware – technical means of the system installed in the cities of Fergana, Tashkent, Samarkand;

• A web server is an application server that collects and processes information.

In order to implement the proposed information and measurement system, three “Software and Hardware measuring systems” have been installed in the cities of Fergana, Tashkent and Samarkand. The measurement results obtained from these complexes are entered into the information base through the web server applications on the website pribori.uz. Figure 3 shows a block diagram of the system.

Fig.– 4. Interface for visualization of system data

Figure 4 below shows the data visualization interface of the system.

Conclusions

During the analysis of the results of preliminary experiments conducted at different times in the cities of Tashkent and Ferghana, an assumption arose about the existence of a correlation between the parameters of an earthquake and neutron and charged particle fluxes, i.e. neutron and charged particle fluxes carry informative signs about upcoming earthquakes in the near future. According to this assumption, they can be attributed to the precursors of earthquakes.

The proposed information and measurement system will allow a deep study of the relationship between the parameters of the upcoming earthquake and the fluxes of neutrons and charged particles.

The conditions for the prediction of all parameters of the upcoming earthquake, such as the hypocenter, magnitude and time of the upcoming earthquakes, are determined.

Literature

1. A. U. Maksudov, M.A. Zufarov, “Predvaritelnye dannye registratsii predvestnikov zemletryaseniya modernizirovannoi ustanovkoi”, Comp. nanotechnol., 2017, no. 3, 33—35

2. Regression models for earthquake prediction. Rakhimov R. H., Umaraliev N., Jalilov M. L., Maksudov A. U. Computational nanotechnology 2018, No. 2, 40—42

3. Makhsudov A. U., Umaraliev N., Jalilov M. L., Zhyraev N. M. //Evaluation of the results of the parameters of the earthquake precursor measured by the detector// Republican Scientific and Technical Conference “Mukobil energiya turlari va ulardan foidalanish istikbollari” FerGU (May 12) Fergana 2017.

4. Asatulla U. Maksudov & Mars A. Zufarov, Measurement of neutron and charged particle fluxes toward earthquake prediction//Earthquake Science, ISSN 1674—4519, Earthq Sci, DOI 10.1007/s11589-017-0198-z;

5. B.S.Yuldashev, R.A.Muminov, A.U.Maksudov, Umaraliev N. et al. A new nuclear-physical method for registering earthquake precursors. DAN RUz, 2018, No. 1, pp. 4—6.

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8. Kurskeev A. K., (2011), Earthquake and seismic safety. Almaty, Kazakhstan, “EVRO”, p. 504.

9. Yuldashbaev T. S., Maksudov A. U., (2010) Development of a method for registering earthquake precursors from observations of temporal variations in the flux of cosmic rays and neutrons. Reports of the Academy of Sciences of the Republic of Uzbekistan. V. 14, No. 3, pp. 144- 148.

10. Yuldashbaev T.S., Maksudov A.U., Preliminary results of the study of temporal variations of cosmic rays in a new experimental setup. Reports of the Academy of Sciences of the Republic of Uzbekistan, 2012, v.14, No. 3, pp. 14—16.

11. Yuldashbaev T.S., Maksudov A.U., et al., (2012), Study of temporal variations in the flux of charged particles and low-energy neutrons. Uzbek Physical journal of the Academy of Sciences of the Republic of Uzbekistan, 3:14—18.

12. Asatulla U. Maksudov & Mars A. Zufarov, Measurement of neutron and charged particle fluxes toward earthquake prediction//Earthquake Science, ISSN 1674—4519, Earth Sci, DOI 10.1007/s11589-017-0198-z;

13. Maksudov A. U., Zufarov M. A., Preliminary registration data for earthquake precursors by a modernized installation. Computational nanotechnology. No.3, M. 2017, p. 33—35;

14. Maksudov A. U., et.al. Modernized registering device of harbingers of earthquakes// special issue of proceedings EMSEV2016 Institute of Earthquake Science, China Earthquake Administration, Fuxing Avenue 63, Haidian District, Beijing, 100036, China. 2017.

15. Rakhimov R. Kh., Makhsudov A. U., Zufarov M. A.//Nuclear-radioactive reactions in earth crust the generator of earthquake harbingers. Computational nanotechnology. No. 3, M. 2018g. p.68—72. http: www.urvak.ru

16. Rakhimov R.Kh., Umaraliev N., Djalilov M.L., Maksudov A.U., (2018), Regressions models for forecasting of earthquakes. Computational nanotechnology. 3: 43—45.

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