The Meso-Structural Characteristics of Crack Generation and Propagation during Rock Fracturing

Meso-structure is an important factor affecting the characteristics of rock fracture. To determine the factors influencing the internal meso-structural characteristics upon the crack generation and extension, rock samples were tested under uniaxial cyclic loading and unloading and examined using computed tomography (CT) scanning. CT scanning was used to visualize and investigate the entire process of fracture source generation and its development in three dimensions, and finally the location information of the fracture source was determined. The mineral composition and structure along the fracture path inside the specimen were studied by using a polarizing microscope, and the evolution of fracture propagation around mineral particles was revealed based on its mineralogical characteristics. Results indicate that based on the fracture source around different rock meso-structure types, the initial fracture source can also be divided into different types, namely, the primary porosity type, the micro-crack type, and the mineral grain type. The strength characteristics of mineral grains can determine whether the crack extends around the gravel or through it. The hard grains at the crack-tip promote the transformation of tensile stress to shear stress, which lead to the change in the direction of crack extension and bifurcation. The spatial shape of the cracks after rock fracture is related to the initial distribution of minerals and is more complicated in areas where minerals are concentrated. The crack extension around gravel particles also generates a mode of failure, affecting large grains with gravel spalling from the matrix. The findings provide a study basis for identifying the potentially dangerous areas and provide early warning for the safety of underground engineering construction operations.

Dependence of the nature of the Holstein polaron motion in a polynucleotide chain subjected to a constant electric field on the initial polaron state and the parameters of the chain

In this work, we consider the motion of a polaron in a polynucleotide Holstein molecular chain in a constant electric field. It is shown that the character of the polaron motion in the chain depends not only on the chosen parameters of the chain, but also on the initial distribution of the charge along the chain. It is shown that for a small set value of the electric field intensity and for fixed values of the chain parameters, changing only the initial distribution of the charge in the chain, it is possible to observe either a uniform movement of the charge along the chain, or an oscillatory mode of charge movement.

SEPARATION OF PARTICLES IN POLYDISPERSE NANOSUSPENSION IN THE LASER RADIATION FIELD

На основе стационарного решения уравнения диффузии изучена сепарация наночастиц в прозрачной полидисперсной водной суспензии с различными типами распределений по размерам под действием силы светового давления, возникающей в поле лазерного излучения интенсивностью 0,5 - 500 кВт/см. Установлено, что на дно кюветы преимущественно будут осаждаться частицы радиусом более 100 нм, а концентрация более мелких наночастиц во всем объеме суспензии останется без изменений. В случае симметричного начальное распределения наночастиц по размерам воздействие интенсивного светового пучка на суспензию приводит к нарушению симметрии кривой функции распределения, а также смещению максимума в область меньших размеров частиц на облучаемой поверхности. Если начальное распределение по размерам имеет несимметричный характер, исходное одномодовое распределение частиц по размерам трансформируется в двумодовое. Данная методика может быть использована для выделения наночастиц определенных размеров в зависимости от плотности мощности излучения. On the basis of a stationary solution of a diffusion equation separation of nanoparticles in a transparent polydisperse aqueous suspension with different types of size distributions was studied under the action of the light pressure arising in the laser radiation field with the intensity of 0,5 - 500 kW/cm. It was found that particles with a radius of more than 100 nm will mainly be precipitated at the bottom of the cell, and the concentration of smaller nanoparticles in the entire volume of the suspension will remain unchanged. In the case of a symmetrical initial distribution of nanoparticles size, the effect of a light beam with high intensity on the suspension leads to a violation of the symmetry of the distribution function curve, as well as a shift of the maximum to the region of smaller particle sizes on the irradiated surface. If the initial size distribution is asymmetric, the initial single-mode particle size distribution is transformed into a two-mode one. This technique can be used to isolate nanoparticles of certain sizes depending on the power density of the radiation.

An electron wave packet in the relativistic strophotron FEL

The theory of free electron lasers (FELs) is well developed both in quantum mechanical and classical approaches. In strophotron FEL, in classical approach, resonance frequency and the gain are strongly dependent on initial parameters of electron beam. In the quantum mechanical approach considered by Zaretsky and Nersesov (1983 JETP 57 518), there is no such dependence. The correspondence between the quantum mechanical and classical approaches in a relativistic strophotron FEL is discussed. We study the initial distribution of electrons over vibrational levels determined by the expansion coefficients in relativistic strophotron FEL. It is shown, (presenting electron wave function in the form of Gaussian wave packet), that the number of the vibrational level most efficiently populated at the initial moment of time can be expressed in terms of the initial parameters of the electron beam.

Statistical description of coalescing magnetic islands via magnetic reconnection

The physical picture of interacting magnetic islands provides a useful paradigm for certain plasma dynamics in a variety of physical environments, such as the solar corona, the heliosheath and the Earth's magnetosphere. In this work, we derive an island kinetic equation to describe the evolution of the island distribution function (in area and in flux of islands) subject to a collisional integral designed to account for the role of magnetic reconnection during island mergers. This equation is used to study the inverse transfer of magnetic energy through the coalescence of magnetic islands in two dimensions. We solve our island kinetic equation numerically for three different types of initial distribution: Dirac delta, Gaussian and power-law distributions. The time evolution of several key quantities is found to agree well with our analytical predictions: magnetic energy decays as $\tilde {t}^{-1}$ , the number of islands decreases as $\tilde {t}^{-1}$ and the averaged area of islands grows as $\tilde {t}$ , where $\tilde {t}$ is the time normalised to the characteristic reconnection time scale of islands. General properties of the distribution function and the magnetic energy spectrum are also studied. Finally, we discuss the underlying connection of our island-merger models to the (self-similar) decay of magnetohydrodynamic turbulence.

The list algorithms accuracy characteristics improvement based on Krohn’s algorithm and its modifications

The research of algorithms for uniform loading of devices for homogeneous information processing systems is a very important science-intensive task. An experimental approach was chosen for the research. This is primarily due to the fact that the analytical solution of the distribution problem gives solutions that are far from reality, since it is unable to take into account many factors that affect the computing machine during its operation. The aim of this research is to improve the accuracy characteristics of the list algorithms through the use of heuristic algorithms, such as Krohn’s algorithm and its modifications. This made it possible to obtain a more even distribution of tasks among executive devices, which can be networked workstations, processors or processor cores. The work uses list algorithms, such as the Critical Path algorithm and Pashkeev’s algorithm, as well as heuristic algorithms - Krohn’s algorithm and its modifications. The main idea of the research is to obtain the best suboptimal solution by improving the quality of the resulting distribution. In this case, with the help of the list algorithms, the initial distribution is formed, and its refinement is carried out through the application of the Krohn’s algorithm and its modifications. In fact, in the work, a number of symbiotic algorithms are examined and analyzed. For this, many computational experiments were carried out and a large amount of output data were collected, on the basis of which conclusions were drawn about the effectiveness of the solution obtained for each symbiotic group and for all groups as a whole.

DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.

Simulated Annealing Applied to HIO Method for Phase Retrieval

Phase retrieval utilizing Fourier amplitudes plays a significant role in image recovery. Iterative phase retrieval algorithms have been developed to retrieve phase information that cannot be recorded by detectors directly. However, iterative algorithms face the problem of being trapped in local minima due to the nonconvexity of phase retrieval, and most existing works addressed this by optimizing in multiple runs parallelly to improve the possibility that one of these could reach the global minimum. Alternatively, we propose in this work to increase the probability of reaching the global minimum with one arbitrary initial distribution by adapting simulated annealing in the standard hybrid input-output (HIO) algorithm. Numerical and experimental results demonstrate that the proposed method reconstructs images with mean square errors 50.12% smaller than those reconstructed by HIO. More importantly, the proposed method can be applied to any HIO-based algorithm with multiple runs to further improve the performance.

Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media

We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We develop a computational model of a society, where each individual is represented as a simple neural network (a perceptron), under the influence of a recommendation system that honestly forward messages (posts) to other individuals that in the past appreciated previous messages from the sender, i.e., that showed a certain degree of affinity. This dynamical affinity database determines the interaction network. We start from a set of individuals with random preferences (factors), so that at the beginning, there is no community structure at all. We show that the simple effect of the recommendation system is not sufficient to induce the isolation of communities, even when the database of user–user affinity is based on a small sample of initial messages, subject to small-sampling fluctuations. On the contrary, when the simulated individuals evolve their internal factors accordingly with the received messages, communities can emerge. This emergence is stronger the slower the evolution of individuals, while immediate convergence favors to the breakdown of the system in smaller communities. In any case, the final communities are strongly dependent on the sequence of messages, since one can get different final communities starting from the same initial distribution of users’ factors, changing only the order of users emitting messages. In other words, the main outcome of our investigation is that the bubble formation depends on users’ evolution and is strongly dependent on early interactions.