Spectral analysis of granular material reaction to long-term weak dynamic effect

The paper demonstrates the similarity of alternating reaction of rocks to explosions to the response of a granular material to repetitive (up to 330 thousands) light shocks (3.85 -10{-3} J, frequency 1.33 Hz). The cause of both reactions is multiple forms of equilibrium of geomaterials. It has been stated that pressure of the sensor in a granular material subject to long-term weak effects doesn't relax to the hydrostatic one, but oscillates about it with the amplitude about its value. The spectral density of this process obeys the power dependence on the frequency; the exponent is typical for black noise

Rolling Friction in Loose Media and its Role in Mechanics Problems

Rolling friction between particles is to be set in problems of granular material mechanics alongside with sliding friction. A classical problem of material passive lateral pressure on the retaining wall is submitted as a case in point. 3D method of discrete elements was employed for numerical analysis. Material is a universe of spherical particles with specified size distribution. Viscose-elastic properties of the material and surface friction are included, when choosing contact forces. Particles' resistance to rolling relative to other particles and to the boundary is set into the model. Kinetic patterns of medium deformations are given. It has been proved that rolling friction can significantly affect magnitude and nature of passive lateral pressure on the retaining wall

On the theory of plasticity, associated with a new integral characteristic of shearing stresses

A new invariant of stress tensor is introduced - the mean shearing stress, resulted from the integration with respect to Mohr's circle. The invariant is used to lay down the terms of plasticity. Determining equations are written on the basis of associated flow law. Rigid variants of the model and elastoplastic ones are obtained. Characteristic surfaces with normals, coinciding with main stresses direction are demonstrated for the rigid-plastic variant

Development of Requirements for a Basic Standardized Mathematical Model of Geokhod

The article revealed the shortcomings of existing mathematical models geokhods, the necessity of a new approach to modeling the processes of internal and external geokhods interaction, formulated the task of building flexible mathematical models

Substantiating Ways of Load Application When Modeling Interaction of a Multiincisal Mining Machine Actuator With Rocks

Two methods of modeling of interaction between a mining machine working body and rocks are considered; a multi-cutter working body sum impact on rock stress-strain state in a cutter action zone is described; practicability of distributed forces application in math modeling is substantiated

Importance of Resultant Action of the Mining Machine Actuator for Stresses in Impact Zone of a Separate Cutter

Two stress levels are considered in the general pattern of stress-strain state of the rock destroyed by the mining machine. The authors also ground the necessity of considering the interaction of all cutters of the actuating device when calculating the process of cutting with a separate cutter

On One Class of Dual Problems of Mechanics of Deformable Solids

Inelastic body's plane deformation is described by two vector fields: vector stress potential (gradient of Airy stress function) and vector displacement field. Conditions for possibility of proceeding to the dual problem, when variables change the roles, are described: stress potential is interpreted as displacement field and vice versa. Both a perfectly plastic body model and its dual model of perfectly solidifying matter are considered

Dissemination of Weak Waves in Granular Materials Under Short-Term Impulse Loads

The results of experiments with dry high-silica sand are presented. Multiple point impacts have been revealed to improve waveguide properties of the material because a conductive channel, containing force "chains", is formed there. Quasistatic alternating shears condition the change in the particle packing; destroy "chains", and deteriorate the channel conductivity. Further multiple impulse loads lead to restoration of the "chains" and conductivity of the channel

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future