MINING BLOCK STABILITY PREDICTION BY THE MONTE CARLO METHOD

This paper analyses the stability of the mining blocks by the Monte Carlo method in Estonian oil shale mines, where the room-and-pillar mining system is used. The pillars are arranged in a singular grid. The oil shale bed is embedded at the depth of 40-75 m. The processes in overburden rocks and pillars have caused the subsidence of the ground surface. Visual Basic for Application was used for the modeling. Through Monte Carlo simulation, room-and-pillar stable parameters can be calculated. Model allows determination of the probability of spontaneous collapse of the pillars and surface subsidence by the parameters of registered collapsed mining blocks. Proposed method suits as an express-method for stability analysis and failure prognosis. It is applicable in different geological conditions, where the room-and-pillar mining system is used

Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation

High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice-ocean model NEMO-LIM3 was modified to improve its simulation quality for appropriate spatio-temporal resolutions. A multigrid model setup with connected coarse- (14 km) and fine-resolution (5 km) model configurations was devised. These two configurations were implemented and run separately. The resulting computational cost was lower when compared to that of the built-in AGRIF nesting system. Ice and tracer boundary-condition schemes were modified to achieve the correct interaction between coarse- and fine grids through a long ice-covered open boundary. An ice-restoring scheme was implemented to reduce spin-up time. The NEMO-LIM3 configuration described in this article provides more flexible and customisable tools for high-resolution regional Arctic simulations

Features of the postoperative period in patients with uncomplicated forms of ureterolithiasis

In order to study the possibilities of non-drainage management of the postoperative period in patients with uncomplicated ureteral stones, we analyzed the results of treatment of 198 patients with uncomplicated ureterolithiasis, in whom it was decided to refuse stenting of the upper urinary tract. In the uncomplicated course of ureterolithiasis, in most cases (68.2% of patients) in the postoperative period, there is no need for drainage of the upper urinary tract with stent. At the same time, in 31.8% of patients when a stent was not installed in the postoperative period, complications occurred that significantly affected the duration and cost of treatment. In the future a more rigorous consideration of risk criteria is needed to make a decision on non-drainage management of the postoperative period in patients with uncomplicated ureteral stones

Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble

The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.Comment: 7 pages, 6 figure

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time

Maxwell-Chern-Simons Models: Their Symmetries, Exact Solutions and Non-relativistic Limits

Two Maxwell-Chern-Simons (MCS) models in the (1 + 3)-dimensional space-space are discussed and families of their exact solutions are found. In contrast to the Carroll-Field-Jackiw (CFE) model [2] these systems are relativistically invariant and include the CFJ model as a particular sector.Using the InNonNu-Wigner contraction a Galilei-invariant non-relativistic limit of the systems is found, which makes possible to find a Galilean formulation of the CFJ model

Aqueous medium toxicity assessment by Daphnia magna swimming activity change

© 2014 AENSI Publisher All rights reserved. This paper presents toxicity evaluation data for the water containing various substances of known concentrations of various substances by Daphnia magna swimming activity change. The toxicity of the following substances was evaluated: potassium dichromate, zinc sulphate, pesticide esfenvalerate and cyanobacterial toxin of microcystin-LR. The swimming activity was determined using a computer vision system under normal conditions and after the toxicant introduction. It has been shown that at exposure time of 30 minutes, the median swimming speed of Daphnia changes. This fact may be used for the rapid assessment of aquatic toxicity, as well as for the development of the biological early warning systems for the contamination presence