Prospects for the development of underground ore mining geotechnologies at the Talnakh and Oktyabrskoye deep mines

Rich sulphide, cuprous and impregnated ores are currently mined in the underground mines of the Talnakh and Oktyabrskoye deposits at the depths from 250 to 1,700 m. The reserves of rich ores are depleted, and therefore the growth of cuprous and impregnated ores is gaining importance. Their share may reach 80% of the total production by 2030. A distinctive feature of such deposits is the occurrence of cuprous and impregnated ores above the rich sulphide ore, which reserves have been mined out using mining systems with curing backfill mixtures. In this context, mining of impregnated ores will be done in the undermined zones, which will lead to significant rock mass deformation, opening of existing natural and formation of new cracks, will affect the stability of mining structures and will require special measures to control rock pressure in the mines. The paper presents the results of assessing the stress-and-strain condition of the undermined mass of impregnated ores mined using the room-and-pillar cut-and-fill method at the depths of 500, 1000 and 2000 m. The assessment shows that no dangerous stress concentrations arise in the mining structures at great depths which creates preconditions for the safe development of such deposits. A significant increase in ore extraction will require upgrading of existing underground facilities. It is proposed to carry out pre-concentration of the mined ore in the underground conditions using modern crushing complexes, high-capacity mine separators to remove waste rock, which can subsequently be used as the backfill material. In this way, a closed-loop mining system is created that meets the efficiency requirements of mining production and integrated subsoil development.

Application of OASys approaches for Prophetic Food Ontology

Ontology is an established knowledge representation enriched with a semantic interpretation that offered a mechanism for sharing mutual ideas and understanding among the members of a related domain. Semantic interpretation provided by the ontology has a structure that could facilitate the presentation of information for the users. This paper presents the ontology construction of prophetic food specifically for Dates and Goats Milk by using the OASys approaches. The ontology content focusing on the dates attributes, the developing stages of dates, defect and diseases of dates, health benefits, its compositions, and the chain of operation. Besides, the ontology content for goat’s milk includes its nutrition, its cure for a medical problem, and the production. The construction of this ontology can be used to answer user queries, data integration to other applications as well as expand the ontology to a context mining semantic information retrieval search engine known as Naqli Aqli Integrated Search Engine (NAISE). This system is a query system based on integrated Naqli and Aqli knowledge heterogeneous sources on prophetic food.

3D Dense Separated Convolution Module for Volumetric Medical Image Analysis

With the thriving of deep learning, 3D convolutional neural networks have become a popular choice in volumetric image analysis due to their impressive 3D context mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data are often limited in biomedical tasks, a trade-off has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of overfitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network’s representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric medical image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.