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Beijing, China

China University of Mining and Technology colloquially 矿大 is a national key university under the direct supervision of the Ministry of Education of China as well as a Project 211 and Project 985 university of China. The university consists of two parts, one located in Xuzhou, Jiangsu Province, the other located in Beijing with the name China University of Mining and Technology, Beijing . The latter is formerly the Graduate School of CUMT. CUMT is a leading multi-disciplinary polytechnic university with mining features. Wikipedia.


Miao X.-X.,China University of Mining and Technology
Meitan Xuebao/Journal of the China Coal Society | Year: 2012

On the basis of a brief overview of mining with backfilling history and the objectives, requirements and difficulties of developing modern mining with backfilling technology, this paper systematically introduced the research progress of fully mechanized mining with solid backfilling technology, focused on the expounding the strata movement theory of mining with dense backfilling, including equivalent mining height theory of strata movement control, continuous media mechanics model of strata movement, calculation formula of strata movement, and stope rock pressure and support stress analysis, which are the prerequisites of the new technology. And then detailedly introduced the system, equipments and technics of fully mechanized mining with solid backfilling technology, and the engineering examples of large-scale mining with backfilling under dense buildings, island village coal pillar, near the loosen aquifer and under the large embankment.


Zhang S.,China University of Mining and Technology
Computers and Mathematics with Applications | Year: 2010

In this paper, we consider the existence of positive solutions to the singular boundary value problem for fractional differential equation. Our analysis relies on a fixed point theorem for the mixed monotone operator. © 2009 Elsevier Ltd. All rights reserved.


Sun J.-P.,China University of Mining and Technology
Meitan Xuebao/Journal of the China Coal Society | Year: 2011

The construction principles of the refuge chamber and rescue capsule underground the coal mine were presented. It includes no excessive output and excessive capacity power supply, no issues in safety, no influence with mine ventilation and safety production, responding the emergency quickly, maintenance-free or easily do it, economical and practical, etc. The survival section and device section in rescue capsule should not be separated, and the rescue capsule should not be built in tunnel. Several small or medium size refuge chambers and rescue capsules should be distributed within the specific region. The refuge chamber and rescue capsule should adopt high pressure gas expansion refrigeration; the air blower should adopt air power fan; it should use water of condensation to remove damp at the heat exchanger's surface of refrigeration system; it should use compressed oxygen to supply oxygen; it should not adopt storage battery or external power to supply illumination system. It should equip with the portable detectors which detect O2, CO, CO2, CH4, temperature, humidity; the sensor and its substation, staff location monitoring substation and dispatcher telephone should use matching equipment of existing system; the air vent holes of rescue capsule and the pipelines connecting with compressed air self rescue, supply water rescue, safety monitoring, staff location monitoring and communication system should be placed on both sides of lane. The soft rescue capsule should not be applied to the coal mine; lanes and workplaces should have no less than 2 safety walk paths to surface safely, excepting heading faces and temporary workplaces.


Fan H.,China University of Mining and Technology
Nonlinear Analysis: Real World Applications | Year: 2014

In this paper, we study the multiplicity results of positive solutions for a semi-linear elliptic system involving both concave-convex and critical growth terms. With the help of Nehari manifold and Lusternik-Schnirelmann category, we prove that the problem admits at least cat(Ω)+1 distinct positive solutions. © 2014 Elsevier Ltd.


Zhang L.,China University of Mining and Technology
BMC genomics | Year: 2012

DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters. The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

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