Miloš Gligorić^1,*, Zoran Gligorić¹, Saša Jovanović², Suzana Lutovac¹, Dragan Pamučar^3,4, Ivan Janković¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2635-2664, 2024, DOI:10.32604/cmes.2024.050898

Abstract Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy. This study develops the novel Gromov-Hausdorff distance for rock quality (GHDQR) methodology for rock mass quality rating based on multi-criteria grey metric space. It usually presents the quality of surrounding rock by classes (metric spaces) with specified properties and adequate interval-grey numbers. Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study. The Gromov-Hausdorff distance is an especially useful discriminant function, i.e., a classifier to… More >

Chen Xing¹, Leihua Yao^1,*, Yingdong Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2825-2848, 2024, DOI:10.32604/cmes.2024.049330

Abstract In pumped storage projects, the permeability of rock masses is a crucial parameter in engineering design and construction. The rock mass permeability coefficient (K) is influenced by various geological parameters, and previous studies aimed to establish an accurate relationship between K and geological parameters. This study uses the improved sparrow search algorithm (ISSA) to optimize the parameter settings of the deep extreme learning machine (DELM), constructing a prediction model with flexible parameter selection and high accuracy. First, the Spearman method is applied to analyze the correlation between geological parameters. A sample database is built by comprehensively… More >

HaiYan Yang^*†, ZhiGang Peng^†, ZhenZhen Da^*, Xin Li^‡, YeXiao Cheng^*, BinBin Tan^§, Xin Xiang^¶, HaiPing Zheng^†, Yan Li^*, LanHua Chen^*, Ning Mo^†, XueXin Yan^†, Xiaolin Li^*, XiaoHua Hu^†

Oncology Research, Vol.25, No.8, pp. 1231-1243, 2017, DOI:10.3727/096504017X14850134190255

Abstract MicroRNAs (miRs) have been demonstrated to be involved in the development and progression of osteosarcoma (OS), but the molecular mechanism still remains to be fully investigated. The present study investigated the function of miR-148a in OS, as well as its underlying mechanism. Our data showed that miR-148a was significantly downregulated in OS tissues compared to their matched adjacent normal tissues, and also in OS cell lines compared to normal human osteoblast cells. Low expression of miR-148a was significantly associated with tumor progression and a poor prognosis for OS patients. Rho-associated coiled-coil kinase 1 (ROCK1) was… More >

Minglei Zhang^*, Dapeng Wang^†, Tongtong Zhu^*, Ruofeng Yin^*

Oncology Research, Vol.25, No.1, pp. 75-81, 2017, DOI:10.3727/096504016X14719078133401

Abstract MicroRNAs (miRNAs) are small conserved RNAs regulating specific target genes in posttranscriptional levels. They have been involved in multiple processes of tumor progression, including cell proliferation. miR-214-5p (also miR-214*) is a newly identified miRNA, and its functions are largely unknown. In this study, we explore the role of miR-214-5p in the proliferation and invasion of human osteosarcoma (OS) cells. The results showed that miR-214-5p was sharply reduced in OS tissues and cell lines, compared with normal tissues and cell lines. In addition, the miR-214-5p mimic greatly increased the miR-214-5p level and significantly decreased the proliferation More >

Yanli Wang^*, Jia Li^†, Chunling Xu^†, Xiaomeng Zhang^†

Oncology Research, Vol.26, No.3, pp. 411-420, 2018, DOI:10.3727/096504017X14974343584989

Abstract Increasing evidence indicates that the dysregulation of microRNAs is associated with the development and progression of various cancers. MicroRNA-139-5p (miR-139-5p) has been reported to have a tumor suppressive role in many types of cancers. The role of miR-139-5p in ovarian cancer (OC) is poorly understood. The purpose of the present study was to explore the expression of miR-139-5p and its function in OC. The results showed that miR-139-5p expression was markedly downregulated in OC tissues and cell lines. In addition, underexpression of miR-139-5p was significantly associated with FIGO stage, lymph mode metastasis, and poor overall… More >

Hao Qi¹, Mohamed Sharaf², Andres Annuk³, Adrian Ilinca⁴, Mohamed A. Mohamed^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1387-1404, 2024, DOI:10.32604/cmes.2024.048672

Abstract Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance More >

Ji Zhou^1,2, Yijun Lu³, Qiong Tian^1,2, Haichuan Liu³, Mahdi Hasanipanah^4,5,*, Jiandong Huang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398

Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

Baojun Qin¹, Guangwei Zhang¹, Wei Zhang^2,*

Structural Durability & Health Monitoring, Vol.18, No.3, pp. 341-360, 2024, DOI:10.32604/sdhm.2024.047405

Abstract This study delves into the effects of shield tunneling in complex coastal strata, focusing on how this construction method impacts surface settlement, the mechanical properties of adjacent rock, and the deformation of tunnel segments. It investigates the impact of shield construction on surface settlement, mechanical characteristics of nearby rock, and segment deformation in complex coastal strata susceptible to construction disturbances. Utilizing the Fuzhou Binhai express line as a case study, we developed a comprehensive numerical model using the ABAQUS finite element software. The model incorporates factors such as face force, grouting pressure, jack force, and… More >

Kai Wang¹, Biao He^2,*, Pijush Samui³, Jian Zhou⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569

Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive… More >

Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables,… More > Graphic Abstract