Results 71 to 80 of about 31,862 (304)

Management plan of geological hazard threats in the fault zone of adjacent open-pit mines

Meitan kexue jishu
In the development of large-scale open-pit coal fields in China, there are often several Coal companies entering the same coal field, and each Coal company is a main body of development, mining coal resources in the state-allocated strip area, and ...
Guangwei LIU, Junxiong TU, Xianshuai MA, Yong YAO, Liang WANG, Meiling ZHAO   +5 more
doaj   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery

International Journal of Applied Earth Observations and Geoinformation
The monitoring of open-pit mining and reclamation activities is an important part of ecological protection across various countries. Among the many methods of monitoring open-pit mining areas, satellite remote sensing is the most widely used and ...
Yongkai Wang, Kai Qin, Zilong Zhang, Qin He, Jason Cohen   +4 more
doaj   +1 more source

Mahoning Mine, churn drill in open cut

, 2022
Date scanned: 2002-6-19.USBM #64077; Churn drill in open-cut. (Note platform and grab-irons at top of mast). Mahoning Mine, Pickands, Mather & Company, Hibbing, Minnesota. - M. S. Petersen - August 1946.Held in the Russell L.
Petersen, Max S.
core  

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Coupled analysis of landslide risk in open-pit mines integrating N-K model and BN model

Gong-kuang zidonghua
Existing coupled-causation analyses of landslide accidents in open-pit mines lack quantitative characterization and inference of the coupling relationships among risk factors, such as coupling strength and sensitivity of risk factors, which have certain ...
YUAN Liwei, ZHOU Yu, NIE Binren, LIU Dingshuai, LI Sumin   +4 more
doaj   +1 more source

Mathematical Model for Flexible Allocation of Multi-Capacity Trucks in Open Pit Mines

, 2005
The objective of flexible allocation of trucks is to increase the productivity of haulage system. In this paper, a comprehensive model is produced for the haulage system in open pit mines, which can optimize the allocation process of trucks by goal ...
Ahmadi, Morteza, Asi, Bahareh, Oraee, Kazem   +2 more
core  

Uranium open pit mine, graben in high wall

, 2022
Date scanned: 2002-7-16.Figure 2 Graben in highwall of open pit uranium mine. Note failure along fault in lower right corner of photograph.Held in the Russell L.

core  

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source