The maximum distance at which an electromagnetic (EM) logging while drilling (LWD) tool senses an approaching boundary is considered to be the depth of detection (DOD). Achieving a large DOD while keeping the tool itself compact is what we have always pursued. We proposed a novel transient multicomponent EM LWD method and studied its capability in detecting the formation boundary. Instead of using the transient triaxial measured data directly, a time domain detection mode is defined to sense the boundary. DOD of this time domain EM LWD method can reach tens of meters with a compact transmitter-receiver spacing. Based on the polarity of the signal, directional measurements can also be achieved. In addition, we find that the cross component decays much faster than the coaxial or coplanar components with time in the formation coordinate system. Thus, an algebraic method is proposed to determine the relative dip angle of stratified formation and the inversion process can be avoided. Theoretical simulation results indicate that this determination method obtains the true value at some particular moments. And it is still stable and valid even when considering some random measurement errors. Moreover, linear relationship between the distance to the boundary (DTB) and the time we measure it is established, providing a method to quickly determine the DTB.
Contributors:I Ketut Gede Darma Putra, I Made Suwija Putra, Putu Jhonarendra
The palmprint dataset is captured on left hand. Palmprint dataset is acquired from 15 people with 5 to 8 images of each person. To increase the amount of data in each person, the raw dataset was filtered with Gabor Filter. The characteristics of the Gabor Filter are good applied to palmprint image because the image has many variations of line direction and the thickness. The palmprint dataset has 20 to 32 images each class after applying the Gabor Filter. The author trains the palmprint dataset using the Convolutional Neural Network method.
Here are the uncropped data used in the paper entitled "MYC Releases Early Reprogrammed Human Cells from Proliferation Pause via Retinoblastoma Protein Inhibition." by Rand et al. published in Cell Reports.
Contributors:mickish zhang, Dan-Ya Wu, Hui Zheng, Yao Wang, Qiao-Ran Sun, Xin Liu, Li-Yan Wang, Wen-Jing Xiong, Qiujun Wang, Kai Xu, lijia li, zili LIN, Guang Yu, Weikun Xia, Bo Huang, zhenhai du, Yao Yao, Yi-Liang Miao, Wei Xie
Somatic cell nuclear transfer (SCNT) can reprogram a somatic nucleus to a totipotent state. However, the re-organization of three-dimensional chromatin structure in this process remains poorly understood. Here, using low-input Hi-C we revealed that during SCNT, the transferred nucleus first enters a mitotic-like state (premature chromatin condensation). Unlike fertilized embryos, SCNT embryos show stronger TADs at the 1-cell stage. TADs become weaker at the 2-cell stage, followed by gradual consolidation. Compartments A/B are markedly weak in 1-cell SCNT embryos, before becoming strengthened at the 2-cell stage and onward. Somatic chromatin compartments, TAD boundaries, and transcriptomes, with a few exceptions, are largely reset to embryonic patterns by the 8-cell stage. Unexpectedly, pre-depleting cohesin in donor cells facilitates minor zygotic genome activation (ZGA) and SCNT development. These data reveal multi-step reprogramming of 3D chromatin architecture during SCNT and support dual roles of cohesin in TAD formation and minor ZGA repression.
This zip file contains the source data, post-processed data, and the program files to create figures in the manuscript.
MATLAB Version: 184.108.40.2063579 (R2017b)
Operating System: Mac OS X Version: 10.12.6 Build: 16G1114
Java Version: Java 1.8.0_121-b13 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
Statistics and Machine Learning Toolbox Version 11.2 (R2017b)
This repository is a copy of the Github repository (v1.0) which is publicly available at https://github.com/UCEEB/Distributed-building-identification. It contains a Matlab source code which implements the Distributed building grey-box model identification algorithm. The contents include a demo example of EnergyPlus building model identification.