Numerical Simulation and Prediction for Sticking Type Breakout Behavior in Slab Contiuous Casting

A two-dimensional heat transfer model was developed to calculate the mould wall temperature field under normal operations condition and to determine its changing behavior when breakout occured. On the numerical simulation of sticking type breakout process and the breakout related wall temperature evolution, parameters of prediction were suggested.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites

DNA methylation analysis has been applied to determine the primary site of cancer;however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites;it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

The Metallogenetic Regularities of Lithium Deposits in China

Lithium resources support the development of high-technology industries. China has abundant lithium resources which are mainly distributed in Tibet, Qinghai, Sichuan and Jiangxi. Salt lakes in China have significant lithium reserves, but lithium is mainly produced from hard rock lithium deposits because the extraction from salt lakes requires further improvements. The hard rock lithium deposits mainly occur in granitic pegmatite in the Altay region of Xinjiang and the Jiajika deposit in western Sichuan Province; they mainly formed in the Mesozoic and occurred in a relatively stable stage during orogenic processes. On the basis of the information from 151 lithium deposits or spots, 14 lithium metallogenic series were identified, and granitic pegmatite, granite, and sedimentary types were considered to be the main prediction types of lithium resources. Twelve lithium mineralization belts were divided and a series of maps showing the lithium metallogenetic regularity in China were drawn. We conclude that the hard rock and brine type of lithium resources possibly have a similar lithium source related to magmatism. The mctallogenic features of the lithium in China were related with the distinct history of tectonic-magmatic activity in China. This study benefits the assessment of, and prospecting for, lithium resources in China.

A Preliminary Review of Metallogenic Regularity of Copper Deposits in China

Copper resources in China are rich, but imported copper products are still required. Researches on metallogenic regularity of major types of copper deposits by geologists have involved in worldwide classification, significant copper belts, representative copper deposits, etc. Studies on metallogenic regularity of copper deposits in China also have made achievements with a long-term work. Combined with latest exploration advances obtained in recent ten years, this review aims to conclude the achievements of researches on copper metallogenic regularity in China. Based on data of 814 copper deposits and other ore （mineralized） occurrences, ten prediction types of copper deposits have been suggested. Porphyry and skarn copper ores are taken as the key targets. Porphyry copper deposits are the most important one which concentrate in Gangdese, Changdu-Sanjiang, Dexing and East Tianshan. The Cenozoic and Mesozoic are the major metallogenic epochs. Four main metallogenic epochs are been studied based on the copper ore geochronological data including Precambrian Era （Archean and Proterozoic）, Paleozoic Era, Mesozoic Era and Cenozoic Era. Based on the study of metallogenic series of ore deposits in China, twenty-seven metallogenic series of copper deposits are proposed. This is suggested to deepen the study of metallogenic regularity of copper ore and provide the theory guide for copper resources prediction in China.

A Preliminary Review of Metallogenic Regularity of Gold Deposits in China

Gold is one of the most important mineral resources in China with its rich mineral resources. In recent years, significant progress has been made on the process of gold resource exploration. Some large and giant gold deposits were newly found and some important expansions in the main mining regions were also been completed. Studies on metailogenic regularity of gold deposits in China also have made achievements with a long-term work. This review aims to conclude the achievements of research on gold metallogenic regularity in China. Based on the data of about 2000 gold deposits and other ore （mineralized） occurrences, gold deposits in China were classified into five prediction types： gold deposits genetically related to granite-greenstone formation, gold deposits related to sedimentary formation （including the Carlin type and the metamorphosed clastic rock related vein gold deposit）, gold deposits genetically related to volcanic rocks （including the continental and marine types）, gold deposits genetically related to intrusions （including the porphyry type and the inner intrusion and contact zone related gold deposit）, gold deposits of supergenesis （including fracture zone-altered rock gold deposit, placer gold deposit, gossan type gold precise chronology data of gold deposits indicate deposit and soil type gold deposit）. Statistics on that there occurred 5 main periods of gold- mineralization in geological history of China. They were Neoarchean to Paleoproterozoic, Meso- Neoproterozoic, Paleozoic, Mesozoic, and Cenozoic. Gold deposits in China mainly formed in the Mesozoic and the Cenozoic. On the studies of the spatial-temporal distribution characteristics of gold deposits, 53 gold-forming belts were delineated in China. The metallogenic regularity of gold deposits was preliminarily summarized and 71 gold metallogenic series were proposed in China. This suggests that it is necceary to deepen the study on metallogenic regularity of gold deposits and to provide the theory guide for the ore-prospecting for gold resources in China.

A Preliminary Review of the Metallogenic Regularity of Nickel Deposits in China

The nickel deposits mainly distributed in 19 provinces and autonomous regions in China are 339 ore deposits/occurrences, including 4 super large-scale deposits, 14 large-scale deposits, 26 middle- scale deposits, 75 small-scale deposits, and 220 mineralized occurrences. The prediction types of mineral resources of nickel deposits are magmatic type, marine sedimentary type and regolith type. The formation age is from the Neoarchean to the Cenozoic with two peaks in the Neoproterozoic and the late Paleozoic. The nickel deposits formed in the Neoproterozoic are located on the margin of the North China Block and Yangtze Block, and those formed in the late Paleozoic are mainly distributed in the Central Asian Orogenic Belt （CAOB）, Emeishan and the Tarim Large Igneous Provinces （LIPs）. Magmatic nickel deposits are mainly related with broken-up continental margin, post-collision extension of the orogenic belt and mantle plume. According to different tectonic backgrounds and main characteristics of magmatism, the Ni-Cu-Co-PGE metallogenie series types of ore deposits related with mantle-derived mafic-ultramafic rocks can be divided into 4 subtypes： （1） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the broken-up continental margin, （2） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in mantle plume magmatism, （3） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the subduction of the orogenic belt, and （4） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in post-collision extension of the orogenic belt. We have discussed in this paper the typical characteristics and metaliogenic models for Neoproterozoic Ni-Cu-（PGE） deposits related with broken-up continental margin, Cambrian marine sedimentary Ni-Mo-V deposits related with black shale, early Permian Ni-Cu deposits related with post-collision extension of the orogenic belt, late Permian Ni-Cu-（PGE） deposits related with Large Igneous Provinces （LIPs）, and Cenozoic Ni-Au deposits related with regolith. The broken-up continental margin, mantle plume and post-collision extension of the orogenic belt are important ore- forming geological backgrounds, and the discordogenic fault, mafic-ultramafic intrusion, high MgO primitive magma （high-MgO basaltic magma）, deep magmatism, sulfur saturation and sulfide segregation are 6 important geological conditions for the magmatic nickel deposits.

Empirically revisiting and enhancing automatic classification of bug and non-bug issues

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and analyzed.To fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches.Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML algorithms.Based on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.