Occurrence and Chemical Composition of Dolomite from Zhenzigou Pb-Zn Deposit, China (중국 젠지고우 연-아연 광상의 돌로마이트 산상과 화학조성)
-
- Korean Journal of Mineralogy and Petrology
- /
- v.34 no.3
- /
- pp.177-191
- /
- 2021
The Zhenzigou Pb-Zn deposit, one of the largest Pb-Zn deposit in the northeast of China, is located at the Qingchengzi mineral field in Jiao Liao Ji belt. The geology of this deposit consists of Archean granulite, Paleoproterozoinc migmatitic granite, Paleo-Mesoproterozoic sodic granite, Paleoproterozoic Liaohe group, Mesozoic diorite and monzoritic granite. The Zhenzigou deposit which is a strata bound SEDEX or SEDEX type deposit occurs as layer ore and vein ore in Langzishan formation and Dashiqiao formation of the Paleoproterozoic Liaohe group. Based on mineral petrography and paragenesis, dolomites from this deposit are classified three type (1. dolomite (D0) as hostrock, 2. dolomite (D1) in layer ore associated with white mica, quartz, K-feldspar, sphalerite, galena, pyrite, arsenopyrite from greenschist facies, 3. dolomite (D2) in vein ore associated with quartz, apatite and pyrite from quartz vein). The structural formulars of dolomites are determined to be Ca1.00-1.03Mg0.94-0.98Fe0.00-0.06As0.00-0.01(CO3)2(D0), Ca0.97-1.16Mg0.32-0.83Fe0.10-0.50Mn0.01-0.12Zn0.00-0.01Pb0.00-0.03As0.00-0.01(CO3)2(D1), Ca1.00-1.01Mg0.85-0.92Fe0.06-0.11 Mn0.01-0.03As0.01(CO3)2(D2), respectively. It means that dolomites from the Zhenzigou deposit have higher content of trace elements compared to the theoretical composition of dolomite. Feo and MnO contents of these dolomites (D0, D1 and D2) contain 0.05-2.06 wt.%, 0.00-0.08 wt.% (D0), 3.53-17.22 wt.%, 0.49-3.71 wt.% (D1) and 2.32-3.91 wt.%, 0.43-0.95 wt.% (D2), respectively. The dolomite (D1) from layer ore has higher content of these trace elements (FeO, MnO, ZnO and PbO) than dolomite (D0) from hostrock and dolomite (D2) from quartz vein. Dolomites correspond to Ferroan dolomite (D0 and D2), and ankerite and Ferroan dolomite (D1), respectively. Therefore, 1) dolomite (D0) from hostrock is a Ferroan dolomite formed by marine evaporative lagoon environment in Paleoproterozoic Jiao Liao Ji basin. 2) Dolomite (D1) from layer ore is a ankerite and Ferroan dolomite formed by hydrothermal metasomatism origined metamorphism (greenschist facies) associated with Paleoproterozoic intrusion. 3) Dolomte (D2) from quartz vein is a Ferroan dolomite formed by hydrothermal fluid origined Mesozoic intrusion.
The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.
The present study was conducted as part of basic research for selecting species of street trees with historical value in Seoul. It also made up a list of traditional landscape trees for a variety of alternatives. The following results are shown below. As to the history of street trees in Korea, records on to-be-estimated street trees are found in historical documents written in King Yangwon during the second year of Goguryeo Dynasty (546) and King Myeongjong during 27 year of Goryeo (1197). However, it is assumed that lack of clarity is found in historical records. During the 23 year of King Sejong in the early Joseon Dynasty (1441), the record showed that the state planted street trees as guideposts on the postal road. The records revealed that Ulmus spp. and Salix spp. were planted as guidance trees. The street tree system was performed in the early Joseon Dynasty as recorded in the first year of King Danjong document. Pinus densiflora, Pinus koraiensis, Pyrus pyrifolia var. culta, Castanea crenata, Styphnolobium japonicum and Salix spp. were planted along the avenue at both left and right sides. Morus alba were planted on streets during the five year of King Sejo (1459). As illustrated in pieces Apgujeong by painter Jeongseon and Jinheonmajeongsaekdo in the reign of King Yeongjo, street trees were planted. This arrangement is associated with a number of elements such as king procession, major entrance roads in Seoul, place for horse markets, prevention of roads from flood and indication. In the reign of King Jeongjo, there are many cases related to planting Pinus densiflora, Abies holophylla and Salix spp. for king procession. Turning king roads and related areas into sanctuaries is considered as technique for planting street trees. During the 32 year of King Gojong after opening ports (1985), the state promoted planting trees along both sides of roads. At the time, many Populus davidiana called white poplars were planted as rapidly growing street trees. There are 17 taxa in the Era of Three Kingdoms records, 31 taxa in Goryeo Dynasty records and 55 taxa in Joseon Dynasty records, respectively, described in historical documents to be available for being planted as street trees in Seoul. 16 taxa are recorded in three periods, which are Era of Three Kingdoms, Goryeo Dynasty and Joseon Dynasty. These taxa can be seen as relatively excellent ones in terms of historical value. The introduction of alien plants and legal improvement in the Japanese colonial period resulted in modernization of street tree planting system. Under the six-year street tree planting plan (1934-1940) implemented as part of expanding metropolitan areas outside the capital launched in 1936, four major street trees of top 10 taxa were a Populus deltoides, Populus nigra var. italica, Populus davidiana, Populus alba. The remaining six trees were Salix babylonica, Robinia pseudoacacia, platanus orientalis, Platanus occidentalis, Ginkgo biloba, and Acer negundo. Beginning in the mid- and late 1930s, platanus orientalis, Platanus occidentalis were introduced into Korea as new taxa of street trees and planted in many regions. Beginning on 1942, Ailanthus altissima was recommended as street trees for the purpose of producing silks. In 1957 after liberation, major street tree taxa included Platanus occidentalis, Ginkgo biloba, Populus nigra var. italica, Ailanthus altissima, Populus deltoides and Salix babylonica. The rank of major street tree species planted in the Japanese colonial period had changed. Tree planting trend around that period primarily representing Platanus occidentalis and Ginkgo biloba still holds true until now.
The results of tracking the symbolism of plants in the introduction factors of Sokhwa(folk painting) are as the following. 1. The term Sokhwa(俗畵) is not only a type of painting with a strong local customs, but also carries a symbolic meaning and was discovered in "Donggukisanggukjip" of Lee, Gyu-Bo(1268~1241) in the Goryo era as well as the various usage in the "Sok Dongmunseon" in the early Chosun era, "Sasukjaejip" of Gang, Hee-mang(1424~1483), "Ilseongrok(1786)" in the late Chosun era, "Jajeo(自著)" of Yoo, Han-joon(1732~1811), and "Ojuyeonmunjangjeonsango(五洲衍文長箋散稿)" of Lee, Gyu-gyung(1788~?). Especially, according to the Jebyungjoksokhwa allegation〈題屛簇俗畵辯證說〉in the Seohwa of the Insa Edition of Ojuyeonmunjangjeonsango, there is a record that the "people called them Sokhwa." 2. Contemporarily, the Korean Sokhwa underwent the prehistoric age that primitively reflected the natural perspective on agricultural culture, the period of Three States that expressed the philosophy of the eternal spirits and reflected the view on the universe in colored pictures, the Goryo Era that religiously expressed the abstract shapes and supernatural patterns in spacein symbolism, and the Chosun Era that established the traditional Korean identity of natural perspective, aesthetic values and symbolism in a complex integration in the popular culture over time. 3. The materials that were analyzed in 1,009 pieces of Korean Sokhwa showed 35 species of plants, 37 species of animals, 6 types of natural objects and other 5 types with a total of 83 types. 4. The shape aesthetics according to the aesthetic analysis of the plants in Sokhwa reflect the primitive world view of Yin/yang and the Five Elements in the peony paintings and dynamic refinement and biological harmonies in the maehwado; the composition aesthetics show complex multi-perspective composition with a strong noteworthiness in the bookshelf paintings, a strong contrast of colors with reverse perspective drawing in the battlefield paintings, and the symmetric beauty of simple orderly patterns in nature and artificial objects with straight and oblique lines are shown in the leisurely reading paintings. In terms of color aesthetics, the five colors of directions - east, west, south, north and the center - or the five basic colors - red, blue, yellow, white and black - are often utilized in ritual or religious manners or symbolically substitute the relative relationships with natural laws. 5. The introduction methods in the Korean Sokhwa exceed the simple imitation of the natural shapes and have been sublimated to the symbolism that is related to nature based on the colloquial artistic characteristics with the suspicion of the essence in the universe. Therefore, the symbolism of the plants and animals in the Korean Sokhwas is a symbolic recognition system, not a scientific recognition system with a free and unique expression with a complex interaction among religious, philosophical, ecological and ideological aspects, as a identity of the group culture of Koreans where the past and the future coexist in the present. This is why the Koran Sokhwa or the folk paintings can be called a cultural identity and can also be interpreted as a natural and folk meaningful scenic factor that has naturally integrated into our cultural lifestyle. However, the Sokhwa(folk paintings) that had been closely related to our lifestyle drastically lost its meaning and emotions through the transitions over time. As the living lifestyle predominantly became the apartment culture and in the historical situations where the confusion of the identity has deepened, the aesthetic and the symbolic values of the Sokhwa folk paintings have the appropriateness to be transmitted as the symbolic assets that protect our spiritual affluence and establish our identity.
This study, based on
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (