• Korean Journal of Psychosomatic Medicine
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• v.27 no.1
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• pp.25-34
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• 2019

Objectives : This study aimed to compare the psychosocial characteristics among patients with functional gastrointestinal disorder (FGID), adults with functional gastrointestinal symptoms, and normal control group and investigate factors related to quality of life (QoL) of FGID patients. Methods : 65 patients diagnosed with FGID were selected. 79 adults were selected as normal control group based on the Rome III diagnostic criteria, and 88 adults who showed functional gastrointestinal symptoms were selected as "FGID positive group". Demographic factors were investigated. Psychosocial factors were evaluated using the Korean-Beck Depression Inventory-II, Korean-Beck Anxiety Inventory, Korean-Childhood Trauma Questionnaire, Multi-dimensional Scale of Perceived Social Support, Connor-Davidson Resilience Scale and WHO Quality of Life Assessment Instrument Brief Form. A one-way ANOVA was used to compare differences among groups. Pearson correlation test was used to analyze correlations between QoL and psychosocial factors in patients with FGID. Results : There were group differences in the education level. Depression (F=29.012, p<0.001), anxiety (F=27.954, p<0.001) and Childhood trauma (F=7.748, p<0.001) were significantly higher in FGID patient group than in both FGID-positive and normal control group. Social support (F=5,123, p<0.001), Resilience (F=9.623, p<0.001) and QoL (F=35.991, p<0.001) were significantly lower in the FGID patient group than in others. QoL of FGID patients showed a positive correlation with resilience (r=0.475, p<0.01), and showed a negative correlation with depression (r=-0.641, p<0.01), anxiety (r=-0.641, p<0.01), and childhood trauma (r=-0.278, p<0.05). Conclusions : FGID patients have distinctive psychosocial factors compared to the both FGID-positive and normal control group. Therefore, the active interventions for psychosocial factors are required in the treatment of patients with FGID.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.3
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• pp.177-191
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• 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 Ca_1.00-1.03Mg_0.94-0.98Fe_0.00-0.06As_0.00-0.01(CO₃)₂(D₀), Ca_0.97-1.16Mg_0.32-0.83Fe_0.10-0.50Mn_0.01-0.12Zn_0.00-0.01Pb_0.00-0.03As_0.00-0.01(CO₃)₂(D₁), Ca_1.00-1.01Mg_0.85-0.92Fe_0.06-0.11 Mn_0.01-0.03As_0.01(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.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.3
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• pp.185-197
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• 2023

The Gubong Au-Ag deposit consists of eight lens-shaped quartz veins. These veins have filled fractures along fault zones within Precambrian metasedimentary rock. This has been one of the largest deposits in Korea, and is geologically a mix of orogenic-type and intrusion-related types. Korea Mining Promotion Corporation drilled into a quartz vein (referred to as the No. 6 vein) with a width of 0.9 m and a grade of 27.9 g/t Au at a depth of -728 ML by drilling (No. 90-12) in the southern site of the deposit, To further investigate the potential redevelopment of the No. 6 vein, another drilling (No. 04-1) was carried out in 2004. In 2004, samples (wallrock, wallrock alteration and quartz vein) were collected from the No. 04-1 drilling core site to study the occurrence and chemical composition of Ti-bearing minerals (ilmenite, rutile). Rutile from mineralized zone at a depth of -275 ML occur minerals including K-feldspar, biotite, quartz, calcite, chlorite, pyrite in wallrock alteration zone. Ilmenite and rutile from ore vein (No. 6 vein) at a depth of -779 ML occur minerals including white mica, chlorite, apatite, zircon, quartz, calcite, pyrrhotite, pyrite in wallrock alteration zone and quartz vein. Based on mineral assemblage, rutile was formed by hydrothermal alteration (chloritization) of Ti-rich biotite in the wallrock. Chemical composition of ilmenite has maximum values of 0.09 wt.% (HfO₂), 0.39 wt.% (V₂O₃) and 0.54 wt.% (BaO). Comparing the chemical composition of rutile at a depth -275 ML and -779 ML, Rutile at a depth of -779 ML is higher contents (WO₃, FeO and BaO) than rutile at a depth of -275 ML. The substitutions of rutile at a depth of -275 ML and -779 ML are as followed : rutile at a depth of -275 ML Ba²⁺ + Al³⁺ + Hf⁴⁺ + (Nb⁵⁺, Ta⁵⁺) ↔ 3Ti⁴⁺ + Fe²⁺, 2V⁴⁺ + (W⁵⁺, Ta⁵⁺, Nb⁵⁺) ↔ 2Ti⁴⁺ + Al³⁺ + (Fe²⁺, Ba²⁺), Al³⁺ + V⁴⁺+ (Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + 2Fe²⁺, rutile at a depth of -779 ML 2 (Fe²⁺, Ba²⁺) + Al³⁺ + (W⁵⁺, Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + (V⁴⁺, Hf⁴⁺), Fe²⁺ + Al³⁺ + Hf ⁴⁺ + (W⁵⁺, Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + V⁴⁺ + Ba²⁺, respectively. Based on these data and chemical composition of rutiles from orogenic-type deposits, rutiles from Gubong deposit was formed in a relatively oxidizing environment than the rutile from orogenictype deposits (Unsan deposit, Kori Kollo deposit, Big Bell deposit, Meguma gold-bearing quartz vein).

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.3
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• pp.344-352
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• 2012

Soil physical properties determine soil quality in aspect of root growth, infiltration, water and nutrient holding capacity. Although the monitoring of soil physical properties is important for sustainable agricultural production, there were few studies. This study was conducted to investigate the condition of soil physical properties of arable land according to land use across the country. The work was investigated on plastic film house soils, upland soils, orchard soils, and paddy soils from 2008 to 2011, including depth of topsoil, bulk density, hardness, soil texture, and organic matter. The average physical properties were following; In plastic film house soils, the depth of topsoil was 16.2 cm. For the topsoils, hardness was 9.0 mm, bulk density was 1.09 Mg $m^{-3}$, and organic matter content was 29.0 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.32 Mg $m^{-3}$, and organic matter content was 29.5 g $kg^{-1}$; In upland soils, depth of topsoil was 13.3 cm. For the topsoils, hardness was 11.3 mm, bulk density was 1.33 Mg $m^{-3}$, and organic matter content was 20.6 g $kg^{-1}$. For the subsoils, hardness was 18.8 mm, bulk density was 1.52 Mg $m^{-3}$, and organic matter content was 13.0 g $kg^{-1}$. Classified by the types of crop, soil physical properties were high value in a group of deep-rooted vegetables and a group of short-rooted vegetables soil, but low value in a group of leafy vegetables soil; In orchard soils, the depth of topsoil was 15.4 cm. For the topsoils, hardness was 16.1 mm, bulk density was 1.25 Mg $m^{-3}$, and organic matter content was 28.5 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.41 Mg $m^{-3}$, and organic matter content was 15.9 g $kg^{-1}$; In paddy soils, the depth of topsoil was 17.5 cm. For the topsoils, hardness was 15.3 mm, bulk density was 1.22 Mg $m^{-3}$, and organic matter content was 23.5 g $kg^{-1}$. For the subsoils, hardness was 20.3 mm, bulk density was 1.47 Mg $m^{-3}$, and organic matter content was 17.5 g $kg^{-1}$. The average of bulk density was plastic film house soils < paddy soils < orchard soils < upland soils in order, according to land use. The bulk density value of topsoils is mainly distributed in 1.0~1.25 Mg $m^{-3}$. The bulk density value of subsoils is mostly distributed in more than 1.50, 1.35~1.50, and 1.0~1.50 Mg $m^{-3}$ for upland and paddy soils, orchard soils, and plastic film house soils, respectively. Classified by soil textural family, there was lower bulk density in clayey soil, and higher bulk density in fine silty and sandy soil. Soil physical properties and distribution of topography were different classified by the types of land use and growing crops. Therefore, we need to consider the types of land use and crop for appropriate soil management.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.5
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• pp.418-428
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• 2007

Physical and chemical properties of the aggregate by-products including sludge and crushed dust samples collected from the 21 private companies throughout the country were analyzed to evaluate possible usage of the by-products as artificial soil materials for plantation. The pH of the materials ranged from 8.0 to 11.0. The organic matter content was $2.85g\;kg^{-1}$, and the total nitrogen content and available phosphate content were low as 0.7 percents and $12.98mg\;kg^{-1}$, respectively. Exchangeable $Ca^{2+}$, $Mg^{2+}$, $K^+$, and $Na^+$ were 2.29, 0.47, 0.02 and $0.05cmol\;kg^{-1}$, respectively. Heavy metal contents were lower than the limits regulated by environmental law of Korea. Textural analysis showed that most of the materials were silt loam with low water holding capacity ranged from 0.67 to 7.41 percents, and with low hydraulic conductivity ranged from 0.4 to $2.8m\;s^{-1}$. Mineralogical analysis showed that the aggregate by product materials were mostly composed of silicate, alumina and ferric oxides except calcium oxide dominant materials derived from limestones. The primary minerals were quartz, feldspars and dolomites derived from granite and granitic gneiss materials. Some samples derived from limestone material showed calcite and graphite together with the above minerals. According to the result, it can be concluded that the materials could be used as the artificial soil material for plantation after proper improvement of the physico-chemical properties and fertility.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.