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Background Extracellular vesicles (EV) have been proven to contain double-stranded DNA reflecting the mutational status of the parental tumor cells in non-small cell lung cancer (NSCLC), which can be translated into clinically useful EV-based liquid biopsy for Epidermal growth factor receptor (EGFR) genotyping using bronchoalveolar lavage fluid (BALF) obtained from tumor site. Methods Patients subjected for an initial lung cancer work-up underwent bronchoscopy and BALF was obtained from tumor site. After isolating EVs from BALF by ultracentrifugation, EV-derived DNA (EV DNA) was extracted for subsequent EGFR genotyping performed through peptide nucleic acid (PNA)-mediated Real-Time PCR. The sensitivity, specificity, and concordance rate of BALF EV-based EGFR genotyping were calculated in comparison to tissue genotyping. Results The average sensitivity and specificity of BALF EV-based EGFR genotyping were 76% and 87%, respectively, while the sensitivity significantly increased as the stage progressed. Especially, in stage IV, BALF EV-based EGFR typing identified all tissue-proven EGFR mutant cases (n=31) and detected 6 additional mutant cases. The concordance rate was 79% in stage I, 100% in stage II, 74% in stage III, and 92% in stage IV. As TNM stage advanced, especially in the presence of metastasis, concordance rate significantly increased (P

The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is needed to determine whether a pass/fail test is accurate or not. In this paper, a deep belief network (DBN)-based multi-classifier is proposed for fault detection prediction in the semiconductor manufacturing process. The proposed method consists of two phases: The first phase is a data pre-processing phase in which features required for semiconductor data sets are extracted and the imbalance problem is solved. The second phase is to configure the multi-DBN using selected features. A DBN classifier is created for each feature and, finally, fault detection prediction is performed. The proposed method showed excellent performance and can be used in the semiconductor manufacturing process efficiently.

To identify and quantify the contribution of the major sources for the ambient carbonaceous aerosols at a Global Atmosphere Watch (GAW) station in Korea, a receptor model, the Positive Matrix Factorization (PMF) model was applied for the one-year long measurement data. Particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5) aerosols were sampled at Anmyeon Island GAW station from June 2015 to May 2016 and carbonaceous species including ~80 organic compounds were analyzed. According to the performance parameter evaluation, 5 or 7 factors were considered as optimal number of factors. It was found out that the results of 7 factors gave less contribution from the factor designated as mixed sources which we could not separate clearly. The major sources with 7 factors were identified with various analyses including chemical characteristics and air parcel movement analysis. The 7 factors with their relative contributions are; anthropogenic Secondary Organic Aerosols (SOA) (14%), biogenic SOA (15%), primary biogenic source (8%), local biomass burning (13%), transported biomass burning (16%), combustion related source (15%), and mixed sources (19%). The air parcel movement analysis results also support the identification of these factors. Thus, the Anmyeon Island GAW station has been affected by both regional and local sources for the carbonaceous aerosols.

It is well-known that cognitive radio (CR) is a fundamental solution to mitigate the spectrum scarcity of today's wireless communication systems. For the realization of CR, there are many obstacles to resolve, including technical and policy perspectives. Since CR is a very sensitive technology, it is particularly important to test the scheme with appropriate operation in a real environment. In this regard, we have developed a testbed of a regional spectrum sensing based CR system for digital TV (DTV) white space. The developed system has a centralized spectrum sensor, and according to the sensing results, mobile WiMAX system provides high quality multimedia service through the spectrum hole in DTV band. There is also a fast frequency hopping capability depending on the sensing results to minimize interference to the actual licensee, the primary DTV system. The performance analysis shows that the developed CR system has excellent performance satisfying the IEEE 802.22 requirements, and is suitable ...

Only a few have evaluated the mitigation of greenhouse emissions and profit analysis along with soil carbon sequestration for corn cultivation. This experiment was conducted to evaluate the carbon sequestration and mitigation of greenhouse gas emissions as well as their profit analysis with different composts mixed with biochar during corn cultivation. This experimental data provided the second year of corn cultivation. The soil type used was clay loam. The application amounts of synthetic fertilizer and biochar were 220–30–155 kg ha−1 (N–P–K) as the recommended amount after soil analysis and 2600 kg ha−1 based on 1.3% of soil bulk density. For the biannual experimental results, it appeared that carbon sequestration in cow manure cooperated with biochar was highest at 2.3 tons ha−1 and recovered from 67.3 to 78.5% with biochar application. Furthermore, mitigation of CO2-eq. emission as greenhouse gases was estimated to be at 7.3–8.4 MT ha−1, and its profit was evaluated from $7.2 to 8.4 as lowest, from $57.2 to 66.6 as medium and from $139.7 to 162.7 as highest per hectare regardless of organic compost types used. For agricultural practice in Korea, it is evaluated that the market price of CO2 in corn field cooperated with 2600 kg ha−1 of biochar application ranged from $57.2 to 162.7 per hectare in Korean Climate Exchange. For corn biomass, the treatment with biochar application did not significantly decrease compared with the only organic compost application. Based on these experimental results, it might be applied for carbon trading with clean development mechanism for agricultural practices.

This paper introduces a novel AI framework for nationwide wireless KPI monitoring and its diagnosis in LTE/5G mobile networks. The proposed method, which has not been provided so far for a commercial use, automatically detects meaningful degradations of cell-specific wireless KPI statistics and also provides an alarm to administrator with possible causes of the corresponding problem. Machine learning and deep learning technologies are properly applied to detect relative KPI degradations on the basis of those estimated globa/local PDFs and also to carry out root cause analysis for the problem diagnosis, respectively. We have confirmed the feasibility of the mentioned AI framework by completing a first PoC through interworking our AI server with commercial LTE/5G networks. Currently, about 20 major monitoring KPIs such as RRE, ENDC,RSSI, BLER, and so on are being considered in current 5G NSA mode.

Electric field inducement technology is one of the most efficient and straightforward way to direct and form a percolation network of conducting fillers in a polymer without necessitating chemical functionalization. However, extended application is very restricted due to the initiation of electrical breakdown of conducting fillers in polymers. Herein, thermal conductive polymer nanocomposites are fabricated using graphene nanoplatelets (GNP) as a conducting filler under application of electric field that avoids electric breakdown. An electrode surface is coated with fuluoacrylate of nano-to-micro-dimensional thickness, which is applied as insulating electrodes to inhibit electric spark under application of electric current. Two different kinds of electric fields; direct current (DC) and switching DC (SWDC), were applied to envisage the enhancement of filler assembly. Analysis showed that an insulating layer with approximately 0.35–18 μm of thickness was successfully coated on the electrodes, which enabled increased formation of through-plane thermal conducting networks as well as enhanced thermal conductivity compared to composites with random filler distribution. The intensity of the available electric field could be increased over 30 times without breakdown with the insulating layer. Moreover, the field-induced GNP assembly generated by SWDC improved the thermal conductivity of the composite of over 112% compared to composite with random GNP distribution. This technique has the potential to control the linear assembly of conducting fillers an under electric field without electric breakdown.

The use of fossil fuels threatens environmental systems and causes an increase in greenhouse gas emissions, thereby leading to global warming. Such a scenario has spurred research into renewable hydrogen energy production as a strategy to replace fossil fuels. In this regard, thermochemical water splitting using redox reactions with metal oxides, which generates neither CO nor CO2 gas, is a promising approach with advantages over general hydrocarbon steam reforming. However, preventing catalytic deactivation due to nanocatalyst agglomeration or sintering during thermocycling at high temperatures (>800 °C) is a significant challenge. In this work, the design, synthesis, and characterization of a new CeO2-based catalytic model were carried out through a combination of theoretical and experimental approaches. From thermodynamic simulations, an optimal support material was first selected. A CeO2 nanoparticle-dispersed porous support structure was subsequently synthesized. The recyclable CeO2–support structure showed good capability and repeatability for hydrogen generation during consecutive thermocycles with no undesirable side reactions or particle sintering. It is anticipated that the results of this study will facilitate greater efficiency in the development of catalytic materials and allow for more effective materials to be designed so as to accelerate the realization of economical green energy production based on thermochemical cycles.

Objective: In current practice, medical experts use the pathological stage predictions provided in the Partin tables to support their decisions. Hence, the Partin tables are based on logistic regression built from the US data. In the present study, we developed a data-mining model to predict the pathologic stage of prostate cancer. In this newly developed model, using the classification and regression tree-particle swarm optimization analysis of the Korean population data, we aim to improve the prediction accuracy of the pathologic state of prostate cancer. Method: A total of 467 patients from the smart prostate cancer database were evaluated. The results were intended to predict the pathologic stage of prostate cancer: organ-confined disease and non–organ-confined disease. The accuracy of 4 classification and regression tree-particle swarm optimization models was compared; furthermore, the models were validated with the Partin tables using the receiver operating characteristic curve. Results: Among the 467 evaluated patients, 235 patients had organ-confined disease and 232 patients had non–organ-confined disease. The area under the receiver operating characteristic curve of the proposed classification and regression tree-particle swarm optimization model (0.858 ± 0.034) was larger than the 1 in the Partin tables (0.666 ± 0.046). Conclusion: The proposed classification and regression tree-particle swarm optimization model was superior to the Partin tables in terms of predicting the risk of prostate cancer. Compared to the validation of the Partin tables for the Korean population, the classification and regression tree-particle swarm optimization model resulted in a larger receiver operating characteristic curve and a more accurate prediction of the pathologic stage of prostate cancer in the Korean population.

Greenhouse-gas emission factors are widely used to estimate emissions arising from a defined unit of a specific activity. Such estimates are used both for international reporting to the United Nations Framework Convention on Climate Change (UNFCCC) and for myriad national and sub-national reporting purposes (for example, European Union Emissions Trading Scheme; EU ETS). As with the other so-called "Kyoto protocol GHGs", the Intergovernmental Panel on Climate Change (IPCC) provides a methodology for national and sub-national estimation of N₂O emissions, based on the sector from which the emissions arise. The objective of this study was to develop a integrated emission factor to estimate the direct N₂O emission from an agricultural field cultivated with the red pepper, soy bean, spring cabbage, autumn cabbage and potato in 2010∼2012. Emission factor of N₂O calculated using accumulated N₂O emission, N fertilization rate, and background N₂O emission over three year experiment was 0.00596±0.001337 kg N₂O-N (N kg) -1 . More extensive studies need to be conducted to develop N₂O emission factors for other upland crops in the various regions of Korea because N₂O emission is influenced by many factors including climate characteristics, soil properties, and agricultural practices.

건축물의 고층화, 복합화, 대형화에 따라 다양한 공법이 개발되고 있어 주요 공종에 대한 공법 선정의 중요성이 대두되고 있다. 그러나 프로젝트의 특성을 충분히 고려하지 못하고 있고 주요 공법의 선정을 위한 객관적 기준이나 자료 또한 부족한 실정이며, 실무자의 경험과 직관에만 의존하여 선정이 이루어지고 있는 점이 지적되어 왔다. 이러한 문제점을 해소하기 위해 퍼지, AHP, CBR 등 인공지능이론을 이용한 주요 공종의 공법 선정을 위한 다양한 연구가 진행되었다. 그러나 실무에서 공법 선정 시 공종별 특성 및 현장별 조건을 고려하여 주요 공종마다 각기 다른 여러가지 공법 선정 모델을 적용하기는 어렵다. 이에 본 연구에서는 매트릭스 분석과 선형변환을 이용하여, 실무에서 활용이 용이한 범용적인 성격의 의사결정지원 모델을 제시하고, 사례 연구를 통해 흙막이 공법 선정 과정에 적용하여 연구모델의 정합성을 검증하였다.

If the cloud computing environment is not sufficiently provide the required resources due to the number of virtual server to process the request, may cause a problem that the load applied to the specific server. Migration administrator receive the resources of each physical server for improving the efficiency of the virtual server that exists in the physical servers, and determines the migration destination based on the simulation results. But, there is more overhead predicting the future resource consumption of all the physical server to decide the migration destination through the simulation process in large and complex cloud computing environments. To solve this problem, we propose an improved prediction method with the simulation-based approach. The proposed method is a fuzzy-logic based priority model for VM migration. We design a proposed model with the DEVS formalism. And we also measure and compare a performance and migration count with existing simulation-based migration method. FPLM shows high utilization.

Environmental effect of water-saving irrigation on the emissions of greenhouse gases (GHGs) has not been well investigated. The objective of this study was to measure the effect of water-saving irrigation on GHGs as well as water use and rice production yield in paddy field condition in Korea. Four experimental runoff plots of 4x35 m in size were prepared at an existing paddy field. GHGs emission was measured during the 2012∼2013 growing seasons while a Japonica rice variety was cultivated. Four different water management methods, 1) Continuous Flooding (CF), 2) Intermittent Drainage (ID), 3) Water Saving (WS), and 4) CF+WS, were used during a rice growing season to compare the effects of water management methods on GHGs emission. CF method is flooding all the time, ID method makes paddy water drained 40 days after transplanting for about two weeks, WS method maintains 2~3 ㎝ water-level, which should be refilled when the water-level decreased to about 0 ㎝, and CF+WS method combines CF method before 30 days after transplanting (DAT) and WS method after 30 DAT. Regardless of water management methods, paddy field water was drained about 30 days before harvest. Amount of GHGs emission from WS plot was reduced by 69.3% compared to that from CF plot and by 59.0% compared to that from ID plot. Amount of GHGs emission from CF+WS plot was reduced by 60.7% compared to that from CF plot and by 47.5% compared to that from ID plot. Weed occurrence in CF+WS plots was reduced to 62.2% in comparison with the WS plot, implying that CF+WS plot showed the best effect to mitigation of the greenhouse gas emission in the atmosphere.

BACKGROUND: For the heavy metal cotamination sites, it is very important to estimate the human bioavailability quotients for heavy metals in paddy soils released from mine tailings, which is a major source of contamination in Korea, and to assess the human health risks of heavy metals. METHODS AND RESULTS: This experiment was carried out to investigate the human bioavailability quotient of the heavy metals in paddy soils below part of the closed metalliferous mine. For estimating the human bioavailability quotients for heavy metals, 30 paddy soils below part of the closed mine were collected, and analyzed for Cd, Cu, Pb, Zn, and As using simple bioavailability extraction test(SBET). The quantities of Cd, Cu, Pb, Zn and As extracted from paddy soils below part of the mine by using the SBET analysis were 28.1, 17.3, 34.1, 14.6 and 2.3% respectively. Specially, the maximum values of Cd, Pb and Zn were 73.3, 81.5 and 58.1% of human bioavailability quotient, respectively, and varied considerably among the sampling sites. The human bioavailability quotient of Cd, Cu, Pb and Zn in soils near the closed mine showed significant positive correlation among soil pH value, O.M. and Ex. Ca. contents, while it correlated negatively between soil Ex. K and Ex. Mg contents in paddy soils. Also, its of Cd, Cu, Pb and Zn in paddy soils showed significant positive correlation with 0.1M HCl extractable and total contents, while in soils, it correlated negatively with As content in soil near the closed mine. CONCLUSION: The results of the simple bioavailability extraction test (SBET) indicate that regular ingestion of soils by the local population could be closed a potential health threat due to long-term heavy metals exposure in these mine areas.

BACKGROUND: Most of the tailings have been left without any management in abandoned meta lliferous mines and have become the main source of heavy metal contamination for agricultural soils and crops in the these areas. METHODS AND RESULTS: This experiment was carried out to investigate the assessment of the heavy metal contamination in paddy soils located on downstream of the closed metalliferous mine. The average total concentrations of cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn), and arsenic (As) in paddy soils were 8.88, 56.7, 809, 754, and 37.9 mg/kg, respectively. Specially, the average concentrations of Cd, Pb and Zn were higher than those of warning criteria for soil contamination(4 mg/kg for Cd, 200 mg/kg for Pb, and 300 mg/kg for Zn) in agricultural soil established by Soil Environmental Conservation Act in Korea. The proportions of 0.1 M HCl extractable Cd, Cu, Pb, Zn, and As concentration to total concentration of these heavy metals in paddy soils were 27.7, 21.3, 35.1, 13.8 and 10.5%, respectively. The pollution index of these five metals in paddy soils ranged from 0.42 to 11.92. Also, the enrichment factor (EFc) of heavy metals in paddy soils were in the order as Cd>Pb>Zn>Cu>As, and the enrichment factor in paddy soil varied considerably among the sampling sites. The geoaccumulation index (Igeo) of heavy metals in soils were in the order as Cd>Pb>Zn>Cu>As, specially, the average geoaccumulation index of Cd, Pb, and Zn (Igeo 2.49 ～3.10) were relatively higher than that of other metals in paddy soils.

Background We have occasionally encountered advanced lung cancer patients with disseminated carcinomatosis throughout the body and/or within the lung. This study investigated the clinical characteristics and outcomes of advanced lung adenocarcinoma patients with miliary disseminated carcinomatosis. Methods Patients with adenocarcinomas harboring epidermal growth factor receptor (EGFR) mutations who presented with miliary disseminated carcinomatosis (either intrapulmonary or distant site) were enrolled in the study. Clinical characteristics, treatment responses, and survival outcomes were collected from medical records. Results The most frequent EGFR mutation was an in-frame deletion in exon 19 (n = 44, 68.8%). Arginine substitution of leucine 858 in exon 21 and alanine substitution of glycine 719 in exon 18 were detected in 19 patients (29.7%) and one patient (1.6%), respectively. Patients with miliary disseminated carcinomatosis tended to be female and non-smokers. They expressed the E19 deletion more frequently than patients without miliary dissemination and had shorter progression-free survival times in response to EGFR tyrosine kinase inhibitors (9.7 vs. 12.8 months, P = 0.003) and poorer overall survival (15.9 vs. 29.0 months, P = 0.077). Multivariate analyses revealed that metabolic tumor volume correlated with shorter overall survival time. Conclusions Our data indicate that lung adenocarcinoma patients with miliary dissemination have relatively shorter survival times than those without miliary dissemination. The poor prognosis of patients with miliary dissemination may reflect a high tumor burden, as represented by metabolic tumor volume.

This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.

The level of nitrous oxide (), a long-lived greenhouse gas, in atmosphere has increased mainly due to anthropogenic sources, especially application of nitrogen fertilizers. Quantifying emission in the agricultural field is essential to develop National inventories of greenhouse gases (GHGs) emission. The objective of this study was to develop emission factor to estimate direct emission from agricultural field by measuring emissions in the red pepper cultivating field from 2010 to 2012. Emission factor of calculated from accumulated emission, nitrogen fertilization rate, and background emission was N resulted from three year experiment of the research sites. More extensive studies need to be conducted to develop emission factors for other upland crops in the various regions of Korea because emission is influenced by many factors including climate characteristics, soil properties, and agricultural practices.