A Quantification Method for the Cold Pool Effect on Nocturnal Temperature in a Closed Catchment (폐쇄집수역의 냉기호 모의를 통한 일 최저기온 분포 추정)
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- Korean Journal of Agricultural and Forest Meteorology
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- v.13 no.4
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- pp.176-184
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- 2011
Cold air on sloping surfaces flows down to the valley bottom in mountainous terrain at calm and clear nights. Based on the assumption that the cold air flow may be the same as the water flow, current models estimate temperature drop by regarding the cold air accumulation at a given location as the water-like free drainage. At a closed catchment whose outlet is blocked by man-made obstacles such as banks and roads, however, the water-like free drainage assumption is no longer valid because the cold air accumulates from the bottom first. We developed an empirical model to estimate quantitatively the effect of cold pool on nocturnal temperature in a closed catchment. In our model, a closed catchment is treated like a "vessel", and a digital elevation model (DEM) was used to calculate the maximum capacity of the cold pool formed in a closed catchment. We introduce a topographical variable named "shape factor", which is the ratio of the cold air accumulation potential across the whole catchment area to the maximum capacity of the cold pool to describe the relative size of temperature drop at a wider range of catchment shapes. The shape factor is then used to simulate the density profile of cold pool formed in a given catchment based on a hypsometric equation. The cold lake module was incorporated with the existing model (i.e., Chung et al., 2006), generating a new model and predicting distribution of minimum temperature over closed catchments. We applied this model to Akyang valley (i.e., a typical closed catchment of 53
Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.
Purpose: This study examined the immunological activity and optimized the mixture conditions of Sargassum horneri (S. horneri) extracts in vitro and in vivo models. Methods: S. horneri was extracted using three different methods: hot water extraction (HWE), 50% ethanol extraction (EE), and supercritical fluid extraction (SFE). Splenocyte proliferation and cytokine production (Interleukin-2 and Interferon-γ) were measured using a WST-1 assay and enzyme-linked immunosorbent assay, respectively. The levels of nitric oxide and T cell activation production were measured using a Griess assay and flow cytometry, respectively. The natural killer (NK) cell activity was determined using an EZ-LDH kit. Results: Among the three different types of extracts, HWE showed the highest levels of splenocyte proliferation and cytokine production in vitro. In the animal model, three different types of extracts were administrated for 14 days (once/day) at 50 and 100 mg/kg body weight. HWE and SFE showed a high level of splenocyte proliferation and cytokine production in the with and without mitogen-treated groups, whereas EE administration did not induce the splenocyte activation. When RAW264.7 macrophage cells were treated with different mixtures (HWE with 5, 10, 15, 20% of SFE) to determine the optimal mixture ratio of HWE and SFE, the levels of nitric oxide and cytokine production increased strongly in the HWE with 5% and 10% of SFE containing group. In the animal model, HWE with 5% and 10% of SFE mixture administration increased the levels of splenocyte proliferation, cytokine production, and activated CD4+ cell population significantly, with the highest level observed in the HWE with 5% of SFE group. Moreover, the NK cell activity was increased significantly in the HWE with 5% of SFE mixture-treated group compared to the control group. Conclusion: The optimal mixture condition of S. horneri with immune-enhancing activity is the HWE with 5% of SFE mixture. These results confirmed that the extracts of S. horneri and its mixtures are potential candidate materials for immune enhancement.
Induced polarization (IP) effect is known to be caused by electrochemical phenomena at interface between minerals and pore water. Spectral induced polarization (SIP) method is an electrical survey to localize subsurface IP anomalies while injecting alternating currents of multiple frequencies into the ground. This method was effectively applied to mineral exploration of various ore deposits. Titanomagnetite ores were being produced by a mining company located in Gonamsan area, Gwanin-myeon, Pocheon-si, Gyeonggi-do, South Korea. Because the ores contain more than 0.4 w% vanadium, the ore deposit is called as Gwanin vanadiferous titanomagnetite (VTM) deposit. The vanadium is the most important of materials in production of vanadium redox flow batteries, which can be appropriately used for large-scale energy storage system. Systematic mineral exploration was conducted to identify presence of hidden VTM orebodies and estimate their potential resources. In geophysical exploration, laboratory geophysical measurement of rock samples is helpful to generate reliable property models from field survey data. Therefore, we performed laboratory SIP data of the rocks from the Gwanin VTM deposit to understand SIP characteristics between ores and host rocks and then demonstrate the applicability of this method for the mineral exploration. Both phase and resistivity spectra of the ores sampled from underground outcrop and drilling cores were different of those of the host rocks consisting of monzodiorite and quartz monzodiorite. Because the phase and resistivity at frequencies below 100 Hz are mainly dependent on the SIP characteristics of the rocks, we calculated mean values of the ores and the host rocks. The average phase values at 0.1 Hz were ores: -369 mrad and host rocks: -39 mrad. The average resistivity values at 0.1 Hz were ores: 16 Ωm and host rocks: 2,623 Ωm. Because the SIP characteristics of the ores were different of those of the host rocks, we considered that the SIP survey is effective for the mineral exploration in vanadiferous titanomagnetite deposits and the SIP characteristics are useful for interpreting field survey data.
Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70