• Journal of the Korean Vacuum Society
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• v.15 no.6
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• pp.563-575
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• 2006

Ti(C,N) films are synthesized by pulsed DC plasma enhanced chemical vapor deposition (PEMOCVD) using metal-organic compounds of tetrakis diethylamide titanium at $200-300^{\circ}C$. To compare plasma parameter, in this study, $H_2$ and $He/H_2$ gases are used as carrier gas. The effect of $N_2\;and\;NH_3$ gases as reactive gas is also evaluated in reduction of C content of the films. Radical formation and ionization behaviors in plasma are analyzed in-situ by optical emission spectroscopy (OES) at various pulsed bias voltages and gas species. He and $H_2$ mixture is very effective in enhancing ionization of radicals, especially for the $N_2$. Ammonia $(NH_3)$ gas also highly reduces the formation of CN radical, thereby decreasing C content of Ti(C, N) films in a great deal. The microhardness of film is obtained to be $1,250\;Hk_{0.01}\;to\;1,760\;Hk_{0.01}$ depending on gas species and bias voltage. Higher hardness can be obtained under the conditions of $H_2\;and\;N_2$ gases as well as bias voltage of 600 V. Hf(C, N) films were also obtained by pulsed DC PEMOCYB from tetrakis diethyl-amide hafnium and $N_2/He-H_2$ mixture. The depositions were carried out at temperature of below $300^{\circ}C$, total chamber pressure of 1 Torr and varying the deposition parameters. Influences of the nitrogen contents in the plasma decreased the growth rate and attributed to amorphous components, to the high carbon content of the film. In XRD analysis the domain lattice plain was (111) direction and the maximum microhardness was observed to be $2,460\;Hk_{0.025}$ for a Hf(C,N) film grown under -600 V and 0.1 flow rate of nitrogen. The optical emission spectra measured during PEMOCVD processes of Hf(C, N) film growth were also discussed. $N_2,\;N_2^+$, H, He, CH, CN radicals and metal species(Hf) were detected and CH, CN radicals that make an important role of total PEMOCVD process increased carbon content.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.924-942
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• 2021

The expansion of the Changjiang Diluted Water (CDW) plume during summer is known to be a major factor influencing phytoplankton diversity, community structure, and the regional marine environment of the northern East China Sea (ECS). The discharge of the CDW plume was very high in the summer of 2020, and cruise surveys and stationary monitoring were conducted to understand the dynamics of changes in environmental characteristics and the impact on phytoplankton diversity and community structure. A cruise survey was conducted from August 16 to 17, 2020, using R/V Eardo, and a stay survey at the Ieodo Ocean Research Station (IORS) from August 15 to 21, 2020, to analyze phytoplankton diversity and community structure. The southwestern part of the survey area exhibited low salinity and high chlorophyll a fluorescence under the influence of the CDW plume, whereas the southeastern part of the survey area presented high salinity and low chlorophyll a fluorescence under the influence of the Tsushima Warm Current (TWC). The total chlorophyll a concentrations of surface water samples from 12 sampling stations indicated that nano-phytoplankton (20-3 ㎛) and micro-phytoplankton (> 20 ㎛) were the dominant groups during the survey period. Only stations strongly influenced by the TWC presented approximately 50% of the biomass contributed by pico-phytoplankton (< 3 ㎛). The size distribution of phytoplankton in the surface water samples is related to nutrient supplies, and areas where high nutrient (nitrate) supplies were provided by the CDW plume displayed higher biomass contribution by micro-phytoplankton groups. A total of 45 genera of nano- and micro-phytoplankton groups were classified using morphological analysis. Among them, the dominant taxa were the diatoms Guinardia flaccida and Nitzschia spp. and the dinoflagellates Gonyaulax monacantha, Noctiluca scintillans, Gymnodinium spirale, Heterocapsa spp., Prorocentrum micans, and Tripos furca. The sampling stations affected by the TWC and low in nitrate concentrations presented high concentrations of photosynthetic pico-eukaryotes (PPE) and photosynthetic pico-prokaryotes (PPP). Most sampling stations had phosphate-limited conditions. Higher Synechococcus concentrations were enumerated for the sampling stations influenced by low-nutrient water of the TWC using flow cytometry. The NGS analysis revealed 29 clades of Synechococcus among PPP, and 11 clades displayed a dominance rate of 1% or more at least once in one sample. Clade II was the dominant group in the surface water, whereas various clades (Clades I, IV, etc.) were found to be the next dominant groups in the SCM layers. The Prochlorococcus group, belonging to the PPP, observed in the warm water region, presented a high-light-adapted ecotype and did not appear in the northern part of the survey region. PPE analysis resulted in 163 operational taxonomic units (OTUs), indicating very high diversity. Among them, 11 major taxa showed dominant OTUs with more than 5% in at least one sample, while Amphidinium testudo was the dominant taxon in the surface water in the low-salinity region affected by the CDW plume, and the chlorophyta was dominant in the SCM layer. In the warm water region affected by the TWC, various groups of haptophytes were dominant. Observations from the IORS also presented similar results to the cruise survey results for biomass, size distribution, and diversity of phytoplankton. The results revealed the various dynamic responses of phytoplankton influenced by the CDW plume. By comparing the results from the IORS and research cruise studies, the study confirmed that the IORS is an important observational station to monitor the dynamic impact of the CDW plume. In future research, it is necessary to establish an effective use of IORS in preparation for changes in the ECS summer environment and ecosystem due to climate change.