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Factors Affecting the Development of Vietnamese Construction and Real Estate Companies
Giang Lam PHAN(Giang Lam PHAN ),Thuy Dieu NGUYEN(Thuy Dieu NGUYEN ),Chi Thi NGUYEN(Chi Thi NGUYEN ),Lan NGUYEN(Lan NGUYEN ),Le Thi TRAN(Le Thi TRAN ) 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.9
This study aims to investigate the factors that contribute to the sustainable development of 334 Vietnamese construction and real estate companies listed on the Stock Exchange of Vietnam over a 5-year period from 2016 to 2020. By using regression analysis with the support of STATA software through examining the financial statements, which involves looking into crucial ratios including capital structure, profitability, firm size, accounts receivable management, and tangible assets investment, this study sheds light on whether these accounting indicators could help predict the construction and real estate companies growing potential in the future. Nevertheless, these ratios slightly contribute to the explanation of the change in revenue growth ratio, with a result of 1.6%, indicating that the value relevance of accounting information provides a modest and insignificant effect on investment decisions. This is understandable because the Vietnamese construction and real estate market still has many shortcomings in handling unexpected events, as well as the industry’s peculiarities related to major capital sources from bank loans. Based on this study, governmental authorities and business executives should plan appropriate risk management policies and measures to contribute to the sustainable development of construction and real estate companies.
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Predicting Financial Distress Distribution of Companies
Giang Huong VU(Giang Huong VU ),Chi Thi Kim NGUYEN(Chi Thi Kim NGUYEN ),Dang Van PHAM(Dang Van PHAM ),Diu Thi Phuong TRAN(Diu Thi Phuong TRAN ),Toan Duc VU(Toan Duc VU ) 한국유통과학회 2022 유통과학연구 Vol.20 No.10
Purpose: Predicting the financial distress distribution of an enterprise is important to warn enterprises about their future. Predicting the possibility of financial distress helps companies have action plans to avoid the possibility of bankruptcy. In this study, the author conducted a forecast of the financial distress distribution of enterprises. Research design, data and methodology: The forecasting method is based on Logit and Discriminant analysis models. The data was collected from companies listed on Vietnam Stock Exchange from 2012 to 2020. In which there are both companies suffer from financial distress and non-financial distress. Results: The forecast analysis results show that the Logistic model has better predictability than the Discriminant analysis model. At the same time, the results also indicate three main factors affecting the financial distress of enterprises at all three research stages: (1) Liquidity, (2) Interest payment, and (3) firm size. In addition, at each stage, the impact of factors on financial distress differs. Conclusions: From the results of this study, the author also made several recommendations to help companies better control company operations to avoid falling into financial distress. Adjustments to current assets, debt, and company expansion considerations are the most important factors for companies.
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Efficient Association Rule Mining based SON Algorithm for a Bigdata Platform
Giang-Truong Nguyen(뉘엔양쯔엉),Van-Quyet Nguyen(뉘엔반퀴엣),Sinh-Ngoc Nguyen(뉘엔신응억),Kyungbaek Kim(김경백) 한국디지털콘텐츠학회 2017 한국디지털콘텐츠학회논문지 Vol.18 No.8
In a big data platform, association rule mining applications could bring some benefits. For instance, in a agricultural big data platform, the association rule mining application could recommend specific products for farmers to grow, which could increase income. The key process of the association rule mining is the frequent itemsets mining, which finds sets of products accompanying together frequently. Former researches about this issue, e.g. Apriori, are not satisfying enough because huge possible sets can cause memory to be overloaded. In order to deal with it, SON algorithm has been proposed, which divides the considered set into many smaller ones and handles them sequently. But in a single machine, SON algorithm cause heavy time consuming. In this paper, we present a method to find association rules in our Hadoop based big data platform, by parallelling SON algorithm. The entire process of association rule mining including pre-processing, SON algorithm based frequent itemset mining, and association rule finding is implemented on Hadoop based big data platform. Through the experiment with real dataset, it is conformed that the proposed method outperforms a brute force method.
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Giang, T.,Kim, J. Springer Science + Business Media 2017 Journal of electronic materials Vol.46 No.1
<P>In a series of papers published recently, we clearly demonstrated that the most important factor governing the thermal conductivity of epoxy-Al2O3 composites is the backbone structure of the epoxy. In this study, three more epoxies based on diglycidyl ester-terminated liquid-crystalline epoxy (LCE) have been synthesized to draw conclusions regarding the effect of the epoxy backbone structure on the thermal conductivity of epoxy-alumina composites. The synthesized structures were characterized by proton nuclear magnetic resonance (H-1-NMR) and Fourier-transform infrared (FT-IR) spectroscopy. Differential scanning calorimetry, thermogravimetric analysis, and optical microscopy were also employed to examine the thermal and optical properties of the synthesized LCEs and the cured composites. All three LCE resins exhibited typical liquid-crystalline behaviors: clear solid crystalline state below the melting temperature (T-m), sharp crystalline melting at T-m, and transition to nematic phase above T-m with consequent isotropic phase above the isotropic temperature (T-i). The LCE resins displayed distinct nematic liquid-crystalline phase over a wide temperature range and retained liquid-crystalline phase after curing, with high thermal conductivity of the resulting composite. The thermal conductivity values ranged from 3.09 W/m-K to 3.89 W/m-K for LCE-Al2O3 composites with 50 vol.% filler loading. The steric effect played a governing role in the difference. The neat epoxy resin thermal conductivity was obtained as 0.35 W/m-K to 0.49 W/m-K based on analysis using the Agari-Uno model. The results clearly support the objective of this study in that the thermal conductivity of the LCE-containing networks strongly depended on the epoxy backbone structure and the degree of ordering in the cured network.</P>
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Giang, Hoang Huong,Viet, Tran Quoc,Ogle, Brian,Lindberg, Jan Erik Asian Australasian Association of Animal Productio 2011 Animal Bioscience Vol.24 No.5
Two experiments were conducted to investigate the effect of dietary supplementation of Bacillus, Saccharomyces and lactic acid bacteria (LAB) on performance and nutrient digestibility in grower and finisher pigs. In Exp. 1, 80 pigs (32 females and 48 males), $28.7{\pm}0.9\;kg$ body weight (BW), were randomly divided into 4 treatment groups balanced for sex and weight (5 pigs per pen, 4 pens per treatment). They were fed one of four diets: a basal grower (20-50 kg BW) and finisher (>50 kg BW) diet without any addition of probiotic or antibiotic (diet C), the basal diet supplemented with Bacillus subtilis H4 (diet B), diet B supplemented with Saccharomyces boulardi Sb (diet BS) and diet BS supplemented with a LAB complex (diet BSL). The LAB complex consisted of Enterococcus faecium 6H2, Lactobacillus acidophilus C3, Pediococcus pentosaceus D7, and Lactobacillus fermentum NC1. In Exp. 2, 16 male pigs, $29.2{\pm}0.8\;kg$ BW, were kept in individual pens and divided into 4 groups (4 pigs in each group). All 4 groups were given exactly the same growing-period diets (diet C, B, BS and BSL) as in Exp 1. The total faeces and urine were collected during 5 days (day 20-24) to determine nitrogen retention and total tract digestibility. In the growing period, average daily feed intake (ADFI), average daily gain (ADG) and feed conversion ratio (FCR) were not affected by diet B and BS (p>0.05), but ADG increased (+5.9%) (p<0.05) and FCR improved (+5.9%) (p<0.05) on diet BSL compared with the control, although ADFI was not different (p>0.05). Digestibility of crude protein and organic matter was higher (p<0.05) in diet BSL and digestibility of crude fibre was higher (p<0.05) in diet BS and BSL than in diet C. Nitrogen retention was not affected by diet (p>0.05). The faecal LAB counts were increased in grower pigs fed diet BSL (p<0.05) and faecal E. coli counts were decreased in pigs fed diets BS and BSL (p<0.05). In the finishing period, no effects of diet were found in ADFI, ADG, FCR, nutrient digestibility, and nitrogen retention (p>0.05). Faecal LAB and E. coli counts in the finisher pigs were not affected by diet (p>0.05). In conclusion, the current study demonstrates that a mixture of bacteria and yeast has the potential to be used as a probiotic dietary supplement in grower pigs.
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A multi-label Classification of Attributes on Face Images
Giang H. Le,Yeejin Lee 한국방송·미디어공학회 2021 한국방송미디어공학회 학술발표대회 논문집 Vol.2021 No.6
Generative adversarial networks (GANs) have reached a great result at creating the synthesis image, especially in the face generation task. Unlike other deep learning tasks, the input of GANs is usually the random vector sampled by a probability distribution, which leads to unstable training and unpredictable output. One way to solve those problems is to employ the label condition in both the generator and discriminator. CelebA and FFHQ are the two most famous datasets for face image generation. While CelebA contains attribute annotations for more than 200,000 images, FFHQ does not have attribute annotations. Thus, in this work, we introduce a method to learn the attributes from CelebA then predict both soft and hard labels for FFHQ. The evaluated result from our model achieves 0.7611 points of the metric is the area under the receiver operating characteristic curve.
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