• Journal of Korea Trade
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• 제24권1호
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• pp.35-58
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• 2020

Purpose - This study examines the determinants of foreign direct investment (FDI) location choice for Chinese firms, focusing on the agglomeration effect for firms of the same nationality. Design/methodology - The empirical data are China's inward FDI from the top 19 economies (excluding tax havens and Taiwan) in terms of FDI during 1997-2015 and China's outward FDI from the top 18 economies (excluding tax havens). This study uses a random effects generalized least squares model for panel data analysis. Findings - The results confirm that both host countries' costs and market conditions and the degree of agglomeration affect these countries' attractiveness for FDI inflows. Specifically, agglomeration has a significant effect on China's inward and outward FDI. This study confirms that the agglomeration of firms of the same nationality has predictive power for multinational enterprises' FDI location choices. The host countries' real GDP and trade openness also positively affect FDI inflows. Interestingly, however, China's production cost has a positive effect. Thus, inward FDI aimed at entering the Chinese market is increasing in recent years relative to the previous efficiency-seeking FDI. Inward FDI in China is therefore the market-entry type, whereas outward FDI by Chinese firms is the market-oriented type. Originality/value - These results suggest that the effects of the potential determinants of Chinese outward FDI are similar to those of inward FDI as China's trade liberalization progresses.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• 산업경영시스템학회지
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• 제41권4호
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• pp.81-90
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• 2018

Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.431-443
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• 2019

Goodness-of-fit techniques are an important topic in statistical analysis. Censored data occur frequently in survival experiments; therefore, many studies are conducted when data are censored. In this paper we mainly consider test statistics based on the empirical distribution function (EDF) to test normal distributions with unknown location and scale parameters when data are randomly censored. The most famous EDF test statistic is the Kolmogorov-Smirnov; in addition, the quadratic statistics such as the $Cram{\acute{e}}r-von$ Mises and the Anderson-Darling statistic are well known. The $Cram{\acute{e}}r-von$ Mises statistic is generalized to randomly censored cases by Koziol and Green (Biometrika, 63, 465-474, 1976). In this paper, we generalize the Anderson-Darling statistic to randomly censored data using the Kaplan-Meier estimator as it was done by Koziol and Green. A simulation study is conducted under a particular censorship model proposed by Koziol and Green. Through a simulation study, the generalized Anderson-Darling statistic shows the best power against almost all alternatives considered among the three EDF statistics we take into account.

• 유통과학연구
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• 제19권1호
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• pp.105-113
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• 2021

Purpose: The rural market in India provides tremendous scope for FMCG consumer healthcare companies to market their products because of a significant increase of rural purchasing power. Many empirical studies in this area highlight the challenges and opportunities for marketers in the FMCG space. Research articles are not in abundance to understand intermediaries' expectations in the supply chain specific to consumer healthcare products. The existing literature did not significantly address the challenges of channel partners in the rural market. The present study aims to determine the retailer expectations from manufacturers and channel members' mutual expectations in the FMCG distribution channel. Research design and Methodology: The present study adopted a qualitative research methodology. Fifty respondents from each level of distribution channel such as super-stockist, distributors and retailers in central India were identified and an interview method was adopted to collect the data. Results: Nineteen factors were identified to influence the intermediaries for involvement in the business with any FMCG brand. Factors like Profit margin, reverse logistics, credit terms, return on investment, timely payments were crucial for managing the expectations of all intermediaries. This study provides academic as well as practical implications in terms of enabling the industry to align its channel management strategies accordingly.

• 전자통신동향분석
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• 제37권2호
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• pp.30-41
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• 2022

As the society is superaging, the number of patients with movement disabilities due to musculoskeletal or nervous system illness is rapidly increasing. To improve public health and reduce medical expenses, it is essential to develop rehabilitation systems that allow patients to resume their daily-life activities. However, the existing musculoskeletal illness diagnosis and rehabilitation method is limited in terms of precision and efficiency because it is based on an empirical diagnosis and prescription without regard for individual characteristics. To overcome these limits, it is critical to design a novel concept of routine rehabilitation therapy device that is capable of inducing musculoskeletal balance by the precise analysis of musculoskeletal usage patterns via the motion and the muscle activity tracking of linked muscles. This study introduces the trend of on-skin sensor device technology for routine musculoskeletal monitoring and therapy. For on-skin rehabilitation systems, skin-adhesive and stretchable motion/posture, electromyography, pressure sensors, small-size and low-power wireless sensor interfaces, and user-friendly rehabilitation contents based on new algorithms are combined.

• The Journal of Asian Finance, Economics and Business
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• 제9권6호
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• pp.81-93
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• 2022

This research aims to construct a model that combines brand love, brand loyalty, brand image, customer satisfaction, and service quality into a single model, with brand loyalty coming foremost, and test its predictive power in building brand love. Moreover, mediating effect of customer satisfaction and brand image on service quality and brand loyalty affecting brand love was checked. The study adopted an alliance context using an existing SERVQUAL model, a bi-dimensional aspect of brand loyalty and parasocial love relationship theory, to identify brand love as a construct or outcome in the consumer-brand relationship. Using a quantitative approach, survey questionnaires were distributed by unrestricted random sampling among 507 Saudia Airlines customers. Data were analyzed using structural equation modeling with SmartPLS 3.0. The results revealed significant relationships between four variables except for the brand image. It was found that brand image had no mediating effect on the relationship between service quality and customer loyalty. The outcome of this study highlights the importance of airline alliances for service quality, which leads to positive customer satisfaction, brand image, and customer loyalty. A unique contribution of the study is that it revealed that brand loyalty is also an antecedent of brand love.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3299-3309
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• 2022

Nuclear energy has the potential to play an influential role in energy transition efforts than is now anticipated by many countries. Realizing sustainable human development and reducing global climate crises will become more difficult without significantly increasing nuclear power. This paper aims to probe the role of nuclear energy, external debt, and financial globalization in sustaining human development and environmental conditions simultaneously in BRICS (Brazil, Russia, India, China, and South Africa) countries. This study applied a battery of second-generation estimation approaches over the period from 1990 to 2019. These methods are useful and robust to cross-countries dependencies, slope heterogeneity, parameters endogeneity, and serial correlation that are ignored in conventional approaches to generate more comprehensive and reliable estimates. The empirical findings indicate that nuclear energy and financial globalization contribute to human development, whereas external debt inhibits it. Similarly, financial globalization accelerates ecological deterioration, but nuclear energy and external debt promote environmental sustainability. Moreover, the study reveals bidirectional feedback causalities between human development, carbon emissions and nuclear energy consumption. The study offers useful policy guidance on accomplishing sustainable and inclusive development in BRICS countries.

• Genomics & Informatics
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• 제20권2호
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.