The Study On The Effect And Determinants Of Small - And Medium-Sized Entities Conducting Tax Avoidance

SME often lack the capacity to keep transparency in management due to a small number of information users. Thus, the adoption of K-IFRS can be burdensome to numerous SME, which led to the enactment of Accounting Standards for Small- and Medium-sized Entities (AS-SME). AS-SME allows the accountants to easily implement accounting rules when writing financial statements and the users to comprehend useful information. SME hold less tax burden since they receive a tax deduction and exemption from the Tax Act. Thus, we conjecture that the financial determinants of tax avoidance between SME and non-SME will differ. We divide the total sample according to the corporate tax avoidance and empirically examine whether the difference actually exists. Our sample consists 18,954 audited firms including those external audited from 2011 to 2013. This study implements BTD, the difference between accounting profit and taxable income and estimated corporate tax avoidance (TS), which is the part that cannot be explained by total accruals in BTD to proxy for tax avoidance. (Desai and Dharmapala 2006). We summarize our findings as below: there is a significant distinction between SME and non-SME regarding the related financial determinants. The result shows that firm size (SIZE), profitability (ROA), leverage (LEV), operating cash flow(CFO), capital intensity (PPE), R&D intensity (RNDS), and growth rate (GS) all influence the corporate tax avoidance of SME. Our result also suggests that there is variation in the determinants among the SME with high corporate tax avoidance. The attempt to investigate the financial determinants of the tax avoidance in SME can be a barometer of the effectiveness of AS-SME, which is enacted to lessen the tax burden of the SME. We intend to provide policy implication regarding SME subsidy by examining the motive for corporate tax avoidance in SME

Cancer stem cell metabolism: target for cancer therapy

Increasing evidence suggests that cancer stem cell (CSC) theory represents an important mechanism underlying the observed failure of existing therapeutic modalities to fully eradicate cancers. In addition to their more established role in maintaining minimal residual disease after treatment and forming the new bulk of the tumor, CSCs might also critically contribute to tumor recurrence and metastasis. For this reason, specific elimination of CSCs may thus represent one of the most important treatment strategies. Emerging evidence has shown that CSCs have a different metabolic phenotype to that of differentiated bulk tumor cells, and these specific metabolic activities directly participate in the process of CSC transformation or support the biological processes that enable tumor progression. Exploring the role of CSC metabolism and the mechanism of the metabolic plasticity of CSCs has become a major focus in current cancer research. The targeting of CSC metabolism may provide new effective therapies to reduce the risk of recurrence and metastasis. In this review, we summarize the most significant discoveries regarding the metabolism of CSCs and highlight recent approaches in targeting CSC metabolism

Reported Profits And Effective Tax Rate Following Accounting Standards Changes Analysis Of Consolidated Financial Statements And Separate Financial Statements

This study empirically examines how the adoption of IFRS affected the reported profits and effective tax rates of firms by analyzing consolidated financial statements and separate financial statements. Firms that adopted IFRS in 2011 were required to disclose consolidated financial statements and separate financial statements in both K-IFRS and K-GAAP for this period. We conjecture that there will be a difference in the reported profits and effective tax rates between the financial statements that adopt the two different accounting standards. This study will provide policy implications with regards to the recent IFRS adoption and the use of accounting standards. The findings of this study are as follows. First, we find that the effective tax rate and corporate tax expenses decreased after the adoption of K-IFRS from K-GAAP. Earnings Before Tax (EBT) and net income also decreased when reported in K-IFRS. When we divide the total sample into the listed firms and KOSDAQ firms, we found a significant difference between the accounting standards in the total sample and listed firms, but did not see such a difference in KOSDAQ firms. In addition, results from the analysis of separate financial statements were analogous to those from consolidated financial statements. Additional analyses examined the effect of the early adoption of IFRS, but a significant influence due to early adoption was not found in consolidated financial statements from both parametric and non-parametric tests. However, the effective tax rate did decrease in the separate financial statements of firms that adopted K-IFRS earlier. The implementation of K-IFRS (changes in accounting standards) has made the managerial performance of firms accounted for in the Equity Method to be reflected in EBT and net income. This entailed an increase (or decrease) in the Equity Method profit, which in turn increased reported profits and decreased effective tax rates. In other words, the total increase of reported profits in consolidated financial statements can be attributed to subsidiary companies. However, the adoption of IFRS also reduced the tax burden, which is considered to be the motivation for firms to adopt IFRS in advance. This article attempts to provide policy implications with regards to the adoption of new accounting standards and its influence on the corporate tax expenses and effective tax rates in listed firms and KOSDAQ firms. &nbsp

Medium Resolution Near-Infrared Spectra of the Host Galaxies of Nearby Quasars

We present medium resolution near-infrared host galaxy spectra of low redshift quasars, PG 0844 + 349 (z=0.064), PG 1226 + 023 (z=0.158), and PG 1426+015 (z=0.086). The observations were done by using the Infrared Camera and Spectrograph (IRCS) at the Subaru 8.2 m telescope. The full width at half maximum of the point spread function was about 0.3 arcsec by operations of an adaptive optics system, which can effectively resolve the quasar spectra from the host galaxy spectra. We spent up to several hours per target and developed data reduction methods to reduce the systematic noises of the telluric emissions and absorptions. From the obtained spectra, we identified absorption features of Mg I (1.503 um), Si I (1.589 um) and CO (6-3) (1.619 um), and measured the velocity dispersions of PG 0844 + 349 to be 132+/-110 km s-1 and PG 1426 + 015 to be 264+/-215 km s-1. By using an M_BH-sigma relation of elliptical galaxies, we derived the black hole (BH) mass of PG 0844+349, log(M_BH/M_SUN) = 7.7+/-5.5 and PG 1426+015, log(M_BH/M_SUN) = 9.0+/-7.5. These values are consistent with the BH mass values from broad emission lines with an assumption of a virial factor of 5.5.Comment: 16 pages, 5 figure

Association Between Macronutrients Intake and Depression in the United States and South Korea

Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was measured from 60,935 participants from the National Health and Nutrition Examination Survey (NHANES) and 15,700 participants from the South Korea NHANES (K-NHANES) databases. When the proportion of calories intake by protein increased by 10%, the prevalence of depression was significantly reduced both in the United States [Odds Ratio, OR (95% CI), 0.621 (0.530–0.728)] and South Korea [0.703 (0.397–0.994)]. An association between carbohydrate intake and the prevalence of depression was seen in the United States [1.194 (1.116–1.277)], but not in South Korea. Fat intake was not significantly associated with depression in either country. Subsequent analysis showed that the low protein intake groups had significantly higher risk for depression than the normal protein intake groups in both the United States [1.648 (1.179–2.304)] and South Korea [3.169 (1.598–6.286)]. In the daily diet of macronutrients, the proportion of protein intake is significantly associated with the prevalence of depression. These associations were more prominent in adults with insufficient protein intake, and the pattern of association between macronutrients and depression in Asian American and South Korean populations were similar. Our findings suggest that the proportion of macronutrients intake in everyday life may be related to the occurrence of depression

Association Between Macronutrients Intake and Depression in the United States and South Korea

Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was measured from 60,935 participants from the National Health and Nutrition Examination Survey (NHANES) and 15,700 participants from the South Korea NHANES (K-NHANES) databases. When the proportion of calories intake by protein increased by 10%, the prevalence of depression was significantly reduced both in the United States [Odds Ratio, OR (95% CI), 0.621 (0.530–0.728)] and South Korea [0.703 (0.397–0.994)]. An association between carbohydrate intake and the prevalence of depression was seen in the United States [1.194 (1.116–1.277)], but not in South Korea. Fat intake was not significantly associated with depression in either country. Subsequent analysis showed that the low protein intake groups had significantly higher risk for depression than the normal protein intake groups in both the United States [1.648 (1.179–2.304)] and South Korea [3.169 (1.598–6.286)]. In the daily diet of macronutrients, the proportion of protein intake is significantly associated with the prevalence of depression. These associations were more prominent in adults with insufficient protein intake, and the pattern of association between macronutrients and depression in Asian American and South Korean populations were similar. Our findings suggest that the proportion of macronutrients intake in everyday life may be related to the occurrence of depression

Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm

Background: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression. Methods: Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4,949 from the South Korea NHANES (K-NHANES) database in 2014. Results: A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74). Conclusions: Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses