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

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

DECAY FACTOR WITH EXPERIMENTAL VARIABLES IN TWO CIRCULATING FLUIDIZED BED (CFB) RISERS

The effects of the riser inlet velocity, solid mass flux and particle size on the axial solid holdup profile and decay factor were investigated using two circulating fluidized beds (CFBs) with FCC (Geldart A) particles as the bed materials. Based on the experimental results from the two-CFBs, the axial solid holdup in the two CFBs were compared with the correlations of previous studies. Also, an empirical correlation was proposed for decay factor that exhibited a good agreement with experimental data

Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings

Long-Term Clinical Outcomes according to Initial Management and Thrombolysis In Myocardial Infarction Risk Score in Patients with Acute Non-ST-Segment Elevation Myocardial Infarction

PURPOSE: There is still debate about the timing of revascularization in patients with acute non-ST-segment elevation myocardial infarction (NSTEMI). We analyzed the long-term clinical outcomes of the timing of revascularization in patients with acute NSTEMI obtained from the Korea Acute Myocardial Infarction Registry (KAMIR). MATERIALS AND METHODS: 2,845 patients with acute NSTEMI (65.6 +/- 12.5 years, 1,836 males) who were enrolled in KAMIR were included in the present study. The therapeutic strategy of NSTEMI was categorized into early invasive (within 48 hours, 65.8 +/- 12.6 years, 856 males) and late invasive treatment (65.3 +/- 12.1 years, 979 males). The initial- and long-term clinical outcomes were compared between two groups according to the level of Thrombolysis In Myocardial Infarction (TIMI) risk score. RESULTS: There were significant differences in-hospital mortality and the incidence of major adverse cardiac events during one-year clinical follow-up between two groups (2.1% vs. 4.8%, p or= 5 points). CONCLUSIONS: The old age, high Killip class, low ejection fraction, high TIMI risk score, and late invasive treatment strategy are the independent predictors for the long-term clinical outcomes in patients with NSTEMI.ope