• Archives of Craniofacial Surgery
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• v.23 no.4
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• pp.163-170
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• 2022

Background: Personal mobility devices (PMDs) have become an increasingly popular transport modality globally. With increasing social interest in and demand for PMDs, the number of individuals visiting emergency departments with PMD-related injuries has also increased annually. This study aimed to evaluate injury patterns and treatment costs for patients treated in the department of plastic surgery in a trauma center. Methods: In this retrospective study, data concerning patients with PMD-related injuries from January 2017 to December 2021 were reviewed. The data retrieved included age, sex, alcohol consumption, helmet use, the type of impact, onset of injury, place of first visit, type of injury, admission status, operation status, and treatment cost. Multiple linear regression analysis was performed to determine the effects of various factors on cost. Results: Data were collected from 93 patients. Until 2019, the annual number of PMD-related accidents was less than 10; however, this number increased sharply in 2020. The average cost of hospitalization was USD 7,698 whereas the average cost of non-hospitalization was USD 631. Only fractures had a significant association with total cost in linear regression analysis (p< 0.001). Conclusion: The prevalence of PMD use and related injuries requiring plastic surgery during the study period showed significant health and financial costs both to the patients involved and to society. This cost could be reduced through stricter regulations concerning PMD use, advocating the use of protective gear, and promoting greater awareness of safety measures and of the consequences of PMD-related accidents.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.312-321
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• 2020

In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.73-92
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• 2022

Purpose - The purpose of this paper is to explore the relationship between corporate social responsibility (CSR) and corporate present and future value. Design/methodology/approach - This paper intends to prove the relationship between CSR and corporate value once again by selecting A-share companies listed on the China Shenzhen Stock Exchange and Shanghai Stock Exchange from 2010 2017. This paper also examines the effect of five dimensions of CSR on corporate value in China. Findings - Empirical evidence shows that CSR is conducive to corporate value. The fulfillment of social responsibilities improves firm value in the future. Further, the regression results show that the social responsibility of the non-state-owned enterprise (Non-SOEs) group has a more significant effect on corporate financial performance than on the state-owned enterprise (SOEs) group. Research implications or Originality - This study has limitations. First, the grouping is only divided into two groups of SOEs and non-SOEs, and we did not consider foreign investments, that is, foreign-funded enterprises, for the comparative analysis. Second, only the linear relationship between CSR and corporate value was tested. In the future, we must determine whether there exists a nonlinear relationship between the two key concepts. Finally, there exists no research on CSR and corporate value by specific industries. Thus, the relationship between the five dimensions of CSR and corporate value should be investigated by specific industries.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2023-2030
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• 2022

A novel pulse fitting analysis (PFA) method is presented for the acquisition of nuclear spectra. The charging process of the feedback capacitor in the resistive feedback charge-sensitive preamplifier is equivalent to the impulsive pulse, and its impulse response function (IRF) can be obtained by non-linear fitting of the falling edge of the nuclear pulse. The integral of the IRF excluding the baseline represents the energy deposition of the particles in the detector. In addition, since the non-linear fitting process in PFA method is difficult to achieve in the conventional architecture of spectroscopy system, a new multi-channel analyzer (MCA) based on Zynq SoC is proposed, which transmits all the data of nuclear pulses from the programmable logic (PL) to the processing system (PS) by high-speed AXI-Stream in order to implement PFA method with precision. The linearity of new MCA has been tested. The spectrum of ¹³⁷Cs was obtained using LaBr₃(Ce) scintillator detector, and was compared with commercial MCA by ORTEC. The results of tests indicate that the MCA based on PFA method has the same performance as the commercial MCA based on pulse height analysis (PHA) method and excellent linearity for γ-rays with different energies, which infers that PFA method is an effective and promising method for the acquisition of spectra. Furthermore, it provides a new solution for nuclear pulse processing algorithms involving regression and iterative processes.

• Nutrition Research and Practice
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• v.10 no.1
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• pp.67-73
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• 2016

BACKGROUND/OBJECTIVES: This study was aimed at examining the association between dietary flavanones intake and lipid profiles according to the presence of metabolic syndrome (MetS) in Korean women with type 2 diabetes mellitus (T2DM). SUBJECTS/METHODS: A cross-sectional analysis was performed among 502 female T2DM patients (non-MetS group; n = 129, MetS group; n = 373) who were recruited from the Huh's Diabetes Clinic in Seoul, Korea between 2005 and 2011. The dietary intake was assessed by a validated semi-quantitative food frequency questionnaire (FFQ) and the data was analyzed using the Computer Aided Nutritional Analysis program (CAN-Pro) version 4.0 software. The intake of flavanones was estimated on the basis of the flavonoid database. RESULTS: In the multiple linear regression analysis after adjustment for confounding factors, daily flavanones intake was negatively associated with CVD risk factors such as total cholesterol, LDL-cholesterol, and apoB and apoB/apoA1 ratio only in the MetS group but not in the non-MetS group. Multiple logistic regression analysis revealed that the odds ratio for a higher apoB/apoA1 ratio above the median (${\geq}0.74$) was significantly low in the $4^{th}$ quartile compared to that in the $1^{st}$ quartile of dietary flavanones intake [OR: 0.477, 95% CI: 0.255-0.894, P for trend = 0.0377] in the MetS group. CONCLUSIONS: Dietary flavanones intake was inversely associated with the apoB/apoA1 ratio, suggesting a potential protective effect of flavanones against CVD in T2DM women with MetS.

• Advances in Computational Design
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• v.5 no.2
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.64-71
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• 2013

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

• Korean Journal of Ecology and Environment
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• v.48 no.2
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• pp.95-107
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• 2015

In order to identify long-term trends of water quality parameters in Lake Kyeongpo, Mann-Kendall test, Sen's slope estimator and linear regression were applied on data, with 15 parameters from three different sites and rainfall, monitored once in every two months from March to November during 1998~2013. Seasonal variation analysis only used Mann-Kendall test and Sen's slope estimator. Analysis result showed that salinity, transparency and nutrient variables (total phosphorus, dissolved inorganic phosphorus, total nitrogen, nitrate nitrogen, ammonia nitrogen) were only parameters having statistically significant trend. In linear regression analysis, salinity (surface and bottom layer of all sites) and transparency (only at site 1), were figured out with statistically significant increasing trend, while in non-parametric statistical method, salinity and transparency in all sites (surface, middle, deep) were figured out with statistically significant increasing trend. Water quality parameters showing statistically significant decreasing trends were dissolved oxygen (surface layer of site 1 and bottom layer of sites 2 and 3), total phosphorus (sites 1 and 2), dissolved inorganic phosphorus, total nitrogen, nitrate nitrogen and ammonia nitrogen in the linear regression analysis and, dissolved oxygen (bottom layer of all sites), total phosphorus, dissolved inorganic phosphorus, total nitrogen, nitrate nitrogen and ammonia nitrogen in the non-parametric method. Seasonal trend analysis result showed that salinity, turbidity, transparency and suspended solids in spring, salinity, transparency, nitrate nitrogen and suspended solids in summer and temperature, salinity, transparency and suspended solids in fall were the variables depending on the season with increasing trends. In general, rainfall during the research period showed decreasing trend. The significant reduction trends of nutrients in Lake Kyeongpo were believed to be related to lagoon restoration and water management project run by Gangneung city and under-water wear removal, but further detailed studies are needed to know the exact causes.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.32 no.3
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• pp.118-125
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• 2021

Objectives: Childhood adversity is a risk factor for anxiety symptoms, but it affects anxiety symptoms in attention-deficit/hyperactivity disorder (ADHD). The current study aimed to examine the association between childhood adversity and anxiety symptoms in participants with and without ADHD. Methods: Data were obtained from a school-based epidemiological study of 1017 randomly selected children and adolescents. The ADHD and non-ADHD groups were divided using the Diagnostic Interview Schedule for Children Predictive Scale (DPS). The DPS was also used to assess comorbidities such as anxiety and mood disorders. The childhood adversities were assessed using the Early Trauma Inventory Self Report-Short Form, and the anxiety symptoms were assessed using the Screen for Child Anxiety Related Disorders. Linear and logistic regression models were used to investigate the association between childhood adversity and anxiety in the ADHD and non-ADHD groups with adjustments for age and sex. Results: This study found that the ADHD group did not show any significant association between anxiety symptoms and childhood adversities, whereas the non-ADHD group always showed a significant association. In a subgroup analysis of the non-ADHD group, the normal group without any psychiatric disorders assessed with DPS demonstrated a statistically significant association between childhood adversities and anxiety symptoms. These results were consistent with the association between childhood adversities and anxiety disorders assessed using DPS, as shown by logistic regression. Conclusion: The association between anxiety symptoms and childhood adversities statistically disappears in ADHD; ADHD may mask or block the association. Further longitudinal research is necessary to investigate this relationship.