• Archives of Obesity and Metabolism
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• v.2 no.1
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• pp.1-5
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• 2023

The importance of weight discrimination for people with obesity has been highlighted by research which has found that more than 40% of those living with obesity have experienced weight discrimination. Evidence suggests that weight bias among obese individuals puts their health at risk more than health issues caused by obesity itself. Although bias, stigma, and discrimination towards individuals living with obesity are factors that make it difficult for them to lose weight, weight bias and stigma among healthcare professionals are common, causing individuals living with obesity to avoid treatment and potentially exacerbating obesity-related health issues. The concept that one's own efforts matter contributes to stigma, discrimination, and bias. This issue will be more frequent among primary care providers treating individuals living with obesity; thus, it is important to acknowledge the issues of bias, stigma, and discrimination towards individuals living with obesity and to seek out solutions. In this review, I will discuss the concept of weight bias, stigma, and discrimination, the problems they cause, and seek solutions to weight prejudice, stigma, and discrimination.

• Research in Community and Public Health Nursing
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• v.32 no.2
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• pp.140-149
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• 2021

Purpose: This study aimed to identify factors (biological, psychological, interpersonal, and contextual factors) associated with the weight bias internalization of the Korean girls in early adolescence. Methods: This study used a cross-sectional design. Data was collected from 233 girls aged 12~14 years with a self-reported questionnaire. With multiple regression analysis, the factors associated with the weight bias internalization of the girls in early adolescence were analyzed. Results: Of psychosocial factors, fear to fat (anti-fat attitude) (β=.43, p<.001) was associated with the weight bias internalization of the girls in early adolescence. In addition, attachment with teachers (β=-.11, p=.029) of an interpersonal factor and perceived socio-cultural pressure regarding weight control (β=.34, p<.001) of a contextual factor were associated with the weight bias internalization of the girls in early adolescence. Conclusion: For releasing the weight bias internalization of the girls in early adolescence, decreasing anti-fat attitude and socio-cultural pressure regarding weight control should be primarily required through social efforts including community and school based interventions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5268-5273
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• 2013

Technique for analyzing pile installed by vibratory pile driver was developed and results of analysis obtained from variation of bias weight were studied. It can be seen from load transfer curve for dynamic skin friction that load transfer curve shift to downward as bias weight increases. Shape of load transfer curve for dynamic skin friction becomes closer to shape of coil as the bias weight decreases. Magnitudes of toe resistances were not affected by the bias weight. Shape of load transfer curve for dynamic toe resistance shows the similar tendency as the load transfer curve for skin friction exhibits. Vertical displacement increases as the bias weight increases and the shape of vertical displacement with time shows more distinct shape of wave.

• Korean Journal of Occupational Health Nursing
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• v.25 no.3
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• pp.199-207
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• 2016

Purpose: The purpose of this study was to identify the obesity stress, weight bias and health care on Body Mass Index (BMI) in soldiers of non-combat area and to provide data for improving the quality of their life. Methods: This research involved 165 soldiers working in non-combat area. Data collection was conducted from November 1 to 20, 2015. Statistical analysis of the collected data were t-test and ANOVA, $Scheff{\acute{e}}$ method post hoc analysis, Pearson's correlation coefficients, and multiple liner regression using IBM SPSS 22.0. Results: The mean score of obesity stress was moderate ($19.05{\pm}5.28$). The mean score of weight bias was 69.03 and health care was 2.41 points. There are a positive correlation between obesity stress and BMI (r=.19, p<.05). Weight bias (r=-.19, p<.01) and health care (r=-.26, p<.01) among the subjects had negative correlations with BMI. In a multiple liner regression, obesity stress (${\beta}=.18$, p<.05), health care (${\beta}=-.18$, p<.05) were associated with BMI. Conclusion: Based on the findings that obesity stress and health care influence BMI, there is a need to control stress and to properly set proper guidelines on health care for soldiers.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.81-90
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• 2018

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.

• Journal of the Korean institute of surface engineering
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• v.28 no.6
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• pp.343-351
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• 1995

Titanium nitride films have been prepared on various substrates (silicon wafer, HSS) by cathode arc ion plating process to measure microhardness, adhesion and wear-resistant behaviors by changing the substrate bias voltages (0∼-300V), thickness and roughness. Microhardnesses were measured by micro vickers hardness tester, the adhesion strengths were evaluated by acoustic signals through the scratch test with incremental applied load. As the substrate bias voltages were increased, the ｛111｝ orientation was predominant, the microhardnesses and adhesion strengths of tool steel were observed to be stronger than those of without subatrate bias voltage. Adhesion strengths of the substrate bias were 4-7 times higher than those of without the substrate bias, confirmed by SEM with EDX. Wear resistances were used pin-on-disk tribotester and TiN costing reduced the abrasive wear. As the substrate bias was increased, the weight loss and the friction coefficient was decreased.

• Journal of KIISE
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• v.44 no.5
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• pp.486-495
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• 2017

Collaborative Filtering is limited because of the cost that is required to perform the recommendation (such as the time complexity and space complexity). The RFWBP (Rating Frequency Weight-based Baseline Predictor) that approximates the precision of the existing methods is one of the efficiency methods to reduce the cost. But, the following issues need to be considered regarding the RFWBP: 1) It does not reduce the error because the RFWBP does not learn for the recommendation, and 2) it recommends all of the items because there is no condition for an appropriate recommendation list when only the RFWBP is used for the achievement of efficiency. In this paper, the BBP (Bias-Based Predictor) is proposed to solve these problems. The BBP reduces the error range, and it determines some of the cases to make an appropriate recommendation list, thereby forging a recommendation list for each case.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.129-134
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• 2004

In this study, we investigate the effects of the weight function in the bounded influence regression quantile (BIRQ) estimator for the AR(1) model with additive outliers. In order to down-weight the outliers of X-axis, the Mallows' (1973) weight function has been commonly used in the BIRQ estimator. However, in our Monte Carlo study, the BIRQ estimator using the Tukey's bisquare weight function shows less MSE and bias than that of using the Mallows' weight function or Huber's weight function.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1222-1226
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• 2002

Estimates of genetic correlation between direct and maternal effects for weaning weight of beef cattle are often negative in field data. The biological existence of this genetic antagonism has been the point at issue. Some researchers perceived such negative estimate to be an artifact from poor modeling. Recent studies on sources affecting the genetic correlation estimates are reviewed in this article. They focus on heterogeneity of the correlation by sex, selection bias caused from selective reporting, selection bias caused from splitting data by sex, sire by year interaction variance, and sire misidentification and inbreeding depression as factors contributing sire by year interaction variance. A biological justification of the genetic antagonism is also discussed. It is proposed to include the direct-maternal genetic covariance in the analytical models.