• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3856-3870
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• 1996

The objective of this study is to evaluate heat transfer coefficient distribution during heat treatment by inverse analysis. As a first procedure, the inverse heat transfer formulation by using two dimensional finite element method has been developed. The formulation can handlematerial nonlinearity and allow arbitrary placement and number of sensors. The formulation was verified through application to simulated exact and inexact measurements.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.343-348
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• 2015

The purpose of this study is a comparison of forward planning(FP) and inverse planning(IP) of a radiosurgery procedure. 10 patients of acoustic schwannoma MR image were used for treatment plan. FP-1,2 and IP were established under the same condition. FP and IP were compared by number of shot, conformity index(CI), paddic conformity index(PCI), gradiant index(GI) and treatment time. On average the treatment plan produced by IP tool provided an improved or similar CI, PCI, GI and reduced treatment time as compared to the FP (CI;FP-1:0.85, FP-2:0.86, IP:0.94, PCI;FP-1:0.79, FP-2:0.81, IP:0.78, GI;FP-1:2.94, FP-2:2.94, IP:3.01). The inverse planning system provides a clinically useful plan while reducing the planning time and treatment time.

• Radiation Oncology Journal
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• v.36 no.1
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• pp.1-10
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• 2018

Intensity-modulated radiation therapy (IMRT) has been considered the most successful development in radiation oncology since the introduction of computed tomography into treatment planning that enabled three-dimensional conformal radiotherapy in 1980s. More than three decades have passed since the concept of inverse planning was first introduced in 1982, and IMRT has become the most important and common modality in radiation therapy. This review will present developments in inverse IMRT treatment planning and IMRT delivery using multileaf collimators, along with the associated key concepts. Other relevant issues and future perspectives are also presented.

• Journal of Environmental Science International
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• v.5 no.3
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• pp.361-367
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• 1996

An inverse fluidized-bed biofilm reactor (IFBBR) was used for the treatment of highly-emulsified oily wastewater. When the concentration of biomass which was cultivated in the synthetic wastewater reached to 6000 mg/1, the oily wastewater was employed to the reactor with a input COD concentration range of 50 mg/1 to 1900 mg/l. Virtually the IFBBR showed a high stability during the long operation period although soma fluctuation was observed. The COD removal efficiency was maintained over 9% under the condition that organic loading rate should be controlled under the value of 1.5 kgCOD/$m^3$/day, and F/M ratio is 1.0 kgCOD/kgVSS/day at $22{\circ}C$ and HRT of 12 hrs. As increasing organic loading rates, the biomass concentration was decreased steadily with decreasing of biofilm dry density rather than biofilm thickness. Based on the experimental jesuits, it was suggested that the decrease in biofilm dry density was caused by a loss of biomass inside the biofilm.

• Journal of Korea Water Resources Association
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• v.36 no.5
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• pp.863-878
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• 2003

In this paper, it was studied on a device for maintaining the instream flow by using treated waste water from sewage treatment plant, and discussed on using the inverse circulation system in stream for directly utilizing treated waste water as instream flow. This system is to secure insufficient discharge at upstream, as treated water is pumped from treatment plant which is located at down stream. Therefore, it will be improved water quality with simple water treatment plant, as it is composed of optimal system by consist various types of scheme for transporting. Also, influx method of transferred treated water to a stream will be improved water quality by aeration and be shown environmental friendly spaces. It was considered water quality and present using condition to use for maintenance water in stream by treated water. The guide line for application of inverse circulation system in domestic streams is suggested.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• International Neurourology Journal
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• v.22 no.4
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• pp.237-245
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• 2018

Purpose: Presenilins are functionally important components of ${\gamma}$-secretase, which cleaves a number of transmembrane proteins. Manipulations of PSEN1 and PSEN2 have been separately studied in Alzheimer disease (AD) and cancer because both involve substrates of ${\gamma}$-secretase. However, numerous clinical studies have reported an inverse correlation between AD and cancer. Interestingly, AD is a neurodegenerative disorder, whereas cancer is characterized by the proliferation of malignant cells. However, this inverse correlation in the PSEN double-knockout (PSEN dKO) mouse model of AD has been not elucidated, although doing so would shed light onto the relationship between AD and cancer. Methods: To investigate the inverse relationship of AD and cancer under conditions of PSEN loss, we used the hippocampus of 7-month-old and 18-month-old PSEN dKO mice for a microRNA (miRNA) microarray analysis, and explored the tumorsuppressive or oncogenic role of differentially-expressed miRNAs. Results: The total number of miRNAs that showed changes in expression level was greater at 18 months of age than at 7 months. Most of the putative target genes of the differentially-expressed miRNAs involved Cancer pathways. Conclusions: Based on literature reviews, many of the miRNAs involved in Cancer pathways were found to be known tumorsuppressive miRNAs, and their target genes were known or putative oncogenes. In conclusion, the expression levels of known tumor-suppressive miRNAs increased at 7 and 18 months, in the PSEN dKO mouse model of AD, supporting the negative correlation between AD and cancer.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.381-397
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• 2023

The most representative design used in clinical trials is randomization, which is used to accurately estimate the treatment effect. However, comparison between the treatment group and the control group in an observational study without randomization is biased due to various unadjusted differences, such as characteristics between patients. Propensity score weighting is a widely used method to address these problems and to minimize bias by adjusting those confounding and assess treatment effects. Inverse probability weighting, the most popular method, assigns weights that are proportional to the inverse of the conditional probability of receiving a specific treatment assignment, given observed covariates. However, this method is often suffered by extreme propensity scores, resulting in biased estimates and excessive variance. Several alternative methods including trimming, overlap weights, and matching weights have been proposed to mitigate these issues. In this paper, we conduct a simulation study to compare performance of various propensity score weighting methods under diverse situation, such as limited overlap, misspecified propensity score, and treatment contrary to prediction. From the simulation results overlap weights and matching weights consistently outperform inverse probability weighting and trimming in terms of bias, root mean squared error and coverage probability.

• Journal of Environmental Science International
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• v.6 no.1
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• pp.17-24
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• 1997

The objective of this study was to examine the transient response to hydraulic shocks in an Inverse fluidized bed bioflm reactor(IFBBR) for the treatment of apartment sewage. The hydraulic shock experiments, when the system were reached at steady state with each HRT 12, 7, and 4hr, were conducted by chancing twice HRT per day during 3days. The SCOD, SS, DO, and pH of the effluent stream were increased with hydraulic shock, but easily recovered to the steady state of pre-hydraulic shock condition. In spite of hydraulic shock, there were not much variation of biomass concentration, biofilm thickness and biofilm dry density.