• Korean Journal of Radiology
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• v.22 no.10
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• pp.1680-1689
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• 2021

Objective: To investigate the diagnostic yield of diffusion-weighted imaging (DWI) in patients with transient global amnesia (TGA) and identify significant parameters affecting diagnostic yield. Materials and Methods: A systematic literature search of the MEDLINE and EMBASE databases was conducted to identify studies that assessed the diagnostic yield of DWI in patients with TGA. The pooled diagnostic yield of DWI in patients with TGA was calculated using the DerSimonian-Laird random-effects model. Subgroup analyses were also performed of slice thickness, magnetic field strength, and interval between symptom onset and DWI. Results: Twenty-two original articles (1732 patients) were included. The pooled incidence of right, left, and bilateral hippocampal lesions was 37% (95% confidence interval [CI], 30-44%), 42% (95% CI, 39-46%), and 25% (95% CI, 20-30%) of all lesions, respectively. The pooled diagnostic yield of DWI in patients with TGA was 39% (95% CI, 27-52%). The Higgins I² statistic showed significant heterogeneity (I² = 95%). DWI with a slice thickness ≤ 3 mm showed a higher diagnostic yield than DWI with a slice thickness > 3 mm (pooled diagnostic yield: 63% [95% CI, 53-72%] vs. 26% [95% CI, 16-40%], p < 0.01). DWI performed at an interval between 24 and 96 hours after symptom onset showed a higher diagnostic yield (68% [95% CI, 57-78%], p < 0.01) than DWI performed within 24 hours (16% [95% CI, 7-34%]) or later than 96 hours (15% [95% CI, 8-26%]). There was no difference in the diagnostic yield between DWI performed using 3T vs. 1.5T (pooled diagnostic yield, 31% [95% CI, 25-38%] vs. 24% [95% CI, 14-37%], p = 0.31). Conclusion: The pooled diagnostic yield of DWI in TGA patients was 39%. DWI obtained with a slice thickness ≤ 3 mm or an interval between symptom onset and DWI of > 24 to 96 hours could increase the diagnostic yield.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.79-92
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• 2009

With the rising reliance on market estimation through customer analysis in customer-centered marketing, there is a rapid increase in the amount of personal data owned by corporations. There has been a corresponding rise in the customers' interest in personal information protection, and the problem of personal information leakage has risen as a serious issue. The purpose of this research is to develop a diagnosis model for personal information protection that is suited to our country's corporate environment, and on this basis, to present diagnostic instruments that can be applied to domestic corporations. This diagnosis model is a structural equation model that schematizes the degree of synthetic effect that administration factors and estimation items have on the protection of personal information owned by corporations. We develop the model- consisting of the administration factors for personal information protection and the measurement items of each factor- using the development method of standardized structural equation model. We then present a tool through which the administration factors and estimation items verified through this model can be used in the diagnosis for personal information protection in corporations. This diagnostic tool can be utilized as a useful instrument to prevent in advance the leakage of personal information in corporations.

• Journal of the Korean institute of surface engineering
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• v.43 no.3
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.197-206
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• 2019

The purpose of this study is to develop and validate a model for diagnosing core competencies at the higher education level. Based on literature reviews, a potential model for core competencies at university was suggested. A tool for validating the model was composed of 24 items, which were delivered to 226 professors and administrative staffs, 730 students, and 134 graduates & external industrial experts. Five constructs (core competencies) were extracted from the data collected among professors and administrative staff responding to the importance of the items. The results of importance and performance surveys on core competencies with students were respectively 3.28 to 3.66 and 2.68 to 3.28 (4-point Likert scale). Statistical differences between importance level and performance level were found in all the sub-categories of core competencies. Borich priority determination formula and Locus for Focus Model were used for the determination of the priority of needs. Importance survey among graduates and external experts showed that the mean of each items ranged from 2.80 to 3.76 (4-point Likert scale). The overall results of the analyses suggest that the final model is appropriate for measuring the core competencies.

• International Journal of Quality Innovation
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• v.4 no.1
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• pp.116-130
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• 2003

The gap-based SERVQUAL model is a popular service quality determinant due to its superior diagnostic capacity over alternative explanatory frameworks. However, some researchers criticize the performance of the SERVQUAL model and propose alternative service Quality measurement constructs. Nevertheless, it is argued that the superior diagnostic capacity of SERVQUAL is its key strength; and that any criticism made of it when making comparison with alternative models does not reflect the differing nature of scales of statistical analysis. Arguably, the only limitation of a gap-based model is misinterpretation of customers' evaluation and perception of a service. In this research, the gap score is transformed into a ratio score. Also, empirical tests and implications are presented to support this alternative contribution to the body of knowledge.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.439-442
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• 1997

For the treatment of arteriovenous malformations (AVM), we developed a new liquid embolic material and named it Embol. The material was pre-tested in an in vitro AVM model and tested in rete mirabile of pigs that is similar in structure with the AVM in human. We report on the construction of the in vitro AVM model, the pre-test results of Embol in the AVM model, and the animal test result in pig rete mirabile.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.2
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• pp.70-76
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• 1984

In this paper, a functional m-redundant system, which is m-fault tolerant, is defined based on the graph-theory. This system is designed to be t(t$\geq$m) fault-diagnosable by comparing its unit's outcomes without additive test functions, so, the system down for diagnosis is not needed. The diagnostic model for this system is presented. It is to avail the redundancy of the system effectively. It is shown that this model can be converted into Preparata's model. Thus, the diagnostic characteristics of a functional m-redundant system is analyzed by the method originated by Preparata et al.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.3
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• pp.63-71
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• 2001

This paper presents a model-based method for detecting and diagnosing some faults in the cooling tower of healing, ventilating, and air-conditioning systems. A simple model for the cooling tower is employed. Faults in cooling tower operation are detected through the deviations in the values of system characteristic parameters such as the heat transfer coefficient-area product, the tower approach, the tower effectiveness, and fan power. Three distinct faults are considered: cooling tower inlet water temperature sensor fault, cooling tower pump fault, and cooling tower fan fault. As a result, most values of the system characteristics parameter variations due to a fault are much higher or lower than the values without faults. This allows the faults in a cooling tower to be detected easily using above methods. The diagnostic rules for the faults were also developed through investigating the changes in the different parameter due to each faults.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.231-239
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• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.4
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• pp.327-338
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• 2000

complexity in atmospheric environment coupled with shoreline and complex terrain often causes local variations of meteorology that are distinct from those representative over larger surrounding area, These kinds of local variations are less significant in usual long-term environmental impact analyses dealing with continuous plume. The variations could however be crucial in predicting dispersion of toxic substance released in a relatively small area for a short duration. In the present paper the effects of spatial and temporal resolution of diagnostic wind field on the dispersion of the released substance are investigated by using a puff model. A hypothetical release scenario assumes that a substance is released from a location in the Yochon Industrial Estate and passively dispersed within a few-kilometer distance for an hour. The results show that diagnostic analysis could resolve more spatial variations to some extent by employing smaller grid size. The peak concentrations and puff trajectories obtained from spatially -and/or tmeporally -varing diagnostic wind field are found appreciably different from those obtained from uniform wind field. Attention to high-resolution wind field in the both spatial and temporal spaces is called in the consequence analysis of toxic substance release.