• Journal of radiological science and technology
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• v.45 no.3
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• pp.213-223
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• 2022

Although the number of computed tomography(CT) is increasing every year, it is insufficient to establish appropriate workload calculation standards of radiologic technologist to provide optimal medical services to patients, such as patient safety management and infection management. The purpose of this study is to present guidelines for calculating the appropriate workload of radiologic technologist by analyzing the work flow of CT procedures and the time required for CT examination in major hospitals. As for the study subjects and methods, the appropriate process for each step of CT examination was investigated to systematically present the process and time required for the actual examination, and the CT procedure time of 104,105 adult patients and 465 pediatric patients under the age of 6 were analyzed. For the time required, data according to the use of contrast medium, procedure type, and adult/child were collected and compared. The test time of CT examination using contrast medium took about 13 minutes when one radiologic technologist worked and about 9 minutes when two radiologic technologists worked. The time required for the procedures were statistically significant depending on the presence or absence of contrast medium, multi-phase procedure, and patient age (considering pediatric patients). As a result, in order to thoroughly perform patient safety and infection management, the appropriate workload increased by about 40% when there were two radiologic technologists. The limit workload was an average of 32 people per day with one radiologic technologist per 15 minutes, and 48 people per day with two radiologic technologist per 10 minutes. This is a marginal workload, and in the case of procedures that require more time to acquire radiographic images, the interval between reservations should be widened.

• Food Science of Animal Resources
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• v.42 no.2
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• pp.225-239
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• 2022

As commensal colonizers in livestock, there has been little attention on staphylococci, especially non-aureus staphylococci (NAS), contaminating meat production chain. To assess prevalence of staphylococci in retail pork and slaughterhouse carcass samples in Korea, we collected 578 samples from Korean slaughterhouses (n=311) and retail markets (n=267) for isolation of staphylococci and determined antimicrobial resistance phenotypes in all the isolates. The presence of and prevalence of fusB-family genes (fusB, fusC, fusD, and fusF) and mutations in fusA genes were examined in fusidic acid resistant isolates. A total of 47 staphylococcal isolates of 4 different species (Staphylococcus aureus, n=4; S. hyicus, n=1; S. epidermidis, n=10; Mammaliicoccus sciuri, n=32) were isolated. Fusidic acid resistance were confirmed in 9/10 S. epidermidis and all of the 32 M. sciuri (previously S. sciuri) isolates. Acquired fusidic acid resistance genes were detected in all the resistant strains; fusB and fusC in S. epidermidis and fusB/C in M. sciuri. Multi-locus sequence type analysis revealed that ST63 (n=10, 31%) and ST30 (n=8, 25%) genotypes were most prevalent among fusidic acid resistant M. sciuri isolates. In conclusion, the high prevalence of fusB-family genes in S. epidermidis and M. sciuri strains isolated from pork indicated that NAS might act as a reservoir for fusidic acid resistance gene transmissions in pork production chains.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.113-119
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• 2022

In software fault prediction, a multi classification model that predicts the fault severity category of a module can be much more useful than a binary classification model that simply predicts the presence or absence of faults. A small number of severity-based fault prediction models have been proposed, but no classifier using deep learning techniques has been proposed. In this paper, we construct MLP models with 3 or 5 hidden layers, and they have a structure with a fixed or variable number of hidden layer nodes. As a result of the model evaluation experiment, MLP-based deep learning models shows significantly better performance in both Accuracy and AUC than MLPs, which showed the best performance among models that did not use deep learning. In particular, the model structure with 3 hidden layers, 32 batch size, and 64 nodes shows the best performance.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.59.2-59.2
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• 2020

Disks around protostars are the birthplace of planets. The first step toward planet formation is grain growth from ㎛-sized grains to mm/cm-sized grains in a disk, particularly in dense regions. In order to study whether grains grow and segregate at the protostellar stage, we investigate the ALMA Band 3 (3.1 mm) and 7 (0.87 mm) dust continuum observations of the protostellar disk WL 17 in ρ Ophiuchus L1688 cloud. As reported in a previous study, the Band 3 image shows substructures: a narrow ring and a large central hole. On the other hand, the Band 7 image shows different substructures: a non-axisymmetric ring and an off-center hole. The two-band observations provide a mean spectral index of 2.3, which suggests the presence of mm/cm-sized large grains. Its non-axisymmetric distribution may imply dust segregation between small and large grains. We perform radiative transfer modeling to examine the size and spatial distributions of dust grains in the WL 17 disk. The best-fit model suggests that large grains (>1 cm) exist in the disk, settling down toward the midplane, whereas small grains (~10 ㎛) well mixed with gas are distributed off-center and non-axisymmetrically in a thick layer. The low spectral index and the modeling results suggest that grains rapidly grow at the protostellar stage and that grains differently distribute depending on sizes, resulting in substructures varying with observed wavelengths. To understand the differential grain distributions and substructures, we discuss the effects of the protoplanet(s) expected inside the large hole and the possibility of gravitational instability.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.433-443
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• 2023

The current paper discusses the dynamic and stability responses of cross-ply composite laminated plates by employing a refined quasi-3D trigonometric shear deformation theory. The proposed theory takes into consideration shear deformation and thickness stretching by a trigonometric variation of in-plane and transverse displacements through the plate thickness and assures the vanished shear stresses conditions on the upper and lower surfaces of the plate. The strong point of the new formulation is that the displacements field contains only 4 unknowns, which is less than the other shear deformation theories. In addition, the present model considers the thickness extension effects (ε_z≠0). The presence of the Winkler-Pasternak elastic base is included in the mathematical formulation. The Hamilton's principle is utilized in order to derive the four differentials' equations of motion, which are solved via Navier's technique of simply supported structures. The accuracy of the present 3-D theory is demonstrated by comparing fundamental frequencies and critical buckling loads numerical results with those provided using other models available in the open literature.

• Analytical Science and Technology
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• v.21 no.6
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• pp.526-534
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• 2008

To determine the trace level of synthetic and natural hormones in food, the improvement of official analytical method and new development of simultaneous determination of hormones were established. On the basis of developed analytical method, the background level of natural hormones and distribution of residual hormones were monitored in meat. Target hormones were six natural hormones such as estrogens ($17{\beta}$-estradiol, $17{\alpha}$-estradiol, estrone), androgens ($17{\beta}$-testosterone, $17{\alpha}$-testosterone), and gestagens (progesterone) and three synthetic hormones such as DES, zeranol, and taleranol. These hormones were analyzed by gas chromatographymass spectrometry. Newly developed multi-residue analysis method was applied for meat sample which were collected from market in the capital region and monitored the presence of residues of synthetic and natural steroid hormones. No residue of synthetic hormones were detected and endogenous level of progesterone was detected in cattle, pig and liver samples tested.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.846-860
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• 2022

Persistent left superior vena cava (PLSVC) is a rare congenital, thoracic, and vascular anomaly. Although PLSVCs generally do not have a hemodynamic effect, several types of PLSVC and some cardiac anomalies may manifest with clinical symptoms. The presence of PLSVC can render catheterization via left subclavian access difficult when placing a pacemaker or central venous catheter. As such, recognizing a PLSVC that is typically incidentally discovered can prevent complications such as vascular injury. Differentiating vessels found in a similar location as PLSVC is necessary when performing thoracic vascular procedures. This pictorial essay explains the multi-detector CT findings of a PLSVC, and provides a summary of other blood vessels that require differentiation during thoracic vascular procedures.

• Korean Journal of Radiology
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• v.1 no.3
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• pp.152-158
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• 2000

Objective: The purpose of this study was to compare the effectiveness of MR urography (MRU) with that of ultrasonography (US) in the evaluation of urinary tract when this failed to opacify during excretory urography (EXU). Materials and Methods: Twelve urinary tracts in 11 patients were studied. In each case, during EXU, the urinary system failed to opacify within one hour of the injection of contrast media, and US revealed dilatation of the pelvocalyceal system. Patients underwent MRU, using a HASTE sequence with the breath-hold technique; multi-slice acquisition was then performed, and the images were reconstructed using maximal intensity projection. Each set of images was evaluated by three radiologists to determine the presence, level, and cause of urinary tract obstruction. Results: Obstruction was present in all twelve cases, and in all of these, MRU accurately demonstrated its level. In this respect, however, US was successful in only ten. The cause of obstruction was determined by MRU in eight cases, but by US in only six. In all of these six, MRU also successfully demonstrated the cause. Conclusion: MRU is an effective modality for evaluation of the urinary tract when this fails to opacify during EXU, and appears to be superior to US in demonstrating the level and cause of obstruction.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.1-11
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• 2024

Geotechnical parameter estimation is critical to the design, performance, safety, and cost and schedule management in Tunnel Boring Machine projects. Since these parameters vary within a certain range, relying on mean values for evaluation introduces significant risks to the project. Due to the non-homogeneous characteristics of geological formation, data may not exhibit a normal distribution and the presence of outliers might be deceptive. Therefore, the use of reliable analyses and simulation models is inevitable in the course of the data evaluation process. Advanced modeling techniques enable comprehensive analysis of the project data and allowing to model the uncertainty in geotechnical parameters. This study involves using Monte Carlo Simulation method to predict probabilistic distributions of field data, and therefore, establish a basis for designs and in turn to minimize project risks. In the study, 166 sets of geotechnical data Obtained from 35 boreholes including Standard Penetration Test, Limit Pressure, Liquid Limit, and Plastic Limit values, which are mostly utilized parameters in estimating project requirements, were used to estimate the geotechnical data distribution of the study field. In this context, firstly, the data was subjected to multi-parameter linear regression and variance analysis. Then, the obtained equations were implemented into a Monte Carlo Simulation, and probabilistic distributions of the geotechnical data of the field were simulated and corresponding to the 90% probability range, along with the minimum and maximum values at the 5% probability levels presented. Accordingly, while the average SPT N30 value is 42.86, but the highest occurrence rate is 50.81. For Net Limit Pressure, the average field data is 17.07 kg/cm², with the maximum occurrence between 9.6 kg/cm² and 13.7 kg/cm². Similarly, the average Plastic Limit value is 22.32, while the most probable value is 20.6. The average Liquid Limit value is 56.73, with the highest probability at 54.48, as indicated in the statistical data distribution. Understanding the percentage distribution of data likely to be encountered in the project allows for accurate forecasting of both high and low probability scenarios, offering a significant advantage, particularly in ordering TBM requirements.