• Natural Product Sciences
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• 제25권2호
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• pp.111-114
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• 2019

The herbal formula, DF-02, consisting of Ephedra intermedia and Rheum palmatum are used for the treatment of the metabolic diseases such as obesity and liver fibrosis in Korean local clinics. We aimed to develop the simultaneous analytical conditions for four standards, (+)-pseudoephedrine (PSEP) and (-)-ephedrine (EP) for E. intermedia, and aloe-emodin (AE) and chrysophanol (CP) for R. palmatum using HPLC-UV techniques. The validated conditions yielded the high precision (relative standard deviation (RSD) < 3.65%) and the recoveries (94 - 106%) using the calibration curves with high linearity ($R^2$ > 0.9994). As a result, four standards of DF-02 were simultaneously determined under the developed method, which will be utilized for the quality control or evaluation of DF-02 and many herbal preparations containing E. intermedia and R. palmatum.

• Journal of Applied Biological Chemistry
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• 제64권3호
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• pp.285-290
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• 2021

Salicornia herbacea is a type of salt marsh plant that has been used in traditional medicine to treat several diseases. Isorhamnetin-3-O-glucoside (I3G) and quercetin-3-O-glucoside (Q3G) are major flavonoids in S. herbacea that are known to exert various pharmacological activities. Therefore, our study sought to validate and optimize an HPLC/UV-based analytical method for I3G and Q3G yield quantification, as well as to determine its limit of detection, limit of quantification, linearity, precision, and accuracy. Upon testing a concentration range of 31.5-1.9 ㎍/mL the results exhibited good linearity (r² ≥0.9996 and r² ≥0.9999 for I3G and Q3G, respectively), and the procedure was deemed precise (relative standard deviation of ≤3.19 and ≤3.85%, respectively), and accurate (102.6-105.0 and 92.9-95.2%, respectively). The results showed that our proposed method could be used for rapid I3G and Q3G evaluation in S. herbacea.

• Safety and Health at Work
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• 제12권2호
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• pp.174-183
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• 2021

Background: Toluene diisocyanate (TDI) is a highly reactive chemical that causes sensitization and has also been associated with increased lung cancer. A risk assessment was conducted based on occupational epidemiologic estimates for several health outcomes. Methods: Exposure and outcome details were extracted from published studies and a NIOSH Health Hazard Evaluation for new onset asthma, pulmonary function measurements, symptom prevalence, and mortality from lung cancer and respiratory disease. Summary exposure-response estimates were calculated taking into account relative precision and possible survivor selection effects. Attributable incidence of sensitization was estimated as were annual proportional losses of pulmonary function. Excess lifetime risks and benchmark doses were calculated. Results: Respiratory outcomes exhibited strong survivor bias. Asthma/sensitization exposure response decreased with increasing facility-average TDI air concentration as did TDI-associated pulmonary impairment. In a mortality cohort where mean employment duration was less than 1 year, survivor bias pre-empted estimation of lung cancer and respiratory disease exposure response. Conclusion: Controlling for survivor bias and assuming a linear dose-response with facility-average TDI concentrations, excess lifetime risks exceeding one per thousand occurred at about 2 ppt TDI for sensitization and respiratory impairment. Under alternate assumptions regarding stationary and cumulative effects, one per thousand excess risks were estimated at TDI concentrations of 10 - 30 ppt. The unexplained reported excess mortality from lung cancer and other lung diseases, if attributable to TDI or associated emissions, could represent a lifetime risk comparable to that of sensitization.

• Mass Spectrometry Letters
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• 제12권1호
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• pp.11-15
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• 2021

Nitarsone is an organoarsenic antiprotozoal drug widely used to treat blackhead disease in turkeys and chickens. However, since its biological conversion into inorganic arsenic, a carcinogen was known, its residue in foods should be regulated. Thus, here, a novel method to determine residual nitarsone in various food commodities (pork, milk, egg, halibut, eel, and shrimp) using QuEChERS and LC-MRM was developed. The developed method was successfully validated through specificity, linearity (coefficient of determination, at least 0.991), recovery (R, 63.6 - 85.6%), precision (the relative standard deviation of R, 0.5 - 10.6%), and sensitivity (the lower limit of quantitation, 5 ppb) by following the Ministry of food and drug safety (MFDS) guidelines. The present method is the first mean to quantitate nitarsone using LC-MRM, and it was designed to be conveniently merged into a new method to quantitate multiple veterinary drugs for the positive list system (PLS). Therefore, the present method could contribute to fortify the food safety system in South Korea.

• 한국축산식품학회지
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• 제41권5호
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• pp.816-825
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• 2021

This study aims to validate a fast method of simultaneous analysis of 365 LCamenable and 142 GC-amenable pesticides in hen eggs by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS), respectively, operating in multiple reaction monitoring (MRM) acquisition modes. The sample preparation was based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction. Key method performance parameters investigated were specificity, linearity, limit of quantification (LOQ), accuracy, precision and measurement uncertainty. The method was validated at two spiking levels (10 and 50 ㎍/kg), and good recoveries (70%-120%) and relative standard deviations (RSDs) (≤20) were achieved for 92.9% of LC-amenable and 86.6% of GC-amenable pesticide residues. The LOQs were ≤10 ㎍/kg for 94.2% of LC-amenable and 92.3% of GC-amenable pesticides. The validated method was further applied to 100 egg samples from caged hens, and none of the pesticides was quantified.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.617-626
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• 2022

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

• Geomechanics and Engineering
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• 제30권2호
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2172-2194
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• 2023

A variational nodal method (VNM) with unstructured-mesh is presented for solving steady-state and dynamic neutron diffusion equations. Orthogonal polynomials are employed for spatial discretization, and the stiffness confinement method (SCM) is implemented for temporal discretization. Coordinate transformation relations are derived to map unstructured triangular nodes to a standard node. Methods for constructing triangular prism space trial functions and identifying unique nodes are elaborated. Additionally, the partitioned matrix (PM) and generalized partitioned matrix (GPM) methods are proposed to accelerate the within-group and power iterations. Neutron diffusion problems with different fuel assembly geometries validate the method. With less than 5 pcm eigenvalue (k_eff) error and 1% relative power error, the accuracy is comparable to reference methods. In addition, a test case based on the kilowatt heat pipe reactor, KRUSTY, is created, simulated, and evaluated to illustrate the method's precision and geometrical flexibility. The Dodds problem with a step transient perturbation proves that the SCM allows for sufficiently accurate power predictions even with a large time-step of approximately 0.1 s. In addition, combining the PM and GPM results in a speedup ratio of 2-3.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.207-208
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• 2021

The non-destructive method using ultrasonic waves has been applied in many studies due to its low damage to the structure and its simple evaluation method and high precision. On the other hand, if the concrete is subjected to a high-temperature, the mechanical properties may be deteriorated due to the micro-crack network and the damage may be severe depending on the strength of the concrete. Therefore, this study attempts to evaluate the fundamental wave behavior of different strength ranges using the ultrasonic non-destructive method for concrete that has been subjected to high-temperature. As a result, the relative power of the fundamental wave was decreased as temperature increase. And it was confirmed that the 2nd and 3rd harmonics were generated at 110 MPa. However, to check the 2nd, 3rd harmonics 110 MPa or less, there is a need for further research considering the ultrasonic output, the output of the sender and receiver, and the appropriate frequency accordingly.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.91-99
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• 2024

Collision avoidance system is important to improve the robustness and functional safety of autonomous vehicles. This paper proposes an object-level distance estimation method to develop a collision avoidance system, and it is applied to golfcarts utilized in country club environments. To improve the detection accuracy, we continually trained an object detection model based on pseudo labels generated by a pre-trained detector. Moreover, we propose object-aware depth estimation (OADE) method which trains a depth model focusing on object regions. In the OADE algorithm, we generated dense depth information for object regions by utilizing detection results and sparse LiDAR points, and it is referred to as object-aware LiDAR projection (OALP). By using the OALP maps, a depth estimation model was trained by backpropagating more gradients of the loss on object regions. Experiments were conducted on our custom dataset, which was collected for the travel distance of 22 km on 54 holes in three country clubs under various weather conditions. The precision and recall rate were respectively improved from 70.5% and 49.1% to 95.3% and 92.1% after the continual learning with pseudo labels. Moreover, the OADE algorithm reduces the absolute relative error from 4.76% to 4.27% for estimating distances to obstacles.