• Korean Journal of Veterinary Service
• /
• v.45 no.3
• /
• pp.243-248
• /
• 2022

The HPLC conditions for analysis of ivermectin in solutions dosage forms of commercial anthelmintics are different for each product. The purpose of this study was to establish a standardized chromatographic method for the quantification of ivermectin in solution. The separation was achieved on Waters Xbridge C18 column (4.6×150 nm, 5 ㎛) using different kinds of mobile phase composed of water/methanol/acetonitrile (15/34/51, v/v and 19.5/27.5/53, v/v), with UV detection at wavelengths 245 nm and 254 nm. A total of five commercial ivermectin in solution samples were analyzed. In this study, the optimal chromatographic conditions for analysis of ivermectin in solution were mobile phase of water/methanol/acetonitrile (15/34/51, v/v) at a flow rate of 1.0 mL/min and a detection wavelength of 245 nm using a Waters Xbridge C18 column (4.6×250 nm, 5 ㎛) at a column temperature of 25℃. The linearity was observed in the concentration range of 50~150 ㎍/mL, with a correlation coefficient, r²= 0.99999. The limit of detection and the limit of quantification were 0.88 and 2.68 ㎍/mL, respectively. The accuracy (% recovery) was found to be 98.9 to 100.3%. Intra-day and Intermediate precisions with relative standard deviations were less than 1.0%. The content of ivermectin for five market samples ranged 91.2~102.7%. The proposed method was also found to be robust, therefore, the method can be used for the routine analysis of ivermectin in solutions dosage forms.

• Korean Journal of Agricultural Science
• /
• v.49 no.2
• /
• pp.193-202
• /
• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Current Photovoltaic Research
• /
• v.11 no.1
• /
• pp.27-33
• /
• 2023

In this paper, a machine learning model by using a regression algorithm is proposed to estimate the power generation performance of the BIPV system. The physical model formula for estimating the generation performance and the proposed model were compared and analyzed. For the physical model formula, simple efficiency model, temperature correction model, and regressive physics model for changing an irradiance were used. As a result, when comparing the regressive physics model for changing an irradiance and the proposed model with the actual generation measured data, the respective RMSE values are 0.1497 kW, 0.0451 kW and the accuracy values are 86.44%, and 96.56%. Therefore, the proposed model implemented in this experiment can be useful in estimating power generation.

• Structural Engineering and Mechanics
• /
• v.86 no.4
• /
• pp.443-459
• /
• 2023

This study investigates the theoretical thermal buckling analyses of thick porous rectangular functionally graded (FG) plates with different geometrical boundary conditions resting on a Winkler-Pasternak elastic foundation using a new higher-order shear deformation theory (HSDT). This new theory has only four unknowns and involves indeterminate integral variables in which no shear correction factor is required. The variation of material properties across the plate's thickness is considered continuous and varied following a simple power law as a function of volume fractions of the constituents. The effect of porosity with two different types of distribution is also included. The current formulation considers the Von Karman nonlinearity, and the stability equations are developed using the virtual works principle. The thermal gradients are involved and assumed to change across the FG plate's thickness according to nonlinear, linear, and uniform distributions. The accuracy of the newly proposed theory has been validated by comparing the present results with the results obtained from the previously published theories. The effects of porosity, boundary conditions, foundation parameters, power index, plate aspect ratio, and side-to-thickness ratio on the critical buckling temperature are studied and discussed in detail.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.397-402
• /
• 2013

In super-tall building construction projects, schedule risk factors which vertically change and are not found in the low and middle-rise building construction influence duration of a project by vertical attribute; and it makes hard to estimate activity or overall duration of a construction project. However, the existing duration estimating methods, that are based on quantity and productivity assuming activities of the same work item have the same risk and duration regardless of operation space, are not able to consider the schedule risk factors which change by the altitude of operation space. Therefore, in order to advance accuracy of duration estimation of super-tall building projects, the degree of changes of these risk factors according to altitude should be analyzed and incorporated into a duration estimating method. This research proposes a simulation model using Monte Carlo method for estimating activity duration incorporating schedule risk factors by weather conditions in a super-tall building. The research process is as follows. Firstly, the schedule risk factors in super-tall building are identified through literature and expert reviews, and occurrence of non-working days at high altitude by weather condition is identified as one of the critical schedule risk factors. Secondly, a calculating method of the vertical distributions of the weather factors such as temperature and wind speed is analyzed through literature reviews. Then, a probability distribution of the weather factors is developed using the weather database of the past decade. Thirdly, a simulation model and algorithms for estimating non-working days and duration of each activity is developed using Monte-Carlo method. Finally, sensitivity analysis and a case study are carried out for the validation of the proposed model.

• Journal of Microbiology and Biotechnology
• /
• v.33 no.9
• /
• pp.1238-1249
• /
• 2023

In this study, we sought to investigate the production and optimization of biosurfactants by soil fungi isolated from petroleum oil-contaminated soil in Saudi Arabia. Forty-four fungal isolates were isolated from ten petroleum oil-contaminated soil samples. All isolates were identified using the internal transcribed spacer (ITS) region, and biosurfactant screening showed that thirty-nine of the isolates were positive. Aspergillus niger SA1 was the highest biosurfactant producer, demonstrating surface tension, drop collapsing, oil displacement, and an emulsification index (E₂₄) of 35.8 mN/m, 0.55 cm, 6.7 cm, and 70%, respectively. This isolate was therefore selected for biosurfactant optimization using the Fit Group model. The biosurfactant yield was increased 1.22 times higher than in the nonoptimized medium (8.02 g/l) under conditions of pH 6, temperature 35℃, waste frying oil (5.5 g), agitation rate of 200 rpm, and an incubation period of 7 days. Model significance and fitness analysis had an RMSE score of 0.852 and a p-value of 0.0016. The biosurfactant activities were surface tension (35.8 mN/m), drop collapsing (0.7 cm), oil displacement (4.5 cm), and E₂₄ (65.0%). The time course of biosurfactant production was a growth-associated phase. The main outputs of the mathematical model for biomass yield were Yx/s (1.18), and µ_max (0.0306) for biosurfactant yield was Y_p/s (1.87) and Y_p/x (2.51); for waste frying oil consumption the So was 55 g/l, and Ke was 2.56. To verify the model's accuracy, percentage errors between biomass and biosurfactant yields were determined by experimental work and calculated using model equations. The average error of biomass yield was 2.68%, and the average error percentage of biosurfactant yield was 3.39%.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.113-118
• /
• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.875-882
• /
• 2023

In this paper, we propose a virtual tuning technique for a temperature controller using the PID algorithm. Virtual simulation on a computer was used using the mathematical expression of the control object. A technique for accurately calculating the gain of the PID algorithm was introduced through detailed computer data analysis, and superior performance compared to conventional experimental tuning results was verified. In addition, it has the advantage of replacing tuning experiments conducted on actual control subjects, so there are no temporal or spatial limitations. Tuning experiments that actually operate the control object do not show detailed data that appears during the process. The accuracy of the experiment could not be guaranteed, and the results could not be confirmed immediately. Through the proposed technique, the entire tuning process can be accurately checked on a computer and the cause of problems that occur can also be analyzed.

• The Korean Journal of Food And Nutrition
• /
• v.36 no.6
• /
• pp.489-495
• /
• 2023

This study evaluated the effects of heat treatment on gastrodin and gastrodigenin content, and antioxidant activities, in Gastrodia elata Blume. Gastrodin and gastrodigenin content was analyzed post-method validation, and antioxidant activity evaluation, including assessing total polyphenol content, DPPH, and ABTS radical scavenging activities, was done. The validation of the analysis method demonstrated excellent linearity. The limits of quantification of gastrodin and gastrodigenin were 2.89 and 3.47 ㎍/mL, respectively. Moreover, the results of intra- and inter-day precision analysis demonstrated relative standard deviation values, within 5%. The recovery rates for gastrodin and gastrodigenin were 97.22~98.85 and 97.99~99.91%, respectively, indicating good accuracy. Under different heat treatment conditions, gastrodin and gastrodigenin content significantly increased (p<0.05), ranging from 91.15 to 310.27 and 559.66 to 830.02 mg/100 g DW, respectively. Additionally, the total polyphenol content exhibited a significant (p<0.05) increasing trend, ranging from 1,444 to 1,798 mg/100 g DW, as the temperature and time of heat treatment increased. The DPPH and ABTS radical scavenging abilities demonstrated an increasing trend at 120℃ during heat treatment. These research findings are expected to enhance our understanding of the changes in gastrodin and gastrodigenin content, and antioxidant effects in Gastrodia elata Blume during heat treatment.

• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.893-899
• /
• 2024

In a secondary cooling system of a sodium-cooled fast reactor (SFR), rapid detection of hydrogen due to sodium-water reaction (SWR) caused by water leakage from a heat exchanger tube of a steam generator (SG) is important in terms of safety and property protection of the SFR. For hydrogen detection, the hydrogen detectors using atomic transmission phenomenon of hydrogen within Ni-membrane were used in Japanese proto-type SFR "Monju". However, during the plant operation, detection signals of water leakage were observed even in the situation without SWR concerning temperature up and down in the cooling system. For this reason, the study of a new hydrogen detector has been carried out to improve stability, accuracy and reliability. In this research, the authors focus on the difference in composition of hydrogen and the difference between the background hydrogen under normal plant operation and the one generated by SWR and theoretically estimate the hydrogen behavior in liquid sodium by using ultra-accelerated quantum chemical molecular dynamics (UA-QCMD). Based on the estimation, dissolved H or NaH, rather than molecular hydrogen (H₂), is the predominant form of the background hydrogen in liquid sodium in terms of energetical stability. On the other hand, it was found that hydrogen molecules produced by the sodium-water reaction can exist stably as a form of a fine bubble concerning some confinement mechanism such as a NaH layer on their surface. At the same time, we observed experimentally that the fine H₂ bubbles exist stably in the liquid sodium, longer than previously expected. This paper describes the comparison between the theoretical estimation and experimental results based on hydrogen form in sodium in the development of the new hydrogen detector in Japan.