• Journal of Pharmacopuncture
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• v.25 no.2
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• pp.114-120
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• 2022

Objectives: Antivenom serums have been used extensively for over a century and are the only effective treatment option for snake bites and other dangerous animal envenomations. In therapeutic serum centers, a wide range of antivenoms is made from animal serum, mainly equine and sheep, that are immunized with single or multiple venoms. This work aimed to use caprylic acid (CA) to purify therapeutic snake antivenom. Methods: Plasma was obtained from equine immunized with a mixture of venoms. Immunized plasma was obtained by precipitation of different concentrations (2-5%) of CA. This methodology was compared to that based on ammonium sulfate (AS) precipitation. Sediment plasma proteins were purified by ion-exchange chromatography. Protein assay, SDSPAGE, and agar gel diffusion were performed. Results: The total protein precipitation with AS was higher than precipitation with CA, but the best results were obtained when CA was added to the plasma until a final CA concentration of 5% was reached. Chromatography and electrophoresis indicated a stronger band for the 5% CA, and the gel diffusion assay showed antigen-antibody interaction in the purified serum. Conclusion: The use of CA compared to the routine method for purifying hyperimmune serums is a practical and cost-effective method for preparing and producing therapeutic serums. It constitutes a potentially valuable technology for alleviating the critical shortage of antivenom in Iran.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• 한국생물공학회:학술대회논문집
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• 2003.04a
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• pp.501-504
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• 2003

A novel prepurification method was developed aiming at increasing yield and purity, also reducing solvent usage for purification of paclitaxel. This method was a simple and efficient procedure, for the isolation and prepurification of paclitaxel from the biomass of Taxus chinensis, consisting of micelle formation, followed by two steps of precipitation. The use of a micelle and precipitation in the prepurification process allows for rapid separation of paclitaxel from interfering compounds and dramatically reduces solvent usage compared to alternative methodologies. This prepurification process serves to minimize the size and complexity of the HPLC operations for paclitaxel purification. This process is readily scalable to a pilot plant and eventually to a production environment where multikilogram quantities of material are expected to be produced. As much as possible, the process has been optimized to minimize solvent usage, complexity, and operating costs.

• Journal of the Korean Ceramic Society
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• v.30 no.5
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• pp.404-410
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• 1993

The characterization and sintering behavior of ZnO powders prepared by precipitation method were investigated. ZnO powders were synthesized using the aqueous solutions of ZnCl2 and NH4OH as a precipitation agent, which were crystallized in the shape of plate-like. The grain growth of ZnO(0.68${\mu}{\textrm}{m}$, 1.3${\mu}{\textrm}{m}$ and 3.4${\mu}{\textrm}{m}$) has been studied for temepratures from 100$0^{\circ}C$ to 130$0^{\circ}C$, and the rate of densification was inversely proportional to the ZnO particle size. Densification proceeded slowly by diffusion mechanisms above at 100$0^{\circ}C$. In this work, the grain growth kinetic exponent(n) was 3. The temperature dependence of ZnO grain growth was plotted, and the activation energy of grain growth was 75~85Kcal/mol.

• Journal of the Korean Ceramic Society
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• v.27 no.3
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• pp.361-368
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• 1990

The properties of the powder of ZrO2＋3m/o Y2O3 system prepared by co-precipitation method at the pH values of 7, 9, 10 and 11 were investigated. ZrOCl2.8H2O and YCl3.6H2O were used as starting materials and NH4OH as a precipitation agent. Zirconium hydroxide near by Zr(OH)4 structure showed more excellent crystallinity and lower formation temperature of tetragonal ZrO2. In the range of this study, cubic ZrO2 was not formed and stability of tetragonal ZrO2 prepared in the conditiion of pH 7 was most excellent. Average particle sizes and specific surface areas of tetragonal ZrO2 powders, prepared as calcining amorphous zirconium hydroxides at $600^{\circ}C$ for 1h, were 0.6-0.8${\mu}{\textrm}{m}$ and 45-70$m^2$/g, respectively.

• Journal of the Korean Ceramic Society
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• v.35 no.2
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• pp.107-114
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• 1998

ZnO powder as phosphor was prepared by precipitation method with zinc acetate and ammonia solution and the size and shapes of precipitates were examined with variation of pH and concentration of solution. Its cathodoluminesence properties was evaluated with various heat tratment condition. Optimum con-dition for uniform precipitates was 11.8 of pH and 0.4M of concentration. ZnO:Zn phosphor was obtained by heat treatment of precipitates in reduction atmosphere using ZnS powder. With addition of 20wt% ZnS and 1 hour firing at 1000$^{\circ}C$ the highest cathodoluminescence was obtained.

• Bulletin of the Korean Chemical Society
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• v.29 no.2
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• pp.328-334
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• 2008

The effects of catalyst preparation on the reaction route and the mechanism of biphenol (BP) hydrogenation, which consists of a long series-reaction, were studied. Pd/C catalysts were prepared by incipient wetness method and precipitation and deposition method. The reaction behaviors of the prepared catalysts and a commercial catalyst along with the final product distributions were very different. The choice of the catalyst preparation conditions during precipitation and deposition including the temperature, pH, precursor addition rate, and reducing agent also had significant effects. The reaction behaviors of the catalysts were interpreted in terms of catalyst particle size, metal distribution, and support acidities.

• Journal of the Korean Ceramic Society
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• v.25 no.2
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• pp.111-116
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• 1988

Aluminum hydrates were prepared by precipitation method using Al2(SO4)3$.$18H2O as a starting material and NH4OH as precipitation agent. The phases of aluminum hydrate were changed from amorphous aluminum hydrate to pseudo-boehmite of AlOOH form and bayerite, gibbsite, hydragillite and norstrandite of Al(OH)3 form with increasing pH. As pH increased, agglomeration phenomena were reduced. Aluminum hydrates of AlOOH and Al(OH)3 form represented dehydration of structural water near 175$^{\circ}C$ and 385$^{\circ}C$, and 280$^{\circ}C$, respectively. As the ratio of Al(OH)3 to AlOOH increased, specific surface area was reduced.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.