Manganese hydrogen phosphate hydrate, $MnHPO_4{\cdot}2.25H_2O$, is a major constituent of the pre-conditioning compositions for the manganese phosphate coating treatment over carbon steel substrate. This compound is conventionally produced by the synthesis in the aqueous solution process followed by the filtration and drying processes and a series of size reduction and classification processes in dry state. However, it is evident that the conventional process is neither environment-friendly nor cost-effective. In this work, a new process principle was examined based on the controlled double-jet precipitation technology to produce the manganese chemical product of fairly uniform particle size distribution in an aqueous solution media. The effects of stabilizing agents were comparatively studied by the scanning electron microscope analysis in a uniformity point of view of the resulting particle size. Polyvinylpyrrolidone and Gum Arabic were excellent in controlling the crystal growth step, resulting in fairly uniform size distributions of the particles from the controlled double-jet process.
High viscosity carbon black dispersions are used in various industrial fields such as color cosmetics, rubber, tire, plastic and color filter ink. However, carbon black particles are unstable to heat due to inherent characteristics, and it is very difficult to keep the quality of the product constant due to agglomeration of particles. In general, particle size analysis is performed by dynamic light scattering (DLS) during the dispersion process in order to select the optimum dispersant in the carbon black dispersion process. However, the existing low viscosity analysis provides reproducible particle distribution analysis results, but it is difficult to select the optimum dispersant because it is difficult to analyze the reproducible particle distribution at high viscosity. In this study, dynamic light scattering (DLS) and asymmetrical flow field-flow fractionation (AsFlFFF) analysis methods were compared for reproducible particle size analysis of high viscosity carbon black. First, the stability of carbon black dispersion was investigated by particle size analysis by DLS and AsFlFFF according to milling time, and the validity of analytical method for the selection of the optimum dispersant useful for carbon black dispersion was confirmed. The correlation between color and particle size of particles in high viscosity carbon black dispersion was investigated by using colorimeter. The particle size distribution from AsFlFFF was consistent with the colorimetric results. As a result, the correlation between AsFlFFF and colorimetric results confirmed the possibility of a strong analytical method for determining the appropriate dispersant and milling time in high viscosity carbon black dispersions. In addition, for nanoparticles with relatively broad particle size distributions such as carbon black, AsFlFFF has been found to provide a more accurate particle size distribution than DLS. This is because AsFlFFF, unlike DLS, can analyze each fraction by separating particles by size.
Kim, Chun Ho;Lee, Eung Suack;Hahm, Young Tae;Kim, Byung Yong;Son, Tae Il
Applied Chemistry for Engineering
/
v.10
no.1
/
pp.19-23
/
1999
Chitosan was partially degraded by using nitrous acid. The deacetylation degree of chitosan decreased with its degree of hydrolysis. Deacetylation degree of each fraction was less than 50%. The degraded product was fractionated by means of precipitation using aqueous methanol solution or aqueous methanol-acetone solution. The molecular weight of each fraction was distributed between 6000 and 4000, and below 2000 for being precipitated using aqueous methanol solution and aqueous methanol-acetone solution respectively. They had narrow molecular weight distributions, and their polydispersities were less than 1.7. The antibacterial activities for each fraction were evaluated against Bacillus subtilis HB 101 and E. coli PP 2, gram-positive bacteria and gram-negative bacteria, respectively. Fraction B (Mw=5100) showed high antibacteiral activity. All fractions were more active against Bacillus subtilis than E. coli.
Side bolster is a part of the vehicle seat that holds the passenger's body from the side to make it more stable when the passenger is seated in the seat. In this study, the structural and fatigue analyses of the side bolsters at a car seat were carried out with two models of A and B. The heavily loaded parts, the damage by fatigue at driving a car and the difference of durability due to the structure were examined and the distributions of stress and deformation, and the fatigue lives were seen. Also, the strength and durability were examined. This study result is thought to be devoted to decrease the fatigue damage and increase the fatigue life and durability according to the design of bolster. This result is able to improve the product by applying the design of automotive side bolster practically. And it is thought to be the advantage to apply this study result to the convergence research with esthetic sense.
Pyrolysis of polyethylene was carried out in the stainless steel reactor of internal volume of $10cm^3$. Pyrolysis reactions were performed at temperature $390{\sim}450^{\circ}C$ and the pyrolysis products were collected separately as reaction products and gas products. The molecular weight distributions(MWDs) of each product were determined by HPLC-GPC and GC analysis. Distribution balance equation for MWDs of random and specific products were proposed to account for initiation-termination and propagation-depropagation, such as hydrogen abstraction, chain cleavage, coupling of polymer and radical. A separate chain-end scission process produces low molecular weight noncondensable gases(C1 through C5) of average molecular weight 38. Activation energies of the random-chain scission and chain-end scission rate parameters, respectively, were determined to be 35, 17 kcal/mole.
KHOEURN SAKSONITA;Jungsung Ha;Wan-Sup Cho;Phyoungjung Kim
Journal of the Korea Society of Computer and Information
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v.28
no.7
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pp.29-37
/
2023
Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel (${\delta}_{N_i}$).
shows various market conditions captured by the two consumer heterogeneities.
(a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in
(c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like (b). Countries like Ukraine and Bulgaria, or the market for "experience goods" such as shoes, could be examples of this market condition.
summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (${\delta}_{N_i}$) is held constant, while the range of consumer distribution (${\chi}_i$) varies from -25 to 25, from -50 to 50, from -100 to 100, from -150 to 150, and from -200 to 200.
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers. illustrates how this happens. When mangers consider the overall impact of the Internet channel, however, they should consider not only channel power, but also sales volume. When both are considered, the introduction of the Internet channel is revealed as more harmful to a physical retailer in Russia than one in Hong Kong, because the sales volume decrease for a physical store due to Internet channel competition is much greater in Russia than in Hong Kong. The results show that manufacturer is always better off with any type of Internet store introduction. The independent physical store benefits from opening its own Internet store when the average travel cost is higher relative to the disutility of using the Internet. Under an opposite market condition, however, the independent physical retailer could be worse off when it opens its own Internet outlet and coordinates both outlets (RI). This is because the low average travel cost significantly reduces the channel power of the independent physical retailer, further aggravating the already weak channel power caused by myopic inter-channel price coordination. The results implies that channel members and policy makers should explicitly consider the factors determining the relative distributions of both kinds of consumer disutility, when they make a channel decision involving an Internet channel. These factors include the suitability of a product for Internet shopping, the level of E-Commerce readiness of a market, and the degree of geographic dispersion of consumers in a market. Despite the academic contributions and managerial implications, this study is limited in the following ways. First, a series of numerical analyses were conducted to derive equilibrium solutions due to the complex forms of demand functions. In the process, we set up V=100, ${\lambda}$=1, and ${\beta}$=0.01. Future research may change this parameter value set to check the generalizability of this study. Second, the five different scenarios for market conditions were analyzed. Future research could try different sets of parameter ranges. Finally, the model setting allows only one monopoly manufacturer in the market. Accommodating competing multiple manufacturers (brands) would generate more realistic results.
With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.
Park, Kyoung-Ho;Lee, Min-Hwa;Lee, Myung-Gull;Kwon, Jun-Soo;Park, Won-Myung;Park, Jin-Seng
YAKHAK HOEJI
/
v.34
no.6
/
pp.375-383
/
1990
The pharmacokinetics of haloperidol were determined after single oral and intravenous doses in 13 male schizophrenic patients. Plasma concentrations of haloperidol(HP) and reduced haloperidol(RH) were measured by high performance liquid chromatography. Plasma concentration data obtained were analyzed by obth model dependent (one-or two exponential decay models using nonlinear regression) and model independent (AUC and first moment curve) approaches. The two methods were found to be in close results. After intravenous injections of HP in 8 patients (10 mg/man), the mean central and peripheral volume of distributions were $2.85\;{\pm}\;1.70$ and $8.09\;{\pm}\;2.10\;l/kg$, respectively, and mean steady state volume of distribution was $11.87\;{\pm}\;3.21\;l/kg$. Mean clearance, MRT and elimination half life were $12.39\;{\pm}\;3.25\;ml/min/kg$, $925.10\;{\pm}\;166.79\;min$ and $676.35\;{\pm}\;126.45\;min$, respectively. After oral administrations of HP in 5 patients, mean peak time and peak concentration were $217.63\;{\pm}\;61.60\;min$ and $9.77\;{\pm}\;2.92\;ng/ml$, respectively. Mean MRT and elimination half life were $1112.23\;{\pm}\;131.73\;min$ and $724.02\;{\pm}\;120.03\;min$, respectively, and these parameters were not significantly different from those of intravenous injection of HP. Absolute bioavailability of HP oral product was found to be about 44%. The profiles of plasma RH concentration-time curves after oral or intravenous doses of HP were similar. Also it was found that the elimination rate of RH was solwer than that of HP by comparing the slopes of plasma concentration-time curves of HP and RH.
Lee, Ji Young;Lee, Hyung Won;Kim, Young-Min;Park, Young-Kwon
Applied Chemistry for Engineering
/
v.29
no.3
/
pp.350-355
/
2018
In this study, the effect of biomass torrefaction on the thermal and catalytic pyrolysis of cork oak was investigated. The thermal and catalytic pyrolysis behavior of cork oak (CO) and torrefied CO (TCO) were evaluated by comparing their thermogravimetric (TG) analysis results and product distributions of bio-oils obtained from the fast pyrolysis using a fixed bed reactor. TG and differential TG (DTG) curves of CO and TCO revealed that the elimination amount of hemicellulose in CO increased by applying the higher torrefaction temperature and longer torrefaction time. CO torrefaction also decreased the oil yield but increased that of solid char during the pyrolysis because the contents of cellulose and lignin in CO increased due to the elimination of hemicellulose during torrefaction. Selectivities of the levoglucosan and phenolics in TCO pyrolysis oil were higher than those in CO pyrolysis oil. The content of aromatic hydrocarbons in bio-oil increased by applying the catalytic pyrolysis of CO and TCO over HZSM-5 ($SiO_2/Al_2O_3=30$). Compared to CO, TCO showed the higher efficiency on the formation of aromatic hydrocarbons via the catalytic pyrolysis over HZSM-5 and the efficiency was maximized by applying the higher torrefaction and catalytic pyrolysis reaction temperatures of 280 and $600^{\circ}C$, respectively.
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