• Bulletin of the Korean Chemical Society
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• v.25 no.9
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• pp.1355-1360
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• 2004

The oxidative decomposition of trichloroethylene (TCE) was investigated using palladium catalysts supported on pure and sulfated zirconia. The reactions were performed under dry and wet conditions in the temperature between 200 and $550^{\circ}C$ keeping GHSV of 14,000 $h^{-1}.$ The products such as $C_2Cl_4,\;C_2HCl_5,\;CO\;and\;CO_2$ were observed in the reaction. The addition of water in the feed affected the distribution of reaction product with dramatically improved catalytic activity. The spectroscopic investigations gave an evidence that the strong acid sites play an important role on controlling the catalytic activity. Among the catalysts investigated, the Pd-loaded sulfated zirconia catalyst with 1 wt% Pd was found to exhibit the highest catalytic activity in the presence of water vapor having the stability for 30 h of the reaction at $500^{\circ}C$. The successful performance of the catalyst might be attributed to promotional effect of Pd active sites and strong acid sites induced from surface sulfate species on zirconia.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.5-12
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• 2015

The authors calculate the long-term predictability of GDP, domestic demand, investment, and net exports for Guangdong province, P.R. China from 2000 to 2013. A vector autoregressive (VAR) model with quarterly data for this period is first co-integrated then the Granger causality test is applied to empirically assess the relationships among gross domestic product (GDP), consumption, investment, and net exports. There is a strong causality effect between investment and net exports in Guangdong province. However, the variance decomposition results indicate that exports respond to foreign shocks rather than domestic ones, making their impact on the Guangdong economy to predict. Results show the stimulating effect of domestic demand on GDP is larger than the stimulating effect of net exports and much larger than even the stimulating effect of investment. The analysis suggests that there are dynamic influences with various levels of persistence between GDP, consumption, investment, and net exports. Macroeconomic policy adjustments are urgently required to expand domestic demand and thereby stimulate economic growth in Guangdong province.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Korean Journal of Materials Research
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• v.32 no.1
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• pp.1-8
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• 2022

In this investigation, Bi₂MoO₆ deposited graphene nanocomposite (BMG) was synthesized using a simple microwave assisted hydrothermal synthesis method. The synthesized BMG nanocomposite was characterized by X-ray diffraction, transmission electron microscopy, scanning electron microscopy with energy dispersive X-ray analysis, and photocurrent analysis. The study revealed that the catalysts prepared have high crystalline nature, enhanced light responsive property, high catalytic activity, and good stability. XRD results of BMG composite exhibit a koechlinite phase of Bi₂MoO₆. The surface property is shown by SEM and TEM, which confirmed a homogenous composition in the bulk particles of Bi₂MoO₆ and nanosheets of graphene. The catalytic behavior was investigated by the decomposition of Rhodamine B as a standard dye. The results exhibit excellent yields of product derivatives at mild conditions under ultrasonic/visible light-medium. Approximately 1.6-times-enhanced sono-photocatalytic activity was observed by introduction of Bi₂MoO₆ on graphene nanosheet compared with control sample P25 during 50 min test.

• Food Science of Animal Resources
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• v.43 no.1
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• pp.1-9
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• 2023

The expansion and advancement of the meat product market have increased the demand for fermented sausages. A typical method for manufacturing high-quality fermented sausages is using a starter culture, which improves the taste, aroma, and texture. Currently, the starter culture for manufacturing fermented sausages is mainly composed of microorganisms such as lactic acid bacteria, yeast, and fungi, which generate volatile compounds by the oxidation of fatty acids. In addition, protein decomposition and changes in pH occur during the fermentation period. It can positively change the texture of the fermented sausage. In this review, we discuss the requirements (improving food safety, the safety of starter culture, enzyme activity, and color) of microorganisms used in starter cultures and the generation of flavor compounds (heptanal, octanal, nonanal, hexanal, 2-pentylfuran, 1-penten-3-ol, and 2-pentanone) from lipids. Furthermore, quality improvement (hardness and chewiness) due to texture changes after starter culture application during the manufacturing process are discussed.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Applied Biological Chemistry
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• v.18 no.1
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• pp.30-41
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• 1975

A substituted diphenyl ether herbicide(MC-4379) was studied on the decomposition by some environmental factors; sunlight, microorganisms, and the crude enzyme in rice plant extract. All the decomposition products were confirmed by means of TLC, GLC, and IR. The parent compound and the decomposition products were put to the test for the effect on the growth of some plants. The results obtained are summarized as follows: 1. Photolysis Amino-MC-4379, 2,4-dichloro-3'-carboxyl-4'-nitrodiphenyl ether, Nitrofen, 2,4-dichloro-3'-carboxyl-4'-amino-diphenyl ether, amino-Nitrofen, 3-carboxymethyl-4-nitrophenol, p-nitrophenol, p-aminophenol, etc. were confirmed as photoproducts, in addition to a relatively small amount of an unknown compound. It was confirmed that the solution-phase photolysis of MC-4379 was accelerated much more by the aid of a photosensitizer benzophenone. 2. Degradation by the crude extract of germinating rice seeds Nitrofen was confirmed as a major degradation product, in addition to a relatively small amount of and unknown compound. Most of the parent compound remained unchanged. 3. Degradation by microorganisms Nitrofen and amino-MC-4379 were confirmed as the major products. in addition to a small amount of an unknown compound. 4. The germinating rice seeds and soybean were grown in the 1,000 ppm emulsions of some chemicals, respectively. The effect on rice plant growth was in the inhibitory order of p-nitrophenol > C-6989> Nitrofen > amino-Nitrofen > MC-4379. The effect on soybean was in the order of Nitrofen > amino-Nitrofen > MC-4379. Two weeds, Amantus blitum and Setaria viridis were grown in the 500 ppm emulsions containing the compounds, respectively. After incubation for 3 days, it was observed that all the shoots had been dead.

• Journal of Life Science
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• v.15 no.5 s.72
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• pp.743-748
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• 2005

Histamine can be produced at early spoilage stage through decarboxylation of histidine in red-flesh fish by Proteus morganii, Hafnia alvei or Klebsiella pneumoniae. Allergic food poisoning is resulted from the histamine produced when the freshness of Mackerel degrades. Conversely it has been reported that there are bacteria which decompose histamine at the later stage. We isolated histamine decomposers from salted mackerel and studied the characteristics to help establish hygienic measure to prevent outbreak of salted mackerel food poisoning. All the samples were purchased through local supermarket. Histamine decomposers were isolated using restriction medium using histamine 10 species were selected. Identification of these isolates were carried out by the comparison of 16S rDNA partial sequence; as a result, we identified Pseudomonas putida strain RA2 and Halomonas marina, Uncultured Arctic sea ice bacterium clone ARKXV1/2-136, Halomonas venusta, Psychrobacter sp. HS5323, Pseudomonas putida KT2440, Rhodococcus erythropolis, Klebsiella terrigena (Raoultella terrigena), Alteromonadaceae bacterium T1, Shewanella massilia with homology of $100\%,{\;}100\%,{\;}99\%,{\;}99\%,{\;}99\%,{\;}99\%,{\;}100\%,{\;}95\%,{\;}99\%,{\;}and{\;}100\%$respectively. Turbidometry determination method and enzymic method were employed to determine the ability of histamine decomposition. Among those species Shewanella massilia showed the highest in ability of histamine decomposition. From these results we confirmed various histamine decomposer were present in salted mackerel product in the market.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.4
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• pp.299-304
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• 2013

The HAN which is an ionic liquid is a non-toxic monopropellant with high storability, and its specific impulse can be increased by blending methanol, thereby it can substitute the hydrazine. The HAN was synthesized by acid-base reaction of hydroxylamine and nitric acid, and the blending ratio of HAN and methanol is 8.2:1. The iridium catalyst was used to decompose the HAN, and 1 N class thruster with shower head type injector having one orifice was used to evaluate the HAN/Methanol propellant. The thermal stability of distributor was increased by using ceramic material to endure the high temperature of product gas. The preheating temperature of catalyst should be $400^{\circ}C$ at least for the complete decomposition. The feeding pressure should be increased to increase the $C^*$ efficiency, thereby the decomposition performance was decreased upstream catalyst, and the performance of thruster was decreased. The fine metal mesh was inserted after the injector to improve the atomization of propellant, thereby it can settle the performance decrease problem. The phenomenon of performance decrease was remarkably improved owing to the insertion of fine metal mesh.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.