In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m3/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.
Purpose : We aimed to investigate the efficacy of and functional recovery after intracerebral transplantation of different doses of mouse mesenchymal stem cells (mMSCs) in immature rat brain with hypoxic-ischemic encephalopathy (HIE). Methods : Postnatal 7-days-old Sprague-Dawley rats, which had undergone unilateral HI operation, were given stereotaxic intracerebral injections of either vehicle or mMSCs and then tested for locomotory activity in the 2nd, 4th, 6th, and 8th week of the stem cell injection. In the 8th week, Morris water maze test was performed to evaluate the learning and memory dysfunction for a week. Results : In the open field test, no differences were observed in the total distance/the total duration (F=0.412, P=0.745) among the 4 study groups. In the invisible-platform Morris water maze test, significant differences were observed in escape latency (F=380.319, P<0.01) among the 4 groups. The escape latency in the control group significantly differed from that in the high-dose mMSC and/or sham group on training days 2-5 (Scheffe's test, P<0.05) and became prominent with time progression (F=6.034, P<0.01). In spatial probe trial and visible-platform Morris water maze test, no significant improvement was observed in the rats that had undergone transplantation. Conclusion : Although the rats that received a high dose of mMSCs showed significant recovery in the learning-related behavioral test only, our data support that mMSCs may be used as a valuable source to improve outcome in HIE. Further study is necessary to identify the optimal dose that shows maximal efficacy for HIE treatment.
Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.
With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.
Lee, Sang Mong;Kim, Yong Gyun;Park, Hyean Cheal;Kim, Keun Ki;Son, Hong Joo;Hong, Chang Oh;Park, Nam Sook
Journal of Life Science
/
v.27
no.2
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pp.247-266
/
2017
Cordyceps is a traditional Chinese medicinal herb well-known in China, Korea and Japan since B.C. 2,000. The original entomopathogenic fungus, Cordyceps sinensis belonging to the genus Cordyceps could not be found inside Korean peninsula due to the absence of the host insect for the corresponding entomogenous fungus. The development of artificial production methods of Korean type Cordyceps using the silkworm Bombyx mori as in vivo culture medium for the the entomopathogenic fungus Paecilomyces tenuipes is the first, and wonderful occasion in the research history of insect industry of this global world. The aim of this article is to review the historical research background, mass-production methods, and pharmacological effects of the silkworm-dongchunghacho (Paecilomyces tenuipes) which is a newly developed Korean medicinal insect-borne mushroom, and another non-insect-borne medicinal mushroom (Cordyceps militaris and Cordyceps pruinosa). Their biological actions include anti-tumor, immunostimulating, anti-fatigue, anti-stress, anti-oxidant, anti-aging, anti-diabetic, anti-inflammatory, anti-thrombosis, hypolipidaemic and insecticidal effects. The bioactive principles are protein-bound polysaccharides (hexose, hexosamin), cordycepin, D-manitol, acidic polysaccharide etc. Protein-bound polysaccharides and n-butanol fractions were demonstrated to show a significant anti-tumor activities but did not show a cytotoxicities. D-mannitol exhibited a significant prolongation of the life span in tumor bearing mice. Ergosterol did not show an efficient anti-tumor activity, but showed a significant phagocytosis enhancing activity. Anti-tumor activity of silkworm-dongchunghacho might be attributed to immuno-stimulating activities rather than cytotoxic effects [164]. Also this review comprises the breeding of Dongchunghacho varieties, optimization of culture conditions, improvement of learning and memory by Dongchunghacho, application of them as foods and chemical constituents.
Seo, Bo-Young;Kim, Min-Jung;Kim, Hyun-Su;Park, Hae-Ryong;Lee, Seung-Cheol;Park, Eun-Ju
Journal of the Korean Society of Food Science and Nutrition
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v.40
no.9
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pp.1208-1214
/
2011
Caffeine, a psychoactive stimulant, has been implicated in the modulation of learning and memory functions due to its action as a non-selective adenosine receptors antagonist. On the contrary, some side effects of caffeine have been reported, such as an increased energy loss and metabolic rate, decrease DNA synthesis in the spleen, and increased oxidative damage to exerted on LDL particles. Therefore, the aim of this study was to develop a safe stimulant from natural plants mixture (Aralia elata, Acori graminei Rhizoma, Chrysanthemum, Dandleion, Guarana, Shepherd's purse) that can be used as a substitute for caffeine. Thirty SD rats were divided into three groups; control group, caffeine group (15.0 mg/kg, i.p.), and natural plants mixture group (NP, 1 mL/kg, p.o.). The effect of NP extract on stimulant activity was evaluated with open-field test (OFT) and plus maze test for measurement of behavioral profiles. Plasma lipid profiles, lipid peroxidation in LDL (conjugated dienes), total antioxidant capacity (TRAP) and DNA damage in white blood, liver, and brain cells were measured. In the OFT, immobility time was increased significantly by acute (once) and chronic (3 weeks) supplementation of NP and showed a similar effect to caffeine treatment. Three weeks of caffeine treatment caused plasma lipid peroxidation and DNA damage in liver cells, whereas there were no changes in the NP group. NP group showed a higher plasma HDL cholesterol concentration compared to the caffeine group. The results indicate that the natural plants mixture had a stimulant effect without inducing oxidative stress.
Journal of The Korean Association For Science Education
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v.42
no.3
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pp.311-324
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2022
This study analyzed the types of scientific emoticons made by science-gifted elementary school students and their perceptions on making scientific emoticons. To do this, 71 students from 4th to 6th graders of two gifted science education center in Seoul were selected. Scientific emoticons made by the students were analyzed according to the number and types. Their perceptions on making scientific emoticons were also analyzed through a questionnaire and group interviews. In the analyses for types of text in the scientific emoticons, 'word type' and 'sentence type' were made more than 'question and answer type'. And the majority of students made more 'pun using pronunciation type' and 'mixed type' than other types. They also made more 'graphic type' and 'animation type' than 'text type' in the images of the scientific emoticons. In the analyses for the information of the scientific emoticons, 'positive emotion type' and 'negative emotion type' of scientific emoticons were made evenly. The students made more 'new creation type' than 'partial correction type' and 'entire reconstruction type'. They also used scientific knowledge that preceded the knowledge of science curriculum in their grade level. The scientific knowledge of chemistry was used more than physics, biology, earth science, and combination field. 'Name utilization type' was more than 'characteristic utilization type' and 'principle utilization type'. Students had various positive perceptions in making scientific emoticons such as 'increase of scientific knowledge', 'increase of various higher-order thinking abilities', 'ease of explanation, use, memory, and understanding of scientific knowledge', 'increase of fun, enjoyment, and interest about science and science learning', and 'increase of opportunity to express emotions'. They were also aware of some limitations related to 'difficulties in the process of making scientific emoticons', 'lack of time', and 'limit that it may end just for fun'. Educational implications of these findings are discussed.
Ha, Gi-Jeong;Lee, Doo Sang;Seung, Tae Wan;Park, Chang Hyeon;Park, Seon Kyeong;Jin, Dong Eun;Kim, Nak-Ku;Shin, Hyun-Yul;Heo, Ho Jin
Korean Journal of Food Science and Technology
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v.47
no.3
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pp.380-387
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2015
The anti-amnesic effect of Artemisia argyi H against trimethyltin (TMT)-induced learning and memory impairment and its neuroprotective effect against $H_2O_2$-inducedoxidative stress were investigated. Cognitive behavior was examined by Y-maze and passive avoidance test for 4 weeks, which showed improved cognitive functions in mice treated with the extract. In vitro neuroprotective effects against $H_2O_2$-induced oxidative stress were examined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium-bromide and lactate dehydrogenase (LDH) assays. A. argyi H. extract showed protective effects against $H_2O_2$-induced neurotoxicity; moreover, LDH release into the medium was inhibited. Finally, high-performance liquid chromatography (HPLC) analysis showed that eupatilin and jaceosidin were the major phenolic compounds in A. argyi H. extract. These results suggest that A. argyi H. could be a good source of functional substances to prevent neurodegenerative diseases.
Kwon, Bong Seok;Guo, Tian Jiao;Park, Seon Kyeong;Kim, Jong Min;Kang, Jin Yong;Park, Sang Hyun;Kang, Jeong Eun;Lee, Chang Jun;Lee, Uk;Heo, Ho Jin
Korean Journal of Food Science and Technology
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v.49
no.6
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pp.649-658
/
2017
The potential of the ethyl acetate fraction from Pteridium aquilinum (EFPA) to improve the cognitive function in high-fat diet (HFD)-induced diabetic mice was investigated. EFPA-treatment resulted in a significant improvement in the spatial, learning, and memory abilities compared to the HFD group in behavioral tests, including the Y-maze, passive avoidance, and Morris water maze. The diabetic symptoms of the EFPA-treated groups, such as fasting glucose and glucose tolerance, were alleviated. The administration of EFPA reduced the acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content in mice brains, but increased the acetylcholine (ACh) and superoxide dismutase (SOD) levels. Finally, kaempferol-3-o-glucoside, a major physiological component of EFPA, was identified by using high-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer (QTRAP LC-MS/MS).
Journal of the Korean Society of Food Science and Nutrition
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v.34
no.4
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pp.466-475
/
2005
Docosahexaenoic acid (22:6n-3, DHA) is highly enriched in membrane of brain and retina, and plays an important role in maintaining an optimal function of the central nervous system. We investigated the effect of n-3 fatty acid deficiency on rat brain, retina and liver fatty acyl composition at two different ages (3 wks and 15 wks) under DHA deficient condition. Rat pups born to dams fed a diet with $3.1\%$ of total fatty acids as $\alpha-linolenic$ acid (LNA) were fed using an artificial rearing system either an n-3 deficient (n-3 Def) or n-3 adequate (n-3 Adq) diet. Both diets contained $17.1\%$ linoleic acid (LA) but the n-3 Adq diet also contained $3.1\%$ LNA. Rats consuming the n-3 Def diet showed a lower brain $(50\%\;in\;13\;wks\;and\;70\%\;in\;15\;wks,\;p<0.05)$ and retinal $(50\%\;in\;13\;wks\;and\;63\%\;in\;15\;wks,\;p<0.05)$ DHA than those on the n-3 Adq diet, which was largely compensated for by an increase in docosapentaenoic acid (22:5n-6, DPAn-6). In the liver of the n-3 Def group, the percentage of DHA decreased by $97\%$ at 3 wks of age with an apparent increase in DPAn-6 relative to the n-3 Adq group (p<0.05), while there was a $65\%$ lower liver DHA in n-3 Def group at 15 wks of age than the n-3 Adq group (p<0.05). Liver arachidonic acid (20:4n-6, AA) was increased at 3 wks of age but decreased at 15 wks of age in the n-3 Def group compared with n-3 Adq group (p<0.05). In conclusion, the replacement of DHA by DPAn-6 in brain and retina fatty acid composition may be related to the suboptimal function in spatial learning, memory and visual acuity. This artificial rearing method presents a first generation model for n-3 deficiency that is similar to the case of human nutrition that commonly employed two generation model.
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