• Journal of the Korea Organic Resources Recycling Association
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• v.22 no.4
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• pp.21-27
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• 2014

Pig slurry has been considered as environmental waste to be treated in an appropriate manner. Moreover, water-born pollution loads by agricultural non-point source(NPS) pollution are expected to become intensified due to ongoing precipitation change. This study was conducted to develop a best management practice to reduce NPS pollution load by agricultural activity with pig manure compost. An eco-friendly way, small drainage pond, was suggested in this study to avoid direct drainage of agricultural runoffs and eventually reduce the amount of pollutants discharged into the surrounding aqua-environment. A small pond($12m^2$) was constructed at the corner of a rice paddy field($17,15m^2$) located in Suwon, Korea. Water was allowed to drain only via a small drainage pond. Sampling was repeatedly made at two locations, one from an entrance and the other from an exit of a pond, during the rice cultivation period(May to October, 2013). Generally, sampling was made only when runoff water drained through a pond, such as during and/or after rain(irrigation). The water quality analysis showed that all quality parameters(SS, $COD_{Mn}$, T-N, and T-P) were improved as water passed through the pond. The amount of runoff water was reduced by 67.8%. Suspended solids and $COD_{Mn}$ concentrations were reduced by 79.8% and 71.9%, respectively. In case of T-N and T-P amounts, the reduction rates were 73.6% and 74.9%, respectively. Our data implies that agricultural NPS pollution from rice paddy fields with pig manure-based fertilizer can be effectively managed when an appropriate drainage water management practice is imposed.

• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.69-79
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• 2007

The purpose of this study was to investigate the effects of composite resin restorations on the stress distribution of notch shaped noncarious cervical lesion using three-dimensional (3D) finite element analysis (FEA). Extracted maxillary second premolar was scanned serially with Micro-CT (SkyScan1072 ; SkyScan, Aartselaar, Belgium). The 3D images were processed by 3D-DOCTOR (Able Software Co., Lexington, MA, USA). ANSYS (Swanson Analysis Systems, Inc., Houston, USA) was used to mesh and analyze 3D FE model. Notch shaped cavity was filled with hybrid or flowable resin and each restoration was simulated with adhesive layer thickness ($40{\mu}m$) A static load of 500 N was applied on a point load condition at buccal cusp (loading A) and palatal cusp (loading B). The principal stresses in the lesion apex (internal line angle of cavity) and middle vertical wall were analyzed using ANSYS. The results were as follows 1. Under loading A, compressive stress is created in the unrestored and restored cavity. Under loading B, tensile stress is created. And the peak stress concentration is seen at near mesial corner of the cavity under each load condition. 2. Compared to the unrestored cavity, the principal stresses at the cemeto-enamel junction (CEJ) and internal line angle of the cavity were more reduced in the restored cavity on both load con ditions. 3. In teeth restored with hybrid composite, the principal stresses at the CEJ and internal line angle of the cavity were more reduced than flowable resin.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.