• Journal of KIISE
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• v.43 no.4
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• pp.450-459
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• 2016

Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.275-284
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• 2022

Changes in air temperature, CO₂ concentration and precipitation due to climate change are expected to have a significant impact on soybean productivity. This study was conducted to evaluate the climate change impact on growth and development of determinate soybean cultivar in the southern parts of Korea. The high temperature during vegetative period, which does not accompany the increase of CO₂ concentration, increased the canopy photosynthetic rate in soybean, but after flowering, the high temperature above the optimal ranges interrupts the photosynthetic metabolism. In yield and yield components, high temperature reduced both the pod and seed number and single seed weight, resulting in a reduction of total seed yield. On the other hand, the increase in CO₂ concentration dramatically increased the canopy photosynthetic rate over the whole growth period. In addition, high CO₂ concentration increased the number of pods and seeds, which had a positive effect on total seed yield. Under concurrent elevation of air temperature and CO₂ concentration, canopy photosynthesis increased significantly, but enhanced canopy photosynthesis did not lead to an increase in soybean seed yield. The increase in biomass and branch by enhanced canopy photosynthesis seems to be attributed to an increase in the total number of pods and seeds per plant, which compensates for the negative effects of high temperature on pod development. However, Single seed weight tended to decrease rapidly by high temperature, regardless of CO₂ concentration level. Elevated CO₂ concentration did not compensate for the poor distribution of assimilations from source to sink caused by high temperature. These results show that the damage of future soybean yield and quality is closely related to high temperature stress during seed filling period.