• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.346-358
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• 2023

Due to the acceleration of climate change or global warming, it is important to predict rice productivity in the future and investigate physiological changes in rice plants. The research aimed to explore how rice adapts to climate change by examining the response of nitrogen absorption and nitrogen use efficiency in rice under elevated levels of carbon dioxide and temperature, utilizing the SPAR system for analysis. The temperature increased by +4.7 ℃ in comparison to the period from 2001 to 2010, while the carbon dioxide concentration was held steady at 800 ppm, aligning with South Korea's late 21st-century RCP8.5 scenario. Nitrogen was applied as fertilizer at rates of 0, 9, and 18 kg 10a^-1, respectively. Under conditions of climate change, there was an 81% increase in the number of panicles compared to the present situation. However, grain weight decreased by 38% as a result of reduction in the grain filling rate. BNUE, indicative of the nitrogen use efficiency in plant biomass, exhibited a high value under climate change conditions. However, both NUEg and ANUE, associated with grain production, experienced a notable and significant decrease. In comparison to the current conditions, nitrogen uptake in leaves and stems increased by 100% and 151%, respectively. However, there was a 25% decrease in nitrogen uptake in the panicle. Likewise, the nitrogen content and NDFF (Nitrogen Derived from Fertilizer) in the sink organs, namely leaves and roots, were elevated in comparison to current levels. Therefore, it is imperative to ensure resources by mitigating the decrease in ripening rates under climate change conditions. Moreover, there seems to be a requirement for follow-up research to enhance the flow of photosynthetic products under climate change conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.207-221
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• 2012

Air temperature in Korea has increased by $1.5^{\circ}C$ over the last 100 years, which is nearly twice the global average rate during the same period. Moreover, it is projected that such change in temperature will continue in the 21st century. The objective of this study was to evaluate the potential impacts of future climate change on the rice production and adaptation methods in Korea. Climate data for the baseline (1971~2000) and the three future climate (2011~2040, 2041~2070, and 2071~2100) at fifty six sites in South Korea under IPCC SRES A1B scenario were used as the input to the rice crop model ORYZA2000. Six experimental schemes were carried out to evaluate the combined effects of climatic warming, $CO_2$ fertilization, and cropping season on rice production. We found that the average production in 2071~2100 would decrease by 23%, 27%, and 29% for early, middle, and middle-late rice maturing type, respectively, when cropping seasons were fixed. In contrast, predicted yield reduction was ~0%, 6%, and 7%, for early, middle, and middle-late rice maturing type, respectively, when cropping seasons were changed. Analysis of variation suggested that climatic warming, $CO_2$ fertilization, cropping season, and rice maturing type contributed 60, 10, 12, and 2% of rice yield, respectively. In addition, regression analysis suggested 14~46 and 53~86% of variations in rice yield were explained by grain number and filled grain ratio, respectively, when cropping season was fixed. On the other hand, 46~78 and 22~53% of variations were explained respectively with changing cropping season. It was projected that sterility caused by high temperature would have no effect on rice yield. As a result, rice yield reduction in the future climate in Korea would resulted from low filled grain ratio due to high growing temperature during grain-filling period because the $CO_2$ fertilization was insufficient to negate the negative effect of climatic warming. However, adjusting cropping seasons to future climate change may alleviate the rice production reduction by minimizing negative effect of climatic warming without altering positive effect of $CO_2$ fertilization, which improves weather condition during the grain-filling period.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.