• Korean Journal of Environmental Biology
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• v.17 no.1
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• pp.1-9
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• 1999

Numerous observations revealed strong evidence of increased middle ultraviolet radiation or UV-B (280 ~ 320 nm) at the earth's surface resulting from stratospheric ozone depletion. UV is the waveband of electromagnetic radiation which is strongly absorbed by nucleic acids and proteins, thus causing damage to living systems. It has been recorded in the East Sea, Korea that solar UV-B impinging on the ocean surface penetrates seawater to significant depths. Recent researches showed that exposure to UV-B for as short as 2h at the ambient level (2.0 Wm$^{-2}$) decreased macroalgal growth and photosynthesis and destroyed photosynthetic pigments. These may suggest that UV-B could be an important environmental factor to determine algal survival and distribution. Some adaptive mechanisms to protect macroalgae from UV-damage have been found, which include photoreactivation and formation of UV-absorbing pigments. Post-illumination of visible light mitigated UV-induced damage in laminarian young sporophytes with blue the most effective waveband. The existence of UV-B absorbing pigments has been recognized in the green alga, Ulva pertusa and the red alga, Pachymeniopsis sp., which is likely to exert protective function for photosynthetic pigments inside the thalli from UV-damage. Further studies are however needed to confirm that these mechanisms are of general occurrence in seaweeds. Macroalgae together with phytoplankton are the primary producers to incorporate about 100 Gt of carbons per year, and provide half of the total biomass on the earth. UV-driven reduction in macroalgal biomass, if any, would therefore cause deleterious effects on marine ecosystem. The ultimate impacts of increasing UV-B flux due to ozone destruction are still unknown, but the impression from UV studies made so far seems to highlight the importance of setting up long-term monitoring system for us to be able to predict and detect the onset of large -scale deterioration in aquatic ecosystem.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.597-632
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• 2010

There is general agreement that global warming is occurring and that the main contributor to this probably is the buildup of green house gasses, GHG, in the atmosphere. Two main contributors are the utilization of fossil fuels and the deforestation of many regions of the world. The burning of fossil fuels increases atmospheric carbon while the burning of fuel-wood reducing fossil fuel consumption along with its forest source maintain an atmospheric carbon level. The standing timber in the forests is a carbon sink, as are wood buildings and structures, and fossil fuel in the ground. This paper is designed to examine a number of current issues related to mitigating the global warming problem through forestry. For this purpose, we develop a modeling approach by integrating timber market, fossil fuel market and carbon cycling model. We use discrete time optimal control theory to identify optimal time paths, the laws of motion, and stationary stats solutions of endogenous variables in the model. On the basis of these results, we identify the optimal amounts of subsidies to be provided or taxes to be imposed by the regulatory agency to mitigate atmospheric carbon accumulation. We also present a numerical example to help illustrate the characteristics of variables in the model when the social cost for atmospheric carbon incrementally shifts upward. A surprising result is that the social cost function for atmospheric carbon has a very smaller impact on the optimal rotation period than previous literature suggested.

• BMB Reports
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• v.55 no.2
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• pp.98-103
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• 2022

Increased mRNA levels of cancer upregulated gene (CUG)2 have been detected in many different tumor tissues using Affymetrix microarray. Oncogenic capability of the CUG2 gene has been further reported. However, the mechanism by which CUG2 overexpression promotes cancer stem cell (CSC)-like phenotypes remains unknown. With recent studies showing that pyruvate kinase muscle 2 (PKM2) is overexpressed in clinical tissues from gastric, lung, and cervical cancer patients, we hypothesized that PKM2 might play an important role in CSC-like phenotypes caused by CUG2 overexpression. The present study revealed that PKM2 protein levels and translocation of PKM2 into the nucleus were enhanced in CUG2-overexpressing lung carcinoma A549 and immortalized bronchial BEAS-2B cells than in control cells. Expression levels of c-Myc, CyclinD1, and PKM2 were increased in CUG2-overexpressing cells than in control cells. Furthermore, EGFR and ERK inhibitors as well as suppression of Yap1 and NEK2 expression reduced PKM2 protein levels. Interestingly, knockdown of β-catenin expression failed to reduce PKM2 protein levels. Furthermore, reduction of PKM2 expression with its siRNA hindered CSC-like phenotypes such as faster wound healing, aggressive transwell migration, and increased size/number of sphere formation. The introduction of mutant S37A PKM2-green fluorescence protein (GFP) into cells without ability to move to the nucleus did not confer CSC-like phenotypes, whereas forced expression of wild-type PKM2 promoted such phenotypes. Overall, CUG2-induced increase in the expression of nuclear PKM2 contributes to CSC-like phenotypes by upregulating c-Myc and CyclinD1 as a co-activator.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1093-1124
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• 2014

Studies on the role of agricultural science colleges are mostly divided into agricultural production, which is the primary function of agriculture, and other functions, which have recently begun to be emphasized as a result of social needs. With the green revolution and the aging of the farming population, there is a strong view that the role of agricultural science colleges should remain as it is. However, agriculture is expanding in terms of concept and content by converging with other industries not traditionally associated with agricultural production. Thus, the fields that now need to form part of agricultural science knowledge are becoming more detailed and expansive. The government's perception remains at the level of merely fostering farmers. This was evident in a survey on the employment rate, a factor used to evaluate colleges, in which the role of agricultural science colleges was limited to fostering farmers. Agro- industry fields, other than agriculturalists, include general industries in which the academic fields of agricultural science are combined with other academic fields. Thus, even when someone is employed in an industry that requires background knowledge of agricultural science, there is often a perception that he or she is employed in a field that is irrelevant to the major. This study examines the role of agricultural science colleges in agriculture and farm villages by focusing on the employment of graduates of these colleges within agro-industry. We categorize academic research on agricultural science into 16 fields, based on the medium level of the National Standard Science and Technology Classification Codes. Then, we categorize the employment fields into 168 fields, based on the small classification level of the inter-industry relations classification. Thus, we investigate 220 departments of 37 colleges, nationwide. Our findings show that the average employment rate of graduates of agricultural science colleges is 69.0%. Furthermore, 33.0% of all employees work in agro-industry fields that require background knowledge in agricultural science, which is one out of three job seekers. Then, 3.6% of employees work in business startups in agro-industry. The aforementioned government survey showed that only 0.1% of all college graduates in Korea were employed as agriculturalists in 2013. However, our results showed that 13.3% of graduates were working as agriculturalists, which is significantly different to the results of the government survey. These results confirm that agricultural science colleges contribute greatly to the employment of graduates, including farmers, agro-industry, and business startups in agro-industry fields.