• CELLMED
• /
• v.11 no.1
• /
• pp.1.1-1.4
• /
• 2021

Background: Amavata is a disease that occurs as a result of the error of metabolism. Poor dietary habits and faulty Dincharya (daily regimen) and ritucharya (seasonal regimen) leading to deranged metabolism and Agni (metabolic fire) which results in the formation of Ama(undigested product of metabolism). When Amaconceals with Vata(subtle energy associated with movement) and circulates in the body under the influence of Vyana Vayu (omnipresent air)it clogs the srotasas (microchannels) and initiates the inflammatory cascade. Amavata is commonly correlated with rheumatoid arthritis (RA) while other forms of auto-immune disorders can also be included in Amavata.Dysbiosis of the gut microbiota (GM) has been connected to the onset of diverse autoimmune diseases. In this study, it was hypothesized that Panchakarma (bio-purificatory methods) based intervention such as Virechana Karma (therapeutic purgation) may influence microbiota. Materials and Methods: Various Ayurvedic literature were reviewed for the etiopathogenesis of Amavata. Different databases were searched with research papers related to Gut Dysbiosis and autoimmunity and management of RA. A connecting link between Intestinal Dysbiosis with the autoimmune mechanisms was established and it was also found that the bowel cleansing introduced a change to the GM. Conclusion: It was concluded that Virechana karma is effective in gut flora Dysbiosis. This study aims to correlate the ancient Ayurvedic principles related to Agni Bala(metabolic energy) and biopurificatory treatment modalities like Virechana karma (therapeutic purgation)with the modern concept of gut microbiota and its role in the pathogenesis of various autoimmune disorders such as rheumatoid arthritis. The article creates an understanding about principles of Ayurveda and its rationality in today's scientific world and thereby opens newer vistas of research in therapeutics from Ayurveda, which may be helpful in the management of various immune-mediated Diseases through Ayurveda.

• Journal of Korean Society for Atmospheric Environment
• /
• v.34 no.2
• /
• pp.321-330
• /
• 2018

Concentrations of the atmospheric radon and gaseous pollutants were measured at the Gosan site on Jeju Island from 2010 to 2015, in order to observe their time-series variation characteristics and examine the concentration change related to the airflow transport pathways. Based on the realtime monitoring of the atmospheric radon and gaseous pollutants, the daily mean concentrations of radon ($^{222}Rn$) and gaseous pollutants($SO_2$, CO, $O_3$, $NO_x$) were $2,400mBq\;m^{-3}$ and 1.3, 377.6, 41.1, 3.9 ppb, respectively. On monthly variations of radon, the mean concentration in October was the highest as $3,033mBq\;m^{-3}$, almost twice as that in July ($1,452mBq\;m^{-3}$). The diurnal variation of radon concentration shows bimodal curves at early morning (around 7 a.m.) and near midnight, whereas its lowest concentration was recorded at around 3 p.m. Several gaseous pollutants($SO_2$, CO, $NO_x$) showed a similar seasonal variation with radon concentration as high in winter and low in summer, whereas the $O_3$ concentrations had a bit different seasonal trend. According to the cluster back trajectory analysis, the frequencies of airflow pathways moving from continental North China, East China, Japan and the East Sea, the Korean Peninsula, and North Pacific Ocean routes were 36, 37, 10, 13, and 4%, respectively. When the airflow were moved to Jeju Island from continental China, the concentrations of radon and gaseous pollutants were relatively high. On the other hand, when the airflows were moved from North Pacific Ocean and East Sea, their concentrations were much lower than those from continental China.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.158-158
• /
• 2018

Drought is a recurrent natural hazard in Bangladesh. It has significant impacts on agriculture, environment, and society. Well-timed information on the onset, extent, intensity, duration, and impacts of drought can mitigate the potential drought-related losses. Thus, drought characteristics need to be explained in terms of frequency, severity, and duration. This paper aims to characterize the spatial and temporal pattern of meteorological drought using EDI and illustrated drought severity over Bangladesh. Twenty-seven (27) station-based daily rainfall data for the study period of 1981-2015 were used to calculate the EDI values over Bangladesh. The evaluation of EDI is conducted for 4 sub-regions over the country to confirm the historical drought record-developed at the regional scale. The finding shows that on average, the frequency of severe to extreme drought is approximately 0.7 events per year. As a result of the regional analysis, most of the recorded historical drought events were successfully detected during the study period. Additionally, the seasonal analysis showed that the extreme droughts were frequently hit in northwestern, middle portion of the eastern and small portion of central parts of Bangladesh during the Kharif(wet) and Rabi(dry) seasons. The severe drought was affected recurrently in the central and northern regions of the country during all cropping seasons. The study also points out that the northern, south-western and central regions in Bangladesh are comparatively vulnerable to both extreme and severe drought event. The study showed that EDI would be a useful tool to identify the drought-prone area and time and potentially applicable to the climate change-induced drought evolution monitoring at regional to the national level in Bangladesh. The outcome of the present study can be used in taking anticipatory strategies to mitigate the drought damages on agricultural production as well as human sufferings in drought-prone areas of Bangladesh.

• Journal of Digital Convergence
• /
• v.16 no.12
• /
• pp.293-299
• /
• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Korean Journal of Remote Sensing
• /
• v.21 no.4
• /
• pp.253-271
• /
• 2005

We have analyzed surface water temperature and seasonal stratification of lakes in the Han river system using time-series Landsat images and in situ measurement data. Using NASA equation, at-satellite temperature is derived from 29 Landsat-5 TM and Landsat-7 ETM+ images obtained from 1994 to 2004, and was compared with in situ surface temperature on river-type dam lakes such as Paro, Chuncheon, Euiam, Chongpyong, Paldang, and with 10m-depth temperature on lake-type dam lake Soyang. Although the in situ temperature at the time of satellite data acquisition was interpolated from monthly measurements, the number of images with standard deviation of temperature difference (at-satellite temperature - in situ interpolated temperature) less than $2^{\circ}C$ was 24 on which a novel statistical atmospheric correction could be applied. The correlation coefficient at Lake Soyang was 0.915 (0.950 after correction) and 0.951-0.980 (0.979-0.997 after correction) at other lakes. This high correlation implies that there exist a mixed layer in the shallow river-like dam lakes due to physical mixing from continuous influx and efflux, and the daily and hourly temperature change is not fluctuating. At Lake Soyang, an anomalous temperature difference was observed from April to July where at-satellite temperature is $3-5^{\circ}C$ higher than in situ interpolated temperature. Located in the uppermost part of the Han river system and its influx is governed only by natural precipitation, Lake Soyang develops stratification during this time with rising sun elevation and no physical mixture from influx in this relatively dry season of the year.

• Journal of The Geomorphological Association of Korea
• /
• v.25 no.4
• /
• pp.89-101
• /
• 2018

In Mt. Halla, an arctic-alpine plant Diapensia lapponica var. obovata largely clings to rock surfaces. We observed the rock-surface temperatures of a rocky ridge on the summit area of the mountain from late April 2009 to early May 2010 in order to examine the diurnal and annual temperature variations and the thermal amplitude. We also investigated temperature regimes such as the frequency of freeze-thaw cycles and the temperature change, which might endanger the habitat through frost weathering. For comparison of slope aspects, temperature monitoring was carried out on the north and south faces of the same rocky ridge. The south face experiences the high daily maximum rock-surface temperatures and the high thermal amplitudes during the unfreezing season of May to November 2009. The temperature regimes are considered to exert physiological stress to the arctic-alpine plant. In addition, the south face shows the high frequency of freeze-thaw cycles during the seasonal freezing period of December 2009 to April 2010. This indicates that the south face is susceptible the exfoliation and granular disintegration of rock surfaces, which results in habitat destruction. As a consequence, the south face is believed to be less favorable for the establishment and growth of the arctic-alpine plant than the north face on the summit area of Mt. Halla.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.34 no.6
• /
• pp.617-623
• /
• 2001

To predict the seasonal variation of dissolved oxygen (DO) in Hakata bay, Japan, possible 20 time-series of different hourly-solar-radiation intensities were generated based on stochastically changing solar radiation intensity, and a numerical simulation on dissolved oxygen (DO) was carried out for each time series by using the Sediment-Water Ecological Model (SWEM). The model, consisting of two sub-models with hydrodynamic and biological models, simulates the circulation process of nutrient between water column and sediment, such as nutrient regeneration from sediments as well as ecological structures on the growth of phytoplankton and zooplankton, The results of the model calibration followed the seasonal variation of observed water quality well, and generated cumulative-frequency-distribution (CFD) curves of daily solar radiation agreed well with observed ones, The simulation results indicated that the exchange of sea water would have a great influence on the DO concentration, and that the concentration could change more than 1 mg/L in a day. This prediction method seems to be an effective way to examine a solution to minimize fishery damage when DO is depleted.

• The Korean Journal of Quaternary Research
• /
• v.14 no.1
• /
• pp.57-63
• /
• 2000

A simulation model was used to examine the effects of climate variation on the tree-ring structure of Larix leptolepis trees growing at a plantation plot in Worak National Park in central Korea. The model uses mathematical equations to simulate processes affecting cell(tracheid) size variations for individual rings using daily precipitation and temperature measurements. Limiting conditions are estimated from temperature, day length and a calculated water balance. The results indicate that the seasonal growth is mostly limited by the soil moisture content and precipitation income during April and May. The April-May temperature also inversely influences the growth by increasing water losses from soil. The global climate-change scenario which includes regional warming(increasing temperature in spring-summer periods) appears to decrease the duration of optimal growths. Consequently, the model estimated that Larix leptolepis would lose the total production of xylem by 25%.

• Journal of Korea Water Resources Association
• /
• v.44 no.12
• /
• pp.929-940
• /
• 2011

In this study, the future expected discharges are analyzed for Daecheong and Yongdam Dam Watershed in Geum River watershed using A1B scenario based RCM with 27 km spatial resolutions from Korea Meteorological Agency and SWAT model. The direct use of GCM and RCM data for water resources impact assessment is practically hard because the spatial and temporal scales are different. In this study, the problems of spatial and temporal scales were settled by the spatial and temporal downscaling from watershed scale to weather station scale and from monthly to daily of RCM grid data. To generate the detailed hydrologic scenarios of the watershed scale, the multi-site non-stationary downscaling method was used to examine the fluctuations of rainfall events according to the future climate change with considerations of non-stationary. The similarity between simulation and observation results of inflows and discharges at the Yongdam Dam and Daecheong Dam was respectively 90.1% and 84.3% which shows a good agreement with observed data using SWAT model from 2001 to 2006. The analysis period of climate change was selected for 80 years from 2011 to 2090 and the discharges are increased 6% in periods of 2011~2030. The seasonal patterns of discharges will be different from the present precipitation patterns because the simulated discharge of summer was decreased and the discharge of fall was increased.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.