• Korean Journal of Environmental Agriculture
• /
• v.37 no.3
• /
• pp.197-206
• /
• 2018

BACKGROUND: The present study aimed to examine the crops capable of growing and adapting to the external environment and various stresses of reclaimed agriculture land for the development of high value-added agricultural utilization technology based on reclaimed land through standardization and empirical study of cultivation environment for cultivating crops. METHODS AND RESULTS: Two crops namely turnips and beets were selected for the salt tolerance test of soil environmental conditions on reclaimed land. Turnip and beet seedlings were planted on the soil collected at the 'Seokmun' reclaimed land. There are five treatments such as non-treatment, 1.0, 2.0 (control), 4.0 and $8.0dS{\cdot}m^{-1}$ of EC. The contents of betacyanin in beet roots was highest in control and decreased with increasing salt concentration. The GSL contents in the turnip roots waswere highest at EC 2.0 and decreased with increasing salt concentration, whereas those in turnip leaves waswere high both in the non-treated control and atthe EC 1.0-treatment. But, tThere was, however, no statistical differences among the treatments. CONCLUSION: The degree of salt tolerance of crops was examined, and the limit EC iswas expected to be $3.0{\sim}4.0dS{\cdot}m^{-1}$ as reported to date. If the soil improvement is performed and irrigation systems are used in the actual reclaimed land, the EC of supplied irrigation will be low, and desalination effecttreatment by the lower EC of the supplied irrigation on the soil will lead to more favorable soil condition of the rhizosphere and cultivation environment offor the crops than those in the port experiment. Therefore, monitoring the salinity, water content and ground water level will enable prediction of the rhizosphere environment, and setting up irrigation management and supplying irrigation will lead to crop cultivation results that are close to normal.

• Journal of Bio-Environment Control
• /
• v.27 no.4
• /
• pp.349-355
• /
• 2018

This study was aimed at predicting the yield of paprika (Capsicum annuum L.) through analyzing the growth characteristics, yield pattern and greenhouse environment. In the greenhouse of the Gyeongnam area (667 m above sea level), the red paprika 'Cupra' and the yellow paprika 'Fiesta' were grown from July 5, 2016 to July 15, 2017. The planting density was $3.66plants/m^2$ and attracted 2 stems. During the cultivation period, the average external radiation of the glasshouse was $14.36MJ/m^2/day$ and the internal average temperature was controlled as $20.1^{\circ}C$. After 42 weeks of planting, the growth rate of 'Cupra' was 7.3 cm/week and that of 'Fiesta' was 6.9 cm/week. The first fruit setting of 'Cupra' appeared at 1.0th node and 'Fiesta' at 2.7th node. The first harvest of 'Fiesta' was 11 weeks after planting and 'Fiesta' was 14 weeks. Comparing the yield per 10 a until the end of the cultivation in July, 'Fiesta' was 19,307 kg, which was 2.4% higher than that of 'Cupra'. And the fruit weight ratio of over 200 g of 'Cupra' was 27.7% which was 7.7% higher than that of 'Fiesta'. The average required days to harvest after fruit setting of 'Cupra' was 72.6 days and 'Fiesta' was 63.8 days. According to the relationship between the average required days to harvest and the cumulative radiation (during from fruit setting to harvest), the more radiation increases the less required days to harvest increases after February. In terms of yield, 'Cupra' increased in yield as the cumulative radiation increased, while 'Fiesta' showed an irregular pattern. Cumulative radiation from fruit setting to harvest was negatively correlated with required days to harvest after February in both cultivars. But in relation to yield, there were difference between 'Cupra' and 'Fiesta'.

• Korean Chemical Engineering Research
• /
• v.58 no.2
• /
• pp.197-208
• /
• 2020

This study was carried out to analyze spatial and temporal incidence characteristics of scrub typhus and predict the future incidence of scrub typhus since the incidences of scrub typhus have been rapidly increased among vector-borne diseases. A maximum entropy (MaxEnt) ecological model was implemented to predict spatial distribution and incidence rate of scrub typhus using spatial data sets on environmental and social variables. Additionally, relationships between the incidence of scrub typhus and critical spatial data were analyzed. Elevation and temperature were analyzed as dominant spatial factors which influenced the growth environment of Leptotrombidium scutellare (L. scutellare) which is the primary vector of scrub typhus. A temporal number of diseases by scrub typhus was predicted by a deep neural network (DNN). The model considered the time-lagged effect of scrub typhus. The DNN-based prediction model showed that temperature, precipitation, and humidity in summer had significant influence factors on the activity of L. scutellare and the number of diseases at fall. Moreover, the DNN-based prediction model had superior performance compared to a conventional statistical prediction model. Finally, the spatial and temporal models were used under climate change scenario. The future characteristics of scrub typhus showed that the maximum incidence rate would increase by 8%, areas of the high potential of incidence rate would increase by 9%, and disease occurrence duration would expand by 2 months. The results would contribute to the disease management and prediction for the health of residents in terms of public health.

• Journal of Forest and Environmental Science
• /
• v.28 no.4
• /
• pp.247-253
• /
• 2012

The purpose of this study was to conduct phenology monitoring of forest plant species in Mt. JuWang National Park, thereby establish long-term prediction and management system for species susceptible to climate change, and utilize the result as basic materials necessary for conservation of plant genetic resources in accordance with changes in their growth environment. Global Positioning System coordinates were marked on each indicator species and a specific number ticket was provided to each plant. Changes in their blooming time, time of blossoms falling, time of leaves bursting into life, and time of leaves turning, and time of leaves falling were recorded. Investigation was made once per week from April 10 in 2010 to November 30 in 2011 except for the time period between July and August when investigation was made biweekly. The investigated plants concerned 12 kinds-nine species of trees and three kinds of herbs. According to the result of the penology monitoring of Mt. JuWang National Park, their time of leaves bursting into life, time of leaves turning, and time of leaves falling were largely earlier in 2011 than in 2010. However, it is hard to say that it is due to the factor of climate change. Long-term collection of climate data and continuous monitoring of plant phenology are considered necessary in order to examine correlation between climate change and seasonal change patterns of plants.

• Advances in Computational Design
• /
• v.4 no.2
• /
• pp.73-90
• /
• 2019

The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.

• KIEAE Journal
• /
• v.12 no.3
• /
• pp.89-94
• /
• 2012

The international community is paying close attention to the climatic changes caused by the meteorological anomalies. In response to such phenomena, after the adoption of the United Nations Framework Convention on Climate Change in 1992, efforts to actively respond to the meteorological changes are proliferating all over the world; even in the Republic of Korea, the issue to tackle the meteorological changes has emerged as a top-priority national agenda. In the year of 2008, after the declaration of the low-carbon, green-growth paradigm by the government, the UNFCCC COP15 has announced a 30% reduction target of the emission of the greenhouse gases by 2020 as compared to the "Business As Usual, BAU" and has also confirmed, as a commitment plan to achieve reduction in the emission of greenhouse gases, the reduction target of greenhouse gases for all sectors, industries and years. (26.9% for buildings) Since the construction of the new apartment houses in the year of 2001, the "Building Energy Efficiency Rating", has been applied to newly constructed building complexes, built in 2010; the accumulated emission reduction has been evaluated at around 450,000toe and the accumulated carbon dioxide emission reduction is at $826,000tCO_2$ And through the prediction of these values under various scenarios (New construction, new construction / expansion of existing uses, when transferred to 1stgrade), the effects on the degree of reduction of greenhouse gases by the increased certification of the Building Energy Efficiency Rating are an alyzed and it is our aim to express the importance of the certification system capable of carrying out a quantitative evaluation of the building energy in order to establish the strategy to reduce the emission of carbon dioxide.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.3989-4006
• /
• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of Marine Bioscience and Biotechnology
• /
• v.6 no.2
• /
• pp.102-109
• /
• 2014

Cetaceans (whales, dolphins, and porpoises) are aquatic mammals that experienced drastic changes during the transition from terrestrial to aquatic environment. Morphological changes include streamlined body, alterations in the face, transformation of the forelimbs into flippers, disappearance of the hindlimbs and the acquisition of flukes on the tail. For a prolonged diving, cetaceans acquired hypoxia-resistance by developing various anatomical and physiological changes. However, molecular mechanisms underlying these adaptations are still limited. CREB-binding protein (CREBBP) is a transcriptional co-activator critical for embryonic development, growth control, metabolic homeostasis and responses to hypoxia. Natural selection analysis of five cetacean CREBBPs compared with those from 15 terrestrial relatives revealed strong purifying selection, supporting the importance of its role in mammals. However, prediction for amino acid changes that elicit functional difference of CREBBP identified three cetacean specific changes localized within a region required for interaction with SRCAP and in proximal regions to KIX domain of CREBBP. Mutations in CREBBP or SRCAP are known to cause craniofacial and skeletal defects in human, and KIX domain of CREBBP serves as a docking site for transcription factors including c-Myb, an essential regulator of haematopoiesis. In these respects, our study provides interesting insights into the functional adaptation of cetacean CREBBP for aquatic lifestyle.

• Research in Plant Disease
• /
• v.26 no.3
• /
• pp.121-133
• /
• 2020

Climate change, increased extreme weather and climate events, and rapidly changing socio-economic environment threaten agriculture and thus food security of our society. Therefore, it is urgent to shift from conventional farming to smart agriculture using big data and artificial intelligence to secure sustainable growth. In order to efficiently manage plant diseases through smart agriculture, agricultural big data that can be utilized with various advanced technologies must be secured first. In this review, we will first learn about agrometeorological big data consisted of meteorological, environmental, and agricultural data that the plant pathology communities can contribute for smart plant disease management. We will then present each sequential components of the smart plant disease management, which are prediction, monitoring and diagnosis, control, prevention and risk management of plant diseases. This review will give us an appraisal of where we are at the moment, what has been prepared so far, what is lacking, and how to move forward for the preparation of smart plant disease management.

• Animal Bioscience
• /
• v.35 no.11
• /
• pp.1760-1770
• /
• 2022

Objective: It is crucial to accurately determine the net energy (NE) values of feed ingredients because the NE system is expected to be applied to the formulation of broilers feed. The NE values of 5 wheat and 5 wheat brans were determined in 12-to 14-day old Arbor Acres (AA) broilers with substitution method and indirect calorimetry method. Methods: A total of 12 diets, including 2 reference diets (REF) and 10 test diets (5 wheat diets and 5 wheat bran diets) containing 30% of test ingredients, were randomly fed to 864 male AA birds with 6 replicates of 12 birds per treatment. These birds were used to determine metabolizable energy (ME) (8 birds per replicate) in the chicken house and NE (4 birds per replicate) in the chamber respectively at the same time. After a 4-d dietary and environment adaptation period, growth performance, energy values, energy balance and energy utilization were measured during the following 3 d. Multiple linear regression analyses were further performed to generate prediction equations for NE values based on the chemical components and ME values. The NE prediction equation were also validated on another wheat diet and another wheat bran diet with high correlation (r = 0.98, r = 0.75). Results: The NE values of 5 wheat and 5 wheat bran samples are 9.34, 10.02, 10.27, 11.33, and 10.49 MJ/kg, and 5.37, 5.17, 4.87, 5.06, and 4.88 MJ/kg DM, respectively. The equation with the best fit were NE = 1.968AME-0.411×ADF-14.227 (for wheat) and NE = -0.382×CF-0.362×CP-0.244×ADF+20.870 (for wheat bran). Conclusion: The mean NE values of wheat and wheat bran are 10.29 and 5.07 MJ/kg DM in AA broilers. The NE values of ingredients could be predicted by their chemical composition and energy value with good fitness.