• Title/Summary/Keyword: E-Journal Management

Search Result 6,665, Processing Time 0.037 seconds

A Study on the Retrieval of River Turbidity Based on KOMPSAT-3/3A Images (KOMPSAT-3/3A 영상 기반 하천의 탁도 산출 연구)

  • Kim, Dahui;Won, You Jun;Han, Sangmyung;Han, Hyangsun
    • Korean Journal of Remote Sensing
    • /
    • v.38 no.6_1
    • /
    • pp.1285-1300
    • /
    • 2022
  • Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

Unusual Delay of Heading Date in the 2022 Rice Growth and Yield Monitoring Experiment (2022년도 벼 작황시험에서 관찰된 출수기 지연 현상 보고)

  • HyeonSeok, Lee;WoonHa, Hwang;SeoYeong, Yang;Yeongseo, Song;WooJin, Im;HoeJeong, Jeong;ChungGen, Lee;HyeongJoo, Lee;JongTae, Jeong;JongHee, Shin;MyoungGoo, Choi
    • Korean Journal of Agricultural and Forest Meteorology
    • /
    • v.24 no.4
    • /
    • pp.330-336
    • /
    • 2022
  • It is likely that the heading would occur early when air temperature increases. In 2022, however, the heading date was delayed unusually, e.g., by 3 to 5 days although temperature during the vegetative growth stage was higher than normal years. The objective of this study was to identify the cause of such event analyzing weather variables including average temperature, sunshine hours, and day-length for each growth stage. The observation data were collected for medium-late maturing varieties, which has been grown at crop yield experiment sites including Daegu, Andong, and Yesan. The difference in heading date was compared between growing seasons in 2021 and 2022 because crop management options, e.g., the cultivars and cultivation methods, were identical at those sites during the study period. It appeared that the heading date was delayed due to the difference in temperature responsiveness under a given day-length condition The effect of the temperature increase on the heading date differed between the periods during which when the day-length was more than 14.3 hours before and after the summer-solstice.. The effect of the temperature decrease during the period from which the day-length decreased to less than 14.3 hours to the heading date was relatively greater. This merits further studies to examine the response of rice to the temperature change under different day-length and sunshine duration in terms of heading.

Ecological Network on Benthic Diatom in Estuary Environment by Bayesian Belief Network Modelling (베이지안 모델을 이용한 하구수생태계 부착돌말류의 생태 네트워크)

  • Kim, Keonhee;Park, Chaehong;Kim, Seung-hee;Won, Doo-Hee;Lee, Kyung-Lak;Jeon, Jiyoung
    • Korean Journal of Ecology and Environment
    • /
    • v.55 no.1
    • /
    • pp.60-75
    • /
    • 2022
  • The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

Identifying sources of heavy metal contamination in stream sediments using machine learning classifiers (기계학습 분류모델을 이용한 하천퇴적물의 중금속 오염원 식별)

  • Min Jeong Ban;Sangwook Shin;Dong Hoon Lee;Jeong-Gyu Kim;Hosik Lee;Young Kim;Jeong-Hun Park;ShunHwa Lee;Seon-Young Kim;Joo-Hyon Kang
    • Journal of Wetlands Research
    • /
    • v.25 no.4
    • /
    • pp.306-314
    • /
    • 2023
  • Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

Gap-Filling of Sentinel-2 NDVI Using Sentinel-1 Radar Vegetation Indices and AutoML (Sentinel-1 레이더 식생지수와 AutoML을 이용한 Sentinel-2 NDVI 결측화소 복원)

  • Youjeong Youn;Jonggu Kang;Seoyeon Kim;Yemin Jeong;Soyeon Choi;Yungyo Im;Youngmin Seo;Myoungsoo Won;Junghwa Chun;Kyungmin Kim;Keunchang Jang;Joongbin Lim;Yangwon Lee
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.6_1
    • /
    • pp.1341-1352
    • /
    • 2023
  • The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

Effects of Seller's Influence Tactics on Customer's Psychological Obligation, Trust, and Repurchase Intention in Offline Cosmetics Selling Channel: Moderating Effect of Perceived Service Quality (오프라인 화장품 구매경로에서 판매원의 판매설득전술이 고객의 심리적의무감과 판매원 신뢰, 재구매의도에 미치는 영향: 지각된 서비스 품질을 조절효과로)

  • Kang, Byeong Jun;Yi, Ho-Taek
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
    • /
    • v.17 no.5
    • /
    • pp.205-221
    • /
    • 2022
  • In this study, the authors investigated the effect of salesperson's Selling Influence Tactics (SIT) on customers' psychological obligation, trust in salespersons and repurchase intentions in the offline cosmetics purchase channel. In addition, we examined the moderating effect of service quality perceived by customers. To this end, a survey was conducted on 298 customers who had purchased cosmetics through the offline sales channel, and the authors conducted hypothesis testing through a structural equation model. As a result of the study, first, among salesperson's sales influence tactics, emotional appeal tactics (H1a), customer ingratiation tactics (H1d), and personal appeal tactics (H1e) were found to affect the psychological obligation of customers, and emotional appeal tactics (H2a), rational persuasion tactics (H2b), information provision tactics (H2c), and customer ingratiation tactics (H2d) were found to affect trust in salespeople. Third, it was found that the psychological obligation did not have a positive (+) effect on the customer's repurchase intention, and the customer's trust in the salesperson had a positive (+) effect on the repurchase intention. Third, perceived service quality showed a significant moderating effect between psychological obligation and repurchase intention, trust in salesperson and repurchase intention. In previous studies on salesperson's Selling Influence Tactics (SIT), many studies examined salesperson's Selling Influence Tactics (SIT) by specifying sub-variables in a limited way, and studies confirming marketing factors such as repurchase intention were also insufficient. Therefore, the results of the empirical research confirmed based on this study are expected to help the standard or direction of the salesperson's Selling Influence Tactics (SIT) in future studies. In addition, this study describes implications for providing help in employee education and management for small business owners who manage and operate offline cosmetics stores, and sales strategies that should be strategically established to improve perceived service quality for customers.

Fish Community Characteristics and the Effects of Water Quality on River Health in the Anyang Stream, Korea (안양천의 어류군집 특성 및 수질이 하천건강성에 미치는 영향)

  • Jae-Woo Joo;Myeong-Hun Ko
    • Korean Journal of Environment and Ecology
    • /
    • v.38 no.2
    • /
    • pp.189-203
    • /
    • 2024
  • This study conducted a survey two times at 20 points from April to June and from August to October 2022 to characterize the fish community and reveal the health of the Anyang stream. The survey collected 3,186 individuals belonging to 5 orders, 10 families and 34 species. The dominant species was Zacco platypus (43.4%), followed by Carassius auratus (10.1%), Cyprinus carpio (7.6%), Zacco koreanus (6.2%), Pungtungia herzi (5.2%), Oryzias sinensis (5.1%), Pseudorasbora parva (4.7%), Squalidus gracilis majimae (4.4%), Rhynchocypris oxycephalus (3.5%), Hemiculter eigenmanni (3.3%), Rhodeus ocellatus (1.4%), and Chelon haematocheilus (1.0%). Among the emerged species, 5 species (14.7%), including Rhodeus uyekii, Squalidus japonicus coreanus, and Odontobutis interrupta, were endemic to Korea, and 4 species (11.8%), including Cyprinus carpio (Israeli type), Lepomis macrochirus, Micropterus salmoides, and Poecilia reticulata, were exotic. The fish community analysis showed that the dominance was higher at the upper reaches of the Anyang stream and decreased downstream, and diversity, richness, and evenness were higher in tributary than mainstream. Stream health (fish assessment index, FAI) was generally poor, as it was rated as moderate (C) at seven sites, poor (D) at eight sites, and very poor (E) at five sites. Water quality was evaluated at eight sites, and it was rated as poor (V) at two sites, slightly poor (IV) at one site, moderate (III) at four sites, and slightly good (II) at one site, indicating that the overall water quality was poor and that the water quality had a significant impact on river health and fish habitat. Moreover, it was found that many weirs without fishways were constructed in the Anyang stream and became an obstacle to fish migration. Therefore, a systematic management plan to improve water quality and add fishways is necessary to improve the stream's health and conserve stable fish habitat.

The Impact of the Internet Channel Introduction Depending on the Ownership of the Internet Channel (도입주체에 따른 인터넷경로의 도입효과)

  • Yoo, Weon-Sang
    • Journal of Global Scholars of Marketing Science
    • /
    • v.19 no.1
    • /
    • pp.37-46
    • /
    • 2009
  • The Census Bureau of the Department of Commerce announced in May 2008 that U.S. retail e-commerce sales for 2006 reached $ 107 billion, up from $ 87 billion in 2005 - an increase of 22 percent. From 2001 to 2006, retail e-sales increased at an average annual growth rate of 25.4 percent. The explosive growth of E-Commerce has caused profound changes in marketing channel relationships and structures in many industries. Despite the great potential implications for both academicians and practitioners, there still exists a great deal of uncertainty about the impact of the Internet channel introduction on distribution channel management. The purpose of this study is to investigate how the ownership of the new Internet channel affects the existing channel members and consumers. To explore the above research questions, this study conducts well-controlled mathematical experiments to isolate the impact of the Internet channel by comparing before and after the Internet channel entry. The model consists of a monopolist manufacturer selling its product through a channel system including one independent physical store before the entry of an Internet store. The addition of the Internet store to this channel system results in a mixed channel comprised of two different types of channels. The new Internet store can be launched by the independent physical store such as Bestbuy. In this case, the physical retailer coordinates the two types of stores to maximize the joint profits from the two stores. The Internet store also can be introduced by an independent Internet retailer such as Amazon. In this case, a retail level competition occurs between the two types of stores. Although the manufacturer sells only one product, consumers view each product-outlet pair as a unique offering. Thus, the introduction of the Internet channel provides two product offerings for consumers. The channel structures analyzed in this study are illustrated in Fig.1. It is assumed that the manufacturer plays as a Stackelberg leader maximizing its own profits with the foresight of the independent retailer's optimal responses as typically assumed in previous analytical channel studies. As a Stackelberg follower, the independent physical retailer or independent Internet retailer maximizes its own profits, conditional on the manufacturer's wholesale price. The price competition between two the independent retailers is assumed to be a Bertrand Nash game. For simplicity, the marginal cost is set at zero, as typically assumed in this type of study. In order to explore the research questions above, this study develops a game theoretic model that possesses the following three key characteristics. First, the model explicitly captures the fact that an Internet channel and a physical store exist in two independent dimensions (one in physical space and the other in cyber space). This enables this model to demonstrate that the effect of adding an Internet store is different from that of adding another physical store. Second, the model reflects the fact that consumers are heterogeneous in their preferences for using a physical store and for using an Internet channel. Third, the model captures the vertical strategic interactions between an upstream manufacturer and a downstream retailer, making it possible to analyze the channel structure issues discussed in this paper. Although numerous previous models capture this vertical dimension of marketing channels, none simultaneously incorporates the three characteristics reflected in this model. The analysis results are summarized in Table 1. When the new Internet channel is introduced by the existing physical retailer and the retailer coordinates both types of stores to maximize the joint profits from the both stores, retail prices increase due to a combination of the coordination of the retail prices and the wider market coverage. The quantity sold does not significantly increase despite the wider market coverage, because the excessively high retail prices alleviate the market coverage effect to a degree. Interestingly, the coordinated total retail profits are lower than the combined retail profits of two competing independent retailers. This implies that when a physical retailer opens an Internet channel, the retailers could be better off managing the two channels separately rather than coordinating them, unless they have the foresight of the manufacturer's pricing behavior. It is also found that the introduction of an Internet channel affects the power balance of the channel. The retail competition is strong when an independent Internet store joins a channel with an independent physical retailer. This implies that each retailer in this structure has weak channel power. Due to intense retail competition, the manufacturer uses its channel power to increase its wholesale price to extract more profits from the total channel profit. However, the retailers cannot increase retail prices accordingly because of the intense retail level competition, leading to lower channel power. In this case, consumer welfare increases due to the wider market coverage and lower retail prices caused by the retail competition. The model employed for this study is not designed to capture all the characteristics of the Internet channel. The theoretical model in this study can also be applied for any stores that are not geographically constrained such as TV home shopping or catalog sales via mail. The reasons the model in this study is names as "Internet" are as follows: first, the most representative example of the stores that are not geographically constrained is the Internet. Second, catalog sales usually determine the target markets using the pre-specified mailing lists. In this aspect, the model used in this study is closer to the Internet than catalog sales. However, it would be a desirable future research direction to mathematically and theoretically distinguish the core differences among the stores that are not geographically constrained. The model is simplified by a set of assumptions to obtain mathematical traceability. First, this study assumes the price is the only strategic tool for competition. In the real world, however, various marketing variables can be used for competition. Therefore, a more realistic model can be designed if a model incorporates other various marketing variables such as service levels or operation costs. Second, this study assumes the market with one monopoly manufacturer. Therefore, the results from this study should be carefully interpreted considering this limitation. Future research could extend this limitation by introducing manufacturer level competition. Finally, some of the results are drawn from the assumption that the monopoly manufacturer is the Stackelberg leader. Although this is a standard assumption among game theoretic studies of this kind, we could gain deeper understanding and generalize our findings beyond this assumption if the model is analyzed by different game rules.

  • PDF

Analysis of Foodborne Pathogens in Food and Environmental Samples from Foodservice Establishments at Schools in Gyeonggi Province (경기지역 학교 단체급식소 식품 및 환경 중 식중독균 분석)

  • Oh, Tae Young;Baek, Seung-Youb;Koo, Minseon;Lee, Jong-Kyung;Kim, Seung Min;Park, Kyung-Min;Hwang, Daekeun;Kim, Hyun Jung
    • Journal of the Korean Society of Food Science and Nutrition
    • /
    • v.44 no.12
    • /
    • pp.1895-1904
    • /
    • 2015
  • Foodborne illness associated with food service establishments is an important food safety issue in Korea. In this study, foodborne pathogens (Bacillus cereus, Clostridium perfringens, Escherichia coli, pathogenic Escherichia coli, Listeria monocytogenes, Salmonella spp., Staphylococcus aureus, and Vibrio parahaemolyticus) and hygiene indicator organisms [total viable cell counts (TVC), coliforms] were analyzed for food and environmental samples from foodservice establishments at schools in Gyeonggi province. Virulence factors and antimicrobial resistance of detected foodborne pathogens were also characterized. A total of 179 samples, including food (n=66), utensil (n=68), and environmental samples (n=45), were collected from eight food service establishments at schools in Gyeonggi province. Average contamination levels of TVC for foods (including raw materials) and environmental samples were 4.7 and 4.0 log CFU/g, respectively. Average contamination levels of coliforms were 2.7 and 4.0 log CFU/g for foods and environmental swab samples, respectively. B. cereus contamination was detected in food samples with an average of 2.1 log CFU/g. E. coli was detected only in raw materials, and S. aureus was positive in raw materials as well as environmental swab samples. Other foodborne pathogens were not detected in all samples. The entire B. cereus isolates possessed at least one of the diarrheal toxin genes (hblACD, nheABC, entFM, and cytK enterotoxin gene). However, ces gene encoding emetic toxin was not detected in B. cereus isolates. S. aureus isolates (n=16) contained at least one or more of the tested enterotoxin genes, except for tst gene. For E. coli and S. aureus, 92.7% and 37.5% of the isolates were susceptible against 16 and 19 antimicrobials, respectively. The analyzed microbial hazards could provide useful information for quantitative microbial risk assessment and food safety management system to control foodborne illness outbreaks in food service establishments.

Analysis of shopping website visit types and shopping pattern (쇼핑 웹사이트 탐색 유형과 방문 패턴 분석)

  • Choi, Kyungbin;Nam, Kihwan
    • Journal of Intelligence and Information Systems
    • /
    • v.25 no.1
    • /
    • pp.85-107
    • /
    • 2019
  • Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.