• Korean journal of applied entomology
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• v.59 no.4
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• pp.295-301
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• 2020

In this study, we aimed to investigate the changes in the catch rate of Monochamus saltuarius, based on sexual maturation by using aggregation-sex pheromone traps. Ovariole development of caught M. saltuarius females was compared to that of the ones not caught using traps. In a mesh cage set up at the Hongneung experimental forest, we placed a multi-funnel trap with or without an aggregation-sex pheromone lure. M. saltuarius adults, which emerged from pine logs, were grouped in four according to the emergence dates (0, 1, 7, and 10 days after emergence [DAE]). We released beetles into the mesh cage to investigate the catch rate using the traps. In each group, a total of 80 beetles (20 beetles × 4 replications) were tested, making it a total of 320 beetles. Among the four groups, M. saltuarius adults in the 7 DAE group were caught more frequently using the traps, especially with a pheromone lure; the other groups showed a low catch rate. A similar number of female and male beetles were caught using the traps. Regarding ovariole development, all the female adults in the 0 and 1 DAE groups were immature, while those in the other two groups were completely developed. Therefore, aggregation-sex pheromone traps might have a limitation in the prevention of pine wilt disease because of the transmission of pine wood nematode during maturation feeding of newly emerged M. saltuarius adults. However, aggregation-sex pheromone traps can be effective for collecting sexually mature M. saltuarius adults, for the investigation of seasonal occurrence of beetles in forests.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.87-96
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• 2022

Recently, the purchase of fresh-cut produce and meal kits has increased. Ready-to-eat (RTE) fresh-cut products have potentially hazard of cross-contamination of various microorganisms in the processes of peeling, slicing, dicing, and shredding. There are frequent cases of protozoa food poisoning, such as Cyclospora and Cryptosporidium, caused by fresh-cut products. The objective of the study is to investigate the microbiological qualities of various types of RTE fresh-cut products in the domestic on/offline markets. RTE fresh-cut fruits cup (n=100), fresh-cut vegetables (n=50), and vegetables in meal kits (Vietnamese spring rolls and white radish rolls kits, n=50) were seasonally analyzed. The contamination levels of hygienic indicator organisms, yeast and mold (YM), and foodborne pathogens (Bacillus cereus, Staphylococcus aureus, Listeria monocytogenes, Salmonella spp., and Escherichia coli O157:H7) were monitored. Overall, the lowest microbiological qualities of meal kits vegetables were observed, followed by RTE fresh-cut fruits cup and fresh-cut vegetables. Contamination levels of total aerobic bacteria, coliforms, and YM in meal kits vegetables were 5.91, 3.90, and 4.71 logs CFU/g, respectively. From the qualitative analysis, 6 out of 200 RTE fresh-cut products (3%) returned positive result for S. aureus. From the quantitative analysis, the contamination levels of S. aureus in purple cabbage from a meal-kit and fresh-cut pineapple were below the acceptable limit (100 CFU/g). Staphylococcus enterotoxin seg and sei genes were detected in RTE fresh-cut celery and red cabbage from meal-kits, respectively. S. aureus contamination must be carefully controlled during the manufacturing processes of RTE fresh-cut products. Neither Cyclospora cayetanensis nor Cryptosporidium parvum was detected in the samples of RTE fresh-cut products and vegetables from meal-kits from the Korean retail markets.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.131-142
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• 2021

Purpose: Clinical practicums via non-face-to-face methods were inevitable due to the COVID-19 pandemic. We developed an online program for emergency nursing care and identified the feasibility of the program and the learning achievements of students. Methods: This was a methodological study. The program was developed by three professors who taught theory and clinical practicum for adult nursing care and clinical experts. Students received four hours of video content and two task activities every week in four-week program. Real-time interactive video conferences were included. Qualitative and qualitative data were collected. Results: A total of 96 students participated in the program. The mean score for overall satisfaction with the online program was 4.72(±1.02) out of 6. Subjects that generally had high learning achievement scores were basic life support care, fall prevention, nursing documentation, infection control, and anaphylaxis care. As a result of a content analysis of 77 reflective logs on the advantages of this program, students reported that "experience in applying nursing process," "case-based learning and teaching method," and "No time and space constraints" were the program's best features. Conclusion: Collaboration between hospitals and universities for nursing is more important than ever to develop online content for effective clinical practicum.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.285-297
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• 2022

We report results of Extended Leak-Off Test (XLOT) conducted in a large diameter borehole, which is drilled for installation of deep borehole geophysical monitoring system to monitor micro-earthquakes and fault behavior of major fault zones in the southeastern Korean Peninsula. The borehole was planned to secure a final diameter of 200 mm (or more) at a depth of ~1 km, with 12" diameter wellbore to intermediate depths, and 7-7/8" (~200 mm) to the bottom hole depth. We drilled first the 12" borehole to approximately 504 m deep and installed American Petroleum Institute standard 8-5/8" casing, then annulus between the casing and bedrock was fully cemented. XLOT was carried out for several purposes such as confirming casing and cementing integrity, measuring rock stress states. To that end, we drilled additional 4 m long open hole interval to directly inject water and pressurize into the rock mass using the upper API casings. During the XLOT, flow rates and interval pressures were recorded in real time. Based on the logs we tried to analyze hydraulic conductivity of the test interval.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.472-489
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• 2023

This study was conducted to derive the optimal estimation equations for deriving the green and dry weights of Larix kaempferi (Japanese larch) and Pinus rigida (Rigida pine), which are major coniferous tree species in South Korea. The equations were then used to develop weight tables. Table development began with the sampling of 150 L. kaempferi and 90 P. rigida trees distributed throughout the national scale, after which green weights were measured on-site. Samples from each stand were then collected, and their dry weights were measured in a laboratory. The equation used to calculate green and dry weights was divided into a one-variable formula that uses only the diameter at breast height (DBH) and a two-variable equation that employs DBH and height. The equations used to estimate the green and dry weights of logs were divided into one- and two-variable equations using DBH. Statistical data, such as the fitness index (FI), root mean square error, standard error of estimation, and residual diagram, were used to verify the suitability of the estimation equations. Applicability was examined by calculating weights using the derived optimal equations. The equation W = bD+cD² was used in measurements involving only DBH, whereas the equation W = aD^bH^c was employed in cases involving both diameter and height at breast height. The FI of W = bD+cD² was 0.91, while that of W = aD^bH^c was 0.95, both of which are high values. With these estimation formulas, weight tables for the green and dry weights of L. kaempferi and P. rigida were prepared and compared with weight tables created 20 years ago. The green and dry weight tables of both species were larger.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.