• Journal of Nutrition and Health
• /
• v.37 no.7
• /
• pp.550-556
• /
• 2004

Dietary pattern analysis is important complementary approach for identifying associations between diet and chronic disease. A case-control study was conducted in order to examine dietary patterns and the risk of colon cancer in Korea. Data were collected from both 137 cases with either colorectal cancer or large bowl adenomatous polyps and 134 controls regarding social-demographic characteristics and food intake using a semi-quantitative food frequency questionnaire. We conducted factor analysis and identified 6 major dietary patterns: 'Well-being diet' characterized by higher intakes of potatoes, yogurt, soybean paste and vegetables, 'Meat & fish', 'Milk & juice', 'Pork & alcohol', 'Rice & kimchi', and 'Coffee & cake'. We calculated factor scores for each participant and examined the associations between dietary patterns and colon cancer risk. After adjusting for potential confounders, there was a relative risk for colon cancer of 0.16 (95% confidence interval, 0.07 - 0.34) when comparing the highest with the lowest tertile of the 'Well-being' pattern. Significant trends of decreasing risk of colon cancer also emerged with the 'Milk & juice' (OR = 0.40, 95% CI = 0.20 - 0.79). In contrast, inverse associations of the risk were found for 'Pork & alcohol' (OR = 1.92, 95% CI = 0.93 - 3.97), 'Coffee & cake' (OR = 2.18, 95% CI = 1.07 - 4.46). For the 'Meat & fish' pattern, the decreased risk of colon cancer was observed in the second tertile, but not in the highest tertile when comparing to the lowest. The 'Rice & kimchi' pattern had a nonsignificant association with the risk. These data suggest that major dietary patterns derived from the FFQ associated with the risk of colon cancer in Korea. Since foods are not consumed in isolation, dietary pattern research in natural eating behavior may be useful for understanding dietary causes of colon cancer.

• Journal of Information Technology Services
• /
• v.19 no.2
• /
• pp.125-138
• /
• 2020

Currently, subway crowding is estimated by observing a specific point at specific hours once or twice every 1 or 2 years. Given the extensive subway network in Seoul Metropolitan Area covering 588 stations, 11 lines and 80 transfer stations as of 2017, implementing crowding mitigation policy may have its limitations due to data uncertainty. A proposal has recently been made to effectively use smart card data, which generates big data on the overall subway traffic related to an estimated 8 million passengers per day. To mitigate subway crowding, this study proposes two viable options based on data related to smart card used in Seoul Metropolitan Area. One is to create a subway passenger pattern model to accurately estimate subway crowding, while the other is to prove effectiveness of early bird policy to distribute subway demand that is concentrated at certain stations and certain time. A subway passenger pattern model was created to estimate the passenger routes based on subway terminal ID at the entrance and exit and data by hours. To that end, we propose assigning passengers at the routes similar to the shortest routes based on an assumption that passengers choose the fastest routes. In the model, passenger flow is simulated every minute, and subway crowding level by station and line at every hour is analyzed while station usage pattern is identified by depending on passenger paths. For early bird policy, highly crowded stations will be categorized based on congestion level extracted from subway passenger pattern model and viability of a policy which transfers certain traveling demands to early commuting hours in those stations will be reviewed. In particular, review will be conducted on the impact of policy implemented at certain stations on other stations and lines from subway network as a whole. Lastly, we proposed that smart card based subway passenger pattern model established through this study used in decision making process to ensure effective implementation of public transport policy.

• Korean Journal of Veterinary Service
• /
• v.41 no.1
• /
• pp.57-61
• /
• 2018

Canine peripheral nerve sheath tumors (PNSTs) are spindle cell tumors that arise from Schwann cells, perineural cells, fibroblasts or all of them. Based on the morphology and biologic behavior, PNSTs are divided into benign PNST (BPNST) and malignant PNST (MPNST) forms. The aim of this study is to diagnose the two cases of neoplastic tissue samples with features of PNSTs by the histopathology and immunohistochemistry. The study was performed using two specimens from small animal clinic. The first case, A was a mass, 3~4 cm in diameter, extruded from vaginal mucosa of 10-year-old spayed female mixed-breed dog. And the second case, B was a subcutaneous mass, 1.5 cm in diameter, which is originated from right hind leg of 9-year-old castrated male mixed-breed dog. Two cases were stained with hematoxylin and eosin (H&E) for histopathological examination. And also immunohistochemistry (IHC) was performed by the avidin-biotin peroxidase complex (ABC) method with antibodies specific for the following proteins: S-100 protein, smooth muscle actin (SMA) and epidermal growth factor receptor (EGFR). In results, Antoni B schwannoma pattern characterized by pleomorphic, round and fusiform polygonal cells was seen in A. In B, Antoni A pattern, densely packed spindle cells arranged in interlacing bundles was seen in addition to Antoni B pattern. In IHC, cytoplasms of neoplastic cells were diffusely labeled for S-100 expression in A and B. For SMA, both A and B show negative expression. And for EGFR, A shows negative expression but B shows partially positive expression in areas of Antoni B schwannoma pattern. The histopathologic features of two cases coupled with the S-100 immunoreactivity led to a diagnosis of PNST. For SMA, both A and B show negative expression. The diagnosis of A will be a BPNST with the negative result and B will be a MPNST with the positive result for EGFR.

• Bulletin of the Korean Chemical Society
• /
• v.30 no.1
• /
• pp.137-144
• /
• 2009

In this study, quantitative and pattern recognition analyses for the quality evaluation of Herba Epimedii using HPLC was developed. For quantitative analysis, five major bioactive constituents, hyperin, epimedin A, epimedin B, epimedin C, and icariin were determined. Analysis was carried out on Capcell pak $C_{18}$ column ($250{\time}4.6$ mm, 5 ${\mu}m$) with a mobile phase of mixture of acetonitrile and 0.1% formic acid, using UV detection at 270 nm. The linear behavior was observed over the investigated concentration range (2-50 ${\mu}g/mL;\;r_2\;>$ 0.99) for all analytes. The intraand inter-day precisions were lower than 4.3% (as a relative standard deviation, RSD) and accuracies between 95.1% and 104.4%. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of one reference sample. The RSD of intra- and inter-day variation of relative retention time (RRT) and relative peak area (RPA) of the 12 selected common peaks were below 0.8% and 4.7%, respectively. The developed methods were applied to analysis of twenty Herba Epimedii extract samples. Contents of hyperin, epimedin A, epimedin B, epimedin C, and icariin were calculated to be 0$\sim$0.79, 0.69$\sim$1.91, 0.93$\sim$9.58, 0.65$\sim$3.05, and 2.43$\sim$11.8 mg/g dried plant. Principal component analysis (PCA) showed that most samples were clustered together with the reference samples but several apart from the main cluster in the PC score plot, indicating differences in overall chemical composition between two clusters. The present study suggests that quantitative determination of marker compounds combined with pattern-recognition method can provide a comprehensive approach for the quality assessment of herbal medicines.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.13 no.1
• /
• pp.109-121
• /
• 1985

A study on the image identification of urban streetscape is valuable for illuminating identity that is not yet fully approached in the field of environmental design. This analysis of urban streetscape for image identification allows us to make a more detailed exploration of an important approaching methods in dealing with the structural characteristics of identity. As a matter of fact, the earlier indirect studies on this image identification were made by environmental designers, such as architectural and urban designer in the field of environmental perception and came to its environmental cognition & environmental pattern research with assistances by such researchers as K. Lynch A. Rapoport & Christopher Alexander. Through its environmental perception research, we can see its structural characteristics that is aesthetic & visual structural contents of physical environmental elements. And we can see its cognitive characteristics through the environmental cognitive research, that is continuity, territoriality, identity of place, uniqueness or individuality, meaning & symbolism. Through its environmental pattern research, we can see its physical, socio - economic, cultural and symbolic pattern identification contents, that is physical form of the city, style of the street, pattern of streetscape, socio- economic & geographical locality, arid life cycle, life style, common style of the behavior, cultural pattern of the activity, socio - cultural expression of the symbol. In these process, we can set up a set of the environmental design criterias from those three integral studies for identity. And for an environmental research, Tongsung-Ro around the CBD (central business district) in Taegu City was selected for a case study, because this streetscape is suitable for that approaching methods in this study.

• Computers and Concrete
• /
• v.31 no.5
• /
• pp.405-417
• /
• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.

• Animal cells and systems
• /
• v.1 no.2
• /
• pp.329-335
• /
• 1997

The final pattern of the skeletal muscle of a vertebrate depends on the position-specific behavior of the muscle precursor cells during early developmental process and the abdominal muscle is made of cells which migrate a relatively long distance from their original tissue, myotome of dorsal mesoderm. We report the spatia-temporal migration pattern of abdominal muscle in Xenopus laevis by in situ hybridization and immunohistological studies. Shortly after hatching tadpole stage (stage 31/32), a group of myotomal cells detaches from the lower tip of the second somite and migrates ventrally to the lower position of abdomen. At stage 34/35, a second cell group migrates away from the third somite. Total 7 myotomal cell groups migrate ventrally one by one from the second to eighth myotome along their own pathways through the cell free space located between epidermis and subepidermal layer of the abdomen. During migration, the sizes of the cell groups (abdominal muscle anlagens) are increased to several tens fold. Around stage 40 all the abdominal muscle anlagens reaches their final positions and are interconnected side by side rostrocaudally. They are also connected to other types of muscles, forming a large multisegmented abdominal muscle. Heat shock study suggests that the disruption of segmentation of somites does not block the detachment of abdominal muscle anlagen, though the treatment gave stage- and dosagedependent effects on the migration speed.

• Proceedings of the KSR Conference
• /
• 2011.05a
• /
• pp.1735-1741
• /
• 2011

The safety of station structures among railway infrastructures should be considered as a top priority because hundreds of thousands passengers a day take a subway. The station structures, which have been being operated since the 1970s, are especially vulnerable to the earthquake and long-term vibrations such as ambient train vibrations as well. This is why the structural-health monitoring system of station structures should be required. For these reason, Korean government has made an effort to develop the structural health-monitoring system of them, which can evaluate the health-state of station structures as well as can monitor the vulnerable structural members in real-time. Then, through the monitoring system, the vulnerable structural members could be retrofitted. For the development of health-state evaluation method for station structures with the real-time sensing data measured in the fields, authors carried out the numerical simulations to develop evaluation algorithms based on statistical pattern recognition techniques. In this study, the dynamic behavior of Chungmuro station in Seoul was numerically analyzed and then critical members were chosen. Damages were artificially simulated at the selected critical members of the numerical model. And, the supervised and unsupervised learning based pattern recognition algorithms were applied to quantify and localize the structural defects.

• Journal of Korean Society for Quality Management
• /
• v.33 no.4
• /
• pp.21-41
• /
• 2005

This study analyzes the Internet utilization pattern of customer by comprehensively investigating the previous studies on the behavior pattern of customer in terms of Internet business. Based on the analysis, this study develops research framework that supports strategic decision-making for resource allocation in Internet business. Such research framework would be helpful for providing the typology of Internet business model that can be specialized by each industry. As a result of the simulation analysis, it was found that the optimal resource allocation portfolio providing maximum profits to the Internet bookstore involves large-scale investment on delivery service and customer support service which are the key factors for post-purchase customer satisfaction, regardless of the growth pattern or size of Internet bookstore market. Consequently, from the above analysis, the investment ratio of resources for the profit maximization of Internet bookstore was drawn. Conclusively, based on the comprehensive examination of the results, this study provided a framework for dynamic resource allocation decision-making, and proposed a management strategy which allows consumers to shop under more favorable environment, and simultaneously enables the Internet bookstore to accomplish management objectives such as continuous growth and profit maximization.

• IE interfaces
• /
• v.24 no.4
• /
• pp.447-456
• /
• 2011

Identifying the major moving patterns of shoppers' movements in the selling floor has been a longstanding issue in the retailing industry. With the advent of RFID technology, it has been easier to collect the moving data for a individual shopper's movement. Most of the previous studies used the traditional clustering technique to identify the major moving pattern of customers. However, in using clustering technique, due to the spatial constraint (aisle layout or other physical obstructions in the store), standard clustering methods are not feasible for moving data like shopping path should be adjusted for the analysis in advance, which is time-consuming and causes data distortion. To alleviate this problems, we propose a new approach to spatial pattern clustering based on longest common subsequence (LCSS). Experimental results using the real data obtained from a grocery in Seoul show that the proposed method performs well in finding the hot spot and dead spot as well as in finding the major path patterns of customer movements.