• Korean Journal of Ecology and Environment
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• v.57 no.1
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• pp.1-16
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• 2024

Most wetlands worldwide have suffered from extensive human exploitation. Unfortunately they have been less explored compared to river and lake ecosystems despite their ecological importance and economic values. This is the same case in Korea. This study was aimed to estimate the assemblage attributes and distribution characteristics of benthic macroinvertebrates for fifty wetlands distributed throughout subtropical Jeju Island in 2021. A total of 133 taxa were identified during survey periods belonging to 53 families, 19 orders, 5 classes and 3 phyla. Taxa richness ranged from 4 to 31 taxa per wetland with an average of 17.5 taxa. Taxa richness and abundance of predatory insect groups such as Odonata, Hemiptera and Coleoptera respectively accounted for 67.7% and 68.2% of the total. Among them Coleoptera were the most diverse and abundant. Taxa richness and abundance did not significantly differ from each wetland type classified in accordance with the National Wetland Classification System. There were three endangered species (Clithon retropictum, Lethocerus deyrolli and Cybister (Cybister) chinensis) and several restrictively distributed species only in Jeju Island. Cluster analysis based on the similarity in the benthic macroinvertebrate composition largely classified 50 wetlands into two major clusters: small wetlands located in lowland areas and medium-sized wetlands in middle mountainous regions. All cluster groups displayed significant differences in wetland area, long axis, percentage of fine particles and macrophyte composition ratio. Indicator Species Analysis selected 19 important indicators with the highest indicator value of Ceriagrion melanurum at 63%, followed by Noterus japonicus (59%) and Polypylis hemisphaerula (58%). Our results are expected to provide fundamental information on the biodiversity and habitat environments for benthic macroinvertebrates in wetland ecosystems, consequently helping to establish conservation and restoration plans for small wetlands relatively vulnerable to human disturbance.

• Reproductive and Developmental Biology
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• v.35 no.1
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• pp.33-45
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• 2011

Heat shock protein 90 (Hsp90) is ATPase-directed molecular chaperon and affects survival of cancer cell. Inhibitory effect of Hsp90 by inducing cell cycle arrest and apoptosis in the cancer cell was reported. However, its role during oocyte maturation and early embryo development is very insufficient. In this study, we traced the effects of Hsp90 inhibitor, 17-allylamino-17-demethoxygeldanamycin (17-AAG), on meiotic maturation and early embryonic development in pigs. We also investigated several indicators of developmental potential, including structural integrity, gene expression (Hsp90-, cell cycle-, and apoptosis-related genes), and apoptosis, which are affected by 17-AAG. Then, we examined the roles of Hsp90 inhibitor on viability of primary cells in pigs. Porcine oocytes were cultured in the NCSU-23 medium with or without 17-AAG for 44 h. The proportion of GV arrested oocytes was significantly different between the 17-AAG treated and untreated group (78.2 vs 34.8%, p<0.05). After completion of meiotic maturation, the proportion of MII oocytes was lower in the 17-AAG treated group than in the control group (27.9 vs 71.0%, p<0.05). After IVF, the percentage of penetrated oocytes was significantly lower in the 17-AAG treated group (25.2%), resulting in lower normal pronucleus formation (2PN of 14.6%). Therefore, the inhibition of meiotic progression by Hsp90 inhibitor played a critical role in fertilization status. Porcine embryo were cultured in the PZM-3 medium with or without 17-AAG for 6 days. In result, significant differences in developmental potential were detected between the embryos that were cultured with or without 17-AAG. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) showed that the number of containing fragmented DNA at the blastocyst stage increased in the 17-AAG treated group compared with control (7.5 vs 4.4, respectively). Blastocysts that developed in the 17-AAG treated group had low structural integrity and high apoptotic nuclei than those of the untreated control, resulting in decrease the embryonic qualities of preimplantation porcine blastocysts. The mRNA expressions of cell cycle-related genes were down-regulated in the 17-AAG treated group compared with control. Also, the expression of the pro-apoptotic gene Bax increased in 17-AAG treated group, whereas expression of the anti-apoptotic gene Bel-XL decreased. However, the expression of ER stress-related genes did not changed by 17-AAG. Cultured pESF cells were treated with or without 17-AAG and used for MIT assay. The results showed that viability of pESF cells were decreased by treatment of 17-AAG ($2{\mu}M$) for 24 hr. These results indicated that 17-AAG decreased cell proliferation and increased cell death. Expression patterns Hsp90 complex genes (Hsp70 and p23), cell cycle-related genes (cdc2 and cdc25c) and apoptosis-related genes (Bax and Bcl-XL) were significantly changed by using RT-PCR analysis. The spliced form of pXbp-1 product (pXbp-1s) was detected in the tunicamycin (TM) treated cells, but it is not detected in 17-AAG treated cells. In conclusion, Hsp90 appears to play a direct role in porcine early embryo developmental competence including structural integrity of blastocysts. Also, these results indicate that Hsp90 is closely associated with cell cycle- and apoptosis-related genes expression in developing porcine embryos.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.284-309
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• 2007

Technology transfer (TT) consortium is an affiliation of two or more public research institutions (PRIs) that participate in a common technology transfer activity or pool their resources together, with the objective of facilitating technology transfer. Based on empirical analysis of five regional TT consortia (2002-2006) operating in Korea, this paper suggests their effectiveness by employing a TT performance index (TTPI) and identifies possible characteristics involved, such as motivations, facilitators, barriers, and challenges. TTPI devised in the paper is a new composite TT performance index to measure how much the TT performance of a PH changed in a designated year compared to a base year. All the performance indicators of TTPI are well-structured based on the unique TT process that is prevalent in Korea. Further, TTPI can bring different size and focus of PRIs to the same scale for comparison by double-normalizing. The paper tests the effectiveness of TT consortium for the escalation of TT performances in member PRIs by highlighting the differences of TTPI's between 2005 and 2001. As a result, the paper found that the escalation of TTPI for member PRIs was greater than that for non-member PRIs. As for the characteristics of TT consortia, their respective factors obtained by TT expert survey were computed with proportion tests of differences (Z tests) to compare two perspectives between intramural and extramural groups. One of key findings is that there is general homogeneity in stakeholder perspectives regarding motivations, facilitators, barriers, and challenges. Some notable responses are as follow; the most probable motivation to join TT consortium is to share or exchange TT competences for enhanced performance. Second, the most probable facilitator is professional capability of consortium-hired personnel. Third, the foremost probable barriers to effective TT consortium are frequent change of consortium director and passive participation of member PRIs. Lastly, both publicizing TT consortia and developing performance metrics are the most important for the improvement of TT consortia. The understanding of the characteristics of TT consortia increases the likelihood of accelerated success, because TT consortia path from formation to termination encompasses many concepts, processes, principles, and factors. Finally, an analysis of the survey data combined with expert interview and observation data led the authors to derive five conditions as being critical to viable TT consortia in Korea at early stage of technology transfer systems. These conditions include policy infrastructure, proactive participation, excellent professionals, personal motivation, and teaming mechanisms. It is expected that the Korean evidence is a starting point to develop and refine the theory of TT consortia and for additional studies in other countries.

• Korea journal of population studies
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• v.26 no.2
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• pp.1-32
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• 2003

An analysis on cause-specific mortality at the provincial level provides essential information for policy formulation and makes it possible to draw hypotheses regarding various diseases and causes of death. Although the mortality level and causes of death at the provincial level are determined by the multiple effects of socioeconomic, cultural, medical and ecological factors, this study primarily intends to examine similarities and differences of cause-specific mortality at the provincial level. Utilizing the registered death and the registered population as of 1998, the delayed death registration and unreported infant deaths were supplemented at the provincial level and age-standardized death rates and life tables were calculated. Regarding the mortality level due to all causes, major findings were as follow: (1) For both sexes as a whole, Seoul showed the lowest mortality level, and Jeonnam showed the highest mortality level; and (2) The differences of the mortality level among provinces were greater for males than females and for those less than 65 years than those 65 years and over. Regarding the cause-specific mortality level revealed in all indicators (cause-specific age-standardized mortality rates and the probability of dying at birth due to a specific cause for males, females, and both sexes combined respectively), the major findings were as follow: (1) The mortality level due to heart diseases was the highest in Busan and the lowest in Gangweon; (2) The mortality level due to liver diseases was the highest in Chonnam; and (3) The mortality level due to traffic accidents was the highest in Chungnam and the lowest in Inchon. As the mortality differentials at the provincial level are related to various factors, exploratory statistical analysis is attempted for the 25 explanatory variables including socioeconomic variables and 90 mortality variables. Mortality due to all causes are related to socioeconomic variables. Among cause-specific mortality, mortality due to liver diseases and traffic accidents is related to socioeconomic variables. Finally, the need to improve the quality of death certificate is discussed.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Sericultural and Entomological Science
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• v.52 no.1
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• pp.73-78
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• 2014

This study was carried out to establish the self-specification and shelf-life by standardization of manufacturing process for Tenebrio molitor larvae. First, standardization of manufacturing process for T. molitor was set up. Sterilization for larvae placed on a multistage shelf with intervals of about 10 cm was carried out at $115^{\circ}C$, $1kgf/cm^2$ for 10 min. After sterilization, T. molitor larvae were frozen at less than $-35^{\circ}C$ for more than 12 hrs. And then, they were dried under $-15^{\circ}C$, 0.5 torr vacuum for more than 30 hrs. Second, we decided self-specification for T. molitor larvae. Their moisture, acidity, peroxide, crude protein and crude fat level should be 5% or less, 3 mg/g or less, 30 meq/g or less, 45% or more, and 25% or more, respectively. Also, oleic acid, representative material, level was set up 11 ~ 16%. Third, we decided shelf-life by analysis of the physicochemical characteristic, sensory evaluation and microbial indicators. The final expiry date for lyophilized T. molitor larvae in PET bottle was calculated as 12 months from date of manufacture. We expect that optimal manufacturing process system, self-specification, and shelf-life proposed in this study can be used in industrial production of T. molitor as a novel food.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.136-144
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• 2017

Objectives : Cognitive complaints are reported frequently after breast cancer treatments. The causes of cognitive decline are multifactorial, a result of the effect of cancer itself, chemotherapy, and psychological factors such as depression and anxiety. However, cognitive decline does not always correlate with neuropsychological test performance. The purpose of this study was to examine the relationship of subjective cognitive decline with objective measurement and to explore associated factors of cognitive function in breast cancer survivors. Methods : We included 29 breast cancer survivors who complain cognitive decline at least 6 months after treatment and 20 age-matched healthy controls. Neuropsychological tests were performed in all participants. Multivariable regression analysis evaluated associations between neuropsychological test scores and psychological distress including depression and anxiety, also considering age, education, and comorbidity. Results : There were no statistically significant differences in neuropsychological test performances. However, the breast cancer survivors showed a significantly higher depression(p=0.002) and anxiety(p<0.001) than the healthy controls did. Among the cancer survivors, poorer executive function was strongly associated with higher depression(${\beta}=-0.336$, p=0.001) and anxiety(${\beta}=-0.273$, p=0.009), after controlling for age, education, and comorbidity. In addition, poorer attention was also significantly related with depression(${\beta}=-0.375$, p=0.023) and anxiety (${\beta}=-0.404$, p=0.013). Conclusions : The results of this study showed the discrepancies between subjective complaints and objective measures of cognitive function in breast cancer survivors. It suggests that subjective cognitive decline could be indicators of psychological distress such as depression and anxiety.

• Journal of Animal Environmental Science
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• v.17 no.sup
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• pp.51-60
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• 2011

The objective of this study was to suggest strategies for improving "Livestock Manure Treatment Systems and Related Technologies Assessment Program". Analytic Hierarchy Process (AHP) was used to evaluate reasonableness of applied weight value for assessment and improve program management strategies. Results of mail survey collected from animal manure treatment technology specialists of 30 companies nationwide were used for AHP. Company's ability, technological prowess, facility's convenience, economic feasibility are four important aspects of assessment program evaluation using AHP. More than 70% of the respondents said they were overall satisfied with the objectivity of assessment program regarding above four evaluation aspects. However, only 36% of them answered that they were very satisfied with the objectivity of assessment program in terms of economic feasibility. The evaluation results revealed that the assessment program needs to be made up for the weak points regarding economic feasibility. The AHP weight calculation results showed that the current assessment program overestimates the technological prowess, especially livestock manure treatment efficiency. It suggests that the weight value of current assessment program in terms of technological prowess needs to be determined carefully. The current assessment program combined with AHP weight value determination approach will be very useful to improve objectivity and reliability of assessment.