• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2000.05a
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• pp.71-73
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• 2000

Campylobacter jejuni is most frequently identified cause of cause of acute diarrhoeal infections in developeed countries, exceeding rates of illness caused by both salmonella and shigilla(Skirrow, 1990 ; Lior 1994). Previous studies on campylobacter jejuni contamination of commercial broiler carcasses in u.s.(Stern, 1992). Most cases of the disease result from indirect transmission of Campylobactor from animals via milk, water and meat. In addition to Campylobactor jejuni. the closely relates species Campylobactor coli and Campylobactor lari have also been implicated as agents of gastroenteritis in humans. Campylobactor coli represented only approximately 3% of the Campylobactor isolates from patients with Campylobactor enteritis(Griffiths and Park, 1990) whereas Campylobactor coli is mainly isolated from pork(Lmmerding et al., 1988). Campylobactor jejuni has also been isolated from cases of bacteremia, appendicitis and, recently, has been associated with Guillai-Barre syndrome(Allos and Blaser, 1994; von Wulffen et al., 1994; Phillips, 1995). Studies in volunteers indicated that the infectious dose for Campylobactor jejuni is low(about 500 organisms)(Robinson, 1981). The methods traditionally used to detect Campylobactor ssp. in food require at least two days of incubation in an enrichment broth followed by plating and two days of incubation on complex culture media containing many antibiotics(Goossens and Butzler, 1992). Finnaly, several biochemical tests must be done to confirm the indentification at the species level. Therfore, sensitive and specific methods for the detection of small numbers of Campylobactor cells in food are needed. Polymerase chain reaction(PCR) assays targeting specific DNA sequences have been developed for the detection of Campylobactor(Giesendorf and Quint, 1995; Hemandex et al., 1995; Winter and Slavidk, 1995). In most cases, a short enrichment step is needed to enhance the sensitivity of the assay prior to detection by PCR as the number of bacteria in the food products is low in comparison with those found in dinical samples, and because the complex composition of food matrices can hinder the PCR and lower its sensitivity. However, these PCR systems are technically demanding to carry out and cumbersome when processing a large number of samples simutaneously. In this paper, an immunomagnetic method to concentrate Campylobactor cells present in food or clinical samples after an enrichment step is described. To detect specifically the thermophilic Campylobactor. a monoclonal antibody was adsorbed on the surface of the magnetic beads which react against a major porin of 45kDa present on the surface of the cells(Huyer et al., 1986). After this partial purification and concentration step, detection of bound cells was achieved using a simple, inexpensive microtitre plate-based hybridization system. We examined two alternative detection systems, one specific for thermophilic Campylobactor based on the detection of 23S rRNA using an immobilized DNA probe. The second system is less specific but more sensitive because of the high copy number of the rRNA present in bacterial cell($10^3-10^4$). By using specific immunomagnetic beads against thermophilic Campylobactor, it was possible to concentrate these cells from a heterogeneous media and obtain highly specific hybridization reactions with good sensitivity. There are several advantages in using microtitre plates instead of filter membranes or other matrices for hybridization techniques. Microtitre plates are much easier to handle than filter membranes during the adsorption, washing, hybridization and detection steps, and their use faciilitates the simultanuous analysis of multiple sample. Here we report on the use of a very simple detection procedure based on a monoclonal anti-RNA-DNA hybrid antibody(Fliss et al., 1999) for detection of the RNA-DNA hybrids formed in the wells.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.105-115
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• 2008

Statement of problem: Proper finishing and polishing enhance both the esthetics and the longevity of restored teeth. Blade finishing technique would be suited for smoothing and finishing. Evaluation of this technique are necessary. Purpose: The purpose of this study was to evaluate the blade finishing and polishing procedures on the surface profile and roughness of ormocer-based composite resin and nanohybrid composite resin. Material and methods: The material included a ormocer-based composite resin ($Admira^{(R)}$ & $Admira^{(R)}$ Flow); a nanohybrid composite resin ($Grandio^{(R)}$ & $Grandio^{(R)}$ Flow). One hundred forty specimens of each group were prepared using a mylar strip and randomly divied into blade finishing and rubber polishing groups (n=10). The average surface roughness (Ra) in micrometers was measured and the surface profile was examined by scanning electron microscopy (SEM) (Magnification ${\times}$ 200). The data were analyzed by Mann-Whitney Test at 0.05 significance level. Conclusion: The results of this study indicated that the mylar strip produced the smoothest surface on all materials and among the finishing-polishing methods was not significanct difference (P>0.05). Ormocer-based flowable composite resin performedthe lowest variability in initial surface roughness among the tested materials.

• Restorative Dentistry and Endodontics
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• v.35 no.2
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• pp.125-133
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• 2010

Objectives: Rapid polymerization of overlying composite resin causes high polymerization shrinkage stress at the adhesive layer. In order to alleviate the shrinkage stress, increasing the light intensity over the first 5 seconds was suggested as an exponential curing mode by an LED light curing unit (Elipar FreeLight2, 3M ESPE). In this study, the effectiveness of the exponential curing mode on reducing stress was evaluated with measuring microtensile bond strength of three adhesives after the overlying composite resin was polymerized with either continuous or exponential curing mode. Methods: Scotchbond Multipurpose Plus (MP, 3M ESPE), Single Bond 2 (SB, 3M ESPE), and Adper Prompt (AP, 3M ESPE) were applied onto the flat occlusal dentin of extracted human molar. The overlying hybrid composite (Denfil, Vericom, Korea) was cured under one of two exposing modes of the curing unit. At 48h from bonding, microtensile bond strength was measured at a crosshead speed of 1.0 mm/min. The fractured surfaces were observed under FE-SEM. Results: There was no statistically significant difference in the microtensile bond strengths of each adhesive between curing methods (Two-way ANOVA, p > 0.05). The microtensile bond strengths of MP and SB were significantly higher than that of AP (p < 0.05). Mixed failures were observed in most of the fractured surfaces, and differences in the failure mode were not observed among groups. Conclusion: The exponential curing method had no beneficial effect on the microtensile dentin bond strengths of three adhesives compared to continuous curing method.

• Journal of Christian Education in Korea
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• v.74
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• pp.107-134
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• 2023

The purpose of this study is to provide a foundation for establishing the correct direction of education that utilizes artificial intelligence, a key technology of the Fourth Industrial Revolution, in the context of Christian education. To achieve this, theoretical and literature research was conducted. First, the research analyzed the historical development of artificial intelligence to understand its characteristics. Second, the research analyzed the use of artificial intelligence in convergence education from an educational perspective and examined the current policy direction in South Korea. Through this analysis, the research examined the direction of Christian education in the era of artificial intelligence. In particular, the research critically examined the perspectives of continuity and change in the context of Christian education in the era of artificial intelligence. The research reflected upon the fundamental educational purposes of Christian education that should remain unchanged despite the changing times. Furthermore, the research deliberated on the educational curriculum and teaching methods that should adapt to the changing dynamics of the era. In conclusion, this research emphasizes that even in the era of artificial intelligence, the fundamental objectives of Christian education should not be compromised. The utilization of artificial intelligence in education should serve as a tool that fulfills the mission permitted by God. Therefore, Christian education should remain centered around God, rooted in the principles of the Bible. Moreover, Christian education should aim to foster creative and convergent Christian nurturing. To achieve this, it is crucial to provide learners with an educational environment that actively utilizes AI-based hybrid learning environments and metaverse educational platforms, combining online and offline learning spaces. Moreover, to enhance learners' engagement and effectiveness in education, it is essential to actively utilize AI-based edutech that reflects the aforementioned educational environments. Lastly, in order to cultivate Christian learners with dynamic knowledge, it is crucial to employ a variety of teaching and learning methods grounded in constructivist theories, which emphasize active learner participation, collaboration, inquiry, and reflection. These approaches seek to align knowledge with life experiences, promoting a holistic convergence of faith and learning.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.49-63
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• 2018

Purpose : The purpose of this study is investigating the changes of treatment plan and comparing skin dose with or without the skin flash. To investigate optimal applications of the skin flash, the changes of skin dose of each plans by various thicknesses of skin flash were measured and analyzed also. Methods and Material : Anthropomorphic phantom was scanned by CT for this study. The 2 fields hybrid IMRT and the 6 fields static IMRT were generated from the Eclipse (ver. 13.7.16, Varian, USA) RTP system. Additional plans were generated from each IMRT plans by changing skin flash thickness to 0.5 cm, 1.0 cm, 1.5 cm, 2.0 cm and 2.5 cm. MU and maximum doses were measured also. The treatment equipment was 6MV of VitalBeam (Varian Medical System, USA). Measuring device was a metal oxide semiconductor field-effect transistor(MOSFET). Measuring points of skin doses are upper (1), middle (2) and lower (3) positions from center of the left breast of the phantom. Other points of skin doses, artificially moved to medial and lateral sides by 0.5 cm, were also measured. Results : The reference value of 2F-hIMRT was 206.7 cGy at 1, 186.7 cGy at 2, and 222 cGy at 3, and reference values of 6F-sIMRT were measured at 192 cGy at 1, 213 cGy at 2, and 215 cGy at 3. In comparison with these reference values, the first measurement point in 2F-hIMRT was 261.3 cGy with a skin flash 2.0 cm and 2.5 cm, and the highest dose difference was 26.1 %diff. and 5.6 %diff, respectively. The third measurement point was 245.3 cGy and 10.5 %diff at the skin flash 2.5 cm. In the 6F-sIMRT, the highest dose difference was observed at 216.3 cGy and 12.7 %diff. when applying the skin flash 2.0 cm for the first measurement point and the dose difference was the largest at the application point of 2.0 cm, not the skin flash 2.5 cm for each measurement point. In cases of medial 0.5 cm shift points of 2F-hIMRT and 6F-sIMRT without skin flash, the measured value was -75.2 %diff. and -70.1 %diff. at 2F, At -14.8, -12.5, and -21.0 %diff. at the 1st, 2nd and 3rd measurement points, respectively. Generally, both treatment plans showed an increase in total MU, maximum dose and %diff as skin flash thickness increased, except for some results. The difference of skin dose using 0.5 cm thickness of skin flash was lowest lesser than 20 % in every conditions. Conclusion : Minimizing the thickness of skin flash by 0.5 cm is considered most ideal because it makes it possible to keep down MUs and lowering maximum doses. In addition, It was found that MUs, maximum doses and differences of skin doses did not increase infinitely as skin flash thickness increase by. If the error margin caused by PTV or other factors is lesser than 1.0 cm, It is considered that there will be many advantages in with the skin flash technique comparing without it.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.327-340
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• 2017

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2016. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of flow, heat and mass transfer, the reduction of pollutant exhaust gas, cooling and heating, the renewable energy system and the flow around buildings. CFD schemes were used more for all research areas. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results of the long-term performance variation of the plate-type enthalpy exchange element made of paper, design optimization of an extruded-type cooling structure for reducing the weight of LED street lights, and hot plate welding of thermoplastic elastomer packing. In the area of pool boiling and condensing, the heat transfer characteristics of a finned-tube heat exchanger in a PCM (phase change material) thermal energy storage system, influence of flow boiling heat transfer on fouling phenomenon in nanofluids, and PCM at the simultaneous charging and discharging condition were studied. In the area of industrial heat exchangers, one-dimensional flow network model and porous-media model, and R245fa in a plate-shell heat exchanger were studied. (3) Various studies were published in the categories of refrigeration cycle, alternative refrigeration/energy system, system control. In the refrigeration cycle category, subjects include mobile cold storage heat exchanger, compressor reliability, indirect refrigeration system with $CO_2$ as secondary fluid, heat pump for fuel-cell vehicle, heat recovery from hybrid drier and heat exchangers with two-port and flat tubes. In the alternative refrigeration/energy system category, subjects include membrane module for dehumidification refrigeration, desiccant-assisted low-temperature drying, regenerative evaporative cooler and ejector-assisted multi-stage evaporation. In the system control category, subjects include multi-refrigeration system control, emergency cooling of data center and variable-speed compressor control. (4) In building mechanical system research fields, fifteenth studies were reported for achieving effective design of the mechanical systems, and also for maximizing the energy efficiency of buildings. The topics of the studies included energy performance, HVAC system, ventilation, renewable energies, etc. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which could be help for improving the energy efficiency of the buildings. (5) The field of architectural environment was mostly focused on indoor environment and building energy. The main researches of indoor environment were related to the analyses of indoor thermal environments controlled by portable cooler, the effects of outdoor wind pressure in airflow at high-rise buildings, window air tightness related to the filling piece shapes, stack effect in core type's office building and the development of a movable drawer-type light shelf with adjustable depth of the reflector. The subjects of building energy were worked on the energy consumption analysis in office building, the prediction of exit air temperature of horizontal geothermal heat exchanger, LS-SVM based modeling of hot water supply load for district heating system, the energy saving effect of ERV system using night purge control method and the effect of strengthened insulation level to the building heating and cooling load.