• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Animal Environmental Science
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• v.10 no.1
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• pp.47-58
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• 2004

This work was carried out to investigate the effects of rice hull continuously utilized and/or replenished on the composting properties and to obtain the fundamental data between an unsupported wall and a soil supported wall during the period of composting with pig slurry in winter season. There were no the temperature holding effects in soil supported wall. New compost facility design for the temperature holding effects from a soil supported wall was required. The results were as follows; 1. Composting 1㎥ of pig slurry caused to save on 0.31㎥ of bulking agent in the unsupported wall in comparison with a soil supported wall in the rice hull single addition, and 0.45㎥ in the rice hull gradual addition. 2. The pile in the rice hull single addition had a high temperature in 4 days of composting indicating $71^{\circ}C$ and had a tendency in repeating periodically between $40^{\circ}C$ and $65^{\circ}C$ till 43 days of composting. And also the temperature of the pile was maintained between $48^{\circ}C$ and $28^{\circ}C$ after 50 days of composting. The pile of a rice hull gradual addition had the lower point of the temperature high increasingly according to adding up rice hull during the 35 days of composting. 3. The pH recorded in the rice hull single addition was higher(8.35∼10.02) compared to the rice hull gradual addition(8.6∼9.8). The pile of a rice hull single addition had a tendency in abruptly decreasing pH of the unsupported wall during the period of between 0.363$\textrm m^3$ and 0.537$\textrm m^3$ as a unit of pig slurry per rice hull. EC depending upon the way in adding rice hull was changed between 1.10 mS/$\textrm {cm}^3$ and 1.87 mS/$\textrm {cm}^3$. 4. The organic matter in an unsupported wall of the hull single addition was maintained the level of 55% during the period between 0.119㎥ and 0.363㎥ as a unit of pig slurry per rice hull while in the soil supported wall between 48 and 70. Water soluble C:N ratio was maintained between 1 and 2 in the rice hull single addition, while between 1 and 3 in the rice hull gradual addition. 5. Fertilizer constituents were detected higher level in the unsupported wall than in the soil supported wall in all treatments. This was dependant upon the input of pig slurry.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.40 no.4
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• pp.268-281
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• 2004

A study was carried out in order to estimate the deformation of the pound net according to the current by the model test in the circulating water channel. The tension of the frame rope and the variation of net shape were measured to investigate the deforming of the model pound net in the flow. The results are obtained as follows; 1. The experimental equation between tensions (R) of the frame rope and velocity (ν)was found to be R=$19.58v^{1.98}$($r^2$=0.98) in case of the upperward flow with fish court net and R=$26.90v^{1.72}$($r^2$=0.95)at the upperward flow with bag net according to the velocity from 0.0m/s to 0.6m/s, respectively. 2. As the variation of flow speed inside of the model net was gradually decreased according as which is passed through netting panels, in case of the upperward flow with fish court net, the flow speed was about 70% of initial flow speed at 0.1m/s, 60% at 0.2m/s, 50% at 0.3m/s and 40% 0.4~0.6m/s at the measurement point(h) inside of the first bag net, respectively. In case of the upperward flow with bag net, as the flow speed was steeply decreased according as which if passed through the second bag net, it was 30~60% of the initial flow speed and was 20~30% inside of the first bag net and was about 10~20% inside of the inclined passage net. 3. In case of the upperward flow with fish court net, the variation of deformed angle of fish court net was from 0$^{\circ}$ to 70$^{\circ}$and that of inclined passage net was from 0$^{\circ}$ to 63$^{\circ}$and that of the second bag net was from 0$^{\circ}$ to 47$^{\circ}$ . 4. In case of the upperward flow with fish court net, the variation of deformed angle of the second bag net was changed from 0$^{\circ}$ to 70$^{\circ}$and that of the inclined passage net was from 0$^{\circ}$ to 55$^{\circ}$ and that of the fish court net was from 0$^{\circ}$ to 50$^{\circ}$. The depth ratio of the first bag net was changed from 0% to 35% and that of the second bag net was from 0% to 20% and that of the inclined passage net was from 0% to 35%. In the flow speed 0.5m/s, the inclined passage net was raised up to the entry of the bag net and then prevented it more over 90%. 5. To be increased the opening volume of pound net, it needs to attach the added weight outside of the fish court net, inclined passage net and bag net. At the same time, it needs to adjust the tension of the twine for maintenance of the shape.

• Journal of Nutrition and Health
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• v.49 no.3
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• pp.165-178
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• 2016

Purpose: This study was performed to examine associations of intakes of milk and dairy products, pulses, and soy foods with bone health in Koreans aged 50 yr and older. Methods: A total of 3,201 men and 3,581 women aged 50 yr and older who participated in the 2008~2011 Korea National Health and Nutrition Examination Survey were grouped by sex and age groups of 50~64 yr and 65 yr and older. Subjects within each sex and age group were divided into three bone health groups: normal, osteopenia, and osteoporosis groups based on bone mineral density. Intakes of nutrients and foods derived from 24-hour recall data were compared among three bone health groups. Associations between intake frequencies of foods, including milk, yogurt, tofu, or soy milk, and osteoporosis risk were evaluated based on confounding risk factor-adjusted logistic regression. Results: Calcium intake was in the order of normal, osteopenia, and osteoporosis in men (p < 0.01) and women (p < 0.05) aged 50~64 yr as well as in men aged 65 yr and older (p < 0.001). In women aged 50~64 yr, intake of milk and dairy products was lower in the osteoporosis group (p < 0.01) as compared with the osteopenia group. Intake of pulses or tofu was not significantly different among bone health groups. Odds ratio (OR) for milk intake frequency (${\geq}2$ times/week) compared to intake frequency less than 1 time/month was 0.45 (95% CI 0.24~0.85, p for trend = 0.022) in men aged 65 yr and older. The OR for yogurt intake frequency (1 time/month~1 time/week) was 0.47 (95% CI 0.30~0.73, p for trend = 0.019) in women aged 50~64 yr. Intake frequency of tofu or soy milk was not associated with reduced risk of osteoporosis in all groups. Conclusion: Dairy food intake was significantly associated with bone health, and its effect was sex- and age group-specific, whereas soy food intake was not. Dietary intervention to prevent osteoporosis would be effective for women aged 50~64 yr old and for men aged 65 yr and older.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.37-56
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• 2001

This study was conducted to analyze the degree of changes in knowledge and attitude toward smoking and to examine the factors affecting knowledge and attitude for smoking after providing a smoking prevention program based on social influence model for a year to middle school students. Study population consists of 665 subjects of middle school students(aged 14 years) in Gumi city in Kyeongsangbukdo Province. Among them three-hundred sixty-seven students(intervention group) were educated to a smoking prevention program for 1 year from April 1999 to April 2000. School-based four-class program to prevent smoking was developed. The program provides instruction about short and long-term negative physiologic and social consequences of smoking and also discussed the health hazards of smoking, social pressure to smoke, peer norms regarding tobacco use, and refusal skill. A 45-item self-administered structured questionnaire was designed to evaluate the change of knowledge, attitude, smoking rate and the amount of smoking. The instrument was comprised of 11 knowledge items, thirteen attitude item and demographic items. Each scales were created by summing responses to each items within each scales and high scores on the knowledge, attitude, and smoking behavioral intention scales indicated positive responses. Based on the changes before and after the implementation of smoking prevention program between intervention and control group, the change of scores on knowledge were significantly different between the control group and the intervention group(p<0.05) and the change of scores on the attitude toward smoking was significantly different between intervention and control group. The change of smoking rate were not showing a significant difference between two groups but the amount of smoking were significantly reduced in intervention group than control group. In multiple regression analysis on changes of knowledge about smoking, the variables of smoking prevention program education, previous knowledge on smoking and students' school performance were selected the significant variables. In multiple regression to analysis of the factors influencing changes in attitude toward smoking, the variables of smoking prevention program education, previous knowledge on smoking were shown to be significant. The smoking prevention program was effective on change of knowledge and attitude of middle school students. In considering that the policy should be needed to extent of implementation of school-based health education curricula based on social influence model and it would contribute to reduce smoking of students.

• Sleep Medicine and Psychophysiology
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• v.5 no.1
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• pp.54-70
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• 1998

Objectives : The purpose of this study is to evaluate the role of structural and functional changes of the brain in the pathophysiology of schizophrenia. Methods : The authors measured the regions of interest on the magnetic resonance imaging of the brain in 20 patients with paranoid schizophrenia(15 men and 5 women) and 23 control subjects(15 men and 8 women). We also assessed the neurocognitive functions with the Wisconsin Card Sorting Test, the Benton Neuropsychological Assessment, and the Weschler IQ test-Korean version, soft neurologic signs, and psychiatric symptoms in the patient group. Results : In the patient group, all ventricles and basal ganglia including caudate nucleus and globus pallidus were significantly enlarged. Although there were no significant differences between the two groups in the values of right frontal lobe and left temporal lobe, there was a tendency of decrease in the values of right frontal lobe and left temporal lobe. There were significant positive correlations between the values of ventricles and the frequency of previous hospitalization. However, there were no significant correlations between other values of regions of interest and clinical data. The value of the right frontal lobe was significantly correlated with the score of soft neurologic signs, which is suggestive of the neurodevelopmental abnormalities. There were significant correlations between the value of frontal lobe and the scores of the various subscales of Benton Neuropsychiatric Inventory. In contrast, the value of left amygdala and putamen showed significant correlation with the score of verbal IQ on the Weschler IQ test. Structural changes of the temporal lobe areas were related with the positive and general symptom scores on PANSS, while those of the basal ganglia were related with the negative symptom scores. Conclusions : These results suggest that the structural changes of the brain in the patients with schizophrenia show the dual process, which is suggestive that the enlarged ventricle show the neurodegenerative process, while enlarged basal ganglia, and shrinked right frontal and left temporal lobe show the neurodevelopmental abnormalities. Among these changes, structural changes of the frontal lobe related with various neuropsychological deficits, while those of left temporal lobe related with language abnormality. Relative to the relation between structural changes and psychiatric symptoms, structural changes of the temporal lobe areas were related with the positive and general symptoms, while those of the basal ganglia were related with the negative symptoms.

• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.1-11
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• 2008

As conventional oil and gas reservoirs become depleted, interests for oil sands has rapidly increased in the last decade. Oil sands are mixture of bitumen, water, and host sediments of sand and clay. Most oil sand is unconsolidated sand that is held together by bitumen. Bitumen has hydrocarbon in situ viscosity of >10,000 centipoises (cP) at reservoir condition and has API gravity between $8-14^{\circ}$. The largest oil sand deposits are in Alberta and Saskatchewan, Canada. The reverves are approximated at 1.7 trillion barrels of initial oil-in-place and 173 billion barrels of remaining established reserves. Alberta has a number of oil sands deposits which are grouped into three oil sand development areas - the Athabasca, Cold Lake, and Peace River, with the largest current bitumen production from Athabasca. Principal oil sands deposits consist of the McMurray Fm and Wabiskaw Mbr in Athabasca area, the Gething and Bluesky formations in Peace River area, and relatively thin multi-reservoir deposits of McMurray, Clearwater, and Grand Rapid formations in Cold Lake area. The reservoir sediments were deposited in the foreland basin (Western Canada Sedimentary Basin) formed by collision between the Pacific and North America plates and the subsequent thrusting movements in the Mesozoic. The deposits are underlain by basement rocks of Paleozoic carbonates with highly variable topography. The oil sands deposits were formed during the Early Cretaceous transgression which occurred along the Cretaceous Interior Seaway in North America. The oil-sands-hosting McMurray and Wabiskaw deposits in the Athabasca area consist of the lower fluvial and the upper estuarine-offshore sediments, reflecting the broad and overall transgression. The deposits are characterized by facies heterogeneity of channelized reservoir sands and non-reservoir muds. Main reservoir bodies of the McMurray Formation are fluvial and estuarine channel-point bar complexes which are interbedded with fine-grained deposits formed in floodplain, tidal flat, and estuarine bay. The Wabiskaw deposits (basal member of the Clearwater Formation) commonly comprise sheet-shaped offshore muds and sands, but occasionally show deep-incision into the McMurray deposits, forming channelized reservoir sand bodies of oil sands. In Canada, bitumen of oil sands deposits is produced by surface mining or in-situ thermal recovery processes. Bitumen sands recovered by surface mining are changed into synthetic crude oil through extraction and upgrading processes. On the other hand, bitumen produced by in-situ thermal recovery is transported to refinery only through bitumen blending process. The in-situ thermal recovery technology is represented by Steam-Assisted Gravity Drainage and Cyclic Steam Stimulation. These technologies are based on steam injection into bitumen sand reservoirs for increase in reservoir in-situ temperature and in bitumen mobility. In oil sands reservoirs, efficiency for steam propagation is controlled mainly by reservoir geology. Accordingly, understanding of geological factors and characteristics of oil sands reservoir deposits is prerequisite for well-designed development planning and effective bitumen production. As significant geological factors and characteristics in oil sands reservoir deposits, this study suggests (1) pay of bitumen sands and connectivity, (2) bitumen content and saturation, (3) geologic structure, (4) distribution of mud baffles and plugs, (5) thickness and lateral continuity of mud interbeds, (6) distribution of water-saturated sands, (7) distribution of gas-saturated sands, (8) direction of lateral accretion of point bar, (9) distribution of diagenetic layers and nodules, and (10) texture and fabric change within reservoir sand body.