• International Journal of Highway Engineering
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• v.19 no.6
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• pp.129-137
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• 2017

PURPOSES : The purpose of this study is to develop bridge deck concrete materials based on ordinary Portland cement concrete, and to evaluate the applicability of the developed materials through material properties tests. METHODS : For field implementation, raw material (cement, fine aggregate, and coarse aggregate) properties, fresh concrete properties (slump and air content), strength (compressive, flexural and bond strength) gain, and durability (freeze-thaw resistance, scaling resistance, and rapid chloride penetrating resistance) performance were evaluated in the laboratory. RESULTS : For the selected binder content of $410kg/m^3$, W/B = 0.42, and S/a = 0.48, the following material performance results were obtained. Considering the capacity of the deck finisher, a minimum slump of 150 mm was required. At least 6 % of air content was obtained to resist freeze-thaw damage. In terms of strength, 51.28 MPa of compressive strength, 7.41 MPa of flexural strength, and 2.56 MPa of bond strength at 28 days after construction were obtained. A total of 94.9 % of the relative dynamic modulus of elasticity after 300 cycles of freeze-thaw resistance testing and $0.0056kg/m^2$ of weight loss in a scaling resistance test were measured. However, in a chloride ion penetration resistance test, the result of 3,356 Coulomb, which exceeds the threshold value of the standard specification (1000 Coulomb at 56 days) was observed. CONCLUSIONS : Instead of using high-performance modified bridge deck materials such as latex or silica fume, we developed an optimum mix design based on ordinary Portland cement concrete. A test construction was carried out at ramp bridge B (bridge length = 111 m) in Gim Jai City. Immediately after the concrete was poured, the curing compound was applied, and then wet mat curing was applied for 28 days. Considering the fact that cracks did not occur during the monitoring period, the applicability of the developed material is considered to be high.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.259-268
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• 2022

Molten salt consisting primarily of eutectic LiCl-KCl is currently being used in electrorefiners in the Fuel Conditioning Facility at Idaho National Laboratory. Options are currently being evaluated for storing this salt outside of the argon atmosphere hot cell. The hygroscopic nature of eutectic LiCl-KCl makes is susceptible to deliquescence in air followed by extreme corrosion of metallic cannisters. In this study, the effect of occluding the salt into a zeolite on water sorption/desorption was tested. Two zeolites were investigated: Na-Y and zeolite 4A. Na-Y was ineffective at occluding a high percentage of the salt at either 10 or 20wt% loading. Zeolite-4A was effective at occluding the salt with high efficiency at both loading levels. Weight gain in salt occluded zeolite-4A (SOZ) from water sorption at 20% relative humidity and 40℃ was 17wt% for 10% SOZ and 10wt% for 20% SOZ. In both cases, neither deliquescence nor corrosion occurred over a period of 31 days. After hydration, most of the water could be driven off by heating the hydrated salt occluded zeolite to 530℃. However, some HCl forms during dehydration due to salt hydrolysis. Over a wide range of temperatures (320-700℃) and ramp rates (5, 10, and 20℃ min^-1), HCl formation was no more than 0.6% of the Cl^- in the original salt.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

• KDI Journal of Economic Policy
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• v.35 no.4
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• pp.31-61
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• 2013

The purpose of this study is to calculate the economic value of transport demand forecasting risks in the road PPP project. Under the assumption that volatility of the road PPP project value occurs only in regard with uncertainty of traffic volume forecasting, this study calculates the economic value of the traffic forecasting risks in the case of the road PPP project. To that end, forecasted traffic volume is assumed to be a stochastic variable and to follow the Geometric Brownian motion as time passes. In particular, this study attempts to differentiate itself from existing studies that simply use an arbitrary assumption by presenting the application of different traffic volume growth volatility and the rates before and after the ramp-up period. Analysis of the case projects reveals that the risk premium related to traffic volume forecast of the project turns out as 7.39~8.30%, without considering option value-such as minimum revenue guarantee-while the project value volatility caused by transport demand forecasting risks is 17.11%. As the discount rate grows higher, the project value volatility tends to decrease and volatility in project value is always suggested to be larger than that in transport volume influenced by leverage effect due to fixed expenditure. The market value of transport demand forecasting risk-calculated using the project value volatility and risk premium-is analyzed to be between 0.42~0.50, implying that a 1% increase or decrease in the transport amount volatility would lead to a 0.42~0.50% increase or decrease in risk premium of the project.

• Korean journal of food and cookery science
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• v.18 no.6
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• pp.644-654
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• 2002

Brown color characteristics and antioxidizing activities were investigated for Doenjang under different processing conditions. Doenjang A was prepared directly from Meju and saline solution whereas Doenjang B was Prepared after separating soy sauce by soaking for 45 days. Both Doenjangs were aged for up to 180 days. Antioxidizing activity was studied in relation to the brown color characteristics using fat-soluble extract and water-soluble extract of Doenjang. The intensity of brown color was higher in the water-soluble Doenjang extract than the fat-soluble Doenjang extract. In the UV-VIS scanning spectra, water-soluble Doenjang extracts showed significant changes as the aging proceeded, but fat-soluble Doenjang extract did not. Antioxidizing activity of fat-soluble Doenjang extract increased as the aging period extended; however, no significant difference was detected in the water-soluble extract. Overall, Doenjang A showed higher contents of amino acids, reducing sugar, brown color, and antioxidizing activity, and the antioxidizing ability was higher in water-soluble Doenjang extract rather than in the fat-soluble Doenjang extract.