• Elastomers and Composites
• /
• v.26 no.4
• /
• pp.287-295
• /
• 1991

The mixing process with carbon black is important in the rubber industries. However, it is difficult to characterize the mixing mechanism of the carbon black. The mixing mechanism(distributive mixing and dispersive mixing) was discribed in this paper. The effect of fill factor on the mixing of the carbon black was studied. The dispersive mixing ability increases with increasing fill factor. However, the distributive mixing ability decreases with increasing fill factor. The effect of the carbon black content on the rheological property of the material was studied in this paper. The viscosity of the material increases with increasing the carbon black content. However, the elasticity of the matarial decreases with the carbon black content.

• Computers and Concrete
• /
• v.30 no.2
• /
• pp.99-111
• /
• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

• International Journal of Knowledge Content Development & Technology
• /
• v.8 no.1
• /
• pp.5-23
• /
• 2018

There have been extensive studies done on webometrics, particularly on the impact of websites and the web impact factor. The present study analyzed the websites of ICAR organizations, according to the webometrics indicator. It examines and explores the 92 ICAR organizational websites in India and identifies a number of web pages and link pages, and calculates the Overall Web Impact Factor (WIF) and Absolute Web Impact Factor (WIF). In this study, all websites were analyzed and data extracted using Google search engine. It suggests that Web Impact Factors can be calculated as a way of comparing the attractiveness of web sites or domains on the Web.

• Journal of Korean Academy of Nursing
• /
• v.53 no.3
• /
• pp.324-339
• /
• 2023

Purpose: This study aimed to valuate the reliability and validity of the Korean version of the 5C Psychological Antecedents of Vaccination (K-5C) scale. Methods: The English version of the 5C scale was translated into Korean, following the World Health Organization guidelines. Data were collected from 316 community-dwelling adults. Content validity was evaluated using the content validity index, while construct validity was evaluated through confirmatory factor analysis. Convergent validity was examined by assessing the correlation with vaccination attitude, and concurrent validity was evaluated by examining the association with coronavirus disease 2019 (COVID-19) vaccination status. Internal consistency and test-retest reliability were also evaluated. Results: Content validity results indicated an item-level content validity index ranging from .83 to 1, and scale-level content validity index, averaging method was .95. Confirmatory factor analysis supported the fit of the measurement model, comprising a five-factor structure with a 15-item questionnaire (RMSEA = .05, SRMR = .05, CFI = .97, TLI = .96). Convergent validity was acceptable with a significant correlation between each sub-scale of the 5C scale and vaccination attitude. In concurrent validity evaluation, confidence, constraints, and collective responsibility of the 5C scale were significant independent predictors of the current COVID-19 vaccination status. Cronbach's alpha for each subscale ranged from .78 to .88, and the intraclass correlation coefficient for each subscale ranged from .67 to .89. Conclusion: The Korean version of the 5C scale is a valid and reliable tool to assess the psychological antecedents of vaccination among Korean adults.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.3
• /
• pp.221-232
• /
• 2023

In this paper, we propose a recommendation system based on the latent factor model using matrix factorization, which is one of the most commonly used collaborative filtering algorithms for recommendation systems. In particular, by introducing the concept of creating a list of recommended content and a list of non-preferred recommended content, and removing the non-preferred recommended content from the list of recommended content, we propose a method to ultimately increase the satisfaction. The experiment confirmed that using a separate list of non-preferred content to find non-preferred content increased precision by 135%, accuracy by 149%, and F1 score by 72% compared to using the existing recommendation list. In addition, assuming that users do not view non-preferred content through the proposed algorithm, the average evaluation score of a specific user used in the experiment increased by about 35%, from 2.55 to 3.44, thereby increasing user satisfaction. It has been confirmed that this algorithm is more effective than the algorithms used in existing recommendation systems.

• Food Engineering Progress
• /
• v.13 no.2
• /
• pp.79-84
• /
• 2009

Effect of baking on the physicochemical properties including pH, moisture content, hardness, color, and spread factor was investigated using a model system of cookies incorporated with strawberry powder as a value-added food ingredient. Strawberry powder was incorporated into cookie dough at 4 levels (0, 2, 4, and 6% w/w) by replacing equivalent amount of wheat flour of the cookie dough. After aging and sheeting, cookies were baked at 170$^{\circ}C$ for 15 min in an oven. The baked cookies were cooled to room temperature for 1 hr and packed in airtight bags prior to all measurements. The pH of dough and hardness of cookies decreased significantly with increase in strawberry powder content (p<0.05). Moisture content of the dough was not significantly affected by strawberry powder but mean values tended to increase as the strawberry powder content increased. Lightness (L$^{*}$-value) and yellowness (b$^{*}$-value) significantly decreased as the strawberry powder content increased; on the other hand, redness (a$^{*}$-value) increased significantly (p<0.05). Spread factor also increased significantly as the strawberry content increased in the formulation (p<0.05). Finally, correlation analysis indicated that level of strawberry powder incorporation was well-correlated with all the physicochemical properties studied. It is also noted that there was a significant positive correlation between the moisture content of dough and spread factor (p<0.05).

• Food Engineering Progress
• /
• v.14 no.1
• /
• pp.60-64
• /
• 2010

Freeze-dried broccoli powder was incorporated into cookie dough at 5 levels (0, 1, 2, 3, and 4%, w/w) by replacing equivalent amount of wheat flour of the cookie dough. After aging and sheeting, cookies were baked at 170${^{\circ}C}$ for 8 min in an oven. The baked cookies were cooled to room temperature for 1 hr and packed in airtight bags prior to all measurements. The pH and moisture content were ranged 6.74-6.90 and 2.67-4.12% (wet basis) depending on the broccoli powder level, respectively. Lightness (L$^{\ast}$-value), redness (a$^{\ast}$-value), and hardness decreased while yellowness (b$^{\ast}$-value) increased significantly as the broccoli powder content increased (p<0.05). Spread factor of the control was significantly lower than that of samples containing broccoli powder regardless of the concentration (p<0.05) and increased significantly with increase in broccoli powder content (p<0.05). The broccoli concentration correlated significantly with most of properties except for pH and spread factor (p<0.05 or p<0.01). Hardness correlated negatively with moisture content (p<0.05) but correlated positively with spread factor (p<0.01).

• Korean Journal of Soil Science and Fertilizer
• /
• v.48 no.6
• /
• pp.712-723
• /
• 2015

This study was conducted to develop the SWCC estimation equation using scaling technique, and to investigate relative sensitivity of the SWCC according to the soil water tension, for the four kinds of soil texture such as Sand [S], Sandy Loam [SL], Loam [L] and Clay Loam [CL]. The SWCC estimation equation of scale factor [${\Theta}sc$] (Eq. 1) was developed based on the log function (Eq. 2) and exponential function (Eq. 3). ${\Theta}sc=[({\Theta}-{\Theta}r)/({\Theta}s-{\Theta}r)]$ (Eq. 1) ${\Theta}sc=-0.196ln(H)+0.4888$ (Eq. 2) ${\Theta}sc=0.3804(H)^{(-0.448)}$ (Eq. 3) where, ${\Theta}$: water content (g/g %), ${\Theta}s$: water content at 0.1bar, ${\Theta}r$: water content at 15bar, H: soil water tension (matric potential) (bar) Relative sensitivity of soil water content was decreased as increase soil water tension, those according to soil water tension were 0.952~0.620 compared to 0.1bar case. Relative sensitivity of scale factor was also decreased as increase soil water tension, those according to soil water tension were 0.890~0.577 compared to 0.2bar case.

• International journal of advanced smart convergence
• /
• v.10 no.3
• /
• pp.122-130
• /
• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• Journal of the Korean Wood Science and Technology
• /
• v.46 no.4
• /
• pp.380-392
• /
• 2018

The importance of polysaccharides is increasing globally due to their role as a significant source of dietary prebiotics in the human diet. In the present study, in order to maximize the yield of crude polysaccharides from Pinus densiflora, response surface methodology (RSM) was used to optimize a two-stage extraction process consisting of steam explosion and water extraction. Three independent main variables, namely, the severity factor (Ro) for the steam explosion process, the water extraction temperature ($^{\circ}C$), and the ratio of water to raw material (v/w), were studied with respect to prebiotic sugar content. A Box-Behnken design was created on the basis of the results of these single-factor tests. The experimental data were fitted to a second-order polynomial equation for multiple regression analysis and examined using the appropriate statistical methods. The data showed that both the severity factor (Ro) and the ratio of water to material (v/w) had significant effects on the prebiotic sugar content. The optimal conditions for the two-stage process were as follows: a severity factor (Ro) of 3.86, a water extraction temperature of $89.66^{\circ}C$, and a ratio of water to material (v/w) of 39.20. Under these conditions, the prebiotic sugar content in the extract was 332.45 mg/g.