• KSCE Journal of Civil and Environmental Engineering Research
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• v.1 no.1
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• pp.69-76
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• 1981

The predictive equations for reservoir sedimentation rate now in use are extensively reviewed, and the equation of multiple regression type, in which the reservoir sedimentation rate is related with the watershed area and the trap-efficiency, is proposed based on the 113 irrigation reservoir resurvey data. The predictive relation so obtained proved to be a reasonable measure for the estimation of reservoir sedimentation rate. The relationship of sediment yield with the watershed area and with the reservoir trap efficiency is also analyzed. The variations of sedimentation rate and of the annual reservoir capacity loss rate was shown to heavily depend on the trap-efficiency of a reservoir. Besides, the effect of sedimentation on stream channels is confirmed and quantified based on the predictive equation derived in the present study.

• Tunnel and Underground Space
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• v.29 no.5
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• pp.356-366
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• 2019

Various TBM performance prediction models have been developed and most of them were considered penetration rate only. Despite the fact that some models have suggested equations and charts for estimating the utilization factor, but there are a few studies to estimate the TBM utilization factor. Utilization factor is affected by the type of TBM machine, operation, maintenance of machine, geological conditions, contractor experience and other factors. In this study, more than 100 case studies are analyzed to determine the relationship between the utilization factor and RMR, geological conditions, TBM types, tunnel length, and TBM diameter. Simple and multiple linear regression analysis are performed to develop predictive models for the utilization factor. The predictive model with explanatory variables of geological conditions, TBM types, tunnel length, and TBM diameter does not give a good correlation. The predictive models with explanatory variable of RMR give higher values of the coefficient of determination.

• Journal of the Korean Dietetic Association
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• v.25 no.1
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• pp.30-43
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• 2019

This study compared the predictive models for the growth kinetics of Staphylococcus aureus in ham rice balls. In addition, a semi-quantitative risk assessment of S. aureus on ham rice balls was conducted using FDA-iRISK 4.0. The rice was rounded with chopped ham, which was mixed with mayonnaise (SHM), soy sauce (SHS), or gochujang (SHG), and was contaminated artificially with approximately $2.5{\log}\;CFU{\cdot}g^{-1}$ of S. aureus. The inoculated rice balls were then stored at $7^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$, and the number of viable S. aureus was counted. The lag phases duration (LPD) and maximum specific growth rate (SGR) were calculated using a Baranyi model as a primary model. The growth parameters were analyzed using the polynomial equation as a function of temperature. The LPD values of S. aureus decreased with increasing temperature in SHS and SHG. On the other hand, those in SHM did not show any trend with increasing temperature. The SGR positively correlated with temperature. Equations for LPD and SGR were developed and validated using $R^2$ values, which ranged from 0.9929 to 0.9999. In addition, the total DALYs (disability adjusted life years) per year in the ham rice balls with soy sauce and gochujang was greater than mayonnaise. These results could be used to calculate the expected number of illnesses, and set the hazard management method taking the DALY value for public health into account.

• Computers and Concrete
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• v.28 no.6
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• pp.533-547
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• 2021

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

• Economic Analysis
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• v.26 no.3
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• pp.1-47
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• 2020

This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1590-1597
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• 2017

Objective: This study aims to identify the relationship between odd- and branched-chain fatty acids (OBCFAs) and microbial nucleic acid bases in the rumen, and to establish a model to accurately predict microbial protein flow by using OBCFA. Methods: To develop the regression equations, data on the rumen contents of individual cows were obtained from 2 feeding experiments. In the first experiment, 3 rumen-fistulated dry dairy cows arranged in a $3{\times}3$ Latin square were fed diets of differing forage to concentration ratios (F:C). The second experiment consisted of 9 lactating Holstein dairy cows of similar body weights at the same stage of pregnancy. For each lactation stage, 3 cows with similar milk production were selected. The rumen contents were sampled at 4 time points of every two hours after morning feeding 6 h, and then to analyse the concentrations of OBCFA and microbial nucleic acid bases in the rumen samples. Results: The ruminal bacteria nucleic acid bases were significantly influenced by feeding diets of differing forge to concentration ratios and lactation stages of dairy cows (p<0.05). The concentrations of OBCFAs, especially odd-chain fatty acids and C15:0 isomers, strongly correlated with the microbial nucleic acid bases in the rumen (p<0.05). The equations of ruminal microbial nucleic acid bases established by ruminal OBCFAs contents showed a good predictive capacity, as indicated by reasonably low standard errors and high R-squared values. Conclusion: This finding suggests that the rumen OBCFA composition could be used as an internal marker of rumen microbial matter.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.42-53
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• 2024

Mathematical modeling is the process of representing physical phenomena using equations, and it often describes various scientific phenomena through differential equations. Numerical analysis, which is capable of approximating solutions to partial differential equations representing physical phenomena, is widely utilized. However, in high-dimensional or nonlinear systems, computational costs can substantially increase, leading to potential numerical instability or convergence issues. Recently, Physics-Informed Neural Networks (PINNs) have emerged as an alternative approach. A PINN leverages physical laws even with limited data to provide highly reliable predictive performance and can address the convergence issues and high computational costs associated with numerical analysis. This paper analyzes the weak signals, research trends, patent trends, and case studies of PINNs. On the basis of this analysis, it proposes directions for the development of PINN techniques in the agricultural field. In particular, the application of PINNs in agriculture is expected to be more effective than in other industries because of their ability to reflect real-time changes in biological processes. While the technology readiness level of PINNs remains low, the potential for model training with minimal data and real-time prediction capabilities suggests that PINNs could replace traditional numerical analysis models. It is anticipated that the research and industrial applications of PINN will develop at an increasing pace while focusing on addressing the complexity of mathematical models in agriculture, mathematical modeling and the application of various biological processes; securing key patents related to PINNs; and standardizing PINN technology in the field of agriculture.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.507-520
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• 2003

A V-shaped riffle is an artificial hydraulic structure haying two wings from the streamside with a narrow opening in between. It is usually made of crushed stones or large boulders. It limits channel width and accelerates the flow through the constricted section causing a local scour just downstream. The V-shaped riffle provides with a unique aquatic habitat by forming a pool and sandbars around the pool edge, increasing local morphologic, hydraulic and sedimentological diversity. This study investigates experimentally the scour characteristics of the V-shaped riffle in the sandbed stream and proposes a predictive equation for the scour. Total 45 cases of experiments were conducted to examine the effect of hydraulic factors and configuration of V-shaped riffle on the geometry of scour holes. From the comparison of the experimental results of this study with the predictive equation of spur dike by Breusers and Raudkivi(1991), it is found that their predictive equation of spur dike underestimates the maximum scour depth downstream of the V-shaped riffle. h new predictive equation for the maximum scour depth was developed using the non-dimensional hydraulic and geometrical variables. The parameters used in the proposed equations were determined using the experimental data. The analysis reveals that the scour depth is dependent dominantly on the Froude number at the opening of the V-shaped riffle, while the angle of riffle and the opening width also affect the scour depth. The proposed equation for the scour of V-shaped riffle well agrees with the experimental data. It can be used for estimating the scour of the V-shaped riffle in sandbed streams.

• International Journal of Highway Engineering
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• v.16 no.6
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• pp.105-113
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• 2014

PURPOSES: The objective of this paper is to select the confidential intervals by utilizing the second moment reliability index(Hasofer and Lind; 1974) related to the number of load applications to failure which explains the fatigue failure and rut depth that it indicates the permanent deformation. By using Finite Element Method (FEM) Program, we can easily confirm the rut depth and number of load repetitions without Pavement Design Procedures for generally designing pavement depths. METHODS : In this study, the predictive models for the rut depth and the number of load repetitions to fatigue failure were used for determining the second moment reliability index (${\beta}$). From the case study results using KICTPAVE, the results of the rut depth and the number of load repetitions to fatigue failure were deducted by calculating the empirical predictive equations. Also, the confidential intervals for rut depth and number of load repetitions were selected from the results of the predictive models. To determine the second moment reliability index, the spreadsheet method using Excel's Solver was used. RESULTS : From the case studies about pavement conditions, the results of stress, displacement and strain were different with depth conditions of layers and layer properties. In the clay soil conditions, the values of strain and stresses in the directly loaded sections are relatively greater than other conditions. It indicates that the second moment reliability index is small and confidential intervals for rut depth and the number of load applications are narrow when we apply the clay soil conditions comparing to the applications of other soil conditions. CONCLUSIONS : According to the results of the second moment reliability index and the confidential intervals, the minimum and maximum values of reliability index indicate approximately 1.79 at Case 9 and 2.19 at Case 22. The broadest widths of confidential intervals for rut depth and the number of load repetitions are respectively occurred in Case 9 and Case 7.

• Journal of the Korean Society of Safety
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• v.22 no.4
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• pp.37-42
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• 2007

Flash points for the flammable binary systems, n-propanol+acetic acid and n-propanol+n-propionic acid, were measured by Cleveland open cup tester. The Raoult's law, the van Laar equation and the UNIQUAC equation were used for predicting flash points and were compared with experimentally-derived data. The calculated values based on the van Laar and UNIQUAC equations were found to be better than those based on the Raoult's law. And the predictive curve of the flash point prediction model based on the UNIQUAC equation described the experimentally-derived data more effectively than was the case when the prediction model was based upon the the van Laar equation.