• 자원환경지질
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• 제55권3호
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• pp.231-239
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• 2022

The irregular shape of the disturbances is a fundamental issue for mining engineers at the Sidi Chennane phosphate deposit in Morocco. A precise classification of disturbed areas is therefore necessary to understand their part in the overall volume of phosphate. In this paper, we investigate the theoretical and practical aspects of studying and measuring multifractal spectrums as a defining and representative parameter for distinguishing between the phosphate deposit of a low rate of disturbances and the deposit of a high rate. An empirical multifractal approach was used by analyzing the disturbed areas through the geoelectric images of an area located in the Sidi Chennane phosphate deposit. The Generalized fractal dimension, D(q), the Singularities of strength, α(q), the local dimension, f(α) and their conjugate parameter the mass exponent, τ(q) as well as f(α)-α spectrum were the common multifractal parameters used. The results reported show wide variations of the analyzed images, indicating that the multifractal analysis is an indicator for evaluate and characterize the disturbed areas within the phosphates deposits through the studied geoelectric images. This could be the starting point for future work aimed at improving phosphate exploration planning.

• 한국축산식품학회지
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• 제42권5호
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• pp.889-902
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• 2022

With increasing consumer demand for "clean label" products, the use of natural ingredients is required in the food industry. Protein/polysaccharide complexes are considered good alternatives to synthetic emulsifiers and stabilizers for formulating stable emulsion-based foods. Milk protein and carrageenan are widely used to improve the physical properties and stability of dairy food products. In a previous study, milk protein isolate (MPI) was conjugated with 𝛋-carrageenan (𝛋-Car) in a wet-heating system through the Maillard reaction, and the Maillard conjugates (MC) derived from MPI and 𝛋-Car effectively improved the stability of oil-in-water emulsions. Therefore, MPI/𝛋-Car conjugates were used in whipping cream as natural emulsifiers in this study, and the physical and rheological properties of whipping creams stabilized using MPI/𝛋-Car MC and MPI/𝛋-Car electrostatic complexes (EC) were investigated. The whipping creams stabilized with MPI/𝛋-Car MC have lower rheological parameters (η_a,50, K, G', and G'') than those of whipping creams stabilized with MPI/𝛋-Car EC. Although the overrun value was slightly reduced owing to the addition of MPI/𝛋-Car MC, the stability of the whipped creams with MC was effectively improved due to enhanced water-holding ability by conjugation.

• 한국지구과학회지
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• 제44권4호
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• pp.275-290
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• 2023

Structural inversion refers to the reverse reactivation of extensional faults that influence basin shortening accommodated by contractional faults or folds. On the Korean peninsula, Miocene inversion structures have been found, but the Cretaceous rocks on Geoje Island may have undergone inversion as early as the Upper Cretaceous. To evaluate the structural inversion on Geoje Island, located on the eastern side of South Korea, and to determine the effects of preexisting weakness zones, field-based geometric and kinematic analyses of faults were performed. The lithology of Geoje Island is dominated by hornfelsified shale, siltstone, and sandstone in the Upper-Cretaceous Seongpori formation. NE and NW-oblique normal faults, conjugate strike-slip (NW-sinistral transpressional and E-W-dextral transtensional) faults, and NE-dextral transpressional faults are the most prominent structural features in Geoje Island. Structural inversion on Geoje Island was evidenced by the sinistral and dextral transpressional reactivation of the NW and NE-trending oblique normal faults respectively, under WNW-ESE/NW-SE compression, which was the orientation of the compressive stress during the Late Cretaceous to Early Cenozoic.

• 대한방사성의약품학회지
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• 제6권2호
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• pp.155-164
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• 2020

Polyethylene glycol (PEG) has been the most commonly used polymer for the past few decades in the field of biomedical applications due to its gold standard stealth effect. PEGylation of antibody-drug conjugates, liposomes, peptides, nanoparticles, and proteins is done to improve their pharmaceutical efficacy and pharmacokinetic properties. PEGylation of antibodies with various PEG linkers improves targeting ability by increasing the blood circulation time and thus enhances the biodistribution profiles. It also assists in minimizing the immediate capture by the reticuloendothelial system. In this review, we summarize the effect of PEG linkers in an antibody conjugation and their pharmacokinetics in the field of biomedical imaging.

• 한국추진공학회지
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• 제26권6호
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• pp.10-20
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• 2022

본 연구에서는 케로신 다단연소사이클 엔진용 예연소기를 설계하기 위해, 고압의 산화제 과잉 조건에서 예연소가스를 계산하고 냉각유로에서 극저온 유체의 복합열전달 및 수력 특성을 해석할 수 있는 설계코드를 개발하였다. 사용자 편의성과 범용성을 가진 오픈 소스 라이브러리 Cantera를 활용하였으며, 실제유체의 열역학/전달 상태량을 정확히 계산하기 위해 관련 소스 코드들을 새로 작성하여 Cantera에 추가하였다. 현재 예비설계 중인 100톤급 부스터 엔진용 예연소기에 적용하였으며, CFD 해석결과와 비교를 통해 설계코드로서의 예측 정확도와 활용성을 확인하였다.

• Wind and Structures
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• 제36권6호
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Geomechanics and Engineering
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• 재33권6호
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• 대한의생명과학회지
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• 제29권4호
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• pp.344-354
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• 2023

This study emphasizes the importance of early diagnosis and response to COVID-19, leading to the development of a rapid diagnostic kit using quantum dots. The research focuses on finely tuning bioconjugation with quantum dots to enhance the accuracy and sensitivity of COVID-19 diagnosis. We have developed a COVID-19 rapid diagnostic kit that exhibits a sensitivity more than 50 times higher than existing COVID-19 diagnostic kits. Quantum dots enable the accurate detection of COVID-19 viral antigens even at low concentrations, providing a rapid response in the early stages of infection. The COVID-19 quantum dot diagnostic kit offers quick analysis time, utilizing the quantum properties of particles to swiftly measure COVID-19 infection for immediate response and isolation measures. Additionally, this diagnostic kit allows for multiple analyses with ease, as multiple quantum dots can detect various antigens and antibodies simultaneously in a single experiment. This efficiency enhances testing, reduces sample requirements, and lowers experimental costs. The application of this diagnostic technology is anticipated in the future for early diagnosis and monitoring of other infectious diseases.

• 한국해양공학회지
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• 제37권5호
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• pp.215-224
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• 2023

While melting glaciers due to global warming have facilitated the development of polar routes, Arctic vessels require reliable anti-icing methods to prevent hull icing. Currently, the existing anti-icing method, i.e., the heating coil method, has disadvantages, such as disconnection and power inefficiency. Therefore, a carbon nanotube-based surface heating element method was developed to address these limitations. In this study, the numerical analysis of the surface heating element method was performed using ANSYS. The numerical analysis included conjugate heat transfer and computational fluid dynamics to consider the conduction solids and the effects of wind speed and temperature in cold environments. The numerical analysis method of the surface heating element method was validated by comparing the experimental results of the heating coil method with the numerical analysis results (under the -30 ℃ conditions). The surface heating element method demonstrated significantly higher efficiency, ranging from 56.65-80.17%, depending on the conditions compared to the heating coil method. Moreover, even under extreme environmental conditions (-45 ℃), the surface heating element method satisfied anti-icing requirements. The surface heating element method is more efficient and economical than the heating coil method. However, proper heat flux calculation for environmental conditions is required to prevent excessive design.