• Journal of People, Plants, and Environment
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• v.23 no.5
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• pp.537-544
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• 2020

Background and objective: Artemisia argyi has a long history as an effective treatment for various diseases. The detection of environmental pollutant benzo(a)pyrene, a known human carcinogen, in the leaves of Artemisia argyi is cause for concern. For medicinal plant extracts, both a reduction of benzo(a)pyrene as well as the maintained effectiveness of the compound are important. Therefore, in this study, we propose an optimized process for the addition and filtration of activated carbon to reduce benzo(a)pyrene and change the contents of the indicating substance(jaceosidine and eupatilin). Methods: Artemisia argyi EtOH extract containing 36 ppb of benzo(a)pyrene was added to 0.1, 0.5, 1.0, and 1.5% (w/w) of activated carbon for 120 min and filtered using an activated carbon filter 1, 2, 3, and 5 times respectively. The content of benzo(a)pyrene and indicating substances in Artemisia argyi extract were then measured with high performance liquid chromatography (fluorescence and UV detectors). Results: As the amounts of activated carbon powder and filtering cycles increased, the content of benzo(a)pyrene in the Artemisia argyi extract decreased. However, when activated carbon powder 1.5% was added to the extract, and when the activated carbon filter was filtered five times, the results were reduced by 15% and 30~40% respectively. The optimal extraction condition for reducing benzo(a)pyrene was adding 1.5% of activated carbon powder. This resulted in reducing benzo(a)pyrene by 83% and indicating substances by about 4%. Conclusions: Here we present a process for reducing benzo(a)pyrene in Artemisia argyi extract using activated carbon to reduce toxicity and minimize the loss of active ingredients. This approach has potential application within a manufacturing process of various medicinal plant extracts.

• The Korea Journal of Herbology
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• v.35 no.3
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• pp.33-45
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• 2020

Objective : The present study aimed to evaluate the change of the content of 7 active components in decoctions produced by various proportional pairs of Coptis chinensis Franch and Scutellaria baicalensis Georgi in 'Mixed decoction (MD)' and 'Single decoction mixture (SDM)'. Methods : The samples of MDs were prepared by decocting C. chinensis : S. baicalensis with the ratios of 10 g:10 g, 10 g:20 g, and 20 g:10 g. Those of SDMs were prepared by blending each single decoction from C. chinensis and S. Baicalensis with the ratios of 1:1, 1:2, and 2:1. The samples were evaluated by high-performance liquid chromatography with statistical analyses. Results : The analytical methods, which were optimized and validated, were reliably applied to present research. The content of all components in both MDs and SDMs at C. chinensis : S. baicalensis = 1:1 ratio were reduced compared with single herb decoction. The components from each compositional herb in MDs were proportionally increased with the ratio of original herb increased, but inversely proportional to paired herb. The contents of components in MDs were significantly lower than those in SDMs at all ratios, except for high content of baicalin at C. chinensis : S. baicalensis = 2:1. Conclusion : It was concluded that MDs and SDMs as well as the proportions of herbs could affect the contents of the components from original herbal medicines. These results provide the information for the quality control of herbal medicine combined C. chinensis with S. baicalensis.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.11-23
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• 2022

The response of pile foundations under lateral loads are usually analyzed using beam-on-nonlinear-Winkler-foundation (BNWF) model framework employing various forms of empirically derived p-y curves and p-multipliers. In practice, the p-y curve presented by the American Petroleum Institute (API) is most often utilized for piles in granular soils, although its shortcomings are recognized. The objective of this study is to evaluate the performance of the BNWF model and to quantify the error in the estimated pile response compared to a rigorous numerical model. BNWF analyses are performed using three sets of p-y curves to evaluate reliability of the procedure. The BNWF model outputs are compared with results of 3D nonlinear finite element (FE) analysis, which are validated via field load test measurements. The BNWF model using API p-y curve produces higher load-displacement curve and peak bending moment compared with the results of the FE model, because empirical p-y curve overestimates the stiffness and underestimates ultimate resistance up to a depth equivalent to four times the pile diameter. The BNWF model overestimates the peak bending moment by approximately 20-30% using both the API and Reese curves. The p-multipliers are revealed to be sensitive on the p-y curve used as input. These results highlight a need to develop updated p-y curves and p-multipliers for improved prediction of the pile response under lateral loading.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.693-707
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• 2022

Bonded joints have proven their performance against conventional joining processes such as welding, riveting and bolting. The single-lap joint is the most widely used to characterize adhesive joints in tensile-shear loadings. However, the high stress concentrations in the adhesive joint due to the non-linearity of the applied loads generate a bending moment in the joint, resulting in high stresses at the adhesive edges. Geometric optimization of the bonded joint to reduce this high stress concentration prompted various researchers to perform geometric modifications of the adhesive and adherends at their free edges. Modifying both edges of the adhesive (spew) and the adherends (bevel) has proven to be an effective solution to reduce stresses at both edges and improve stress transfer at the inner part of the adhesive layer. The majority of research aimed at improving the geometry of the plate and adhesive edges has not considered the effect of temperature and water absorption in evaluating the strength of the joint. The objective of this work is to analyze, by the finite element method, the stress distribution in an adhesive joint between two 2024-T3 aluminum plates. The effects of the adhesive fillet and adherend bevel on the bonded joint stresses were taken into account. On the other hand, degradation of the mechanical properties of the adhesive following its exposure to moisture and temperature was found. The results clearly showed that the modification of the edges of the adhesive and of the bonding agent have an important role in the durability of the bond. Although the modification of the adhesive and bonding edges significantly improves the joint strength, the simultaneous exposure of the joint to temperature and moisture generates high stress concentrations in the adhesive joint that, in most cases, can easily reach the failure point of the material even at low applied stresses.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.501-512
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• 2021

Recently, a breakthrough has been made in the NLP area by Transformer techniques based on encoder-decoder. However, this only can be used in mainstream languages where millions of dataset are well-equipped, such as English and Chinese, and there is a limitation that it cannot be used in non-mainstream languages where dataset are not established. In addition, there is a deflection problem that focuses on the beginning of the document in mechanical summarization. Therefore, these methods are not suitable for documents with flows such as fairy tales and novels. In this paper, we propose a hybrid summarization method that does not require a dataset and improves the deflection problem using GAN with two adaptive discriminators. We evaluate our model on the CNN/Daily Mail dataset to verify an objective validity. Also, we proved that the model has valid performance in Korean, one of the non-mainstream languages.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.155-165
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• 2021

The objective of this research is to suggest the creativity revelation model and to verify the relationships among knowledge capabilities and creativity processes including exploration and exploitation. Also, we investigate whether there are differences in creativity revelation processes from the perspective of psychological empowerment. To achieve the purpose of the research, a survey was conducted targeting employees of software development companies that require creativity in work performance. Empirical results show that knowledge capabilities have positive effect on creativity revelation processes. The notable point of the results is the role of psychological empowerment such that individuals with high psychological empowerment have more exploration-centric revelation, and those with low psychological empowerment have more exploitation-centric on the other hand. These results are interpreted that the behavioral patterns of organizational members may vary depending on the level of psychological empowerment in the creativity revelation, and therefore could suggest several managerial implications regarding creativity management and organizational development in an environment where convergence becomes more important.

• Animal Bioscience
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• v.35 no.6
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• pp.858-868
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• 2022

Objective: This study was conducted to evaluate the effects of dietary yeast hydrolysate (YH) supplementation on intestinal morphology, barrier, and anti-inflammatory functions of broilers. Methods: A total of 320 one day old male broilers were randomly allocated into four groups with eight replicates of ten broilers each. The broilers were supplemented with a basal diet (the control group) or basal diets adding 50, 100, 150 mg/kg YH, respectively. This trial lasted for 42 days. The orthogonal polynomial contrasts were used to determine the linear and quadratic effects of increasing levels of YH. Results: In our previous research, supplementing YH improved growth performance by enhancing body weight gain but decreased feed-to-gain ratio. In this study, compared with the control group, dietary YH addition linearly and quadratically decreased serum diamine oxidase activity (p<0.05). Additionally, supplementing YH linearly and/or quadratically decreased jejunal crypt depth (CD), tumor necrosis factor-alpha (TNF-α) concentration as well as mucin 2, interleukin-6 (IL-6), IL-1β, TNF-α, nuclear factor kappa B, and myeloid differentiation factor 88 gene expression levels (p<0.05). Whereas the jejunal villus height (VH), VH/CD, IL-10 concentration as well as zonula occludens-1 and IL-10 gene expression levels were linearly and/or quadratically increased by YH supplementation (p<0.05). Conclusion: Dietary YH supplementation improved intestinal morphology, barrier and anti-inflammatory functions while decreased intestinal permeability of broilers, which might be related with altering pertinent genes expression. This study provides evidence of YH as a promising feed additive for broilers.