• Smart Media Journal
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• v.12 no.11
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• pp.77-85
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• 2023

After COVID-19, communication through online platforms has increased, leading to an accumulation of massive amounts of conversational text data. With the growing importance of summarizing this text data to extract meaningful information, there has been active research on deep learning-based abstractive summarization. However, conversational data, compared to structured texts like news articles, often contains missing or transformed information, necessitating consideration from multiple perspectives due to its unique characteristics. In particular, vocabulary omissions and unrelated expressions in the conversation can hinder effective summarization. Therefore, in this study, we restructured by considering the characteristics of Korean conversational data, fine-tuning a pre-trained text summarization model based on KoBART, and improved conversation data summary perfomance through a refining operation to remove redundant elements from the summary. By restructuring the sentences based on the order of utterances and extracting a central speaker, we combined methods to restructure the conversation around them. As a result, there was about a 4 point improvement in the Rouge-1 score. This study has demonstrated the significance of our conversation restructuring approach, which considers the characteristics of dialogue, in enhancing Korean conversation summarization performance.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.746-754
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• 2023

Aircraft accidents are characterized by a low probability of survival compared to other means of transportation, and the main causes appear to be human factors such as violation of regulations and communication. In order to activate the safety management system to prevent such accidents, an important key variable is to recognize the importance of safety culture and actively engage in safety behavior rather than simply emphasizing compliance with regulations to flight crew members. Even if there are well-established regulations, safety culture, The effectiveness varies depending on the safety atmosphere and level of safety behavior. In this study, the correlation between safety culture and safety behavior was verified through a survey of domestic flight crew members' awareness of safety culture. The results showed that fair culture and self-reporting were not activated enough to have a significant impact on safety behavior. We aim to improve the performance of the safety management system by confirming the characteristics of safety culture and safety behavior.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• FOCUS: LIFE SCIENCE
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• no.1
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• pp.3.1-3.7
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• 2024

The article discusses the critical role of chromatography in the analysis and purification of proteins in biopharmaceuticals, emphasizing the importance of comprehensive characterization for ensuring their safety and efficacy. It highlights the use of Avantor® ACE® HPLC columns for the separation and purification of proteins, focusing on the analysis of intact proteins using reversed-phase liquid chromatography (RPLC) with fully porous particles. This article also details the application of different mobile phase additives, such as TFA and formic acid, and emphasizes the advantages of using type B ultra-pure silica-based columns for efficiency and peak shape in biomolecule analysis. Additionally, it addresses the challenges of analyzing intact proteins due to slow molecular diffusion and introduces the concept of solid-core (or superficially porous) particles, emphasizing their benefits over traditional porous particles for the analysis of therapeutic proteins. Furthermore, it discusses the development of Avantor® ACE® UltraCore BIO columns, specifically designed for the high-efficiency separation of large biomolecules, such as proteins, and demonstrates their effectiveness in achieving high-resolution separations, even for higher molecular weight proteins like monoclonal antibodies (mAbs). In addition, it underscores the complexity of analyzing and characterizing intact protein biopharmaceuticals, requiring a range of analytical techniques and the use of wide-pore stationary phases, operated at elevated temperatures and with relatively shallow gradients. It highlights the comprehensive range of options offered by Avantor® ACE® wide pore columns, including both fully porous and solid-core particles, bonded with a variety of complementary stationary phase chemistries to optimize selectivity during method development. The use of ultrapure and highly inert base silica is emphasized for enabling the use of lower concentrations of mobile phase modifiers without compromising analyte peak shape, particularly beneficial for LC-MS applications. Then the article concludes by emphasizing the significance of reversed-phase liquid chromatography and its compatibility with mass spectrometry as a valuable tool for the separation and analysis of intact proteins and their closely related variants in biopharmaceuticals.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.247-259
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• 2024

This study assessed the effectiveness of brand image communication on consumer perceptions of cruelty-free fashion brands. Brand messaging data were gathered from postings on the official Instagram accounts of three cruelty-free fashion brands and consumer perception data were gathered from Tweets containing keywords related to each brand. Web crawling and natural language processing were performed using Python and sentiment analysis was conducted using the BERT model. By analyzing Instagram content from Stella McCartney, Patagonia, and Freitag from their inception until 2021, this study found these brands all emphasize environmental aspects but with differing focuses: Stella McCartney on ecological conservation, Patagonia on an active outdoor image, and Freitag on upcycled products. Keyword analysis further indicated consumers perceive these brands in line with their brand messaging: Stella McCartney as high-end and eco-friendly, Patagonia as active and environmentally conscious, and Freitag as centered on recycling. Results based on the assessment of the alignment between brand-driven images and consumer-perceived images and the sentiment evaluation of the brand confirmed the outcomes of brand communication performance. The study revealed a correlation between brand image and positive consumer evaluations, indicating that higher alignment of ethical values leads to more positive consumer assessments. Given that consumers tend to prioritize search keywords over brand concepts, it's important for brands to focus on using visual imagery and promotions to effectively convey brand communication information. These findings highlight the importance of brand communication by emphasizing the connection between ethical brand images and consumer perceptions.

• Journal of East Asia Management
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• v.4 no.2
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• pp.63-92
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• 2023

This study analyzes the effect of job crafting behavior on career satisfaction and job satisfaction that allows active participants to perform their jobs in a work environment where autonomy and delegation are emphasized, and how the degree of person-job fit plays a role in the relationship between the three variables. The results of an empirical analysis of 360 employees of domestic companies are as follows. First, job crafting was found to have a positive (+) effect on career satisfaction and job satisfaction, respectively, confirming the importance of job crafting in a situation where the work environment changes rapidly. Second, job crafting was found to have a positive (+) effect on desire-supply fit and ability-demand fit, respectively, which are components of person-job fit. This means that person-job fit can be improved through task, cognitive, and relationship crafting. Third, it was found that desire-supply fit and ability-demand fit had a positive (+) effect on career satisfaction and job satisfaction. This means that the higher the person-job fit the more satisfied the career and job. Finally, desire-supply fit has a partial mediating effect in the relationship between job crafting, career satisfaction, and job satisfaction and ability-demand fit has a partial mediating effect in the relationship between job crafting, job satisfaction. In summarizing the above research results, this study suggested in a changing organizational environment that it is necessary to provide individual active work performance (job crafting) opportunities for career satisfaction and job satisfaction, and that it is important to create an organization's support environment to enhance person-job fit.

• Earthquakes and Structures
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• v.26 no.4
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• pp.311-326
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• 2024

Transmission tower structures are particularly susceptible to damage and even collapse under strong seismic ground motions. Conventional seismic analyses of transmission towers are usually performed by considering only ground motion uncertainty while ignoring structural uncertainty; consequently, the performance evaluation and failure prediction may be inaccurate. In this context, the present study numerically investigates the seismic responses and failure mechanism of transmission towers by considering multiple sources of uncertainty. To this end, an existing transmission tower is chosen, and the corresponding three-dimensional finite element model is created in ABAQUS software. Sensitivity analysis is carried out to identify the relative importance of the uncertain parameters in the seismic responses of transmission towers. The numerical results indicate that the impacts of the structural damping ratio, elastic modulus and yield strength on the seismic responses of the transmission tower are relatively large. Subsequently, a set of 20 uncertainty models are established based on random samples of various parameter combinations generated by the Latin hypercube sampling (LHS) method. An uncertainty analysis is performed for these uncertainty models to clarify the impacts of uncertain structural factors on the seismic responses and failure mechanism (ultimate bearing capacity and failure path). The numerical results show that structural uncertainty has a significant influence on the seismic responses and failure mechanism of transmission towers; different possible failure paths exist for the uncertainty models, whereas only one exists for the deterministic model, and the ultimate bearing capacity of transmission towers is more sensitive to the variation in material parameters than that in geometrical parameters. This research is expected to provide an in-depth understanding of the influence of structural uncertainty on the seismic demand assessment of transmission towers.

• Journal of Veterinary Science
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• v.25 no.4
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• pp.58.1-58.8
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• 2024

Importance: Over the past decade, catfish farming has increased in Southeast Asia. However, there has been no existing for pharmacokinetic data in the hybrid catfish (Clarias macrocephalus x C. gariepinus). Objective: This study was designed to evaluate the pharmacokinetic characteristics of oxytetracycline (OTC) in the hybrid catfish, following single intravascular (IV) or oral (PO) administration at a single dosage of 50 mg/kg body weight (BW). Methods: In total, 140 catfish (each about 100-120 g BW) were divided into two groups (n = 70). Blood samples (0.6-0.8 mL) were collected from ventral caudal vein at pre-assigned times up to 144 h (sparse samples design). OTC plasma concentrations were analyzed using high-performance liquid chromatography-photodiode array detector. Results: The pharmacokinetic parameter of OTC was evaluated using a non-compartment model. OTC plasma concentrations were detectable for up to 144 and 120 h after IV and PO, respectively. The elimination half-life value of OTC was long with slow clearance after IV administration in hybrid catfish. The average maximum concentration value of OTC was 2.72 ㎍/mL with a time at the maximum concentration of 8 h. The absolute PO bioavailability was low (2.47%). Conclusions and Relevance: These results showed that PO administration of OTC at a dosage of 50 mg/kg BW was unlikely to be effective for clinical use in catfish. The pharmacodynamic properties and clinical efficacy of OTC after multiple medicated feed are warranted.