• 대한원격탐사학회지
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• 제40권3호
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of Ginseng Research
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• 제48권4호
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• pp.373-383
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• 2024

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

• Geomechanics and Engineering
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• 제37권1호
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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.

• 자원리싸이클링
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• 제33권2호
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• pp.46-53
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• 2024

전기로 제강공정에서 연속주조 조업 완료 후 ladle에 잔존하는 슬래그의 헌열과 슬래그 중 잔존 CaO를 활용하기 위해 전기로 출강 후 ladle 상부에 슬래그를 투입하여 Ladle Furnace(LF) 공정에서의 전력과 생석회의 사용량을 저감하는 공정이 연구되어 산업현장에서 활용되고 있다. 하지만 이러한 공정은 LF 공정과 연속주조 공정상 시점이 맞지 않으면 재활용율이 낮아진다. 슬래그 재활용율을 높이기 위해서 시점이 맞지 않는 경우 ladle의 슬래그를 슬래그 포트에 미리 부은 후 재활용하는 방법에 대해서 LF 조업 영향성을 분석하였다. Ladle 용융 슬래그를 재활용 방법에 대해 열역학 프로그램 Factsage 8.3에서 FSsteel(steel database)와 FToxid(oxide database)를 활용하여 슬래그 조성에 대한 액상화율을 계산하였고, 재활용 방법에 따라 각 10heats 조업 적용을 통해 슬래그 중 탈황능과 LF 조업성에 대해서 비교하였다. 그 결과 연속주조 조업 완료 후 바로 ladle에 슬래그를 재활용하는 방법에서 전력 사용량이 0.3MWh 낮고, LF 조업시간은 1.2분 단축되었으며, 탈황율은 5.8% 높은 결과를 얻었다.

• Journal of Microbiology and Biotechnology
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• 제34권4호
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• pp.958-968
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• 2024

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

• 전자통신동향분석
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• 제39권4호
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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.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.148-156
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• 2024

The research paper presents the results of an experimental research of the development of critical thinking in third-year students majoring in 013 "Primary Education" in studying a special course "From Reflection to Self-Assessment: Critical Thinking Skills" (based on Lauren Starkey methodology). The research was conducted during the first half of 2019-2020 academic year. The sample representativeness was ensured by the method of random selection, the strategy of randomization according to the criteria of age, gender, level of academic performance was described. Given the confidence interval p=95% and the confidence interval of the error Δ=±0.05, the sample size was 94 people, including of the experimental group and 49 students of the control group. The peculiarities of the development of such critical thinking skills as reflective thinking, self-analysis, awareness of one's own achievements and shortcomings, choice of problem-solving strategy, use of cognitive models of learning are revealed. It was found that the development of critical thinking was achieved through a comprehensive combination of self-assessment and reflection, performing exercises to develop the ability to clearly articulate the problem, find, analyse and interpret relevant information, draw the right conclusions and explanations.

• The Korean journal of internal medicine
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• 제39권2호
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• pp.248-260
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• 2024

Background/Aims: The model for end-stage liver disease (MELD) serves as an indicator for short-term mortality among patients diagnosed with liver cirrhosis (LC) and is used to prioritize patients for liver transplantation. In 2021, the updated version of MELD, MELD-3.0, was introduced to improve the accuracy of the mortality prediction of MELD. Therefore, this study aimed to compare the efficacy of MELD 3.0 and MELD-Na in predicting mortality among Korean patients with LC. Methods: A retrospective review was conducted using the medical records of patients diagnosed with LC who were admitted to Konkuk University Hospital From 2011 to 2021. The study calculated the predictive values of MELD-Na and MELD-3.0 for 3- and 6-months mortality using the area under the receiver operating curve (AUROC) and compared the results using the DeLong test. Results: Of the 3,034 patients enrolled in the study, 339 (11.2%) died within 3 months and 421 (14.4%) died within 6 months. The AUROCs values for predicting 3 months mortality were 0.846 for MELD-Na and 0.851 for MELD-3.0. The corresponding AUROC values for predicting 6 months mortality were 0.843 for MELD-Na and 0.848 for MELD-3.0. MELD-3.0 exhibited better discrimination ability than MELD-Na for both 3 (p = 0.03) and 6 months mortality (p = 0.01). Conclusions: Our study found a significant difference between the performance of MELD-3.0 and MELD-Na in Korean patients with LC.

• 대한토목학회논문집
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• 제44권2호
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• pp.133-139
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• 2024

본 연구는 현재 내진 구조 분야에서 압축부재로 사용되고 있는 폴리우레탄의 한계를 극복하기 위해 폴리케톤이라는 신소재 제안을 목적으로 한다. 기존 폴리우레탄은 우수한 탄성 특성을 지녔음에도 불구하고 구조물에 발생하는 변위를 회복하기에는 부족한 경향이 있다. 반면, 폴리케톤은 뛰어난 강도 성능을 보유함과 동시에 친환경 소재로 주목받고 있다. 이러한 장점을 가진 폴리케톤의 압축특성 평가를 위하여 기존에 사용되고 있던 폴리우레탄과의 비교실험을 진행하고자 한다. 단순압축실험과 반복 하중 조건에서의 실험 속도 변화를 통해 폴리케톤의 속도 의존성을 파악하고, 추가적으로 선행압축을 적용하여 압축거동 특성을 확인하였다. 폴리케톤은 폴리우레탄에 비하여 약 10배가량 높은 압축강도를 나타내었으며 비교적 작은 변위에서는 14배가량 높은 변형 회복능력으로 폴리케톤의 우수한 회복특성을 입증하였다.

• 한국산업정보학회논문지
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• 제29권4호
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• pp.67-76
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• 2024

본 연구에서는 다중 에이전트를 이용한 타깃 감지 문제를 다루는데, 특히 이동식 에이전트를 활용한 감지 문제는 경로 계획에 대한 전략이 추가로 필요하다. 문제의 목표는 특정 기간 내 감지 프로세스를 통해 총 효용을 극대화할 수 있는 각 에이전트의 경로를 찾는 것인데, 시간에 따라 타깃의 사건 발생 확률이 변하도록 하는 포아송 프로세스(Poisson process) 기반의 확률적 프로세스(stochastic process)를 고려하여 현실적인 효용 값을 반영한다. 본 감지 문제의 목적함수는 비선형(non-linearity)이고, NP-난해(NP-hard) 문제로 표현된다. 효율적인 계산 시간 내에 효과적인 해를 찾기 위해, 본 연구에서는 하위모듈성(submodularity)의 특성을 갖는 목적함수임을 증명하고, 이를 활용해 비교적 낮은 계산 시간으로 합리적인 전략을 얻기 위한 휴리스틱 알고리즘을 제안한다. 제안한 알고리즘은 해의 성능과 적절한 계산 시간 내에 해를 도출할 수 있다는 측면에서 우수한 알고리즘임을 이론 및 실험적으로 제시한다.