• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.298-305
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• 2023

Burkitt's lymphoma is a distinct subtype of non-Hodgkin's lymphoma originating from B-cells that is notorious for its aggressive growth and association with immune system impairments, potentially resulting in rapid and fatal outcomes if not addressed promptly. Optimizing the use of Food and Drug Administration-approved medications, such as combining known safe drugs, can lead to time and cost savings. This method holds promise in accelerating the progress of novel treatments, ultimately facilitating swifter access for patients. This study explores the potential of a dual-targeted therapeutic strategy, combining the bruton tyrosine kinase-targeting drug Ibrutinib and the epidermal growth factor receptor/human epidermal growth factor receptor-2-targeting drug Lapatinib. Ramos and Daudi cell lines, well-established models of Burkitt's lymphoma, were used to examine the impact of this combination therapy. The combination of Ibrutinib and Lapatinib inhibited cell proliferation more than using each drug individually. A combination treatment induced apoptosis and caused cell cycle arrest at the S and G2/M phases. This approach is multifaceted in its benefits. It enhances the efficiency of the drug development timeline and maximizes the utility of currently available resources, ensuring a more streamlined and resource-effective research process.

• Industry Promotion Research
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• v.9 no.1
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• pp.21-29
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• 2024

In the rapidly advancing information society, electronic devices, including smartphones and tablets, are increasingly digitized and equipped with high-performance features such as flexible displays. This study focused on optimizing the manufacturing process for Transparent Conductive Films (TCF) by using the cost-effective conductive polymer PEDOT and transparent substrate PET as alternatives to expensive materials in flexible display technology. The variables considered are production speed (m/min), coating maximum temperature (℃), and PEDOT supply speed (rpm), with surface resistivity (Ω/□) as the response parameter, using Response Surface Methodology (RSM). Optimization results indicate the ideal conditions for production: a speed of 22.16 m/min, coating temperature of 125.28℃, and PEDOT supply at 522.79 rpm. Statistical analysis validates the reliability of the results (F value: 18.37, P-value: < 0.0001, R²: 0.9430). Under optimal conditions, the predicted surface resistivity is 145.75 Ω/□, closely aligned with the experimental value of 142.97 Ω/□. Applying these findings to mass production processes is expected to enhance production yields and decrease defect rates compared to current practices. This research provides valuable insights for the advancement of flexible display manufacturing.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.311-320
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• 2024

Climate change has been intensifying drought frequency and severity. Such prolonged droughts reduce reservoir levels, thereby exacerbating drought impacts. While previous studies have focused on optimizing reservoir operations using historical data to mitigate these impacts, their scope is limited to analyzing past events, highlighting the need for predictive methods for future droughts. This research introduces a novel approach for predicting minimum inflow at the Seomjingang dam which has experienced significant droughts. This study utilized the Stochastic Analysis Modeling and Simulation (SAMS) 2007 to generate inflow sequences for the same period of observed inflow. Then we simulate reservoir operations to assess firm yield and predict minimum inflow through synthetic inflow analysis. Minimum inflow is defined as the inflow where firm yield is less than 95% of the synthetic inflow in many sequences during periods matching observed inflow. The results for each case indicated the firm yield for the minimum inflow is on average 9.44 m³/s, approximately 1.07 m³/s lower than the observed inflow's firm yield of 10.51 m³/s. The minimum inflow estimation can inform reservoir operation standards, facilitate multi-reservoir system reviews, and assess supplementary capabilities. Estimating minimum inflow emerges as an effective strategy for enhancing water supply reliability and mitigating shortages.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.403-421
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• 2023

As the number of underground structures has increased in recent decades, it has become crucial to predict geological hazards ahead of a tunnel face during tunnel construction. Consequently, this study developed a finite element (FE) numerical model to simulate electrical resistivity surveys in tunnel boring machine (TBM) operations for predicting mixed ground conditions in front of tunnel faces. The accuracy of the developed model was verified by comparing the numerical results not only with an analytical solution but also with experimental results. Using the developed model, a series of parametric studies were carried out to estimate the effect of geological conditions and sensor geometric configurations on electrical resistivity measurements. The results of these studies showed that both the interface slope and the difference in electrical resistivity between two different ground formations affect the patterns and variations in electrical resistivity observed during TBM excavation. Furthermore, it was revealed that selecting appropriate sensor spacing and optimizing the location of the electrode array were essential for enhancing the efficiency and accuracy of predictions related to mixed ground conditions. In conclusion, the developed model can serve as a powerful and reliable tool for predicting mixed ground conditions during TBM tunneling.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.4
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• pp.394-399
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• 2024

An irradiator is developed using two UVA wavelength ranges of SMD LEDs as a curing light source. This module has dimensions of 545×111×300 mm³ and is equipped with a TIR bar-shaped lens made of PDMS silicone resin. The developed irradiator offers high uniformity, with 89% in the centerline of the horizontal axis direction, for two different wavelength ranges of 365 nm and 385 nm. The radiation intensity from the light source module shows highly directional characteristics, and the irradiator provides a maximum irradiance of 1,634 mW/cm² at a working distance of 50 mm. During the initial 5 minutes of operation, the irradiance experiences a rapid decrease. However, this issue is addressed by optimizing the LED's current reduction characteristics and managing the Transistor's temperature rise in the constant current circuit. After continuous operation for approximately 60 minutes. The highest temperature, near the central part of the irradiating surface, reaches 69.7℃, while the lowest temperature, near the edges, is 41.1℃.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.63-68
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• 2024

With the increasing use of transparent displays and flexible devices, polymer substrates offering excellent flexibility and strength are in demand. Since polymers are sensitive to heat, precise temperature control during the process is necessary. The study proposes a temperature measurement system for the laser processing area within the polymer base, aiming to address the drawbacks of using these polymer bases in laser-based selective processing technology. It presents the possibility of optimizing the process conditions of the polymer substrate through local temperature change measurements in the laser processing area. We developed and implemented the PDPT (Photodiode-based Planck Thermometry) to measure temperature in the laser-processing area. PDPT is a non-destructive, contact-free system capable of real-time measurement of local temperature increases. We monitored the temperature fluctuations during the laser processing of the polymer substrate. The study shows that the proposed laser-based temperature measurement technology can measure real-time temperature during laser processing, facilitating optimal production conditions. Furthermore, we anticipate the application of this technology in various laser-based processes, including essential micro-laser processing and 3D printing.

• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.9-15
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• 2024

This paper details research on the optimized design and fabrication of a 4x4 microstrip array antenna intended for X-Band applications. The study focuses on achieving the desired resonance frequency and gain by modifying the microstrip patch and array antenna parameters, including substrate type and patch size. It presents results from designing and fabricating a 4x4 array antenna with optimum substrate materials to enhance X-Band resonance characteristics and gain. The antenna dimensions are 10mm(W)x7.4mm(L)x 0.79mm(H), with an Rogers RO 4350B dielectric substrate (ε_r=3.54) and an inset-fed feeding method to minimize antenna size. Both the single patch and 4x4 array antennas demonstrated stable SWR (<1.5) and a gain of 18.5dBi at the target frequency of 10.3GHz in simulations. The fabricated antenna showed performance consistent with simulation results. This antenna design is suitable for X-Band applications, including military, satellite communications, and biomedical fields.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Applied Chemistry for Engineering
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• v.35 no.4
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• pp.316-320
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• 2024

To optimize the emulsion stability and antibacterial activity against Escherichia coli (E. coli) of cosmeceuticals using Scutellaria baicalensis extracts and olive wax as natural emulsifiers, we conducted a study. The independent variables were the amounts of Scutellaria baicalensis extracts and olive wax added. The response variables included the emulsion stability index (ESI) of the cosmeceuticals product and the inhibition diameter against E. coli. Through central composite design-response surface methodology (CCD-RSM), we obtained a statistically significant and reliable regression equation within a 95% confidence interval. By optimizing multiple responses, we determined that the optimal emulsification conditions that satisfied both ESI and E. coli inhibition diameter were 3.7 wt% of Scutellaria baicalensis extracts and 2.7 wt% of olive wax. The predicted ESI and E. coli inhibition diameter were 97.9% and 9.7 mm, respectively. When actual experiments were conducted under the optimal conditions, the measured ESI and E. coli inhibition diameter were 95.0% and 9.4 mm, respectively, with an average error rate of 3.2 ± 0.4%.