• Clean Technology
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• v.29 no.4
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• pp.321-326
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• 2023

Recently, there has been a growing interest in clean hydrogen energy that does not emit carbon dioxide during combustion due to the increasing focus on carbon neutral. Research related to hydrogen production continues, and in this study, we applied waste-derived synthesis gas to the water-gas shift reaction to simultaneously treat waste and produce high-purity hydrogen. To enhance catalytic activity in the high-temperature water-gas shift (HT-WGS) reaction, magnesium was used as a support material alongside cerium. Cu-CeO₂-MgO catalysts were synthesized, with copper acting as the active component for the HT-WGS reaction. A study on the catalytic activity based on the preparation method was conducted, and the Cu-CeO₂-MgO catalyst prepared by impregnation method exhibited the highest activity in the HT-WGS reaction. The observed superior performance of the Cu-CeO₂-MgO catalyst prepared through the impregnation method can be attributed to its significantly higher oxygen storage capacity and amount of active Cu species.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.437-442
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• 2023

This research aims to enhance the efficiency of Pt/C catalysts due to the limited availability and high cost of platinum in contemporary fuel cell catalysts. Nano-sized platinum particles were distributed onto a carbon-based support via the polyol process, utilizing the metal precursor H₂PtCl₆·6H₂O. Key parameters such as pH, temperature, and RPM were carefully regulated. The findings revealed variations in the particle size, distribution, and dispersion of nano-sized Pt particles, influenced by temperature and pH. Following sodium hydroxide treatment, heat treatment procedures were systematically executed at diverse temperatures, specifically 120, 140, and 160 ℃. Notably, the thermal treatment at 140 ℃ facilitated the production of Pt/C catalysts characterized by the smallest platinum particle size, measuring at 1.49 nm. Comparative evaluations between the commercially available Pt/C catalysts and those synthesized in this study were meticulously conducted through cyclic voltammetry, X-ray diffraction (XRD), and field-emission scanning electron microscopy-energy dispersive X-ray spectroscopy (FE-SEM EDS) methodologies. The catalyst synthesized at 160 ℃ demonstrated superior electrochemical performance; however, it is imperative to underscore the necessity for further optimization studies to refine its efficacy.

• Computers and Concrete
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• v.33 no.2
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Clinical and Experimental Vaccine Research
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• v.12 no.4
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• pp.328-336
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• 2023

Purpose: Human peripheral blood mononuclear cell (PBMC)-based in vitro systems can be of great value in the development and assessment of vaccines but require the right medium for optimal performance of the different cell types present. Here, we compare three commonly used media for their capacity to support innate and adaptive immune responses evoked in PBMCs by Toll-like receptor (TLR) ligands and whole inactivated virus (WIV) influenza vaccine. Materials and Methods: Human PBMCs were cultured for different periods of time in Roswell Park Memorial Institute (RPMI), Dulbecco's minimal essential medium (DMEM), or Iscove's modified DMEM (IMDM) supplemented with 10% fetal calf serum. The viability of the cells was monitored and their responses to TLR ligands and WIV were assessed. Results: With increasing days of incubation, the viability of PBMCs cultured in RPMI or IMDM was slightly higher than that of cells cultured in DMEM. Upon exposure of the PBMCs to TLR ligands and WIV, RPMI was superior to the other two media in terms of supporting the expression of genes related to innate immunity, such as the TLR adaptor protein gene MyD88 (myeloid differentiation factor 88), the interferon (IFN)-stimulated genes MxA (myxovirus resistance protein 1) and ISG56 (interferon-stimulated gene 56), and the leukocyte recruitment chemokine gene MCP1 (monocyte chemoattractant protein-1). RPMI also performed best with regard to the activation of antigen-presenting cells. As for adaptive immunity, when stimulated with WIV, PBMCs cultured in RPMI or IMDM contained higher numbers of IFNγ-producing T cells and secreted more immunoglobulin G than PBMCs cultured in DMEM. Conclusion: Taken together, among the different media assessed, RPMI was identified as the optimal medium for a human PBMC-based in vitro vaccine evaluation system.

• 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.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.109-117
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• 2024

We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform server can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.1
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

• Journal of Platform Technology
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• v.12 no.2
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• pp.12-22
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• 2024

With the rapid increase in digital data in modern society, digital forensics plays a crucial role, and file type identification is one of its integral components. Research on the development of identification models utilizing artificial intelligence is underway to identify file types swiftly and accurately. However, existing studies do not support the identification of file types with high domestic usage rates, making them unsuitable for use within the country. Therefore, this paper proposes a more accurate file type identification model using Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). To overcome limitations of existing methods, the proposed model demonstrates superior performance on the FFT-75 dataset, effectively identifying file types with high domestic usage rates such as HWP, ALZ, and EGG. The model's performance is validated by comparing it with three existing research models (CNN-CO, FiFTy, CNN-LSTM). Ultimately, the CNN and GRU based file type identification and classification model achieved 68.2% accuracy on 512-byte file fragments and 81.4% accuracy on 4096-byte file fragments.