• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.465-475
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• 2023

In this study, we demonstrated a silica nanofibrous membrane based on the electrospinning process and evaluated its DNA isolation and purification performance in PCR pretreatment. Generally, silica membranes made of non-woven fabric are used for PCR pretreatment, but this study aimed to improve the efficiency of the pretreatment process by developing a nanofiber-type silica membrane with high specific surface area and porosity. In order to manufacture a nanofiber-shaped silica film while maintaining the original physical properties of silica, nanofiber membranes produced by adding various concentrations of PEO (5 wt%, 8 wt%, and 10 wt%) to silica prepared by the sol-gel method were compared. In terms of nanofiber membrane production, the higher the PEO concentration, the more effective it was in producing nanofiber membranes. The produced silica nanofiber membrane was inserted to a pretreatment device used in commercial PCR equipment, and the pretreatment performance was compared and verified using Salmonella bacteria. When Salmonella was used, samples containing 5 wt% PEO showed superior PCR efficiency compared to samples containing 8 wt% and 10 wt% PEO. These results show that adding 5 wt% of PEO can effectively improve DNA purification and separation by producing a nanofiber-shaped silica film while maintaining the physical properties of silica. We expect that this study will contribute to the development of effective PCR pretreatment technology essential for various molecular biology applications.

• Land and Housing Review
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• v.14 no.4
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• pp.103-110
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• 2023

People who spend most of their day indoors are continuously exposed to internally and externally generated indoor pollutants. According to a 2022 report from the World Health Organization (WHO), air pollution is the cause of more than 7 million deaths annually worldwide, emphasizing the seriousness of indoor air pollutants. Air pollutants include nitrogen oxides (NOx), formaldehyde (HCHO), and volatile organic compounds (VOCs), which have serious effects on the human body. Photocatalyst is a material that can remove these indoor air pollutants. Photocatalysts not only have the ability to remove dust precursors, but also have antibacterial, sterilizing, and deodorizing functions, making them effective in improving indoor air quality. This study suggests areas and methods in which photocatalysts can be applied to buildings. Fields of application include interior and exterior construction materials such as concrete, as well as organic paints and ventilation devices. If appropriate utilization plans are developed, it may be possible to improve the built environment through reduced indoor and outdoor pollutant levels.

• Steel and Composite Structures
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• v.48 no.1
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• pp.19-25
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• 2023

Due to high application of concrete structures in construction industry, however, the quality improvement is essential. One of the new ways for this purpose is adding the nanoparticles to the concrete. In this work, vibration analysis of concrete beams reinforced by graphene oxide (GO) nanoparticles based on mathematical model has been investigated. For the accuracy of the presented model, the experimental study is done for comparing the compressive strength. Since the nanoparticles can not be solved in water without any specific process, at the first, GO nanoparticles should be dispersed in water by using shaker, magnetic striker, ultrasonic devices and finally mechanical mixer. For modelling of the strucuture, sinusoidal shear deformation beam theory (SSDBT) is utilized. Mori-Tanak model model is utilized for obtaining the effective properties of the beam including agglomeration influences. Utilizing the energy method and Hamilton's principal, the motion equations are calculated. The frequency of the concrete beam is obtanied by analytical method. Three samples with 0.02% GO nanoparticles are built and its compressive strength is compared which shows a good accuracy with maximum 1.29% difference with mathematical model and other papers. The aim of this work from the theoretical study is investigating the effects of nanoparticles volume percentage and agglomeration, length and thickness of the beam on the frequency of the structure. The results show that the with enhancing the GO nanoparticles, the frequency is increased. For example, with enhancing the volume percent of GO nanoparticles from zero to 0.08%, the compressive strength is increased 48.91%. and 46.83%, respectively for two cases of with and without agglomeration.

• Journal of Electrochemical Science and Technology
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• v.15 no.1
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• pp.96-110
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• 2024

Polymer electrolyte membrane fuel cells (PEMFCs) are green energy conversion devices, for which commercial markets have been established, owing to their application in fuel cell vehicles (FCVs). Development of cathode electrocatalysts, replacing commercial Pt/C, plays a crucial role in factors such as cost reduction, high performance, and durability in FCVs. PtNi octahedral catalysts are promising for oxygen reduction reactions owing to their significantly higher mass activity (10-15 times) than that of Pt/C; however, their application in membrane electrode assemblies (MEAs) is challenged by their low stability. To overcome this durability issue, various approaches, such as third-metal doping, composition control, halide treatment, formation of a Pt layer, annealing treatment, and size control, have been explored and have shown promising improvements in stability in rotating disk electrode (RDE) testing. In this review, we aimed to compare the features of each strategy in terms of enhancing stability by introducing a stability improvement factor for a direct and reasonable comparison. The limitations of each strategy for enhancing stability of PtNi octahedral are also described. This review can serve as a valuable guide for the development of strategies to enhance the durability of octahedral PtNi.

• Clean Technology
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• v.30 no.1
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• pp.13-22
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• 2024

In this study, blended 6,13-Bis(triisopropylsilylethynyl)pentacene (TP):2-Decyl-7-phenyl[1]benzothieno[3,2-b][1] benzothiophene (BT):Poly styrene (PS) TFT at different ratios were explored for their potential application as light absorption sensors. Due to the mixing of BT, both off current reduction and on/off ratio improvement were achieved at the same time. In particular, the TP:BT:PS (1:0.25:1 w/w) sample showed excellent light absorption characteristics, which proved that it is possible to manufacture a high-performance light absorption device. Through analysis of the crystal structure and electrical properties of the various mixing ratios, it was confirmed that the TP:BT:PS (1:0.25:1 w/w) sample was optimal. The results of this study outline the expected effects of this innovation not only for the development of light absorption devices but also for the development of mixed organic semiconductor (OSC) optoelectronic systems. Through this study, the potential to create a multipurpose platform that overcomes the limitations of using a single OSC and the potential to fabricate a high-performance OSC TFT with a fine-tuned optical response were confirmed.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.5
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• pp.253-259
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• 2023

Bioreactors are devices used by sewage treatment plants to process sewage and which produce active sludge, and sediments separated by solid-liquid are treated in anaerobic digestion tanks. In anaerobic digestion tanks, the volume of active sludge deposits is reduced and biogas is produced. After dehydrating the digestive sludge generated after anaerobic digestion, anaerobic digested wastewater, which features a high concentration of organic matters, is generated. In this study, the decomposition of organic carbon and nitrogen was studied by advanced oxidation process. Ozone-microbubble flotation process was used for oxidation pretreatment. During ozonation, the TOC decreased by 11.6%. After ozone treatment, the TOC decreased and the removal rate reached 80.4% as a result of the Ultra Violet-Advanced Oxidation Process (UV-AOP). The results with regard to organic substances before and after treatment differed depending on the organic carbon index, such as CODMn, CODCr, and TOC. Those indexes did not change significantly in ozone treatment, but decreased significantly after the UV-AOP process as the linkage treatment, and were removed by up to 39.1%, 15.2%, and 80.4%, respectively. It was confirmed that biodegradability was improved according to the ratio of CODMn to TOC. As for the nitrogen component, the ammonia nitrogen component showed a level of 3.2×10² mg/L or more, and the content was maintained at 80% even after treatment. Since most of the contaminants are removed from the treated water and its transparency is high, this water can be utilized as a resource that contains high concentrations of nitrogen.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.507-516
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• 2024

Through the use and development of underground spaces, subways have become a representative means of transportation in the city center. Due to the extension of subway lines and the increase in the number of subway users, there is a risk that safety for evacuating human lives in case of fire will not be secured. Therefore, this study tried to identify citizens' perceptions of evacuation facilities through questionnaires and find alternatives. An online survey of 115 subway passengers was conducted to learn about citizens' awareness of subway evacuation facilities. As a result of the survey, awareness of evacuation facilities was high in the order of guidance lights, life-saving devices, emergency lights and portable lights, special evacuation stairs, and evacuation stairs. In addition, the majority of the respondents said they were not well aware of evacuation facilities, and people's indifference was the most common cause. As a solution to the lack of awareness, responses to active advertising and promotion, education and experience activities were the highest.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.