• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.17-31
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• 2023

Amidst escalating global warming fueled by indiscriminate fossil fuel consumption, concerted efforts are underway worldwide to mitigate atmospheric carbon dioxide (CO₂) levels. Electrochemical CO₂ reduction technology is recognized as a promising and environmentally friendly approach to convert CO₂ into valuable hydrocarbon compounds, deemed essential for achieving carbon neutrality. Copper, among the various materials used as CO₂ reduction electrodes, is known as the sole metal capable of generating C₂₊ compounds. However, low conversion efficiency and selectivity have hindered its widespread commercialization. This review highlights diverse research endeavors to address these challenges. It explores various studies focused on utilizing copper-based electrodes for CO₂ reduction, offering insights into potential solutions for advancing this crucial technology.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Clinical and Experimental Vaccine Research
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• v.12 no.3
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• pp.209-215
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• 2023

Purpose: The delay in acceptance or refusal to get vaccinated despite the availability of services is called vaccine hesitancy. The Global Polio Eradication Initiative in Pakistan faced consistent barriers preventing the eradication of the disease in the country. Similarly with the advent of the coronavirus disease 2019 (COVID-19) pandemic mass vaccination drives were initiated to a vaccine hesitant population. The aim of this study is to explore the prevalence and reasons for COVID-19 vaccine hesitancy in the Pakistani population. Materials and Methods: Cross-sectional study conducted during July to September 2021 using a snowball sampling technique targeting the adult population of Pakistan. The modified version of the vaccine hesitancy questionnaire related to the Strategic Advisory Group of Experts on Immunization Vaccine Hesitancy matrix was distributed online. Results: Out of 973 participants, 52.4% were immediately willing to take the vaccine and constituted the acceptance group whereas the remaining 47.6% who were still not sure formed the hesitant group. Support from leaders was found to be statistically significant for the difference between the hesitant and acceptance groups (p-value=0.027). Hesitant people were concerned about the effectiveness of the vaccine (60.9%) and potential side effects (57.9%) as it was not sufficiently tested prior to launch (44.7%). Age and education were significant factors affecting the acceptance of vaccination. The most trusted source of information regarding vaccination was health care workers (43.8%). Conclusion: A moderately high prevalence of vaccine hesitancy was reported in Pakistan. To overcome it, policymakers need to address the reasons for it. Leaders, celebrities, and healthcare workers can play an instrumental role in dispelling conspiracy theories regarding vaccines and making the vaccination drive a success.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.697-711
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• 2023

The global waste crisis has been escalating and its consequent impact on soil, water, air pollution, and eventually climate change acceleration has shed light on the importance of reducing waste. Amidst COVID-19 and the following surge in single-use plastics for food delivery, waste generation is on the incline. Companies and governments have embarked on developing various eco-friendly packaging technologies, but their effectiveness on the consumers is vague as definitions of eco-friendly packaging are vague, and research on its link to purchase intention remains scarce. Thus, the adoption of eco-friendly packaging has been slow. To address this issue, this study analyzes the awareness and purchase intention of four visual attributes of eco-friendly packaging-material, verbal statement, eco-label, and color-along with the environmental consciousness among undergraduate university students in Korea through online surveys and the ordered logit regression model. The study distinguished the attributes into evidence-based and conjectural categories. The findings revealed that eco-friendly visual attributes had a positive effect on purchase intention amongst undergraduate students in Korea; however the level of environmental consciousness had marginal effect on the purchase intention of eco-friendly visual attributes. The level of effectiveness also varied with each visual element. Analyses revealed that visual attributes to eco-friendly material had marginal effect on purchase intention; color was deemed not an "Eco-friendly attribute" by most students, and although eco-friendly labels were deemed as an eco-friendly attribute, trust in the labels varied according to environmental consciousness. These findings have implications for businesses and policymakers aiming to promote eco-friendly consumption within packaged food products.

• Journal of the Chungcheong Mathematical Society
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• v.37 no.1
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• pp.27-40
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• 2024

In this paper, we consider the higher-order HartreeFock equations. The higher-order linear Schrödinger equation was introduced in [5] as the formal finite Taylor expansion of the pseudorelativistic linear Schrödinger equation. In [13], the authors established global-in-time Strichartz estimates for the linear higher-order equations which hold uniformly in the speed of light c ≥ 1 and as their applications they proved the convergence of higher-order Hartree-Fock equations to the corresponding pseudo-relativistic equation on arbitrary time interval as c goes to infinity when the Taylor expansion order is odd. To achieve this, they not only showed the existence of solutions in L² space but also proved that the solutions stay bounded uniformly in c. We address the remaining question on the convergence of higherorder Hartree-Fock equations when the Taylor expansion order is even. The distinguished feature from the odd case is that the group velocity of phase function would be vanishing when the size of frequency is comparable to c. Owing to this property, the kinetic energy of solutions is not coercive and only weaker Strichartz estimates compared to the odd case were obtained in [13]. Thus, we only manage to establish the existence of local solutions in H^s space for s > $\frac{1}{3}$ on a finite time interval [-T, T], however, the time interval does not depend on c and the solutions are bounded uniformly in c. In addition, we provide the convergence result of higher-order Hartree-Fock equations to the pseudo-relativistic equation with the same convergence rate as the odd case, which holds on [-T, T].

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Membrane Journal
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• v.34 no.1
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• pp.30-37
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• 2024

As a promising method to address global water scarcity, sorbent-assisted water harvesting from air has shown great potential to deliver drinking water for inlands lacking traditional water sources. In this article, the recent studies of using metal-organic frameworks (MOFs) as sorbents to harvest atmospheric water will be introduced. Compared to the other sorbent materials such as zeolites or silica-based materials, MOFs have shown prospective properties such as the water isotherm inflection points as low as ~10%, which are suitable for harvesting water at dry regions. Due to this property, recently, MOFs have been extensively adopted to develop practical water harvesting devices that can harvest water. Since atmospheric water is accessible anywhere and anytime in the world, this technology is expected to open a new avenue in terms of securing safe water for the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.