Showing papers by "Yonsei University published in 2022"

A large‐scale meta‐analytic atlas of mental health problems prevalence during the COVID‐19 early pandemic

Abstract: The COVID-19 pandemic and related restrictions can impact mental health. To quantify the mental health burden of COVID-19 pandemic, we conducted a systematic review and meta-analysis, searching World Health Organization COVID-19/PsycInfo/PubMed databases (09/29/2020), including observational studies reporting on mental health outcomes in any population affected by COVID-19. Primary outcomes were the prevalence of anxiety, depression, stress, sleep problems, posttraumatic symptoms. Sensitivity analyses were conducted on severe mental health problems, in high-quality studies, and in representative samples. Subgroup analyses were conducted stratified by age, sex, country income level, and COVID-19 infection status. One-hundred-seventy-three studies from February to July 2020 were included (n = 502,261, median sample = 948, age = 34.4 years, females = 63%). Ninety-one percent were cross-sectional studies, and 18.5%/57.2% were of high/moderate quality. The highest prevalence emerged for posttraumatic symptoms in COVID-19 infected people (94%), followed by behavioral problems in those with prior mental disorders (77%), fear in healthcare workers (71%), anxiety in caregivers/family members of people with COVID-19 (42%), general health/social contact/passive coping style in the general population (38%), depression in those with prior somatic disorders (37%), and fear in other-than-healthcare workers (29%). Females and people with COVID-19 infection had higher rates of almost all outcomes; college students/young adults of anxiety, depression, sleep problems, suicidal ideation; adults of fear and posttraumatic symptoms. Anxiety, depression, and posttraumatic symptoms were more prevalent in low-/middle-income countries, sleep problems in high-income countries. The COVID-19 pandemic adversely impacts mental health in a unique manner across population subgroups. Our results inform tailored preventive strategies and interventions to mitigate current, future, and transgenerational adverse mental health of the COVID-19 pandemic.

Electronic interaction between transition metal single-atoms and anatase TiO₂ boosts CO₂ photoreduction with H₂O

Abstract: In the current work, we demonstrate that single Cu atoms, which are site-specifically stabilized in Ti vacancy of TiO 2 , interact with surrounding TiO 2 and control the overall electronic properties (reducibility and defect formation) of TiO 2 .

Improving streamflow prediction in the WRF-Hydro model with LSTM networks

Abstract: Researchers have attempted to use machine learning algorithms to replace physically based models for streamflow prediction. Although existing studies have contributed to improving machine learning methods, they still have weaknesses, such as large dataset requirements and overfitting. Therefore, we propose an approach that combines the Weather Research and Forecasting hydrological modeling system (WRF-Hydro) and the Long Short-Term Memory (LSTM) network, i.e., WRF-Hydro-LSTM, to improve streamflow simulations. In this approach, LSTM was employed to predict the residual errors of WRF-Hydro; in contrast, the conventional approach with LSTM predicts streamflow directly. Here, we performed numerical experiments to predict the inflow of Soyangho Lake in South Korea using WRF-Hydro-LSTM, WRF-Hydro-only, and LSTM-only. WRF-Hydro-LSTM and LSTM-only showed better results (NSE = 0.95 and R greater than 0.96) compared to WRF-Hydro-only (NSE = 0.72 and R = 0.88); however, in terms of the percent bias, WRF-Hydro-LSTM had a better value (1.75) than LSTM-only (17.36). While the LSTM-only follows objective functions and not physical principles, WRF-Hydro-LSTM simulates residual errors and efficiently decreases uncertainties that are inherent with conventional methods. Furthermore, a sensitivity test on the training dataset indicated that the correlation coefficient and NSE value were not overly sensitive, but the PBIAS value differed substantially depending on the training set. This study demonstrates that WRF-Hydro-LSTM is particularly useful for representing real-world physical constraints and thus can potentially improve streamflow prediction compared to using either of the two approaches exclusively.

Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes

Abstract: Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have representative information of the high-dimensional process data remains a challenge. In this study, we propose an adversarial autoencoder (AAE) based process monitoring system. AAE which combines the advantages of a variational autoencoder and a generative adversarial network enables the generation of features that follow the designed prior distribution. By employing the AAE model, features that have informative manifolds of the original data are obtained. These features are used for constructing and monitoring statistics and improve the stability and reliability of fault detection. Extracted features help calculate the degree of abnormalities in process variables more robustly and indicate the type of fault information they imply. Finally, our proposed method is testified using the Tennessee Eastman benchmark process in terms of fault detection rate, false alarm rate, and fault detection delays.

Protonated phosphonic acid electrodes for high power heavy-duty vehicle fuel cells

Abstract: State-of-the-art automotive fuel cells that operate at about 80 °C require large radiators and air intakes to avoid overheating. High-temperature fuel cells that operate above 100 °C under anhydrous conditions provide an ideal solution for heat rejection in heavy-duty vehicle applications. Here we report protonated phosphonic acid electrodes that remarkably improve the performance of high-temperature polymer electrolyte membrane fuel cells. The protonated phosphonic acids comprise tetrafluorostyrene-phosphonic acid and perfluorosulfonic acid polymers, where a perfluorosulfonic acid proton is transferred to the phosphonic acid to enhance the anhydrous proton conduction of fuel cell electrodes. By using this material in fuel cell electrodes, we obtained a fuel cell exhibiting a rated power density of 780 mW cm–2 at 160 °C, with minimal degradation during 2,500 h of operation and 700 thermal cycles from 40 to 160 °C under load. High-temperature polymer electrolyte membrane fuel cells are promising for heavy-duty vehicle applications, but strides in performance are needed to improve their commercial viability. Here it is demonstrated that protonating phosphonic acid electrodes greatly enhances power density and durability.

Sustainability analysis of a hybrid renewable power system with battery storage for islands application

Abstract: High energy consumption and exhaust emissions in the ocean have gradually come into view, and in the 21st century, most countries bordering the sea are gradually strengthening the construction of the ocean. Therefore, it is urgent to reform the energy structure of the ocean engineering platform, so as to increase the use of renewable energy in the ocean engineering platform. This paper reviewed the application of hybrid renewable energy systems (HRESs) consisting of photovoltaic systems (PV), wind turbines (WT), diesel generators (DG) and battery energy storage systems (BESS) in remote areas, ships and islands. Furthermore, the article made an economic analysis of a HRES based on the actual situation of an island in Qingdao, China. The results show that PV (110 kW)/DG (210 kW)/BESS (355 kWh) hybrid system is introduced as a suitable HRES can reduce 0.153 $/kWh cost of energy (COE) and 235,945 kg/year emission of CO2 for the island. In addition, HRESs that include BESS have significant advantages in terms of fuel usage and greenhouse gas emissions compared to HRESs without BESS. Compared with a single DG, the diesel consumption of PV/DG/BESS system is reduced by 90,137 L/year and CO2 emissions are reduced by 235,945 kg/year Therefore, the HRES composed of PV, WT, DG and BESS has good economic benefits and application prospects.

Density-Corrected DFT Explained: Questions and Answers

Abstract: HF-DFT, the practice of evaluating approximate density functionals on Hartree-Fock densities, has long been used in testing density functional approximations. Density-corrected DFT (DC-DFT) is a general theoretical framework for identifying failures of density functional approximations by separating errors in a functional from errors in its self-consistent (SC) density. Most modern DFT calculations yield highly accurate densities, but important characteristic classes of calculation have large density-driven errors, including reaction barrier heights, electron affinities, radicals and anions in solution, dissociation of heterodimers, and even some torsional barriers. Here, the HF density (if not spin-contaminated) usually yields more accurate and consistent energies than those of the SC density. We use the term DC(HF)-DFT to indicate DC-DFT using HF densities only in such cases. A recent comprehensive study (J. Chem. Theory Comput. 2021, 17, 1368-1379) of HF-DFT led to many unfavorable conclusions. A reanalysis using DC-DFT shows that DC(HF)-DFT substantially improves DFT results precisely when SC densities are flawed.

Drivers and determinants of strain dynamics following fecal microbiota transplantation

Abstract: Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor-recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

Interface rich CuO/Al₂CuO₄ surface for selective ethylene production from electrochemical CO₂ conversion

Abstract: In this work, we designed a novel CuO/Al 2 CuO 4 catalyst by a phase and interphase engineering approach, which enables the electrochemical conversion of carbon dioxide to ethylene with ultrahigh activity and selectivity.

Phase change material heat storage performance in the solar thermal storage structure employing experimental evaluation

Abstract: One of the most investigated and broadly used mediums in the solar thermal storage systems is using phase change materials. In this research, a comprehensive performance test bench for solar thermal utilization system using a controllable heater to substitute different levels of solar input was established. The test bench is not limited by the weather and equipped with alternative heat storage tanks for different PCMs. The heat storage structure and the performance of paraffin in low temperature system was examined using numerical simulation method. The results showed that the heating power received by PCM was stable at 6–8 kW under the heating condition of 85 °C. At the stage of incompletely melting, the temperature difference between the inside and outside was as high as 31.6, which can reduce the loss of heat to a great extent.