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Globally, aerosols, emissions, and greenhouse gases have an impact on the environment. With the help of satellite data and field instruments we can understand and continue to study the atmosphere. Specifically, in terms of understanding air quality and aerosol optical depth (AOD), the radiative transfer model is traditionally used but can have unpredictability and is time consuming. With use of machine learning, one can improve accuracy and can be more time efficient. In this paper, we present machine learning methods to estimate AOD from the Geostationary Environmental Monitoring Spectrometer (GEMS). GEMS has a hyperspectral scanning spectrometer that monitors air pollutants over Asia by different observation nodes. Random Forest (RF), and Light Gradient Boosting Machine (LGBM) with auxiliary. meteorological, and ground-based observation data were used to estimate hourly AOD. Inclusion of meteorological data can support the model in performance and reflecting dynamic conditions in the atmosphere. The two machine learning models were evaluated by random, spatial, and temporal 10-fold cross validation to test the transferability and robustness. The results showed that random forest model performed lower than the light-gradient boosting model. LGBM produced R2 of 0.286 – 0.680 and RSME of 0.025-0.057. Random forest produced R2 of 0.283 – 0.643 and RSME of 0.028-0.057. Overall, the model was able to show that AOD can be retrieved from machine learning methods from the Geostationary Environmental Monitoring Spectrometer (GEMS).

Atmospheric aerosols are closely related to climate phenomena such as Earth’s energy budget and the formation of clouds. Anthropogenic aerosols have rapidly increased since the industrial revolution, which is damaging to human health, leading to cardiovascular, respiratory, and allergic diseases. Consequently, comprehensive knowledge of aerosol distribution is critical, particularly at detailed spatial scales. AOD measures the vertically integrated extinction of solar radiation by atmospheric aerosol particles. Ground-based sun photometers and satellite remote sensing are mainly used to retrieve AOD as a trade-off relationship. Ground-based measurements have been considered ground truth data, and satellite remote sensing has been used to derive the spatial variability of AOD over vast areas in near real-time. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellite is one of the main operation instruments to retrieve AOD, which has conducted atmospheric observations for almost two decades. MODIS has two well-known aerosol retrieval algorithms, Dark Target (DT) and Deep Blue (DB). Recently, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed to retrieve high-resolution AOD at a 1 km scale with better performance than DT and DB products. However, DT, DB, and MAIAC algorithms used radiative transfer models (RTM) and lookup tables (LUTs). LUTs was precalculated for a specific aerosol model using meteorological data, atmospheric gases, and constant geometry viewings, which required a high computation. The current LUT-based AOD model has reported uncertainties by aerosol model assumptions. Thus, there is room for complementing the existing AOD retrieval. Recently, machine learning (ML) has been applied with great performance for AOD retrieval. The ML-based AOD retrievals can be processed much faster and simpler without sensitive assumptions of the existing MODIS AOD algorithms. This study developed ML-based AOD retrievals that produce different resolutions of AODs (250m, 500m, and 1km) using MODIS data. The developed AODs at 250m, 500 m, and 1 km showed comparable performance, and 250 m AOD especially caught the spatial dynamics over urban areas well. When compared to MAIAC, 77.8 % of the 250 m AOD values are within the MODIS expected error (EE) envelope of ± (0.05 + 15%), followed by 500 km (76.5 % within EE), 1 km (76.3 % within EE), and MAIAC (70.08% within EE). Even ML-based AOD showed similar performance to MAIAC with three times more samples in the region where MAIAC AOD was unavailable. Our findings suggest the feasibility of ML-based estimation of high-resolution AOD using only satellite data.

Direct-acting oral anticoagulant (DOAC) use has increased dramatically since their introduction because of the growing evidence of proven efficacy and enhanced safety compared with warfarin and the low-molecular-weight heparins in the general population. Unfortunately, there is a dearth of quality data regarding the safety and efficacy of the DOACs in patients awaiting organ transplant and those who received a solid organ transplant. This review aims to evaluate the available literature and considerations regarding anticoagulation use in transplant recipients, focusing on preoperative, perioperative, and postoperative DOAC use.

Oxyapatite[Ca2Nd8(SiO4)6O2] glass-ceramics have been suggested as wasteforms for the immobilisation of rare-earth radioactive nuclides because of their high waste-loading capability and good chemical durability. In particular, a partitioning effect is predicted to contribute to an enhancement of corrosion resistance in glass-ceramics compared with that of conjugate glasses of the same composition. Because rare-earths are inherently insoluble nuclides, detection of changes in corrosion behavior between glass-ceramics and conjugate glasses under normal conditions is not easy. In this study, therefore, we revealed the partitioning effect by exposing glass-ceramics and glasses to solution of pH 2, 7 and 10 at 90 °C for 20 d. In addition, we proposed the corrosion mechanism for oxyapatite glass-ceramics under various corrosion conditions. Especially, the glassy phase dissolved first, followed by the oxyapatite phase during pH 7 corrosion.

Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00–07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R2 = 0.89–0.93 and RMSE = 0.071–0.091 for AOD and R2 = 0.67–0.81 and RMSE = 0.079–0.105 for FMF) and RF (R2 = 0.88–0.92 and RMSE = 0.080–0.095 for AOD and R2 = 0.59–0.76 and RMSE = 0.092–0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i.e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces.

This study aims to investigate if an education program based on positive psychology for the depressed elderly living alone is effective in reducing depression and promoting happiness. The research subjects were 15 depressed elderly people over 65 who were using the Seniors Welfare Center located in U Metropolitan City. Seven people were assigned to an experimental group, and eight to a control group to implement the education program based on positive psychology. The control group did not participate in the education program. As for the research tools, the Korean Short Geriatric Depression Scale (K-SGDS) and Happiness Scale (MUNSH Scale, translated by Jang Hye-kyung 1998) were used. The education program based on positive psychology was composed of a total of eight sessions, each for 90 minutes per week. As for the period, this program was implemented from May through July 2019. As for the verification of the effectiveness of the program, this study conducted a pre-and post-test and used Wilcoxon’s signed-ranks test with the experimental group and control group. For qualitative analysis, the contents evaluation for each session and changes by group members were analyzed. The results of this study can be summarized as follows: First, depression and happiness of the elderly living alone are very closely related. Second, after implementing the positive psychological-based education program, the depression of the elderly living alone decreased, and their happiness improved, and it could be verified that this program was effective in improving the depression and happiness of the depressed elderly living alone. This study proposes that it is necessary to implement many education programs based on positive psychology in the future.

Background Early aspirin (ASA) use after orthotopic heart transplantation (OHT) has been associated with lower rates of cardiac allograft vasculopathy (CAV). We hypothesized that the inverse association between ASA use and CAV incidence may be most pronounced in patients with allograft rejection. Methods Patients receiving OHT at a single center 2004-2010 (n = 120) were categorized by early ASA use post-transplant (ASA use for > 6 months in the first year) and the presence of biopsy-defined acute cellular rejection (ACR) and/or antibody-mediated rejection (AMR) during 5 year followup. Propensity scores for ASA treatment were estimated using boosting models and applied by inverse probability of treatment weighting. The association between ASA use and time to moderate/severe CAV (ISHLT ≥ 2) was investigated. Results Among patients with ACR or AMR, ASA therapy was associated with significantly lower rates of CAV≥ 2 (3.3 vs 30.1%; p = 0.001; HRadj 0.07; 95% CI 0.01-0.52) whereas ASA therapy was not associated with lower rates of CAV in patients with no rejection (5.6 vs 5.3%; p = 0.90; HRadj 1.26; 95% CI 0.08-20.30; pinteraction = 0.09). Conclusions Early ASA use after OHT was associated with lower rates of moderate to severe CAV only in those patients with episodes of allograft rejection. This article is protected by copyright. All rights reserved.

Gross primary production (GPP) is a crucial factor in the carbon cycle especially for the absorption of carbon dioxide into the biosphere from the atmosphere. A large discrepancy between a satellite-based GPP product named MOD17A2H GPP and in-situ data measured at eddy covariance flux towers has been identified over East Asia where the biome types and climatic characteristics are heterogeneous with rugged terrain. For that reason, this study focuses on two potential major error sources in MOD17A2H GPP, which are the coarse resolution land cover information and inappropriate meteorological parameters. The finer resolution observation and monitoring global land cover (FROM-GLC) and the MODIS land cover product collection 5.1 (MCD12Q1) were used to describe biome types in detail, by combining spatial distribution from FROM-GLC and the phenological characteristics of land cover from MCD12Q1. Meteorological parameters were optimized using the 55-years Japanese reanalysis meteorological data (JRA-55). The light use efficiency of the MOD17 GPP algorithm was modified using the combined land cover information (FROM-MCD). Although the use of FROM-MCD and JRA-55 did not improve MOD17A2H GPP, the optimization of two meteorological parameters—daily minimum air temperature (TMIN) and vapor pressure deficit (VPD) significantly improved the GPP algorithm for East Asia. The results show that the root mean square errors (RMSEs) between the estimated and in situ GPPs decreased from 21.83 (gC/m2/8days) to 16.11 (gC/m2/8days) through optimizing the two parameters at 9 flux tower sites. The optimized TMIN and VPD thresholds in the MOD17 GPP algorithm were applied to the entire study area (i.e., East Asia) according to the Koppen-Geiger climate classes. The estimated GPP using the proposed approach was compared to GPPs from widely used process-based models (i.e., VISIT, BEAMS, and BESS), which confirmed that the proposed approach with locally optimized meteorological parameters improved on the underestimation of the MOD17 GPP algorithm for East Asia. The uncertainty of the VPDmin parameter was revealed to be larger than that of TMINmax.

Transparent glass of xEu2O3-(100-x)TeO2 binary system is obtained by using the melt-quenching method and especially studied for x = 5 in xEu2O3-(100-x)TeO2 composition. Optical properties such as transmission, excitation, and emission spectra of Eu2O3-TeO2 glass-ceramics are studied and carefully compared for the samples heated at different temperatures. The one step heat-treatment affects the crystallinities as well as phase formation and yields cubic [Eu2Te6O15], orthorhombic [TeO2], and tetragonal [TeO2] phases depending on the heat treating temperature. Glass-ceramics of Eu3+-doped tetragonal [TeO2] shows stronger emission intensity at wavelength of 613 nm with an excitation wavelength of 394 nm compared with that from cubic [Eu2Te6O15] or Eu3+-doped orthorhombic [TeO2]. Enhancement of emission intensity in Eu3+-doped tetragonal [TeO2] is related to the local environment of Eu3+ ions substituted in tetragonal TeO2 lattice.

Rising expenditures threaten healthcare sustainability. While transplant programs are typically considered profitable, transplant medications are expensive and frequently targeted for cost savings. This review aims to summarize available literature supporting cost-containment strategies used in solid organ transplant. Despite widespread use of these tactics, we found the available evidence to be fairly low quality. Strategies mainly focus on induction, particularly rabbit antithymocyte globulin (rATG), given its significant cost and the lack of consensus surrounding dosing. While there is higher-quality evidence for high single-dose rATG, and dose-rounding protocols to reduce waste are likely low risk, more aggressive strategies, such as dosing rATG by CD3+ target-attainment or on ideal-body-weight, have less robust support and did not always attain similar efficacy outcomes. Extrapolation of induction dosing strategies to rejection treatment is not supported by any currently available literature. Cost-saving strategies for supportive therapies, such as IVIG and rituximab also have minimal literature support. Deferral of high-cost agents to the outpatient arena is associated with minimal risk and increases reimbursement, although may increase complexity and cost-burden for patients and infusion centers. The available evidence highlights the need for evaluation of unique patient-specific clinical scenarios and optimization of therapies, rather than simple blanket application of cost-saving initiatives in the transplant population.

Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

This contribution presents a technique for the machine-learning-based retrieval of cloud liquid water path. Cloud effects are among the major uncertainties in climate models for estimating and predicting the Earth’s energy budget. The study of cloud processes requires information on cloud physical properties, such as the liquid water path (LWP), which is commonly retrieved from satellite sensors using look-up table approaches. However, the accuracy of LWP varies temporally and spatially, also due to assumptions inherent in any physical retrieval. The aim of this study is to improve the accuracy of LWP and analyze quantitatively the accuracy and its errors. To this end, a statistical LWP retrieval was developed using spectral information from geostationary satellite channels (Meteosat Spinning-Enhanced Visible and Infrared Imager, SEVIRI), and satellite viewing geometry. The machine-learning method chosen is gradient-boosted regression trees (GBRTs), which is an ensemble of decision trees but more effective than traditional tree-based models. This study reports on first results, as well as a comparison between the GBRT-derived LWP estimates and those from the SEVIRI-based products of the Climate Monitoring Satellite Application Facility (CM-SAF, CLAAS-A2), as well as MODIS products. We use case studies for individual in-situ measurement sites in Europe under varying meteorological conditions to determine the factors influencing LWP retrieval quality.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial pneumonia of unknown aetiology [1] with aging being one of the major risk factors [2]. Several aging-related changes, such as the overproduction of mitochondrial reactive oxygen species, low adenosine triphosphate production, reduced mitochondrial biogenesis, and inadequate mitochondrial DNA (mtDNA) repair [3] have also been reported in the IPF lungs [4, 5] and in a bleomycin-induced mouse model [6]. Plasma cell-free DNA, including mtDNA, is released from dying cells into the circulatory system in response to injury [7]. Since circulating mtDNA contains CpG-rich sequences similar to bacterial DNA, disengaged mtDNA can activate the innate immune system by functioning as a damage-associated molecular pattern [8] and contribute to the stimulation of the pro-fibrotic pathway [9]. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. Yoon has nothing to disclose. Conflict of interest: Dr. Choi has nothing to disclose. Conflict of interest: Dr. Kim has nothing to disclose. Conflict of interest: Dr. Kim has nothing to disclose. Conflict of interest: Dr. Song has nothing to disclose.

To mitigate CO2 emission, the utilization of renewable resources is expected to grow with the wide and active implementation of electrified energy systems. Process industry, one of the most energy-intensive sectors, is strongly demanded to keep up with the trend to improve sustainability. The application of Power-to-Heat technologies to industrial energy systems would bring enormous benefits to the environment, but a systematic design methodology to evaluate the feasibility of electrification in practice has not been fully discussed. This study proposes a framework to evaluate the feasibility of electrification based on Heat Integration. The cost-effective and/or energy-efficient technology is selected by analyzing different characteristics of several power-to-heat technologies, with which the implementation strategy of electrification is designed based on the electrification potential. Economics of electrified energy systems are evaluated, which is compared with conventional ones based on burning fossil fuels. A case study is presented to demonstrate the applicability of the proposed design methodology. From the results, it is indicated that the electrification with a heat pump and an electrode boiler is a promising strategy, while the introduction of an electric heater becomes economically viable at the 23% of considerable reduction in the cost of renewable electricity.

Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better than widely used and adopted machine learning techniques. The aim of this study is to investigate the application of CNNs for land cover classification by using two-dimensional (2-D) spectral curve graphs from multispectral satellite images. The land cover classification was conducted in Concord, New Hampshire, USA, and South Korea by using multispectral images acquired from 30-m Landsat-8 and 500-m Geostationary Ocean Color Imager images. For the construction of input data specific to CNNs, two seasons (winter and summer) of multispectral bands were transformed into 2-D spectral curve graphs for each class. Land cover classification results of CNNs were compared with the results of support vector machines (SVMs) and random forest (RFs). The CNNs model showed higher performance than RFs and SVMs in both study sites. The examination of land cover classification maps demonstrates a good agreement with reference maps, Google Earth images, and existing global scale land cover map, especially for croplands. Using the spectral curve graph could incorporate the phenological cycles on classifying the land cover types. This study shows that the use of a new transformation of spectral bands into a 2-D form for application in CNNs can improve land cover classification performance.

Calcium-aluminoborate (CAB) glasses were developed to sequester new waste compositions made of several rare-earth oxides generated from the pyrochemical reprocessing of spent nuclear fuel. Several important wasteform properties such as waste loading, processability and chemical durability were evaluated. The maximum waste loading of the CAB compositions was determined to be ~56.8 wt%. Viscosity and the electrical conductivity of the CAB melt at 1300 °C were 7.817 Pa·s and 0.4603 S/cm, respectively, which satisfies the conditions for commercial cold-crucible induction melting (CCIM) process. Addition of rare-earth oxides to CAB glasses resulted in dramatic decreases in the elemental releases of B and Ca in aqueous dissolution experiments. Normalized elemental releases from product consistency standard chemical durability test were −5 g·m−2 for Nd, 0.009 g·m−2 for Al, 0.067 g·m−2 for B and 0.073 g·m−2 for Ca (at 90, after 7 days, for SA/V = 2000m−1); all meet European and US regulation limits. After 20 d of dissolution, a hydrated alteration layer of ~ 200-nm-thick, Ca-depleted and Nd-rich, was formed at the surface of CAB glasses with 20 mol% Nd2O3 whereas boehmite [AlO(OH)] secondary crystalline phases were formed in pure CAB glass that contained no Nd2O3.

The possibility of vitrifying the fly ash generated from municipal solid-waste incinerators and the immobilisation effect of toxic elements were investigated using several batches of fly ash and two kinds of common waste glass (waste glass from end liquid crystal display device and soda lime silicate waste glass). Fly ash could be vitrified by melting at 1300 °C prior to the introduction of a minimum of 30 wt% waste glass to each batch. Leaching tests were performed using the TCLP (Toxicity Characteristic Leaching Procedure) for three types of glass to identify the concentrations of five harmful elements (Pb, Ba, As, Cd, Cr) in the extraction fluid and a surface analysis using SIMS (Secondary Ion Mass Spectrometry) was performed to reveal the elemental distribution of the glass. The results confirmed that the regulatory levels (Pb

Background Cardiac allograft vasculopathy (CAV) remains a leading cause of morbidity and mortality after orthotopic heart transplantation (OHT). Little is known about the influence of aspirin on clinical expression of CAV. Methods We followed 120 patients with OHT at a single center for a median of 7 years and categorized them by the presence or absence of early aspirin therapy post-transplant (aspirin treatment ≥6 months in the first year). The association between aspirin use and time to the primary end-point of angiographic moderate or severe CAV (International Society for Heart and Lung Transplantation grade ≥2) was investigated. Propensity scores for aspirin treatment were estimated using boosting models and applied by inverse probability of treatment weighting (IPTW). Results Despite a preponderance of risk factors for CAV among patients receiving aspirin (male sex, ischemic heart disease as the etiology of heart failure, and smoking), aspirin therapy was associated with a lower rate of moderate or severe CAV at 5 years. Event-free survival was 95.9% for patients exposed to aspirin compared with 79.6% for patients without aspirin exposure (log-rank p = 0.005). IPTW-weighted Cox regression revealed a powerful inverse association between aspirin use and moderate to severe CAV (adjusted hazard ratio 0.13; 95% confidence interval 0.03–0.59), which was directionally consistent for CAV of any severity (adjusted hazard ratio 0.50; 95% confidence interval 0.23–1.08). Conclusions This propensity score–based comparative observational analysis suggests that early aspirin exposure may be associated with a reduced risk of development of moderate to severe CAV. These findings warrant prospective validation in controlled investigations.