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The spatial distribution of oxide abundances and Mg# (Mg/(Mg + Fe)) on the lunar surface is of great significance for in-depth understanding the origin and evolution of the Moon. China's Chang'E−5 (CE-5) mission returned young lunar soils for the first time, providing a new ground truth for the inversion of oxide abundances. In this study, the relationship between multi-source remote sensing data (including Chang'E−1 Interference Imaging Spectrometer (CE-1 IIM) data and the new global Christiansen feature (CF) product, named IIM-CF data), and the abundances of six oxides (FeO, TiO 2 , MgO, SiO 2 , Al 2 O 3 and CaO) measured at 40 lunar sampling sites including CE-5 were analyzed. The use of IIM-CF data as the input features of the selected inversion models for obtaining the abundances of oxides, and the oxide abundances measured at the 40 sampling sites were used as the ground truth. The models selected for this investigation contain three typical algorithms − random forest (RF), extreme gradient boosting (XGBoost) and partial least squares regression (PLSR), and a new method integrates RF, XGBoost and PLSR together named RXP was developed in this study. The modeling results of the abundances of the six oxides derived from the above four algorithms show that the RXP algorithm outperforms the other three algorithms. The distributions of the six oxides and Mg# on the lunar surface covering the range from 70° N to 70° S (70° N/S) with a resolution of about 200 m/pixel were generated using the proposed RXP algorithm. Our results indicate that, compared with the result of a single data source, the use of IIM-CF data improved the accuracy of the modeling of oxide abundances and Mg#. It is expected that the CE-5 samples can bring additional references to the studies of the inversion for the oxides of the lunar surface and deepen our understanding over this issue. • The changes in inversion results of the Moon due to the utilization of CE-5 samples. • RXP outperforms other three models since RXP take advantages of the three algorithms. • IIM-CF data avoid the limitation of single data and enhance accuracy of the inversion. • Mg# calculated from inverted FeO and MgO more accurate than Mg# directly inverted. [ABSTRACT FROM AUTHOR]

Background: Unlike β1- and β2-adrenergic receptors (ARs), β3-AR stimulation inhibits cardiac contractility and relaxation. In the failing left ventricular (LV) myocardium, β3-ARs are upregulated, and can be maladaptive in the setting of decompensation by contributing to LV dysfunction. This study examined the effects of intravenous infusions of the β3-AR antagonist APD418 on cardiovascular function and safety in dogs with systolic heart failure (HF).Methods and Results: Three separate studies were performed in 21 dogs with coronary microembolization-induced HF (LV ejection fraction [LVEF] of approximately 35%). Studies 1 and 2 (n = 7 dogs each) were APD418 dose escalation studies (dosing range, 0.35-15.00 mg/kg/h) designed to identify an effective dose of APD418 to be used in study 3. Study 3, the sustained efficacy study, (n = 7 dogs) was a 6-hour constant intravenous infusion of APD418 at a dose of 4.224 mg/kg (0.70 mg/kg/h) measuring key hemodynamic endpoints (e.g., EF, cardiac output, the time velocity integral of the mitral inflow velocity waveform representing early filling to time-velocity integral representing left atrial contraction [Ei/Ai]). Studies 1 and 2 showed a dose-dependent increase of LVEF and Ei/Ai, the latter being an index of LV diastolic function. In study 3, infusion of APD418 over 6 hours increased LVEF from 31 ± 1% to 38 ± 1% (P < .05) and increased Ei/Ai from 3.4 ± 0.4 to 4.9 ± 0.5 (P < .05). Vehicle had no effect on the LVEF or Ei/Ai. In study 3, APD418 had no significant effects on the HR or the systemic blood pressure.Conclusions: Intravenous infusions of APD418 in dogs with systolic HF elicit significant positive inotropic and lusitropic effects. These findings support the development of APD418 for the in-hospital treatment of patients with an acute exacerbation of chronic HF. [ABSTRACT FROM AUTHOR]

Background: Sacubitril/valsartan (Sac/Val), a combined angiotensin-II receptor blocker (Val) and neprilysin inhibitor (Sac) in a 1:1 molar ratio, was shown to decrease the risk of cardiovascular death or heart failure (HF) hospitalization in patients with HF and reduced left ventricular (LV) ejection fraction. This study examined the effects of Sac/Val on LV structure, function, and bioenergetics, and on biomarkers of kidney injury and kidney function in dogs with experimental cardiorenal syndrome.Methods and Results: Fourteen dogs with cardiorenal syndrome (coronary microembolization-induced HF and renal dysfunction) were randomized to 3 months Sac/Val therapy (100 mg once daily, n = 7) or no therapy (control, n = 7). LV ejection fraction and troponin-I, as well as biomarkers of kidney injury/function including serum creatinine and urinary kidney injury molecule-1 were measured before and at end of therapy and the change (treatment effect change) calculated. Mitochondrial function measures, including the maximum rate of adenosine triphosphate synthesis, were measured in isolated cardiomyocytes at end of therapy. In Sac/Val dogs, the change in ejection fraction increased compared with controls, 6.9 ± 1.4 vs 0.7 ± 0.6%, P < .002, whereas change in troponin I decreased, -0.16 ± 0.03 vs -0.03 ± 0.02 ng/mL, P < .001. Urinary change in kidney injury molecule 1 decreased in Sac/Val-treated dogs compared with controls, -17.2 ± 7.9 vs 7.7 ± 3.0 mg/mL, P < .007, whereas the change in serum creatinine was not significantly different. Treatment with Sac/Val increased adenosine triphosphate synthesis compared with controls, 3240 ± 121 vs 986 ± 84 RLU/µg protein, P < .05.Conclusions: In dogs with cardiorenal syndrome, Sac/Val improves LV systolic function, improves mitochondrial function and decreases biomarkers of heart and kidney injury. The results offer mechanistic insights into the benefits of Sac/Val in HF with compromised renal function. [ABSTRACT FROM AUTHOR]

Coal separation based on computer vision has attracted substantial attention in recent years. However, developing reliable object detection models relies on large-scale annotated dataset, which in industrial practice is time-consuming and labor-intensive to obtain. In this paper, we propose a novel data augmentation model called dual attention deep convolutional generative adversarial network (DADCGAN) to expand dataset scale and improve object detection. For the first time, the proposed DADCGAN, which adopts DCGAN as its foundation architecture, introduces efficient channel attention and external attention mechanisms to capture essential feature information from the channel and spatial dimensions of images, respectively. Moreover, spectral normalization and two time-scale update rule strategies are incorporated to stabilize the training process. The implementation of our proposed data augmentation strategy includes two steps. First, traditional pixel transformation is used to expand an original small dataset. Then, our GAN-based data augmentation is executed to further expand the dataset by generating synthetic images. Experimental results show that our DADCGAN model achieves the lowest FID value, decreasing the FID by 21.30–71.96 % compared to other baseline GAN models, showcasing its ability to produce more realistic coal-gangue images. Finally, the data augmentation strategies are applied to the YOLOv4 model, enhancing the mAP by 9.26 %, highlighting its significance in enhancing coal-gangue object detection. These results have important implications for the development and implementation of computer vision-based technologies, enabling the realization of cleaner and more efficient coal separation methods. • Propose GAN-based data augmentation to generate synthetic coal-gangue images. • Apply efficient channel and external attention to capture key features of images. • Use spectrum normalization and imbalanced learning rate to stabilize GAN training. • Produce more realistic images using DADCGAN with lower FID than regular GAN models. • Improve coal-gangue detection accuracy with our data augmentation strategies. [ABSTRACT FROM AUTHOR]

Abstract With the rapid increase in the numbers of new generation Global Navigation Satellite Systems (GNSS) satellites, signal frequencies and ground tracking stations, the burden on data processing increases significantly, especially for those real-time or near real-time applications, e.g. generating ultra-rapid satellite orbit and Earth rotation parameters (ERP) products. In order to reduce the number of observations used to estimate the orbit and ERP unknown parameters for better computational efficiency, this study first introduced a parameter called orbit and ERP dilution of precision (OEDOP) factor and a method in "optimally" selecting multi-GNSS tracking stations based on the OEDOP factor is investigated to minimize the data processing burden without significantly sacrificing the accuracy and precision of the satellite orbit and ERP determination. The trade-off between computational efficiency and quality of results is primary focus of this research. The contribution of each tracking station to the precision of the parameter estimates is investigated first, according to the location and multi-GNSS data measurement capacity of the station as well as the length of observations, then those stations that contribute least will be identified and excluded in the estimation system. It aims to use as a fewer number of tracking stations as possible but the degradation in the precision of the solution is still under a desired level. The method was tested using GNSS observations from 409 International GNSS service (IGS) stations over a one-month period. Results showed that when the "degradation" factor of the precision of satellite orbit and ERPs solutions is 5%, 10%, 15% and 20% the accuracy of the satellites orbit and polar motion parameters estimated from an optimal minimum number of stations (in comparison with the results from all stations) reduced about 0.33–9.92 cm and 5.77–41.53 μas respectively, and the accuracy of UT1–UTC reduced 10.63–15.50 μs; while their computational speed was improved by 196%, 332%, 527% and 617% respectively. This suggests that our method is a good trade-off method and an ideal option in cases that rapid solutions are required, e.g. ultra-rapid determination of orbit and ERP using multi-GNSS measurements from global ground tracking stations. [ABSTRACT FROM AUTHOR]

EI Niño–Southern Oscillation (ENSO) is a complex ocean-atmosphere interaction phenomenon occurring in nature that has a profound impact on global atmospheric circulation. As ENSO is a coupled ocean-atmosphere phenomenon, in addition to the commonly used sea surface temperature (SST), water vapor in the atmosphere can be used to monitor the evolution of ENSO and to investigate its consequences (e.g., droughts and flooding). The Global Navigation Satellite System (GNSS), in addition to its applications for positioning, timing, and navigation, is another established atmospheric observing system used to remotely sense precipitable water vapor (PWV) in the atmosphere. The accuracy of the GNSS-derived PWV measurements was assessed from 12 stations based on observations made at co-located radiosonde stations as a reference. The results show that mean values of the root-mean-square error (RMSE) and biases of 6-hourly GNSS-derived PWV derived from all 12 stations are valued at 1.48 mm and −0.30 mm, respectively. Regarding monthly means, mean values of the RMSE and biases of the GNSS-derived PWV are valued at 0.66 mm and −0.23 mm, respectively. The variability in PWV estimated from 56 GNSS stations positioned close to the sea indicates that it is significantly affected by ENSO events. Generally, a 1-K increase in SST will lead to an 11% increase in PWV across all of the stations. A case study conducted at the TOW2 station in Australia shows that the non-linear trend of the PWV depicts the evolution of two severe flood events and one severe drought event occurring in this region. Comparative results derived from TOW2 and from another 24 stations show a good agreement between PWV and total precipitation. These results suggest that GNSS-derived PWV together with other climatic variables (e.g., SST) can be used as an indication of the evolution of ENSO events and as a possible indicator of drought and flood occurrence. [ABSTRACT FROM AUTHOR]

Accurate prediction of carbon price effectively ensures the stability of the carbon trading market and reduces carbon emissions. However, making accurate prediction is challenging because the carbon price is highly nonlinear and nonstationary due to complex influential factors. Thus, we propose a multifactorial hybrid forecasting framework, ET-MVMD-LSTM, to integrate three advanced algorithms for a reliable multi-step ahead prediction of the carbon price. First, extremely randomized tree (ET) is used to determine the optimal input variables for the modeling to follow. Then, multivariate variational mode decomposition (MVMD) is executed to simultaneously decompose the screened input variables into relatively regular sub-modes, which reflect characteristics at different scales. Subsequently, long short-term memory (LSTM) with a stable forecasting ability is employed to model each mode individually to effectively extract the long-term trend and short-term fluctuation features. The final forecast is reconstructed by the ensemble of the predictions of all sub-modes. Last, systematical studies on two European Union Emissions Trading Scheme carbon price datasets indicate that the proposed ET-MVMD-LSTM framework outperforms several advanced baseline models in terms of accuracy and stability, which prove the framework is deemed promising and practical for carbon price prediction. • Propose a novel hybrid framework for multi-step carbon price forecasting. • Consider multiple influential factors for carbon price forecasting. • Use extremely randomized tree for feature selection. • MVMD algorithm decomposes multiple variables simultaneously. • The proposed model outperforms the baseline models in two EU ETS dataset. [ABSTRACT FROM AUTHOR]

A gravitational field of an ore-bearing asteroid, which usually has an irregular shape and heterogeneous density, is a prerequisite for asteroid exploration (e.g., space mining) missions. Thus, it is a hot topic in planetary science to model the external gravitational field of the ore-bearing asteroid in an efficient and accurate way. In this study, a computational fluid dynamics (CFD)-based method proposed in our previsous studies is investigated further. Firstly, six types of density distribution in asteroid Bennu are simulated as six experimental cases; then the gravitational fields are derived by using the CFD-based method; and finally, the results are compared to the other two solutions derived from the mascons gravity model method (as a benchmark method) and polyhedron gravity model method, respectively. The CFD-based method shows a superior performance in modelling the gravitational field of an irregularly-shaped asteriod with heterogeneous density in terms of both accuracy and efficiency. For example, the CFD-based method only costs 340 s obtaining 1,650,000 gravitational vectors outside the asteroid with a relative error of 1.27 %, compared to the computation time of 14,520 s and the solution accuracy of 3.84 % for the polyhedron gravity model method on the same testing points. The comparison study demonstrates a good potential application of the CFD-based gravitational field modeling method in asteroid exploration missions. • A CFD method can solve the gravity field of asteroids with various density types. • A CFD method is more efficient in solving gravitational field than other methods. • The mass distribution of an asteroid can be reflected in the external gravity field. • The CFD method exhibits efficient, accurate and stable properties. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) water vapor tomography has been proved to be an effective method for retrieving three-dimensional (3D) water vapor distribution. Currently, due to the limitation on the availability and the poor distribution of observations, the ill-posed problem of the tomographic system still needs to be solved. In this study, a refined extended tomographic model with combining virtual signals is proposed to address the observational geometry defect and improve the performance of tomographic solutions. The new refined model was generated by adding auxiliary voxels of the same size as the original voxels around the original tomographic model and its tomographic body is with an inverted cone shape. Then, "virtual" signals that pass through the sides of the original tomographic body and the top boundary of the extended region are introduced. The slant wet delays (SWDs) of these virtual rays were obtained from the tropospheric parameters estimated from GNSS data processing and pre-defined elevation and azimuth angles. Three experimental schemes based on GNSS data from the Hong Kong reference network during the 30-day period in July 2019 were implemented to evaluate the proposed tomographic model. Statistical results showed that, compared with the tomographic models of other schemes, the new model shows strong robustness in terms of observational geometry and the accuracy of the 3D water vapor field inferred. The mean value of the root-mean-square errors (RMSEs) of the tomographic solutions during the period studied obtained from the proposed method was improved by 12% and 11% compared with Schemes I and II, when radiosonde data were used as the reference. Furthermore, compared to other schemes, the condition number of the design matrix reduced after the side-crossing virtual signals were added, implying improvements in the ill-condition of a tomographic system for the proposed method. All these results suggest the good performance of the proposed method. • An improved tomographic model is proposed to address the observational geometry defect and improve the performance. • The virtual signals penetrating from the side-faces of the tomographic model are incorporated into the observation eqs. • The new model with an inverted cone shape is built to utilize these virtual signals. [ABSTRACT FROM AUTHOR]

[Display omitted] • A forecasting system is proposed for multi-step ahead PM 2.5 and PM 10 forecasting. • Spatial-temporal attention favors extracting spatial-temporal dependency. • Residual learning avoids degradation of model performance. • The developed forecasting system outperforms baseline models in most scenarios. Accurate and reliable forecasting of PM 2.5 and PM 10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM 2.5 and PM 10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM 2.5 and PM 10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM 2.5 and PM 10 , respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution. [ABSTRACT FROM AUTHOR]

Abstract: Background: Apelin-13 (APLN) through apelin receptor (APJ) exerts peripheral vasodilatory and potent positive inotropic effects. We examined the effects of exogenous intravenous infusion of APLN on left ventricular (LV) systolic function in dogs with heart failure (HF, LV ejection fraction, EF∼30%). Methods and Results: Studies were performed in 7 dogs with microembolization-induced HF. Each dog received an intravenous infusion of low dose and high dose APLN followed by washout period. LV end-diastolic volume (EDV), end-systolic volume (ESV) and LV EF were measured at specified time points. APLN protein level was determined in plasma at all time points. mRNA and protein levels of APLN and APJ in LV tissue were also measured in 7 normal (NL) and 7 heart failure (HF) dogs. APLN reduced EDV only at the high dose, significantly reduced ESV and increased EF with both doses. In plasma of HF dogs, APLN levels were reduced significantly compared to NL dogs. APLN treatment in HF dogs significantly increased the plasma APLN levels at both low and high doses. Expression of APLN, but not of APJ, was reduced in LV tissue of HF dogs compared to NL. Conclusions: Exogenous administration of APLN improved LV systolic function in dogs with advanced HF. [Copyright &y& Elsevier]

Abstract: There has been an increase in popularity of wheelchair sports. It is necessary to develop mobile on-board systems to do measurements in field. This paper presents a low cost on-board velocity and position measurement system for field environments. In the system, two MEMS gyroscopes and two GPS receivers are fixed on the rear wheels’ axles. A Kalman filter is used to integrate position data with velocity data to produce accurate velocity and displacement estimations. The wheelchair’s kinematics can be identified by using the estimations. All of the measurement data are transferred to a computer via a wireless network. [Copyright &y& Elsevier]

• Propose a novel hybrid model for multi-step ahead daily coal price forecasting. • Decompose original data into multiple modes via variational mode decomposition. • Use long short-term memory network containing attention layer to predict each mode. • Predicted mode results are ensembled by support vector regression model. • Proposed hybrid model is superior to baseline models in coal price forecasting. Accurate and reliable coal price prediction is of great significance to enhance the stability of the coal market. Numerous methods have been developed to improve the prediction performance. However, most of the studies adopt single model for coal price forecasting, and their accuracy and applicability are usually restricted. In this paper, we propose a novel hybrid VMD-A-LSTM-SVR model to achieve accurate multi-step ahead prediction of coal price. The proposed model consists of three valuable strategies. First, variational mode decomposition (VMD) decomposes the original coal price into several relatively regular sub modes to reduce the non-stationarity and uncertainty of the data. Second, the long short-term memory (LSTM) integrated with attention mechanism trains and predicts the decomposed modes individually to better capture the temporal information of historical data. Lastly, a support vector regression (SVR) model ensembles the predicted results of each mode into the final forecasted coal price. The experimental results of three typical coal price datasets demonstrate that the proposed strategies are all valuable for improving the forecasting performance. Moreover, the proposed model outperforms all state-of-the-art baseline models in terms of both model accuracy and stability. Extensive cross-comparisons of performance between models clearly indicate that the proposed hybrid algorithm is more effective and practical for coal price forecasting. [ABSTRACT FROM AUTHOR]

Air pollution has endangered both ecological environment and human health. Long-term prediction of air quality index (AQI) is an effective approach to early warning of, and prompt response to, pollution events to support cleaner industrial production. However, existing approaches to forecasting long-term air quality need further improvement. In this paper, we proposed a novel spatial-temporal deep learning algorithm based on bidirectional gated recurrent unit integrated with attention mechanism (BiAGRU), for more accurate air quality forecasting. The historical air quality measurements and meteorological monitoring data were constructed as a spatial-temporal matrix suitable for model input. The performance of the proposed BiAGRU model was evaluated by a series of metrics. The RMSE, MAE, R 2 and Fractional Bias (FB) values of the proposed BiAGRU model are 31.10, 23.06, 0.60, and 0.015, respectively, for 24 h multi-step ahead prediction assignments using Huaihai Economic Zone dataset. Quantitative comparison between models indicates the developed BiAGRU model outperformed various traditional machine learning algorithms and advanced deep neural network methods in term of lower error bias and higher accuracy and regression scores. This work is of importance to strengthen regional prevention and control of air pollution. Image 1 • A novel artificial intelligence methodology for multi-step ahead forecasting and analysis of air quality. • Inclusion of spatial information improves regional air quality forecasting. • Model performance is evaluated against comprehensive metrics. • The novel spatial-temporal BiAGRU model outperformed several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The β 3 -adrenergic receptor was initially identified in fat and subsequently found in the myocardium. Unlike β 1 and β 2 -adrenergic receptors, β 3 receptor stimulation inhibits cardiac contraction and relaxation through its link to inhibitory G proteins (Gi). Stimulation of cardiac β 3 receptors has been shown to have a negative effect on the cardiac contractile state without eliciting direct chronotropic effects. In the failing left ventricular (LV) myocardium, β 3 -adrenergic receptors are upregulated, a maladaptation that can contribute to LV systolic dysfunction characteristic of the heart failure (HF) state. This study examined the effects of a single dose, 6 hours intravenous infusions of the β 3 -adrenergic receptor antagonist APD418 on LV systolic and diastolic function in dogs with HF (LV ejection fraction, EF∼35%). Studies were performed in 7 dogs with coronary microembolizations-induced HF. Dogs were randomized to receive a 6 hour infusion of APD418 (4.224 mg/kg) or a 6 hour infusion of 0.9% NaCl (vehicle) administered 1 week apart. Hemodynamic, ventriculographic and echocardiographic-Doppler measurements were made at baseline and at 2 and 6 hours after administration of APD418 or vehicle. Heart rate (HR), mean aortic pressure (mAoP), LV end-diastolic (EDV) and end-systolic (ESV) volumes, EF, cardiac output (CO), systemic vascular resistance (SVR) and myocardial oxygen consumption (MVO 2) as well as the diastolic function measures Ei/Ai and mitral inflow velocity deceleration time (DCT) were measured at each time point. Infusion of vehicle for 6 hours had no significant effects on any of the ventriculographic and echocardiographic-Doppler measures but caused lowering of mAoP and SVR likely due to duration of infusion under general anesthesia. Infusion of APD418 had no significant effects on HR, mAoP, EDV or MVO 2 , decreased ESV, significantly decreased SVR and significantly increased EF, CO, Ei/Ai and DCT (Table). Comparison of treatment effect Δ (change between baseline and 6 hours) between vehicle and APD418 showed that APD418 significantly decreased ESV, and significantly increased EF, SV, CO, Ei/Ai, mAoP and DCT with no changes to EDV, HR, and SVR (data not shown). The results of the study indicate that 6 hours intravenous infusions of APD418 in dogs with systolic HF elicit positive inotropic and lusitropic effects. The data support the continued development of APD418 for the treatment of patients with HF. [ABSTRACT FROM AUTHOR]

Efficient, stable, and selective photocatalytic conversion of nitric oxide (NO) into nitrogen dioxide (NO 2) is highly desirable but remains a big challenge. We prepare single atom catalyst (SAC) by anchoring single Pd atoms onto graphitic carbon nitride (CNPd) via chemical impregnation followed by calcination. The prepared CNPd SAC is confirmed by aberration-correction high-angle-annular-dark field scanning transmission electron microscopy and X-ray absorption fine structure spectroscopy. The synthesized SAC outperforms its competitors reported earlier for photocatalytic removal of NO under visible light and simulated sunlight irradiation in terms of selectivity and stability. The SAC maintains a NO removal efficiency of 83% and an instantaneous selectivity for NO 2 of 92.8% over 117.6 h under simulated sunlight with the inlet NO concentration of 12 ppm. This duration is about 23.5 times the longest duration tested for other catalysts in the literature. The experimental results and density functional theory (DFT) calculations reveal that single Pd atom promotes the photocatalytic degradation of NO. Moreover, the DFT calculations prove that the nitrate ions that accumulate on the surface of the SAC can react with NO to produce NO 2. This reaction enhances the selectivity for NO 2 and stability of the SAC. The CNPd catalyst exhibits high photocatalytic activity, selectivity and stability for the removal of NO under visible light. [Display omitted] • The CNPd SACs exhibit stable and high photocatalytic removal of NO. • The CNPd SACs show ultra-high selectivity for the conversion of NO into NO 2 which can be easily addressed by wet scrubbing. • NO removal efficiency of CNPd SACs remain 83% after solar irradiation over 117.6 h. • DFT calculations reveal the mechanism of photocatalytic over CNPd SACs. • DFT calculations explain the reason for selective conversion of NO to NO 2. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. In this study, a neural network-based (NN-based) approach taking seven meteorological variables as input data was developed based on the back propagation (BP) algorithm for detecting heavy precipitation. Apart from the surface meteorological variables of temperature, pressure and relative humidity, the model has also adopted other information such as day-of-year, hour-of-day and GNSS-derived zenith total delay and precipitable water vapor (PWV) as input variables. The feasibility of using these variables for developing the BP-NN-based model was elaborated by conducting the feature analysis of the seven input variables. In addition, the criterion for selecting a proper size of training sample was also briefly investigated by studying the impact and sensibility of the sample lengths in the model. The proposed model was developed using a sample size of an 8-year (2010–2017) period in the summer at a pair of co-located GNSS/weather stations−HKSC-KP in Hong Kong. The use of a long-term data is to "reliably" capture the characteristics of the selected variables. The detection results for the summer months in 2018 and 2019 were then compared against corresponding precipitation records to valid the effectiveness of the newly proposed model. Results of the correct detection and false alarm rates were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models. [Display omitted] • Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. • A neural network-based approach taking seven meteorological variables as input data was developed based on the back propagation algorithm for detecting heavy precipitation. • The rationality of using those variables for developing the new model was elaborated by conducting a preliminary feature analysis. • The criterion for selecting a proper size of training sample was briefly investigated by studying the impact and sensibility of the sample lengths in the new model. • Results of the correct detection and false alarms were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models. [ABSTRACT FROM AUTHOR]

• There were remarkable discrepancies between COSMIC and ionosonde with a maximum amplitude of 25%. • The remarkable discrepancies are dependent on universal time, geographic latitude, and season. • Ionospheric model applied by the method improves significantly in accuracy and physical features. There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde's observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde's data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations. [ABSTRACT FROM AUTHOR]

Tomography is an important technique for ionosphere investigation. For a voxel-based tomography method, the way the voxel model is constructed is crucial and may affect the quality of the reconstructed image. However, previous research has paid less attention to voxel model construction and how this may improve or reduce the quality of the produced tomography image. To mitigate this issue, a new method is proposed named Image Reconstruction based on Hybrid Grids (IRHG). In IRHG, two hybrid grid models, each with a top and a bottom component (separated by a splitting height) that have different voxel resolution configurations, are adopted for tomographic inversions. Thereafter, the advantageous components of the two reconstructed images are combined to produce a new image (i.e., the image for IRHG). Initial testing showed that a slight improvement was achieved when compared to a uniformly spaced voxel model. This was further enhanced by changing the splitting height to 400 km and the use of different vertical and horizontal voxel resolutions. Finally, an improvement in root mean square error (RMSE) and mean absolute error (MAE) of 28.24% and 23.24% (for quiet ionosphere days), and 5.96% and 9.01% (for disturbed days) respectively, were achieved. The IRHG method is supposed to be independent of the inversion algorithm, e.g., the improved algebraic reconstruction technique (IART) used in this paper, and promises to hold benefits for other algorithms, which may together improve the reconstructed tomographic image. [ABSTRACT FROM AUTHOR]

Abstract The vertical distribution characteristics of aerosols are key uncertain factors for studying the effect on radiative forcing and trans-regional transport of pollutants. This paper used three years (2013–2015) LiDAR measurements at a site in the Yangtze River Delta region of China to investigate the aerosol vertical distribution and transport sources of aerosol-aloft by using the Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT) models. The results indicated that there were 230 haze days accounted for 21% of all the days, including 142 damp haze days and 88 dry haze days during the study period. The aerosols below 2 km accounted for >89% of the total aerosol optical depth (AOD). Compared to other seasons, aerosols in winter were more likely to accumulate below 1 km (>69%). In summer, although atmospheric convention was strong leading to a high planetary boundary layer height (PBLH) and the concentration of PM 2.5 was low, the AOD was largest because of high relative humidity that caused hygroscopic growth of particles. Due to the stable weather condition in winter, the PBLH was low with the largest concentration of PM 2.5 , so the occurrence of haze days was most frequent. The PSCF and CWT results revealed that the high-level aerosols mainly came from local areas; the CWT model showed considerable long-distance transports of dust from northern/northwestern China, as far as Mongolia, Gansu Province and Xinjiang Uygur Autonomous Region, in spring, autumn and winter. Southern sources were more obvious in winter that could contribute more anthropogenic aerosols and biomass burning emissions. Highlights • Long-term vertical distribution of aerosols over the YRD region from 3-years (2013–2015) LiDAR observations. • LiDAR observations were combined with backward trajectory model. • PSCF and CWT models were used to investigate aerosol-aloft sources. [ABSTRACT FROM AUTHOR]

Abstract Compared to the space-borne estimation of PM 2.5 (particulate matter with aerodynamic diameter ≤2.5 μm), the investigation of PM 1 (≤1 μm) remains less intensive and thus unclear. Here we estimated four years (2014–2017) of ground-level PM 1 concentrations from MODIS aerosol optical depth (AOD) in attempt to gain a better understanding of much finer particles. The Yangtze River Delta (YRD) region, with a relatively dense ground-based PM 1 station network, was selected as the study area. The geographically and temporally weighted regression (GTWR) model simultaneously accounting for spatial and temporal variability existing within various predictors was constructed. Validation of satellite-estimated PM 1 against ground-measured PM 1 yields a high consistence, significant improvement over previous work (R2 = 0.74 VS 0.59, RMSE = 13.02 μg/m3 VS 22.5 μg/m3). This suggests the PM 1 estimates from GTWR model are reliable and robust enough to obtain large-scale fine particle contents. The population exposure of air pollution in the YRD region, therefore, has been analyzed by calculating population-weighted mean PM 1 concentrations, which reaches as high as 37.22 μg/m3. Further analysis indicates that near half the people live in locations with high-level PM 1 concentration (>35 μg/m3), which has profounding implication for improving our understanding of human exposure to fine aerosol particles. Highlights • Much improved ground-level PM 1 estimation was achieved over the YDR in China. • New merged 3 km MODIS AOD made better PM 1 prediction accuracy and spatial details. • Four-year (2014–2017) population-weighted mean PM 1 concentrations were calculated. [ABSTRACT FROM AUTHOR]

Abstract Atmospheric drag, as one of the largest non-gravitational perturbations in low Earth orbit (LEO), can dramatically decay the orbit of LEO satellites with both secular and periodic effects. Hence, it plays a critical role in orbit prediction related products, and research on orbit determination, orbital uncertainty propagation and collision avoidance. Although many empirical thermospheric mass density (TMD) models have been proposed in the past few decades, precise determination of atmospheric drag is still a challenging task. In order to give a comprehensive review of the current empirical TMD models, focusing on their impact on orbital dynamics, this review summarises and investigates the most representative classes of models, including the Jacchia, Mass Spectrometer Incoherent Scatter (MSIS), Jacchia-Bowman (JB), and Drag Temperature Model (DTM). Twelve representative models are selected for further comparison in terms of spatial variations and assessing their ability to capture complex features, e.g., equatorial mass density anomaly (EMA). Further validation is done with accelerometer-derived TMD from LEO satellites. The results show that only DTM2013 can capture the EMA feature and the drag coefficient calculated by physical models used in the TMD estimation may be underestimated. The performance of these models in orbit prediction is comprehensively evaluated under different solar and geomagnetic conditions. JB2008 and DTM2013 outperform the other selected models during low and high solar activity. Standard deviation is found to be less affected by the bias in the accelerometer- and model-derived TMD, than mean value and root-mean-square error. The coupling effect between the TMD and ballistic coefficient, and the potential directions for future efforts in TMD modelling are also discussed. [ABSTRACT FROM AUTHOR]

Abstract Initial orbit determination (IOD) for space objects is challenging, especially in the case where only optical observations, i.e. angles-only observations, are available and the optical observing arcs are very short (i.e. the too-short arc (TSA) problem). One approach to address the TSA problem is to associate several short-arc tracklets to targets across varying time intervals. In order to achieve better association and run-time performance, this study proposes an improvement to the traditional initial value problem (IVP) solution that determines the association by searching for the global minimum of a new loss function defined in a nonsingular canonical space. The improved IVP method was validated using optical data of space objects at different altitudes collected from the Mount Stromlo Observatory and compared with traditional IVP and another popular tracklet association method: the boundary value problem (BVP) approach. Results illustrate that the improved IVP method is superior to IVP and BVP in terms of association performance, and it also achieves good run-time performance. In addition, traditional methods suffer the drawback of incorrectly associating tracklets from different objects in the same constellation. A new approach dubbed the common ellipse method is presented to address this issue. The common ellipse method is tested with 86 Iridium constellation tracklets, and results show that it significantly improves the true negative rate for the tested scenario. Graphical abstract Image 1 Highlights • A new improved IVP has been developed for trackelet association for space objects. • A new loss function defined in a nonsingular canonical space was used for association. • The tracklet association and run time performance were validated by the data collected from the Mount Stromlo Observatory. • A new common ellipse method was proposed to associate constellation tracklets. [ABSTRACT FROM AUTHOR]

The Circum-Pacific seismic belt is the region heavily affected by earthquakes in the world. The relationship between earthquake (e.g., the geographic location, occurrence time, magnitude, and focal depth) and ionospheric anomalies in the belt was investigated using 100 M7.0+ earthquakes during 2006–2015. The ground-based GPS measurements and global ionosphere map (GIM) data were used for the analyses of the ionospheric variations preceding the earthquakes. The results indicated that the occurrence rate of total electron content (TEC) anomalies was proportional to the magnitude and inversely proportional to the focal depth to a certain degree, and the occurrence frequency of anomalies had a rising trend with the days getting close to the main shock. The occurrence rate of TEC anomalies in the Southern hemisphere was larger than that in the Northern hemisphere. Besides, the spatial characteristics of TEC anomalies showed that the anomalies in low-middle latitudes did not coincide with the epicenter, sometimes the anomalies were also observed in the corresponding conjugated region. However, the TEC anomalies in the high latitude usually appeared around the epicenter and within the seismogenic zone while no TEC anomalies appeared in the conjugated area. These results may have potential applications to the earthquake prediction in the Circum-Pacific seismic belt. [ABSTRACT FROM AUTHOR]

East Asia and North America are the regions most heavily affected by powerful cyclones. In this paper we investigate the morphological characteristics of ionospheric disturbances induced by cyclones in different continents. The global ionosphere map supplied by the Center for Orbit Determination in Europe (CODE), International Reference Ionosphere Model (IRI) 2012, and Wallops Island ionosonde station data are used to analyse the ionospheric variations during powerful typhoons/hurricanes in East Asia and North America, respectively. After eliminating the ionospheric anomalies due to the solar-terrestrial environment, the total electron content (TEC) time series over the point with maximum wind speed is detected by the sliding interquartile range method. The results indicate that significant ionospheric disturbances are observed during powerful tropical cyclones in East Asia and North America, respectively, and that all the ionospheric anomalies are positive. In addition, the extent and magnitude of travelling ionospheric disturbances are associated with the category of tropical cyclone, and the extent of TEC anomalies in longitude is more pronounced than that in latitude. Furthermore, the maximum ionospheric anomaly does not coincide with the eye of the storm, but appears in the region adjacent to the centre. This implies that ionospheric disturbances at the edges of cyclones are larger than those in the eye of the winds. The phenomenon may be associated with the gravity waves which are generated by strong convective cells that occur in the spiral arms of tropical cyclones. This comprehensive analysis suggests that the presence of powerful typhoons/hurricanes may be a possible source mechanism for ionospheric anomalies. [ABSTRACT FROM AUTHOR]

Abstract: The Global Navigation Satellite Systems (GNSS) are one of the very few tools that can provide continuous, unbiased, precise and robust atmosphere condition information. The extensive research of GNSS space-based segment (e.g. available precise, real-time satellite orbits and clocks), unlimited access to the ground-based Continuously Operating Reference Stations (CORS) GNSS networks along with the well established data processing methods provides an unprecedented opportunity to study the environmental impacts on the GNSS signal propagation. GNSS measurements have been successfully used in precise positioning, tectonic plate monitoring, ionosphere studies and troposphere monitoring. However all GNSS signals recorded on the ground by CORS are subject to ionosphere delay, troposphere delay, multipath and signal strength loss. Nowadays, the GNSS signal delays are gradually incorporated into the numerical weather prediction (NWP) models. Usually the Zenith Total Delay (ZTD) or Integrated Water Vapour (IWV) have been considered as an important source of water vapour contents and assimilated into the NWP models. However, successful assimilation of these products requires strict accuracy assessment, especially in the challenging severe weather conditions. In this study a number of GNSS signal processing strategies have been verified to obtain the best possible estimates of troposphere delays using a selection of International GNSS Service (IGS) orbit and clock products. Three different severe weather events (severe storm, flash flooding, flooding) have been investigated in this paper. The strategies considered are; 1) Double Differenced (DD) network solution with shortest baselines, 2) DD network solution with longest baselines, 3) DD baseline-by-baseline solution (tested but not considered), 4) Zero Differenced (ZD) Precise Point Positioning (PPP) based on ambiguity float solutions, all with precise orbits and clocks, and real time clocks and predicted orbits. The quality of the estimates obtained has been evaluated against radiosonde measurements, Automatic Weather Station (AWS) observations, NWP (assimilation step without ground-based GNSS data) and ZTD estimates from the well established IGS processing centre, the Center of Orbit Determination in Europe (CODE). It shows that the ZTD and IWV estimates from the DD short baseline solution are robust with usually a very small bias (−2.7 to −0.8mm) and errors of less than 10mm (7.6–8.5) (ZTD) or 3mm (2.6–2.7) (IWV). The DD short baseline network solution was found to be the most reliable method in the considered case studies, regardless of the type of orbits and clocks applied. [Copyright &y& Elsevier]

The OMI NO 2 standard product, OMNO2d, has been widely used in estimating surface NO 2 concentrations. The Peking University Ozone Monitoring Instrument NO 2 product (POMINO) is claimed to provide an improved quality over east-central China. This study estimated one year (Dec.2016–Nov.2017) of surface NO 2 concentrations at satellite overpass time based on OMNO2d data and POMINO data, respectively. We used an extra-trees (ET) regression model to convey the non-linear relationship between surface NO 2 and predictors, and compared the prediction accuracy with that of random forests (RF) regression model. The ET model showed a better estimation performance than the RF model, with the cross-validation R2 of 0.72 (RMSE = 9.20 μg/m3) and R2 of 0.70 (RMSE = 9.42 μg/m3) based on POMINO and OMNO2d data, respectively. The POMINO-derived monthly mean surface NO 2 concentrations were closer to ground NO 2 measurements than that OMNO2d-derived. Although the estimations from both satellite products were underestimated in polluted situations, the use of POMINO reduced the underestimation as compared to the use of OMNO2d data. • POMINO data with extra-trees model produces highest cross-validation R2 of 0.72 • POMINO increases surface NO 2 concentration compared with OMNO2d in polluted situation. • Monthly NO 2 from POMINO are consistent with ground sites, but OMNO2d underestimates in Jan and Oct. [ABSTRACT FROM AUTHOR]

In this paper, a design of reconfigurable optical add-drop multiplexer (ROADM) is proposed, which consists of magnetically-coupled switches, bus waveguide, drop waveguide, add waveguide, and reflection cavity based on a photonic crystal (PC). The ferrite materials are introduced to form microcavities in a two-dimensional PC. Then, the magnetic permeability of ferrite rods is changed by adjusting the magnetic field strength, and a scheme for magnetic permeability distribution of magnetically-coupled switches is proposed. The simulation results indicate that the incident wave with wavelength 1550 nm can be downloaded and uploaded successfully, and the transmissivity of wave is both above 90% when the add function and drop function are conducted. The quality (Q) factor is approximately 103. Moreover, the overall size of the device is 13.5 μ m × 10.5 μ m , which is highly suitable for wavelength division multiplexing (WDM) system in the photonic integrated circuits. [ABSTRACT FROM AUTHOR]