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The results of studies on intrauterine fetal death (IUFD) caused by exposure to fine particulate matter (PM2.5) during pregnancy are inconsistent. Further exploration of the dose–response relationship and exposure window is required. We aimed to provide a reference for policy formulation by estimating the exposure-lag relationship of PM2.5 on IUFD and looking for sensitive exposure windows. IUFD data was obtained from China Children Under 5 Death Surveillance Network in Linxia Hui Autonomous Prefecture from 2016 to 2020. Air pollution data and temperature data were obtained from ambient air monitoring stations and China Meteorological Data Network, respectively. The moving average is used to describe the trend and seasonality of PM2.5 exposure; the distributed lag non-linear model (DLNM) is used to estimate the exposure-lag effect; the sandwich estimators are used to correct the variance–covariance matrix; and the model selected by Akaike's Information Criterion (AIC) finally adjusts gender, temperature, and district. About 180,622 infants were enrolled in the study, including 952 IUFDs (5.27‰). The median of PM2.5 exposure is 34.08 μg/m3. There is an exposure-lag effect of PM2.5 on IUFD approximate to a wavy shape; the concentration with effect is 40–90 μg/m3; and the sensitive lag time is 1, 2, 3, 8, 9, and 10 months. The maximum RR value of the exposure-lag effect of PM2.5 on IUFD is 1.61 [95% CI 1.19, 2.19], in which the concentration of PM2.5 is 62 μg/m3, and the lag month is 9 months. In the case of less than 6 months lag, the maximum RR value of the exposure-lag effect of PM2.5 on IUFD is 1.43 [95% CI 1.24, 1.67], in which the concentration of PM2.5 is 73 μg/m3, and the lag month is 3 months. Exposure to PM2.5 concentrations above 40 μg/m3 may increase the risk of IUFD, especially in the first and third trimesters. [ABSTRACT FROM AUTHOR]

The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D) electron density nowcasting model for China and its adjacent regions was developed. Unlike the traditional Gaussian background covariance model, the error covariance of the IRI model, based on the IGS grid TEC data, is estimated in this work. Due to the large scale of the high-resolution covariance matrix, it cannot be stored and calculated directly on a personal computer. The covariance localization (CL) technique is introduced to sparse the covariance matrix while removing the pseudo-correlation of the covariance matrix. After localization, the covariance matrix can be converted into a sparse matrix for storage and calculation, which greatly reduces the computer memory requirement of the assimilation model and improves the calculation speed of the model. Based on this algorithm, a series of experiments were carried out in this work. The experimental results show that this algorithm can effectively assimilate the observed GNSS to the background field, make up for the temporal and spatial limitations of the observed data, and improve the accuracy of the ionospheric electron density nowcast. Compared with the digisonde observed foF2 (the critical frequency of the ionospheric F2 layer), the RMSE of the assimilation model is 0.44 MHz lower than that of the IRI model. [ABSTRACT FROM AUTHOR]

Gas flooding has proven to be a promising method of enhanced oil recovery (EOR) for mature water-flooding reservoirs. The determination of optimal well control parameters is an essential step for proper and economic development of underground hydrocarbon resources using gas injection. Generally, the optimization of well control parameters in gas flooding requires the use of compositional numerical simulation for forecasting the production dynamics, which is computationally expensive and time-consuming. This paper proposes the use of a deep long-short-term memory neural network (Deep-LSTM) as a proxy model for a compositional numerical simulator in order to accelerate the optimization speed. The Deep-LSTM model was integrated with the classical covariance matrix adaptive evolutionary (CMA-ES) algorithm to conduct well injection and production optimization in gas flooding. The proposed method was applied in the Baoshaceng reservoir of the Tarim oilfield, and shows comparable accuracy (with an error of less than 3%) but significantly improved efficiency (reduced computational duration of ~90%) against the conventional numerical simulation method. [ABSTRACT FROM AUTHOR]

The China-specific currency market framework of 'one currency, two markets' provides us with a unique natural experiment to investigate how the active offshore exchange rate frequently diverges from the onshore exchange rate. From an interdisciplinary perspective, we propose a methodological framework that first establishes four convexity–concavity indicators, and then employ time series clustering/segmentation techniques to explore the evolutionary patterns of the onshore and offshore Renminbi exchange rates from 2 May 2012 to 29 December 2017. The empirical results show that the methodology is able to recognize five scenarios in which the exchange rates behave in an unsupervised manner, arriving at a diagnosis of the evolutionary patterns for these two markets. The estimated inverse covariance matrices and the associated graphical representations highlight the assembled timestamps of clustering assignments and reveal time-invariant structures of the market state, with all the most relevant dependencies directly interconnected in these two markets. It also suggests that intervention operations should take into account investor attention, varying arbitrage opportunities for market participants in both markets. [ABSTRACT FROM AUTHOR]

Previous studies showed that 4D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously, and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation, which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper. [ABSTRACT FROM AUTHOR]

We develop a Multivariate Heterogeneous Autoregressive (MHAR) model with time-varying sparsity (TVS), or the TVS-MHAR-X model, to model and forecast the realized covariance matrices by employing the data from China's financial markets. We employ the matrix decomposition method to ensure the positivity of the forecasted covariance matrix and incorporate a set of predictors including the lagged daily, weekly and monthly volatilities, the leverage variables, and the jump variables. The proposed model allows the sparsity of coefficients to change over time based on the importance of predictors. We compare the forecast performances of the proposed models with the competing models based on the statistical evaluation and the economic evaluation. The results show that the proposed MHAR-TVS-X model outperforms the competing models for the short-term forecasts in terms of statistical evaluation. The results also suggest that the MHAR-TVS-X model significantly improves the efficient frontier and economic values for the short-term and long-term forecasts in terms of economic evaluation. [ABSTRACT FROM AUTHOR]

With the development of transportation in the west of China, tunnel construction in mountain cities is becoming very important and widespread. Tunneling safety in the tunnels is usually controlled by faults, and the advanced prediction of faults by seismic detection method has become a research hotspot in the field of engineering geophysics. Unlike seismic exploration on the ground, the sources and receivers are not properly arranged due to the limitation of the tunnel detection space, so as to cause migration artifacts problem in the process of advanced migration imaging. The problem results in inaccurate imaging of the faults. To solve the problem, this paper proposed a new polarization migration method. The method makes use of the polarization characteristic of three-component seismic signals. The principal polarization direction is calculated by Hilbert transform and complex covariance matrix analysis. A weighted function of the principal polarization direction factor is incorporated into the migration calculation. To verify the effectiveness of the polarization migration method, this paper carries out numerical simulations. Test results demonstrate that the artifacts are eliminated by the polarization migration, and occurrence parameters of faults, such as dip and trend are calculated accurately. The field detection case shows that seismic advanced prediction which is based on polarization migration provided parameters of faults in the front of the tunnel face with 100m, and the distance error is less than 2m, and the dip error is less than 3°, which ensures efficient and safe construction of tunneling. [ABSTRACT FROM AUTHOR]

We normalize data from 43 Chinese observatories and select data from ten Chinese observatories with most continuous records to assess the secular variations (SVs) and geomagnetic jerks by calculating the deviations between annual observed and CHAOS-6 model monthly means. The variations in the north, east, and vertical eigendirections are studied by using the covariance matrix of the residuals, and we find that the vertical direction is strongly affected by magnetospheric ring currents. To obtain noise-free data, we rely on the covariance matrix of the residuals to remove the noise contributions from the largest eigenvalue or vectors owing to ring currents. Finally, we compare the data from the ten Chinese observatories to seven European observatories. Clearly, the covariance matrix method can simulate the SVs of Dst, the jerk of the northward component in 2014 and that of the eastward component in 2003.5 in China are highly agree with that of Vertically downward component in Europe, compare to CHAOS-6, covariance matrix method can show more details of SVs. [ABSTRACT FROM AUTHOR]

Investment in the fast-growing new-energy of China has gradually attracted great attention. At present, more than 100 new-energy stocks are listed on China's stock markets. How to effectively manage the investment risk of these stocks and achieve better returns is of great interest to investors and policymakers. In finance, mean–variance portfolio (MVP) is often applied to solve portfolio allocation problems. However, when investing in numerous stocks, the classical MVP is no longer applicable. For large portfolio allocation, this study constructs a comprehensive MVP model, called an unconstrained regression model with latent factors (URELAF). It applies a factor structure with latent factors to modify the covariance matrix estimation in the classical MVP, using an unconstrained penalized regression to estimate the allocation. In addition, to more realistically simulate the investment of stock markets, transaction costs are considered. The empirical results show that URELAF model can control the risk with Sharpe ratio reaching 6 times as many as MVP model, and even improve about 26% compared with the sub-optimal portfolio model. URELAF also outperforms other portfolios with transaction costs. Finally, hypothesis tests further verify that URELAF can significantly improve portfolio performance compared with other portfolio methods for China's new-energy market. • Achieve a good balance of risks and returns for China's new-energy stocks. • The URELAF model can generate the optimal allocation of a large portfolio. • Modify estimation of high-dimensional covariance matrices. • Use a factor model with latent factors and apply a suitable structure to the disturbance term. [ABSTRACT FROM AUTHOR]

Highlights: [•] Surface soil moisture is assimilated into SWAT model via EnKS. [•] Heterogeneous spatial information may affect the performance of EnKS. [•] SLS-based inflation provide more accurate covariance matrix estimation. [•] Stable improvements can be achieved with localization utilized in DA. [ABSTRACT FROM AUTHOR]

Individual trees are characterized by various sizes and forms, such as diameter at breast height, total height (H), height to crown base (HCB), crown length (CL), crown width, and crown and stem forms. Tree characteristics are strongly related to each other, and studying their relationships is very important. The knowledge of the compatibility and additivity properties of the major tree characteristics, such as H, CL, and HCB, is essential for informed decision-making in forestry. H can be used to represent site quality and CL represents biomass and photosynthesis of crown, which is the performance of individual tree vigor and light interception, and the longer the crown length (or shorter HCB) is, the more vigorous the tree would be. However, none of the studies have uncovered their inherent relationships quantitatively. This study attempts to explore such relationships through the application of appropriate modeling approaches. We applied seemingly unrelated regression, such as nonlinear seemingly unrelated regression (NSUR), which is commonly used for exploring the compatibility and additivity properties of the variables, for the proposes. The NSUR involves the variance and covariance matrices of the sub-models that are used for the interpretation of the correlations among the variables of interest. The data set acquired from Mongolian oak forest and spruce-fir forest in the Jingouling forest farm of the Wangqing Forest Bureau in the Northeast of China were used to construct two types of model systems: a compatible model system (the model system of H, CL, and HCB can be estimated simultaneously) and an additive model system (the sum of HCB and CL is H, the form of the H sub-model equals the sum of the HCB and CL sub-models) from the individual models of H, CL, and HCB. Among the various tree-level and stand-level variables evaluated, D (diameter at breast), Dg (quadratic mean diameter), DT (dominant diameter), CW (crown width), SDI (stand density index), and BAS (basal area of stand) contributed significantly highly to the variations of the response of the variables of interest in the model systems. Modeling results showed the existence of the compatibility and additivity of H, CL, and HCB simultaneously. The additive model system exhibited better fitting performance on H and HCB but poorer fitting on CL compared with the simultaneous model system, indicating that the performance of the additive model system could be higher than that of the simultaneous model system. Model tests against the validation data set also confirmed such results. This study contributes a novel approach to solving the compatibility and additivity of the problems of H, CL, and HCB models through the application of the robust estimating method, NSUR. The results and algorithm presented will be useful for constructing similar compatible and additive model systems of multiple tree-level models for other tree species. [ABSTRACT FROM AUTHOR]

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China. [ABSTRACT FROM AUTHOR]

This study focuses on the sustainability efficiency of the Chinese transportation system by investigating the relationship between CO 2 emission levels and the respective freight and passenger turnovers for each transportation mode from January 1999 to December 2017. A novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed by taking carbon inequality into account. In this model, the aggregated variance/covariance matrix for the three classical distributional assumptions of the inefficiency term—Gamma, Exponential, and Half-Normal—is minimized, yielding lower Deviance Information Criteria when compared to each classical assumption separately. Results are controlled for the impact of major macro-economic variables related to fiscal policy, monetary policy, inflationary pressure, and economic activity. Results indicate that the Chinese transportation system shows high sustainability efficiency with relatively small random fluctuations explained by macro-economic policies. Waterway, railway, and roadway transportation modes improved sustainability efficiency of freight traffic while only the railway transportation mode improved sustainability efficiency of passenger traffic. However, the air transportation mode decreased sustainability efficiency of both freight and passenger traffic. The present research helps in reaching governmental policies based not only on the internal dynamics of carbon inequality among different transportation modes, but also in terms of macro-economic impacts on the Chinese transportation sector. • Examine carbon inequality for China's four transportation modes in 1999–2017. • a novel Robust Bayesian Stochastic Frontier Analysis was employed. • Chinese transportation system has high sustainability efficiency with fluctuations. • Waterway, railway, roadway improved sustainability efficiency of freight traffic. • Railway improved sustainability efficiency of passenger traffic. [ABSTRACT FROM AUTHOR]

A novel algorithm named the stepwise genetic algorithm (SGA) is proposed to optimize the air quality monitoring network of mainland China under the framework of adaptive management. SGA is adapted from the genetic algorithm by modifying the operators of "mutation" and "crossover" to increase the number of removed sites by one at each step. Approximately half of the sites are adequate to achieve the same mean kriging variance (MKV) as that from all the sites, and the PM 2.5 maps interpolated from these two site sets are very similar. Based on the site array proposed by SGA, the MKV shows a U-shaped trend with the number of removed sites, where the initial decrease of MKV (indicating improvement of interpolation accuracy by removing some sites) has only rarely been reported before. Mathematical proof demonstrates that the clustered sites tend to cause collinearity in the covariance matrix and hence result in MKV inflation. Image 1 • Topological indices quantify clustering of air quality monitoring network of China. • A novel Stepwise Genetic Algorithm is proposed to facilitate adaptive management. • Clustered monitoring sites tend to cause higher kriging interpolation uncertainty. • This is due to negative values in inverse of covariance matrix for clustered sites. • The PM 2.5 map derived from 50% selective sites approximates that from all sites. [ABSTRACT FROM AUTHOR]