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An extension of the linear H ∞ filter, presented here as the extended H ∞ particle filter (E H ∞ PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H ∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H ∞ particle filter (E H ∞ PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The E H ∞ PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the E H ∞ PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles. [ABSTRACT FROM AUTHOR]

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the "spatial-attribute" unified distance metric is useful, and that the SANNWR model showed the best performance. [ABSTRACT FROM AUTHOR]

We investigate the order of exponential sums involving the coefficients of general L -functions satisfying a suitable functional equation and give some new estimates, including refining certain results in preceding works [X. Ren and Y. Ye, Resonance and rapid decay of exponential sums of Fourier coefficients of a Maass form for G L m (ℤ) , Sci. China Math.58(10) (2015) 2105–2124; Y. Jiang and G. Lü, Oscillations of Fourier coefficients of Hecke–Maass forms and nonlinear exponential functions at primes, Funct. Approx. Comment. Math.57 (2017) 185–204]. [ABSTRACT FROM AUTHOR]

The site formation for the construction of a new airport in a mountainous region is typically performed by cutting and filling a hill section. The fill materials are subjected to seasonal changes and large variations in water content. The water content change renders the fill material to be characterized as unsaturated or saturated. This study aims to investigate the influence of matric suction on the time-dependent compression behavior of one local soil as a fill material for the construction of a new runway of an airport in Chongqing city, a mountainous region in China. A series of unsaturated drained triaxial tests were conducted on this coarse-grained soil to obtain the relationship between the effective stress parameter, χ, and the matric suction. Subsequently, multistaged compression tests were performed on this soil using a newly designed suction-controlled oedometer apparatus. The influence of suction on the time-dependent compression behavior of the fill material is emphasized. The results indicate that the matric suction can increase the compression stiffness, and that the unloading–reloading index varies nonlinearly with suction. A linear relationship between the time-dependent compression coefficient and normalized effective vertical loading is established. The linear relationship is subsequently used to predict the time-dependent compression coefficient to describe the time-dependent behavior of the fill under unsaturated conditions. Further, a nonlinear function based on the work of Yin (Géotechnique 49(5):699–707, 1999) is adopted to describe the development of time-dependent compression. The results indicate that the prediction obtained from the two newly proposed methods are promising, and can predict the nonlinear time-dependent compression behavior of this coarse-grained soil. [ABSTRACT FROM AUTHOR]

• A test method was improved for hydraulic properties determination of fault zones. • Flow in the fault zone was divided into three flow phases. • The threshold of injection pressure for flow transition to nonlinear was obtained. • Temporal change in permeability of the fault zone induced by injections was studied. Coal remains the largest contributor to China's energy structure. However, coal production has frequently been threatened by groundwater inrush incidents induced by fault zones. The variations in the hydraulic properties of fault zones under high water pressure in relation to groundwater inrush risk are poorly understood. This paper presents high-pressure fluid injection tests used to investigate the variations in hydraulic properties of a fault zone at depth. The analysis of experimental results shows that the injection rate and hydraulic conductivity increase as nonlinear functions of injection pressure. The evolution process induced by high water pressure can be divided into three flow phases, including a Darcy flow phase, a flow transition phase and an unstable flow phase. Two thresholds of injection pressure for the onset of nonlinear flow and the onset of substantial fracture dilation and hydraulic fracturing were identified. The hydraulic conductivity of the fault zone is closely related to the water pressure, with an evident increase in permeability resulting from fracture extension and enhancement of fracture connectivity, which were attributed to water injection. Finally, a conceptual model was developed to demonstrate the textural and hydraulic evolution of the fault zone. [ABSTRACT FROM AUTHOR]

Abstract The raindrop size distribution (DSD) characteristics during the precipitation season are analyzed using data collected by an OTT Particle Size Velocity (Parsivel) disdrometer and a Vertical Pointing Radar with C-band Frequency Modulation Continuous Wave (VPR-CFMCW) technology. The two datasets were collected at the same site in Longmen, Guangdong Province, which is the precipitation center of south China, from June to July in 2016 and 2017. We evaluate different fitting methods for the gamma model function and choose a nonlinear least-squares method to fit DSD. Based on the radar reflectance obtained by VPR-CFMCW, the precipitating clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convective, stratiform, mixture, and shallow). The characteristic parameters and the gamma model parameters of different precipitation types are compared. Avoiding the limitations of rainfall classification at the surface, the new classification quantifies the characteristics of mixture and shallow precipitation. The results show that the stratiform precipitation makes up 43.1% of the summer precipitation process in south China, and the contribution of convective precipitation to total rainfall is 62.7%. The precipitation parameters of the four types of precipitation, such as the rain rate (R), the mass-weighted mean diameter (D m), the radar reflectance (Z), and the liquid water content (LWC), follow the pattern: convective > mixture > stratiform > shallow. The DSD characteristics of the four precipitating cloud types are investigated. For the DSD of convective and mixture precipitation, the spectra width is similar but the rain drop concentration of the mixture is smaller. For the DSD of stratiform and shallow clouds, the rain drop concentrations are similar, but the spectra width of the shallow clouds are smaller. In addition, the relationships between μ-Λ, D m -N w , D m -R, and Z -R are obtained. These new relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitating clouds in south China. Highlights • The surface precipitation is associated with the high altitude precipitation cloud structure, the application of vertical detection radar enhances the study of precipitation. The results show the particularity of precipitation in south China. • Based on the radar reflectance obtained by VPR-CFMCW, the precipitation clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convection, stratiform, mixture, and shallow). • New relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitation clouds in south China. • Different methods of DSD gamma fitting were evaluated. [ABSTRACT FROM AUTHOR]

With increasing emissions of industrial wastewater and poor control measures, environmental pollution has become a serious issue haunting China's economic development. Meanwhile, the current pollution management policy system in China is mainly under the supervision of the central government and executed by local governments. Under the current economic growth model of China, the industrial sector remains the dominant segment of our economy, which makes the Total Factor Efficiency (TFE) evaluation and policy analysis of industrial wastewater control decisive factors concerning China's future economic growth and sustainable development. Based on existing studies of China and abroad, and with the help of a Data Envelope Analysis (DEA) model, this paper used 39 industrial sectors and their input-output data from 2003 to 2014 of China as Decision Making Units to calculate the TFE of wastewater control in different industrial sectors of China. Moreover, we have designed and adopted our own MATLAB programming for optimization solutions of multi-variable constrained nonlinear functions in order to obtain a more accurate estimation of the TFE of wastewater control. Based on our calculation results, this paper further explained the difference in TFE and policy implications across typical industries in China, and offered policy recommendations accordingly. [ABSTRACT FROM AUTHOR]

• The mechanism of triggering and driving drought propagation differs. • Triggering drought propagation highly relies on meteorological drought patterns. • Driving drought propagation is primarily depends on underlying surface. • Meteorological and hydrological drought condition will intensify in future scenarios. Exploring the propagation from meteorological drought to hydrological drought and its potential influence factors is crucial for early warning of hydrological drought. As a case study in the Xijiang River Basin (XRB), South China, the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) series from 1961 to 2013 were used to represent the meteorological drought and hydrological drought with daily runoff generated by VIC model. An identification process for the threshold and the mechanism of drought propagation was conducted based on rank order analysis and Copula theory. The drought propagation regulations of drought variables, geographical location, and lag-time were established by linear or non-linear functions. Eventually, the propagation regulations were applied to the hydrological drought prediction from 2021 to 2050. Results indicated that: (1) The spatial-temporal patterns of meteorological drought are the critical factors in triggering drought propagation, and the effects of meteorological drought area, drought severity, drought duration, drought spatial concentration, and drought persistent duration increase successively. The critical threshold for triggering drought propagation is the combined frequency of the above five drought variables greater than 0.14. (2) The geographic location, center number, duration, severity, and area of two drought types follow the linear, two-piecewise, power, linear and logarithmic functions, respectively. Drought mainly propagation from west to east in three-dimensional space, and the lag-time was less than three months, with the maximum being 78 days. Unlike triggering drought propagation, driving drought propagation is synthetically associated with meteorological, soil water, groundwater, and topography conditions. (3) Under the MRI-ESM2-0 model, the severity and area of hydrological drought under different scenarios in the 2021–2050 will increase compared with the reference period 1961–2013, and the extreme hydrological drought exceeding history may occur under scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. The findings can help policy-makers manage the early stages of hydrological drought based on meteorological drought conditions and thus reduce the negative impact of drought hazards. [ABSTRACT FROM AUTHOR]

Mappings of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance as well as the potential for use in flood forecasting applications. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for flood calculation because the stage, discharge, and other hydrological variables often are functions of many influential variables. Herein, we propose a flood forecasting system with related application, based on ANN. This method offers better performance and efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Based on the status quo of carbon emissions in China, this paper introduces dynamic evolutionary factors, advance rate, critical time and evolutionary coefficients of carbon emissions, deducing relative theories, such as Change Trends Theorem and Evolutionary Theorem. Least-square method is used to establish the dynamic evolutionary system of carbon emissions. The system predicts carbon emissions by means of nonlinear predictive function and control function, which facilitates carbon policy regulation and the system’s external influence. In addition, this paper studies dynamic evolutionary rules of dynamic evolutionary factors and advance rate of carbon emissions from the angle of dynamic system. Furthermore, the dynamic evolutionary model is introduced and evolutionary scenario analysis is also conducted by modulating evolutionary coefficient and critical time. Four different scenarios of carbon emissions are set quantitatively for evolutionary analysis of China’s carbon emissions. Based on the above observations, carbon emissions of China in 2020, 2030, 2050 under four evolutionary scenarios are predicted. It is proved that the evolutionary scenario analysis in this research is more scientific with good logic. [Copyright &y& Elsevier]

• Phenotypic traits derived from the SFM-MVS method can estimate sugar beet economic benefit using the PLSR model constructed with multi-year data. • We designed a non-linear function to determine sugar beet purchase price suitable for sugar beet production in Inner Mongolia, China. • We developed an automatic multi-view image acquisition device of beet taproot. • We proposed an automatic pipeline based on SFM-MVS method to reconstruct 3D point clouds and to obtain 10 phenotypic traits of beet taproot. Selecting and breeding crop varieties with high economic benefits is of great significance for social stability and development. The economic benefit of crops is usually reflected by the purchase price. Traditional estimation of economic benefits using purchase price formula based on manual measured traits is time-consuming. Structure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits and has the potential for the efficient and timely estimation of economic benefits for sugar beet. In this study, a framework was developed to obtain phenotypic traits in order to estimate the economic benefits of sugar beet with 207 genotypes based on the calculation of a non-linear formula and the partial least square regression (PLSR) model. The first part of the framework was the designing of a low-cost portable equipment that can be used to obtain multi-view images of taproot in order to facilitate its three-dimensional (3D) reconstruction based on SFM-MVS method. The following part was the development of an automated pipeline for estimating ten traits from the reconstructed 3D taproot. Good agreement was found between measured and estimated traits with R2 >0.97. The PLSR model constructed using the data in 2018 was used to predict the data in 2019 with moderate performance (R2 = 0.5). A new PLSR model built using 70 % of the data collected in 2018 and 2019 could predict the remaining 30 % of the data with a higher R2 of 0.61. The model built with multi-years data had a higher accuracy in estimating phenotypic traits, which suggests that PLSR model can estimate beet economic benefit by using the SFM-MVS method with multi-year data. The current method is more efficient than the manual measurement and may provide a basis for selecting and cultivating sugar beet with high economic benefit. [ABSTRACT FROM AUTHOR]

Azimuthal gamma-ray logging instruments play an important role in the geological steering technology (geosteering) of directional drilling engineering. While their logging response characteristics in various geology bodies are ambiguous, and a fast prediction methods of the distances between logging instruments and formation boudnaries, which are suitable for real time geosteering in drilling, are rare. In order to settle these problems, a Monte Carlo model is established based on the two-layer model and the multi-probe azimuthal gamma-ray logging (MPAGR) instrument structure. Monte Carlo N -Particle Transport (MCNP) Code is employed to simulate the logging responses of MPAGR when drilling into various geology structure bodies (layerd formations, fault, pinchouts and lens), which commonly drilled by honrizontal wells, and then a logging response characteristic scheme in eight spatial relationships between horizontal wells and formations is analyzed and summerized for guiding geosteering qualitatively. Based on these numerical simulations, a fast prediction method, a series of non-linear functions which can describe the relationships between logging responses and boundary distances, is proposed to guide realtime geosteering quantitatively. Finally, the qualitative scheme and the quantitative prediction method are applied in two horizontal wells in the Songliao Basin, China. The application results and effects demonstrate the availability of the scheme and the reliability of the fast prediction method. The scheme and the prediction method can provide reference and alternative solutions for real-time geosteering when drilling horizontal wells, especially in the formations where the geosteering function of resistivity logging is limited, e.g., shale gas/oil, coalbed methane, thin layer and low resistivity contrast reservoirs. • MPAGR responses when drilling into various geology structure bodies are simulated. • A response scheme is established to judge whether and how well path passes boundaries. • A fast prediction method is proposed to quantify the distances between well paths and boundaries. [ABSTRACT FROM AUTHOR]

This paper investigates the nonstationarity of precipitation extremes by incorporating time-varying and physical-based explanatory covariates, using daily precipitation data across the Beijing-Tianjin-Hebei (BTH) region, China. We perform the stationary and nonstationary generalized extreme value (GEV) models based on the Bayesian framework to estimate the expected return levels of precipitation extremes with the 90% credible intervals. Results reveal that the nonstationarity of precipitation extremes is not prominently visible for the majority of sites in BTH. However, the nonstationary GEV models exhibit better performance to capture the variations of precipitation extremes by comparison to the stationary models based on four evaluation criteria. Further, this work attempts to determine the best covariate to illustrate the possible effects of environmental changes on the frequency analysis. Results indicate that the El Nino-Southern Oscillation (ENSO) is the top of the best covariates, followed by the East Asian summer monsoon, North Atlantic Oscillation (NAO) and local temperature anomaly. Moreover, the best covariates are dominated by the physical-based covariates, and the best models with nonlinear functions of covariates are found in the majority of sites. Finally, the best-fitted models are used to estimate the design values of return levels in precipitation extremes. Results illustrate that the differences between the stationary modeling and nonstationary modeling in the median condition of covariates are not significant for most of the sites. But the discrepancies will be enhanced if the covariates locate in a high (95-percentile) or low (5-percentile) value. Our findings suggest that the nonstationary modeling of precipitation extremes might prove more useful and reliable, especially in the uncommon conditions of physical-based covariates. Unlabelled Image • Nonstationary frequency analysis can better capture the temporal evolution of precipitation extremes • The best covariates for nonstationary modeling of precipitation extremes are dominated by the physical-based covariates • Higher return period indicates higher uncertainty in expected return levels both for stationary and nonstationary models [ABSTRACT FROM AUTHOR]

• Parameter c of GNAA model was calibrated to 6.94 at annual scale on the Loess Plateau. • The upper and lower limits of SGCF were constrained within (0, 1) at annual scale. • GNAA and SGCF could estimate annual actual evaporation well in study area. Generalized complementary functions, which describe the relationship between the ratio of actual evaporation over the Penman potential evaporation (E/E Pen) and the proportion of the radiation term in E Pen (E rad /E Pen) have not been widely used at annual time scales. In this study, the generalized nonlinear advection-aridity function (GNAA) and sigmoid generalized complementary function (SGCF) were evaluated for annual evaporation estimation with calibrated parameters in the Loess Plateau of China. For all 15 catchments, the lowest values of annual E rad /E Pen were found to be much larger than zero, and the annual E/E Pen increased approximately linearly with annual E rad /E Pen. This complementary evaporation relationship at an annual timescale differs from those at daily timescales, and requires different parameterizations of the GNAA and SGCF. For the GNAA, parameter c , which is often set to zero for daily time scales, need to be well calibrated with available data. For the SGCF, the upper and lower limits of E rad /E Pen must be constrained. After calibration, both the SGCF and GNAA performed well at estimating annual evaporation. In addition, the calibrated Priestley-Taylor coefficient from the SGCF was found to be closer to the widely accepted value (1.26) than that determined from the GNAA. [ABSTRACT FROM AUTHOR]