Evaluating soil acidification risk and its effects on biodiversity–ecosystem multifunctionality relationships in the drylands of China

Background:Soil acidifcationn caused by anthropogenic activities may aft soil biochemical cydling,bidiversity,productivity,and multiple eosystem-related functions in drylands.However,to date,such information is lacking to support this hypothesis.Methods Based on a transect survey of 78 naturally assembled shrub communities,we caloulated acid deposition flux in Northwest China and evaluated its likely ecological ffets by testing three altemnative hypotheses,namely:.nidche complementarity,mass ratio,and vegetation quantity hypotheses Rao's quadratic entopy and community-weighted mean traits were employed to represent the complementary aspect of niche complementarity and mass ratio effects,respectively.Resulbs:We observed that in the past four decades,the concentrations of exchangeable base cations in soil in Northwest China have decreased significantly to the extent of having faced the risk of depletion,whereas changes in the calium carbonate content and pH of soil were not significant.Adid deposition primani ly increased the aboweground biomass and shrub density in shrublands but had no sigmificant effect on shrub richness and ecasystem multifunctionality(EMF),indicating that acid deposition had positive but weak ecological effects on dryland ecosystems.Community wd ghted mean of functional traits(representing the mass ratio hypothesis)correlated negatively with EMF,whereas both Rao's quadratic entropy(representing the niche complementarity hypothesis)and aboveground biomass(representing the vegetation quantity hypothesis)correlated positively but insignifcantly with EMF.These biodiversity-EMF relationships highlight the fragility and instability of drylands relative to forest ecasystems.Concuions:The findings from this study serve as important reference points to understand the ris of soil acidification in arid regions and its impacts on biodiversity-EMF relationships.

Leveraging the Empirical Wavelet Transform in Combination with Convolutional LSTM Neural Networks to Enhance the Accuracy of Polar Motion Prediction

High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation.Polar motion is affected by a variety of excitation factors,and nonlinear prediction methods are more suitable for polar motion prediction.In order to explore the effect of deep learning in polar motion prediction.This paper proposes a combined model based on empirical wavelet transform(EWT),Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM).By training and forecasting EOP 20C04 data,the effectiveness of the algorithm is verified,and the performance of two forecasting strategies in deep learning for polar motion prediction is explored.The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days,while direct multi-step prediction is more suitable for medium and long-term forecasts.In the 365 days forecast,the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas,respectively,which is 23.5% and 16.2% higher than the accuracy of Bulletin A.The results show that the algorithm has a good effect in medium and long term polar motion prediction.

A 16-parameter GEO Broadcast Ephemeris

Currently,the broadcast ephemerides used in GEOs are same as those of the MEOs and IGSOs in the BeiDou navigation constellation.However,a trade-off strategy,i.e.an orbital inclination of 5°rotation,is needed in the fitting algorithm to solve the ephemeris parameters as well as the user satellite position computation for GEOs.Based on the standard broadcast ephemerides,the representations of both the orbit and its perturbation were revised according to the second class of nonsingular orbital elements.In this research,a 16-parameter broadcast ephemeris is presented specifically for GEOs,and user satellite position computation formulas were derived correspondingly.Fit simulations show that the root of mean squares(RMS)of user range error(URE)with two hour and three hour data sets are better than 0.05 m and 0.1 m,respectively.

有限循环群的膨胀

本文给出了群膨胀的一些性质,然后证明了群膨胀是左-半群的一个充要条件。最后,给出了有限循环群膨胀的基本性质。

Orbital Elements Ephemerides and Interfaces Design of LEO Satellites

Low earth orbit satellites,with unique advantages,are prosperous types of navigation augmentation satellites for the GNSS satellites constellations.The broadcast ephemeris element needs to be designed as an important index of the augmented LEOs.The GPS ephemerides of 16/18 elements cannot be directly applied to the LEOs because of the poor fitting accuracies in along-track positional component.Besides,the ill-conditioned problem of the normal-matrix exists in fitting algorithm due to the small eccentricity of the LEO orbits.Based on the nonsingular orbital elements,5 sets of ephemerides with element numbers from 16 to 19 were designed respectively by adding or modifying orbital elements magnifying the along-track and radial positional components.The fitting experiments based on the LEO of 300 to 1500 km altitudes show that the fitting UREs of the proposed 16/17/18/18*/19-element ephemerides are better than 10/6/4/5/2.5 cm,respectively.According to the dynamical range of the fitting elements,the interfaces were designed for the 5 sets of ephemerides.The effects of data truncation on fitting UREs are at millimeter level.The total bits are 329/343/376/379/396,respectively.29/15 bits are saved for the 16/17-element ephemerides compared with the GPS16 ephemeris,while 64/61/41 bits can be saved for the 18/18*/19-element ephemerides compared with the GPS18 elements ephemeris.

Comparison of “Normal” Craniocerebral Computed Tomography of Deceased and Living Individuals

To compare“normal”craniocerebral computed tomography(CT)of deceased and living individuals.Nineteen parameters of craniocerebral CT scans of 50 deceased and 50 living individuals that met specific filtering criteria were measured separately:The intensity(CT value)ratio of gray matter to white matter(GM/WM),maximum and minimum length of frontal horn of ventricle,transverse diameter of cerebral parenchyma,length of choroid plexus,maximum external diameter of body of lateral ventricle,maximum internal transverse diameter of cranium,length of cerebral longitudinal fissure,length between two calvarium,transverse and longitudinal diameter of the third and fourth ventricle,length of the cerebral longitudinal fissure,Hackman value,ventricular index(D/A),index of the somatic part of lateral ventricle(F/E),lateral ventricular body index(G/E),frontal horn index(G/A),and ventriculocranial ratio(VCR).The values of these 19 parameters for the deceased and living individuals were performed using statistical methods.There were significant statistic differences between deceased and living individuals in terms of eight craniocerebral CT parameters,including GM/WM,D/A,transverse diameter of the fourth ventricle,and length of the cerebral longitudinal fissure.The craniocerebral CT findings differ between deceased and living individuals.Knowledge of the normal postmortem craniocerebral CT parameters is key to correct postmortem craniocerebral radiopathological diagnosis.

Polyurethane networks based on disulfide bonds:from tunable multi-shape memory effects to simultaneous self-healing

With the prompt development in intellectualization nowadays, the smart materials with multifunctionality or multi-responsiveness are highly expected. But it is a big challenge to integrate the different actuating units into a single system in a synergy pattern. Herein, we put forward a new strategy to develop the polyurethane networks which can present shape-memory effect and self-healing effect in independent way as well as simultaneous acting mode. To realize this goal, poly(tetremethylene ether) glycol was chosen as the soft segment to ensure the polymer chains a good mobility, and disulfide bond as the dynamic covalent bond was embedded in the backbone of polyurethane to endow it with desirable self-healing capacity under mild condition. Moreover, a rational control of the architecture of the networks by adjusting the content of disulfide bond and the degree of cross-linking, a broad glass transition temperature(T_g) was achieved, which enabled the network a versatile shape-memory effect, covering from dual-, triple-so far as to quadrupleshape memory effect. More importantly, the shape recovery and healing process can be realized simultaneously because of the highly matched actuating condition in this system.

Robust radar automatic target recognition algorithm based on HRRP signature

Automatic target recognition （ATR） is an important function for modern radar. High resolution range profile （HRRP） of target contains target struc- ture signatures, such as target size, scatterer distribu- tion, etc, which is a promising signature for ATR. Sta- tistical modeling of target HRRPs is the key stage for HRRP statistical recognition, including model selection and parameter estimation. For statistical recognition al- gorithms, it is generally assumed that the test samples follow the same distribution model as that of the train- ing data. Since the signal-to-noise ratio （SNR） of the received HRRP is a function of target distance, the as- sumption may be not met in practice. In this paper, we present a robust method for HRRP statistical recogni- tion when SNR of test HRRP is lower than that of train- ing samples. The noise is assumed independent Gaus- sian distributed, while HRRP is modeled by probabilistic principal component analysis （PPCA） model. Simulated experiments based on measured data show the effective- ness of the proposed method.

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.