GRACE-based estimates of groundwater variations over North America from 2002 to 2017

GRACE-based estimates for groundwater storage(GWS)changes in North America substantially depend upon correction of glacialisostatic adjustment(GIA)effects,which are usually removed with GIA models.In this study,GIA effects are eliminated by employing an independent separation approach with the aid of Global Navigation Satellite System(GNSS)vertical velocity data.Our goal is to provide an independent estimate for monthly GWS changes within North America in 1-degree-grids and their trends over the whole GRACE mission lifetime from April 2002 to June 2017.This estimate is derived from the release-6 version of GRACE monthly level-2 data,GNSS data,land surface models for soil moisture and snow wa-ter equivalent,and satellite altimetric lake leveldata.We find a GWS anomaly in form of an increasing trend in Saskatchewan,which affects the Saskatchewan Province and the states of Montana,North Dakota and Minnesota,and 4 GWS anomalies with declining trends in Nevada,California,Arizona and Texas,respec-tively.The monthly changes of these GWS anomalies,except for the one in Nevada,are validated by well level data.We provide results for average monthly GWS changes and the trends for the 5 anomalies but also in separate form for the 13 affected states or provinces.The increasing trends of the Saskatchewan GWS anomaly and the affected 3 states are related to increasing precipitation and can be elucidated by the decreasing drought intensity level.On the contrary,the declining trends in GWS can be explained by weakening precipitation and are mostly supported by the increasing drought intensity level in the other 4 anomalies and the affected states,which are Nevada,California,Arizona,New Mexico,Texas,Oklahoma,Kansas,and Colorado.Ourestimates of monthly GWS changes and their trendscan serveas alternativeand beneficial input for the sustainable management of groundwater resources in North America.

肺吸虫感染致胸腔积液6例临床分析

目的探索肺吸虫致胸腔积液的临床诊断经验,避免误诊或延误诊断。方法回顾性分析2018年5月至2022年12月十堰市太和医院呼吸与危重症医学科收治的6例肺吸虫感染所致胸腔积液患者的临床资料及诊治过程。收集患者人口学特征、临床症状、暴露史、实验室指标、胸水细胞学检查、胸膜活检组织病理学检查、肺吸虫特异性抗原皮内试验、抗寄生虫治疗后转归以及后期随访结果。结果分别有3例男性、3例女性患者入组,平均年龄为(46.5±5.1)岁。患者外周血嗜酸性粒细胞绝对计数和百分比均不同程度升高(12.6%~54.0%);4例患者胸水细胞学显示存在少量或大量嗜酸性粒细胞;4例患者行内科胸腔镜胸膜活检,其中3例提示间质可见嗜酸性粒细胞广泛浸润,1例患者可见寄生虫虫卵,另1例患者显示非特异性炎症。6例患者中4例有生食淡水蟹或饮用溪水的暴露史,同时6例患者肺吸虫特异性抗原皮内试验(IDTPA)阳性和血清肺吸虫抗体酶联免疫吸附试验(ELISA)阳性,排除肺外脏器肺吸虫感染,诊断为肺吸虫所致胸腔积液。6例患者均接受口服吡喹酮治疗(25 mg/kg、3次/d,连用3 d为1个疗程,间歇7 d后行第2个疗程),胸部计算机断层扫描(CT)随访显示治疗后胸腔积液减少或消失。结论在诊断不明原因胸腔积液时,对于外周血、胸水或胸膜组织嗜酸性粒细胞异常增多,且生食淡水蟹或者生溪水的患者应高度怀疑肺吸虫感染所致胸腔积液,尤其是来自肺吸虫感染流行地区的患者。

Causative fault and seismogenic mechanism of the 2010 Suining M5.0 earthquake from joint modeling of seismic and InSAR data

Although the Sichuan basin is a stable block with low historical seismicity,the Suining M5.0 earthquake on January31,2010 occurred near the center of the basin,causing casualty and substantial damage.Previous studies have shown that the earthquake is very shallow and may occur in the sedimentary cover rocks,but its causative fault has not been identified.Based on local broadband seismic waveform data as well as a pair of ALOS PALSAR ascending orbit data,we explore the seismogenic mechanism via further constraining the source depth and the ruptured fault.The earthquake caused ground uplift in the southeast of the epicenter area,with a maximum line of sight displacement of about 13.6 cm,much larger than the displacement caused by a M5 earthquake at a typical depth of 10 km,which indicates that the earthquake is very shallow.Through joint inversion of seismic waveform and InSAR data,we obtain the moment magnitude of Suining earthquake as MW4.5,with the strike,dip,and rake of its fault plane as 17°,66° and 90°,respectively,and the centroid depth less than 1 km,supporting that the earthquake occurred at the shallow part of a high angle thrust fault dipping to the southeast.It is further confirmed that the earthquake may be triggered by the diffusion of high-pressure fluid migrating from the underside gas reservoir.

Complexities of the Turkey-Syria doublet earthquake sequence

WHAT,WHERE,AND WHEN In the early morning of February 6th,2023,an M7.8 earthquake occurred in southeastern T€urkiye near the northern border of Syria.The event initiated a complex sequence of aftershocks,including an M7.6 earthquake about 9 h later and 90 km to the north(Figures 1A and 1B).The earthquake sequence is also referred to as a strong doublet earthquake sequence.Aftershocks of the two strong earthquakes occurred along two separate branches of the East Anatolia Fault,with lengths of up to 300 km,and some aftershocks occurred in Syria(NEIC/USGS,2023).

Scalable wideband equivalent circuit model for silicon-based on-chip transmission lines

A scalable wideband equivalent circuit model of silicon-based on-chip transmission lines is presented in this paper along with an efficient analytical parameter extraction method based on improved characteristic function approach,including a relevant equation to reduce the deviation caused by approximation.The model consists of both series and shunt lumped elements and accounts for high-order parasitic effects.The equivalent circuit model is derived and verified to recover the frequency-dependent parameters at a range from direct current to 50 GHz accurately.The scalability of the model is proved by comparing simulated and measured scattering parameters with the method of cascade,attaining excellent results based on samples made from CMOS 0.13 and 0.18 μm process.

An improved single-π equivalent circuit model for on-chip inductors in GaAs process

An improved single-π equivalent circuit model for on-chip inductors in the GaAs process is presented in this paper. Considering high order parasites, the model is established by comprising an improved skin effect branch and a substrate lateral coupling branch. The parameter extraction is based on an improved characteristic function approach and vector fitting method. The model has better simulation than the previous work over the measured data of 2.5r and 4.5r on-chip inductors in the GaAs process.

Network-based naive Bayes model for social network

Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.

A review of distributed statistical inference

The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been proposed.They were developed to deal with large-scale statistical optimization problems.This paper aims to provide a comprehensive review for related literature.It includes parametric models,nonparametric models,and other frequently used models.Their key ideas and theoretical properties are summarized.The trade-off between communication cost and estimate precision together with other concerns is discussed.

A case study on the shareholder network effect of stock market data:An SARMA approach

One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.

Rejoinder on‘A review of distributed statistical inference’

We thank the editor,Professor Jun Shao,for orga-nizing this stimulating discussion.We are grateful to all discussants for their insightful comments on our review article on the distributed statistical inference.Due to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical prob-lems.Meanwhile,some important theoretical results were established.While we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ago.However,we are glad to see the discussants provide reviews of some new works and references.We hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area.

Antarctic-wide annual ice flow maps from Landsat 8 imagery between 2013 and 2019

Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise.However,in Antarctica,high-resolution and continuous ice velocity estimates remain elusive,which is key to unravel Antarctica’s present-day ice mass balance processes.Here,we present a suite of newly estimated Antarctic-wide,annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data.We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarcticwide seamless ice velocity products.The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019.The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow,and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling,as compared to existing data products.Validation studies confirmed improved accuracy and consistency of this new data product,when compared with independently estimated optical and radar ice velocity data products,as well as in situ data.