Trust in China: A Cross-Regional Analysis

Using the cross-regional data, this paper shows that trust has a strong effect on uneven development of economy in China. As is discovered in many studies, it is found that trust affects the growth of economy, size distribution of enterprise, and FDI inflow and so on. We also find that cross-regional differences of trust in China are reflections of the regional diversities of education, marketization of economies, urbanization, population density and transportation facilities. Although not statistically significant, “too many officials” may damage social trust. The paper demonstrates that trust cannot simply be taken as a cultural heritage. The paper also argues that sustainability of further economic development of China much depends on how fast China can build trust-facilitating institution, and that the most fundamental institution for trust is the property right.Trust, Economic performance, Information Repeated game, Transaction

Trust in China: A Cross-Regional Analysis

Using the cross-regional data, this paper shows that trust has a strong effect on uneven development of economy in China. As is discovered in many studies, it is found that trust affects the growth of economy, size distribution of enterprise, and FDI inflow and so on. We also find that cross-regional differences of trust in China are reflections of the regional diversities of education, marketization of economies, urbanization, population density and transportation facilities. Although not statistically significant, “too many officials” may damage social trust. The paper demonstrates that trust cannot simply be taken as a cultural heritage. The paper also argues that sustainability of further economic development of China much depends on how fast China can build trust-facilitating institution, and that the most fundamental institution for trust is the property right.http://deepblue.lib.umich.edu/bitstream/2027.42/39972/3/wp586.pd

HORMETIC EFFECTS OF ACUTE METHYLMERCURY EXPOSURE ON GRP78 EXPRESSION IN RAT BRAIN CORTEX

This study aims to explore the expression of GRP78, a marker of endoplasmic reticulum (ER) stress, in the cortex of rat brains acutely exposed to methylmercury (MeHg). Thirty Sprague-Dawley (SD) rats were randomly divided into six groups, and decapitated 6 hours (h) after intraperitoneal (i.p.) injection of MeHg (2, 4, 6, 8 or 10 mg/kg body weight) or normal saline. Protein and mRNA expression of Grp78 were detected by western blotting and real-time PCR, respectively. The results showed that a gradual increase in GRP78 protein expression was observed in the cortex of rats acutely exposed to MeHg (2, 4 or 6 mg/kg). Protein levels peaked in the 6 mg/kg group (p \u3c 0.05 vs. controls), decreased in the 8 mg/kg group, and bottomed below the control level in the 10 mg/kg group. Parallel changes were noted for Grp78 mRNA expression. It may be implied that acute exposure to MeHg induced hormetic dose-dependent changes in Grp78 mRNA and protein expression, suggesting that activation of ER stress is involved in MeHg-induced neurotoxicity. Low level MeHg exposure may induce GRP78 protein expression to stimulate endogenous cytoprotective mechanisms

Two-path network with feedback connections for pan-sharpening in remote sensing

High-resolution multi-spectral images are desired for applications in remote sensing. However, multi-spectral images can only be provided in low resolutions by optical remote sensing satellites. The technique of pan-sharpening wants to generate high-resolution multi-spectral (MS) images based on a panchromatic (PAN) image and the low-resolution counterpart. The conventional deep learning based pan-sharpening methods process the panchromatic and the low-resolution image in a feedforward manner where shallow layers fail to access useful information from deep layers. To make full use of the powerful deep features that have strong representation ability, we propose a two-path network with feedback connections, through which the deep features can be rerouted for refining the shallow features in a feedback manner. Specifically, we leverage the structure of a recurrent neural network to pass the feedback information. Besides, a power feature extraction block with multiple projection pairs is designed to handle the feedback information and to produce power deep features. Extensive experimental results show the effectiveness of our proposed method

HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China

Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT. The daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R²), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 °C, 1.62 °C 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R², and NSE were 1.08°C, 1.53 °C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R², and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other three existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution and over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather

HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China

Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT. The daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using meteorological station observation data. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R^2), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 ℃, 1.62 ℃, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R^2, and NSE were 1.08 ℃, 1.53 ℃, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R^2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other two existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution and over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather

Annual average temperature (maximum temperature and minimum temperature) and annual accumulated precipitation across China from 1961-2019

The annual datasets as "teaser data", can make it much more easy for potential users to have a brief look at daily datasets. The datasets are annual average temperature (maximum temperature and minimum temperature) and annual accumulated precipitation with 1 km spatial resolution over 1961-2019, and are calculated from the daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China (https://doi.org/10.1594/PANGAEA.941329)

Phase retrieval from single interferogram without carrier using Lissajous ellipse fitting technology

Abstract Phase extraction from single interferogram is of high significance and increasingly interest in optical metrology. In this contribute we propose an advanced Pixel-level Lissajous Ellipse Fitting (APLEF) method to extract the phase from single interferogram without carrier. At each pixel, a Lissajous figure is created by plotting N against D, where N and D are subtractions and additions of intensities of adjacent pixels in a small window. The so created Lissajous figure is already in phase quadrature because of the subtraction and addition process, and the Lissajous Figure is forced to be closed by taking the opposite values of N and D, i.e. –N and -D into account. The closed and in phase quadrature Lissajous Figure is the key point for APLEF to demodulate the single inteferogram without carrier in theoretically. The simulation shows its higher accuracy than existed SPT and Garbusi’s method and the experiments finally corroborate its effectiveness

The Structure Research and Design for Beam Steering and Adjustment in Golay3 Sparse-Aperture Imaging System

In order to further explore the technical difficulties involved in orienting the sparse-aperture imaging system towards practical applications, this paper proposes an optical–mechanical structure design scheme and performance simulation, as well as actual alignment on the beam steering and adjustment structure, which is the core component of the Golay3 sparse-aperture imaging system. The beam steering and adjustment structure corrects the cophasing error by using a combination of mechanical rough adjustment and piezoelectric ceramic precision adjustment. The beam adjustment capability of the beam steering and adjustment structure is analyzed by simulation when the system contains the piston error and the tilt error. The piston error is controlled within 12 μm and the tilt error is controlled within 1200 μrad through the mechanical rough adjustment light path using a large-diameter collimation as the point light source, which realizes the confocality of three beams and mutual interference between two beams. With the USAF1951 resolution test panel display in the distance as the surface target, the adjustable piezoelectric ceramics control the piston error within 55 nm and the tilt error within 0.25 μrad, obtaining the surface target imaging result of the sparse aperture