Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

A comparative study of spatial interpolation methods fordetermining fishery resources density in the Yellow Sea

Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation(IDW), global polynomial interpolation(GPI), local polynomial interpolation(LPI) and ordinary kriging(OK).A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect.Using a paired-samples t test, no significant differences(P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few 'bull's-eye' patterns in some areas.However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.

Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods

A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.

Comparing different spatial interpolation methods to predict the distribution of fishes:A case study of Coilia nasus in the Changjiang River Estuary

Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.

GIS Analysis of Spatial Distribution of Crop Incidence

Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.

Numerical solutions for two nonlinear wave equations

The split-step pseudo-spectral method is a useful method for solving nonlinear wave equations. However, it is not widely used because of the limitation of the periodic boundary condition. In this paper, the method is modified at its second step by avoiding transforming the wave height function into a frequency domain function. Thus, the periodic boundary condition is not required, and the new method is easy to implement. In order to validate its performance, the proposed method was used to solve the nonlinear parabolic mild-slope equation and the spatial modified nonlinear Schrodinger （MNLS） equation, which were used to model the wave propagation under different bathymetric conditions. Good agreement between the numerical and experimental results shows that the present method is effective and efficient in solving nonlinear wave eouations.

Analysis of Reflection Characteristics for Foam Filled Grid Structure

The reflection characteristics of gird structures are calculated by the spatial network method in the case of normal incidence plane electromagnetic wave. The numerical result shows that the grid panels without electromagnetic wave absorbing foams are not ideal. However, the absorbing ability can be achieved as low as -25 dBsm from 8 GHz to 12 GHz when the grid cells are filled with foam absorbers. Also it is noted from computation that the foam filled grid structures with larger cell size, higher and thinner ribs will improve the absorbing abilities, which illustrates that they can be used as the effective light-weight stealth structures for aeronautical application.

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

The water quality grades of phosphate（PO4-P） and dissolved inorganic nitrogen（DIN） are integrated by spatial partitioning to fit the global and local semi-variograms of these nutrients. Leave-one-out cross validation is used to determine the statistical inference method. To minimize absolute average errors and error mean squares,stratified Kriging（SK） interpolation is applied to DIN and ordinary Kriging（OK） interpolation is applied to PO4-P.Ten percent of the sites is adjusted by considering their impact on the change in deviations in DIN and PO4-P interpolation and the resultant effect on areas with different water quality grades. Thus, seven redundant historical sites are removed. Seven historical sites are distributed in areas with water quality poorer than Grade IV at the north and south branches of the Changjiang（Yangtze River） Estuary and at the coastal region north of the Hangzhou Bay. Numerous sites are installed in these regions. The contents of various elements in the waters are not remarkably changed, and the waters are mixed well. Seven sites that have been optimized and removed are set to water with quality Grades III and IV. Optimization and adjustment of unrestricted areas show that the optimized and adjusted sites are mainly distributed in regions where the water quality grade undergoes transition.Therefore, key sites for adjustment and optimization are located at the boundaries of areas with different water quality grades and seawater.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Detection of landuse/landcover changes using remotely-sensed data

We evaluated the use of spatial sampling and satellite images to identify deforested areas in Wonju, South Korea. The changes in land cover were identified using a grid of sample points overlaid onto medium and high-resolution remote sensing （RS） satellite images. Deforestation identified in this way （hereafter, RSD） was compared to administrative data on deforestation. We also compared high-resolution satellite images （HR-RSD） and actual deforestation based on categories which were Intergovernmental Panel on Climate Change data. RSD generated by medium-resolution satellite images overesti- mated the amount of deforested area by 1.5-2.4 times the actual deforested area, whereas RSD generated by HR- RSD underestimated the amount of deforested area by 0.4-0.9 times the actual area. The highest degree of matching （90 %） was found in HR-RSD with a grid interval of 500 m and the accuracy of HR-RSD was the highest, at 67 %. The results also revealed that the largest cause of deforestation was the establishment of settlements followed by conversion to cropland and grassland. We conclude that for the identification of deforestation using satellite images, HR-RSD with a grid interval of 500 m is most suitable.

Spatial charge and compensation method in a whirler

Based on particle-in-cell simulation, we studied the motions of ions and electrons. The results have shown that electrons are bounded by a magnetic field and only a small number of electrons can pass through the whirler channel. The plasma becomes non-neutral when it is emitted from the whirler, and the spatial charge leads to a beam divergence, which is unfavorable for mass separation. In order to compensate the spatial charge, a cathode is designed to transmit electrons and the quasi-neutral plasma beam. Experiment results have demonstrated that the auxiliary cathode can obviously improve the compensation degree of the spatial charge.

Wall temperature effects on the stability of leeward vortices of a blunt inclined cone

Streamwise vortex instability is one of the most potent mechanisms for the transition of the three-dimensional boundary layers.By using the global stability analysis methods,stability characteristics of the leeward vortex over a blunt cone with an angle of attack under a typical wind tunnel condition are studied and are compared to the case with a smaller wall temperature ratio(corresponding to a flight condition).The vortical structure features inward and outward vortices,similar to that in the flight con-dition.Unlike the flight condition,the outward vortices appear stronger than the inward vortices,resulting in stronger outer-mode instabilities.Although the inner mode is heavily stabilized compared to the flight condition,it can still radiate apparent acoustics.The acoustic sources are computed based on Lighthill's acoustic analogy,showing that the entropy term measuring the deviation from the isentropic relation is dominant.While Mack second mode is shown to most likely trigger the transition in the flight condition,it is absent in the wind tunnel condition,and a shear-layer mode turns out to be the most dangerous instead.Moreover,the instability frequencies and growth rates of the wind tunnel case are much smaller than those of the flight case,indicating that wall heating may stabilize the leeward vortices.

Impacts of climate change on seasonal extreme waves in the Northwest Atlantic using a Spatial Neural Gas clustering method

Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities,such as coastal management,design of inshore and offshore structures,marine transport,coastal recreational activities,fisheries,etc.This study investigates the efficiency of a state-of-the-art spatial neutral gas clustering method in the classification of wind/wave data and the evaluation of extreme values of significant wave heights(Hs),mean wave direction(MWD)and mean wave periods(T0)for two 39-year time periods;from 1979 to 2017 for the present climate,and from 2060 to 2098,for a future climate change scenario in the Northwest Atlantic.These data were constructed by application of a numerical model,WAVEWATCHIII TM(hereafter,WW3),to simulate the wave climate for the study area for both present and future climates.Data from the model was extracted for the wave climate,in terms of the wave parameters,specifically Hs,MWD and T0,which were analyzed and compared for winter and summer seasons,for present and future climates.In order to estimate extreme values in the study area,a Natural Gas(hereafter,NG)clustering method was applied,separate clusters were identified,and corresponding centroid points were determined.To analyze data at each centroid point,time series of wave parameters were extracted,and using standard stochastic models,such as Gumbel,exponential and Weibull distribution functions,the extreme values for 50 and 100-year return periods were estimated.Thus,the impacts of climate change on wave regimes and extreme values can be specified.

Do Free Trade Zones Improve Air Quality?

As a new window of opening up to the outside world in the new era,the establishment of Free Trade Zones(FTZs)in China is an important national strategy for promoting high-quality economic development wherein the prevention and control of pollution is an important hurdle to be surmounted throughout the process.Based on data taken from model cities for environmental protection in China from 2008 to 2017,given the effect of policy spillover,this study considers the establishment of FTZs to be a"quasinatural experiment."It uses the general analysis paradigm of spatial difference-indifference(DID)to systematically examine the impact of FTZs on air pollution as well as to conduct an in-depth analysis of their spatial heterogeneity and mechanism of action.The study shows that the establishmentofFTZs significantlyreduces the concentration of air pollutants in cities.If the spatial DID method is adopted to measure the policy spillover effect of the establishment of free trade zones,urban air pollutants declines by 12%to 17%,while the estimated result using the traditional DID method is only 7%.The establishment of FTZs significantly alleviates air pollution in neighboring non-pilot cities as well.Based on the range of the spillover effect from the center of the FTZ,it is found that the average spatial spillover effect presents as an inverted"U"curve as the research radius increases,with an optimal policy spillover effect radius of about 200km,while the policy spillover effect between pilot cities remains poor.The establishment of an FTZ not only improves the local atmospheric environment by promoting industrial structure optimization and green technology innovation in pilot cities but also generates a spillover effect on neighboring non-pilot cities through the same mechanism,thus contributing to improving the atmospheric environment in those non-pilot cities.These findings remain valid following a series of robustness tests such as the spatial parallel trend test and the placebo test.This study offers an answer to the key question of whether free trade zone policy can truly promote high-quality economic development in the new era.It provides useful policy insights for further expanding opening up,winning the battle for the prevention and control of pollution,and promoting the high-quality development of China's economy.

Assessing Hourly Precipitation Forecast Skill with the Fractions Skill Score

Statistical methods for category（yes/no） forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called ＂double-penalty＂ problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score（FSS）, a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts.We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES（Global/Regional Assimilation Prediction System） regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation.Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.

Double-slit diffraction of terahertz wave generated by tilted-pulse-front pumping

We propose a spatial diffraction diagnostic method via inserting a millimeter-gap double slit into the collimated terahertz beam to monitor the minute variation of the terahertz beam in strong-field terahertz sources,which is difficult to be resolved in conventional terahertz imaging systems.To verify the method,we intentionally fabricate tiny variations of the terahertz beam through tuning the iris for the infrared pumping beam before the tilted-pulse-front pumping setups.The phenomena can be well explained by the theory based on the tilted-pulse-front technique and terahertz diffraction.

Top-k probabilistic prevalent co-location mining in spatially uncertain data sets

A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions： 1） the concept of the top-k prob- abilistic prevalent co-locations based on a possible world model is defined; 2） a framework for discovering the top- k probabilistic prevalent co-locations is set up; 3） a matrix method is proposed to improve the computation of the preva- lence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for ex- act solutions; 4） a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5） an ap- proximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.

Spatial diversity and combination technology using amplitude and phase weighting method for phase-coherent underwater acoustic communications

The UWA channel is characterized as a time-dispersive rapidly fading channel, which in addition exhibits Doppler instabilities and limited bandwidth. To eliminate inter- symbol interference caused by multipath propagation, spatial diversity equalization is the main technical means. The paper combines the passive phase conjugation and spatial processing to maximize the output array gain. It uses signal-to-noise-plus-interference to evaluate the quality of signals received at different channels. The amplitude of signal is weighted using Sigmoid function. Second order PLL can trace the phase variation caused by channel, so the signal can be accumulated in the same phase. The signals received at different channels need to be normal- ized. It adopts fractional-decision feedback diversity equalizer （FDFDE） and achieves diversity equalization by using different channel weighted coefficients. The simulation and lake trial data processing results show that, the optimized diversity receiving equalization algorithm can im- prove communication system＇s ability in tracking the change of underwater acoustic channel, offset the impact of multipath and noise and improve the performance of communication system. The performance of the communication receiving system is better than that of the equal gain combination. At the same time, the bit error rate （BER） reduces 1.8%.