Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Spatial distribution characteristics and influence factor analysis of landslides——case study of the Hanwang area in Qinba Mountains

The geological hazards of landslides in Hanwang Town, Ziyang County, Ankang City, Shaanxi Province, have emerged. Yet, the current understanding of the spatial distribution characteristics and influencing factors of landslides in this area remains unclear. Combining the results of remote sensing interpretation and field investigation, seven influencing factors, namely, elevation, slope direction, slope gradient, distance from rivers, distance from faults, engineering geologic lithology, and distance from roads, are selected for the study. The distribution characteristics of landslides in each influencing factor and the influence of the resolution of the Digital Elevation Model(DEM) on the results are statistically and analytically analyzed. Furthermore, two highrisk landslides within the study area were subjected to comprehensive analysis, integrating the findings from drilling and other field investigations in order to examine their deformation mechanisms. Based on this analysis,the following conclusions were derived:(1) 34 landslides in the study area, mainly small earth landslides, with a distribution density of 0.42/km~2, threatening 414 people and property of about 55.87 million Yuan.(2)The landslides in the study area easily occur in the <400 m elevation range;the landslides are developed in all slope directions, the gradient is mainly concentrated in the range of 10°–40°, the distribution density of the landslides is higher in the closer distance from the river and the faults(0–200 m), the landslide-prone strata are mainly the softer and weaker metamorphic rocks, and the landslides are mainly around roads.(3) The resolution of the DEM should be selected based on the specific conditions of the study area, the requirements of the investigation, and the scale of the landslide. Opting for an appropriate DEM resolution is advantageous for understanding the patterns of landslides and conducting risk assessments in the region.(4) The Zhengjiabian landslide is a traction Landslide. The landslide body is a binary structure of gravel soil and slate weathering layer, and the damage process can be divided into three stages:(1)damage to the leading edge and stress release,(2)continuous creep and cracking,(3)rainfall infiltration and damage. The predominant slope material in the Brickyard landslide comprises clay, and the landslide is triggered by a combination of the traction effect resulting from the excavation at the slope's base and the nudging effect caused by the stacking load of the brick factory. Additionally, the Brickyard landslide exhibits persistent creep deformation. The study results provide a scientific basis for disaster prevention and mitigation in the Hanwang Township area.

Behavior, distribution and environmental influence of arsenic in a typical lead smelter

A field study was conducted to determine the behavior and distribution of arsenic during the pyrometallurgy process in a typical SKS(Shuikoushan) lead smelter in Hunan province, China. Environmental influences of arsenic in selected samples were evaluated. Arsenic contents in all input and output samples vary from 0.11% in raw lead to 6.66% in collected dust-2. More arsenic is volatilized in blast furnace and fuming furnace(73.02% of arsenic input) than bottom blowing furnace(10.29% of arsenic input).There are 78.97%, 13.69%, 7.31% of total arsenic distributed in intermediate materials, stockpiled materials and unorganized emissions, respectively. Matte slag-2, collected dust-1 and secondary zinc oxide are hazardous based on the arsenic concentrations of toxicity characteristic leaching procedure. According to risk assessment code(RAC) guideline, arsenic in collected dust-1 poses a very serious risk to the surrounding environment, arsenic in speiss, matte slag-2, water-quenched slag and secondary zinc oxide show low risk, while arsenic in matte slag-1, collected dust-2 and post dust has no risk to the environment.

The Influence of Magnetic Turbulence on the Electron Distribution Function in LHCD Plasma

A conservative transport operator in space (v//,r) and moment equations are used to describe the combined effects of a stochastic magnetic field and a radial ambipolar electric field on the electrons. The transport operator is coupled with Fokker-planck and Ohmic heating terms to compute the electron distribution function. A physical picture exhibits the possible importance of the turbulent magnetic field on the suprathermal electrons, which may be concerned with plasma confinement.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

Preliminary Study on the Temporal Distribution of PM_( 2.5 ) Pollution and Influence of Meteorological Factors in Bengbu City

Based on the data of six automatic air monitoring stations in Bengbu City,the pollution characteristics and temporal distribution of fine particulate matter PM 2.5 in the air in Bengbu City from 2015 to 2019 were studied,and the correlation between meteorological factors and PM 2.5 concentration was analyzed.The results showed that from 2015 to 2019,PM 2.5 pollution in Bengbu City was relatively heavy in winter and spring and relatively light in summer and autumn,and PM 2.5 concentration had two peaks during the day and night.Precipitation,relative humidity,wind direction and wind speed had certain effects on PM 2.5 concentration in Bengbu City.The research provides reference for the monitoring,early warning and prevention of PM 2.5 pollution in the city.

Coupling effects of influence factors on probability of corrosion initiation time of reinforced concrete

The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributions of the critical chloride concentration Cc, the chloride diffusion coefficient D, and the surface chloride concentration Cs were determined based on the collected natural exposure data. And the estimation of probability of corrosion initiation considering the coupling effects of influence factors is presented. It is found that the relative humidity and curing time are the most effective factors affecting the probability of corrosion initiation before and after 10 years of exposure time. At the same exposure time, the influence of load-induced crack and stress state on the probability of corrosion initiation is obvious, in which the effect of crack is the most one

ASSESSING LOCAL PRIOR INFLUENCE IN BAYESIAN ANALYSIS

A general method for assessing local influence of minor perturbations of prior in Bayesian analysis is developed in this paper. U8ing some elementary ideas from differelltial geometryl we provide a unified approach for handling a variety of problexns of local prior influence. AS applications, we discuss the local influence of small perturbstions of normal-gamma prior density in linear model and investigate local prior influence from the predictive view.

SPATIO-TEMPORAL CHARACTERISTICS OF NO2 IN PRD URBAN GROUP AND THE ANTHROPOGENIC INFLUENCES ANALYSIS BASED ON OMI REMOTE SENSING DATA

Spatio-temporal distribution characteristics and variation trends of tropospheric NO_2 in Pearl River Delta(PRD) urban group and its adjacent areas were analyze from 2005 to 2013 based on remote sensing data from ozone monitoring instrument(OMI) satellite, and further explored the impact of human activities on NO_2. Compared with the ground observation data, the OMI NO_2 remote sensing data displayed high reliability. Due to active industrial production, high car ownership, great energy and power consumption, the average tropospheric NO_2concentration(7.4×1015molec/cm2) of PRD region is about 3 times of the adjacent areas. At the same time, the regional high pollution NO_2 in PRD region as a whole, the urban group effect is remarkable. Sinusoidal model can well fit the periodic variation of the NO_2 in PRD and adjacent areas. NO_2 concentration was highest in winter while lowest in summer. The concentration of NO_2 in PRD region is decreasing in recent 9 years, which has a significantly negative correlation with the second industry output and car ownership. This suggests that the nitrogen oxide emissions governance in PRD region had achieved initial results. The concentration of NO_2 increased significantly in the eastern and northern Guangdong Province, there are good positive correlations with the second industrial outputs and car ownerships, it is thus clear that industrial emissions and automobile exhausts are important sources of NO_2 in these regions. The concentration of NO_2 in western Guangdong area is stable.

Local Influence Analysis of Generalized Linear Model

Nearly thirty years, the diagnosis and influence analysis of linear regression model has been fully developed. So far the local influence analysis of the generalized linear model has not yet seen in the literature. In this paper, local influence is discussed. Then, concise influence matrix is obtained. At last, an example is given to illustrate our results.

MODEL B-I FOR MACRO RECTANGULAR ELEMENT AND PROBLEMS OF INFLUENCE SURFACE FOR PLATE STRUCTURES

Model B-I for marco rectangular element is presented for the first time in this paper. To establish the influence surf ace for resultant R of bending plates, a number of generalized distributive loads q are defined. It is shown by numerical examples that Model B-I and the formula for the generalized distributive loads advanced in this paper are featured by high accuracy, low memory space and flexibility in practical application, and that they are especially effective for plate structures subject to moving loads, such as the two-dimensional continuous plates of highway bridges and the flat stabs in piled jetty engineering.

Spatial-temporal Variation Characteristics of Sunshine Duration in China and Its Influence Factors

Sunshine duration has an important impact on agriculture.In order to study the temporal and spatial variation of sunshine duration in China under the background of climate change,based on the observation data of 2089 national meteorological stations during 1961-2017,trend analysis,mutation analysis,partial correlation analysis,and Mann-Kendall mutation analysis were used to analyze the temporal and spatial variation characteristics of sunshine duration in different regions of China,and the influence of main climatic factors and human activities.The results showed that:the sunshine duration in China showed a significant decreasing trend with a rate of-45.8 h/10 a,and the sunshine duration in 7 regions of Northwestern China,Northern China,Northeastern China,Center China,Eastern China,Southern China,and Southwestern China also showed a significant decrease.The spatial distribution of sunshine duration in China was characterized by"less in the south and more in the north",and the sunshine duration in the northern region was significantly higher than those in the southern region.The sunshine duration in Tibet,Qinghai,Gansu and west Inner Mongolia was higher,while it was obviously lower in Sichuan Basin.Through M-K mutation test analysis,it was found that sunshine duration in the whole country decreased significantly,but there was no mutation station,while mutation occurred at 1989 in Southwestern China,1983 in Northwestern China,1985 in Northeastern China.Seasonally,there was the highest sunshine duration in summer,followed by spring,autumn,and winter;the decline rate was also the highest in summer,followed by autumn,winter,and spring.Sunshine duration had a highly negative correlation with total cloud amount,visibility,haze,and precipitation,while had a strongly positive correlation with wind speed,and had no significant correlation with fog.

The influence of pore size distribution on thermal conductivity,permeability, and phase change behavior of hierarchical porous materials

Porous foams have been widely employed to improve heat storage rate and prevent leakage of phase change materials(PCMs).Actual porous foams have non-uniform or hierarchical pore size distribution, which is usually neglected in most researches.Here, we establish hierarchical porous models considering different pore size distributions by using Voronoi tessellations. Effects of pore size distribution on thermal conductivity, permeability, and phase change behavior of hierarchical porous foams/PCMs composites are investigated. Uneven pore size distributions are found to decrease the thermal conductivity of porous foams to some extent. On the other hand, the permeability can be reduced by 27.6%, and the heat storage rate can be improved by 7.7% by introducing moderate hierarchy without changing the total porosity. This work opens a new route to enhance heat storage performance of PCMs via leveraging hierarchy of pore size distribution of porous foams.

Influence of Superplasticizer Type and Dosage on Early-age Drying Shrinkage of Cement Paste with Consideration of Pore Size Distribution and Water Loss

We introduced a parameter r_s(the radius of the pores where the meniscus forms),which is composed of two factors,i e,water loss and cumulative pore size distribution(PSD),to provide a better explanation of the influence of superplasticizers(SPs)on early-age drying shrinkage.In our experiments,it is found that the addition of three types of SPs leads to a significant increase in the early-age drying shrinkage of cement paste,and drying shrinkage increases with the dosage of SPs.Based on the results above,we further studied the mechanism of the effects of SPs on the early-age drying shrinkage of cement paste by PSD and water loss,which are two components of r_s.The experimental results indicate that r_s can be a better index for the early-age drying shrinkage of cement-based materials with SPs than a single factor.In addition,the effects of SPs on other factors such as hydration degree and elastic modulus were also investigated and discussed.

Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.

Influence mechanism of pore-scale anisotropy and pore distribution heterogeneity on permeability of porous media

Based on micro-CT scanning experiments, three-dimensional digital cores of tight sandstones were established to quantitatively evaluate pore-scale anisotropy and pore-distribution heterogeneity. The quartet structure generation set method was used to generate three-dimensional anisotropic, heterogeneous porous media models. A multi-relaxation-time lattice Boltzmann model was applied to analyze relationships of permeability with pore-scale anisotropy and pore distribution heterogeneity, and the microscopic influence mechanism was also investigated. The tight sandstones are of complex pore morphology, strong anisotropy and pore distribution heterogeneity, while anisotropy factor has obvious directivity. The obvious anisotropy influences the orientation of long axis of pores and fluid flow path, making tortuosity smaller and flowing energy loss less in the direction with the greater anisotropy factor. The strong correlation of tortuosity and anisotropy is the inherent reason of anisotropy acting on permeability. The influence of pore distribution heterogeneity on permeability is the combined effects of specific surface area and tortuosity, while the product of specific surface area and tortuosity shows significantly negative correlation with heterogeneity. The stronger the pore distribution heterogeneity, the smaller the product and the greater the permeability. In addition, the permeability and tortuosity of complex porous media satisfy a power relation with a high fitting precision, which can be applied for approximate estimation of core permeability.

Pore-scale investigation of forced imbibition in porous rocks through interface curvature and pore topology analysis

Forced imbibition,the invasion of a wetting fluid into porous rocks,plays an important role in the effective exploitation of hydrocarbon resources and the geological sequestration of carbon dioxide.However,the interface dynamics influenced by complex topology commonly leads to non-wetting fluid trapping.Particularly,the underlying mechanisms under viscously unfavorable conditions remain unclear.This study employs a direct numerical simulation method to simulate forced imbibition through the reconstructed digital rocks of sandstone.The interface dynamics and fluid–fluid interactions are investigated through transient simulations,while the pore topology metrics are introduced to analyze the impact on steady-state residual fluid distribution obtained by a pseudo-transient scheme.The results show that the cooperative pore-filling process promoted by corner flow is dominant at low capillary numbers.This leads to unstable inlet pressure,mass flow,and interface curvature,which correspond to complicated interface dynamics and higher residual fluid saturation.During forced imbibition,the interface curvature gradually increases,with the pore-filling mechanisms involving the cooperation of main terminal meniscus movement and arc menisci filling.Complex topology with small diameter pores may result in the destabilization of interface curvature.The residual fluid saturation is negatively correlated with porosity and pore throat size,and positively correlated with tortuosity and aspect ratio.A large mean coordination number characterizing global connectivity promotes imbibition.However,high connectivity characterized by the standardized Euler number corresponding to small pores is associated with a high probability of non-wetting fluid trapping.

Numerical simulation of rock pore-throat structure effects on NMR T_2 distribution

We built a three-dimensional irregular network model which can adequately describe reservoir rock pore-throat structures. We carried out numerical simulations to study the NMR T2 distribution of water-saturated rocks. The results indicate that there is a good correlation between T2 distribution and the pore radius frequency histogram. The total T2 distribution can be partitioned into pore body and pore throat parts. The effect of parameters including throat radius, pore-throat ratio, and coordination number of the micro- pore structure on the T2 distribution can be evaluated individually. The result indicates that： 1 ） with the increase of the pore throat radius, the T2 distribution moves toward longer relaxation times and its peak intensity increases; 2） with the increase of the pore-throat ratio, the T2 distribution moves towards longer T2 with the peak intensity increasing and the overlap between pore body T2 and pore throat T2 decreasing; 3） With the increase of connectivity, the short T2 component increases and peak signal intensity decreases slightly.

Numerical Simulation of the Influence of Drop Size Distribution Shape on Cloud and Precipitation

At present,parameterization methods to describe cloud and precipitation processes are widely used in cloud and mesoscale models,but with different drop size distributions.When precipitation formation mechanism,weather modification technique,and mechanism of hail suppression with seeding are studied by using these models,a question that needs to be addressed is：what is the influence of different drop size distributions and related parameters on cloud and precipitation？In this paper,by using a three-dimensional hail cloud numerical model developed by the Institutes of Atmospheric Physics,Chinese Academy of Sciences, we performed numerical experiments with varied drop size distribution parameters for two hail storms,and analyzed the influence of shape parameters（ar,ai,and ag）of raindrops,ice crystal,and graupel size distributions on rainfall,hail amount,and microphysical processes in clouds.The results show that the variation of ar has no effect on precipitation formation on the whole,but affects directly the production rates for the physical processes related to raindrop.The ag variation has a less obvious effect on rainfall amount,but has a significant effect on hail amount,hailfall rate,and rainfall intensity.It impacts noticeably on the generation rate of the number and mass of ice crystal,graupel,and hail,and also to various degrees on all the microphysical processes in clouds.The ag variation also influences the growing process of the hydrometeors.The effects of the ai variation on part of the generation and growing processes of all the hydrometeors are significant,and even dramatic,such as the collection process of cloud water to rain through melting ice crystal（T CLcir）.However,for clouds located in different geographic regions,the variation of ai has different effects on precipitation,which reflects the complexity of the impact of drop size distribution on cloud and precipitation.At last,some issues about the application of cloud models are also discussed.

Nanoscale pore morphology and distribution of lacustrine shale reservoirs:Examples from the Upper Triassic Yanchang Formation,Ordos Basin

Pore structure plays an important role in the gas storage and flow capacity of shale gas reservoirs. Fieldemission environmental scanning electron microscopy（FE-SEM） in combination with low-pressure carbon dioxide gas adsorption（CO2GA）,nitrogen gas adsorption（N2GA）,and high-pressure mercury intrusion（HPMI） were used to study the nanostructure pore morphology and pore-size distributions（PSDs） of lacustrine shale from the Upper Triassic Yanchang Formation,Ordos Basin. Results show that the pores in the shale reservoirs are generally nanoscale and can be classified into four types： organic,interparticle,intraparticle,and microfracture. The interparticle pores between clay particles and organic-matter pores develop most often,l with pore sizes that vary from several to more than 100 nm. Mercury porosimetry analysis shows total porosities ranging between 1.93 and 7.68%,with a mean value of 5.27%. The BET surface areas as determined by N2 adsorption in the nine samples range from 10 to 20 m2/g and the CO2 equivalent surface areas（2 nm）vary from 18 to 71 m2/g. Together,the HPMI,N2 GA,and CO2 GA curves indicate that the pore volumes are mainly due to pores 100 nm in size. In contrast,however,most of the specific surface areas are provided by the micropores. The total organic carbon（TOC） and clay minerals are the primary controls of the structures of nanoscale pores（especially micropores and mesopores）. Micropores are predominantly determined by the content of the TOC,and mesopores are possibly related to the content of clay minerals,particularly the illite-montmorillonite mixed-layer content.