Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Spatial-temporal dynamics of desert vegetation and its responses to climatic variations over the last three decades:a case study of Hexi region in Northwest China

Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

SPATIAL-TEMPORAL VARIATION OF HEAVY METAL ELEMENTS CONTENT IN COVERING SOIL OF RECLAMATION AREA IN FUSHUN COAL MINE

Grid method is employed for sampling covering soil at the test field,whic h is reclamation area filled by coal mining wastes for cropland in th e Fushun coal mine,Liaoning Province,the Northeast China.The soil samp les are taken at different locations,inclu ding three kinds of covering soil,th ree different depths of soil layers a nd four different covering ages of covering soil.The s patial-temporal variation of heavy metal element content in reclamatio n soil is stud-ied.The results indicate that the co ntent of heavy metal elements is decreasing year after year;the determin ant reason why the content of heavy metal elemen ts at 60cm depth layer is higher than t hat at 30cm depth layer and surface is fertiliz-er and manure application;the metal elements mainly come from external environment;there is no metal pollut ion coming from mother material(coal mining wastes)in plough layer of covering soil.

Spatial-temporal Variation of Extreme Precipitation Events in Northwest China During 1961-2010

Using the daily precipitation data of 118 meteorological stations in Northwest China from January 1, 1961 to December 31,2010, we analyzed extreme precipitation events from prime precipitation data by applying R-language Climate Index （RClimDex）. The spatial-temporal change characteristics in the past 50 years have been examined using the method of trend analysis, Mann-Kendall and the spatial analysis module of Arcgis9.2. The results show that the spatial distribution of the indices for extreme precipitation in Northwest China is greatly influenced by geographic location, atmospheric circulation and topography, and the spatial difference of extreme precipitation events is very evident, while the indices reduce from the southeast to the northwest except Consecutive Dry Days （CDD）. In Xinjiang region, high values appear in Tianshan Mountains and decrease towards the south and north respectively. In the past 50 years, the temporal variation tendency of the indices for extreme precipitation in Northwest China has a great spatial distinction. It shows that the variation tendency is opposite between the east （decrease） and the west （increase）, and CDD has a decreasing tendency while other indices increase. For each region, it is found that the indices for extreme precipitation in Xinjiang and Qinghai Province shows an increasing trend, and it is remarkable in Tianshan Mountains, the north of Xinjiang and the northeast of Qinghai Province. The temporal variation tendency of the indices for extreme precipitation in Ningxia, Shaanxi and Gansu has a large spatial distinction. The stations which have an increasing tend are mainly found in the north of Ningxia, south of Shaanxi and Hexi Corridor of Gansu. However, the south of Ningxia, north of Shaanxi and Longnan of Gansu Province mainly present a decreasing trend. The temporal variation tendency of the indices for extreme precipitation in Inner Mongolia is not obvious. Overall, the east part of Northwest China has a dry tendency, while the west part has an opposite trend.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Spatial-temporal variation of phytoplankton community structure in Jiaozhou Bay, China

To better understand the spatial-temporal variation in phytoplankton community structure and its controlling factors in Jiaozhou Bay, Qingdao, North China, four seasonal sampling were carried out in 2017. The phytoplankton community structure and various environmental parameters were examined. The phytoplankton community in the bay was composed of mainly diatoms and dinofl agellates, and several other species of Chrysophyta were also observed. Diatoms were the most dominant phytoplankton group throughout the year, except in spring and winter, when Noctiluca scintillans was co-dominant. High Si/N ratios in summer and fall refl ect the high dominance of diatoms in the two seasons. Temporally, the phytoplankton cell abundance peaked in summer, due mainly to the high temperatures and nutrient concentrations in summer. Spatially, the phytoplankton cell abundance was higher in the northern part of the bay than in the other parts of the bay in four seasons. The diatom cell abundances show signifi cant positive correlations with the nutrient concentrations, while the dinofl agellate cell abundances show no correlation or a negative correlation with the nutrient concentrations but a signifi cant positive correlation with the stratifi cation index. This discrepancy was mainly due to the diff erent survival strategies between diatoms and dinofl agellates. The Shannon-Wiener diversity index ( H′) values in the bay ranged from 0.08 to 4.18, which fell in the range reported in historical studies. The distribution pattern of H′ values was quite diff erent from that of chlorophyll a , indicating that the phytoplankton community structure might have high biomass with a low diversity index. Compared with historical studies, we believe that the dominant phytoplankton species have been changed in recent years due mainly to the changing environment in the Jiaozhou Bay in recent 30 years.

Spatial-temporal characters of Antarctic sea ice variation

Using sea ice concentration dataset covering the period of 1968-2002 obtained from the Hadley Center of UK, this paper investigates characters of Antarctic sea ice variations .The finding demonstrates that the change of mean sea-ice extent is almost consistent with that of sea-ice area, so sea-ice extent can be chosen to go on this research. The maximum and the minimum of Antarctic sea ice appear in September and February respectively. The maximum and the maximal variation of sea ice appear in Weddell Sea and Ross Sea, while the minimum and the minimal variation of sea-ice appear in Antarctic Peninsula. In recent 35 years, as a whole, Antarctic sea ice decreased distinctly. Moreover, there are 5 subdivision characteristic regions considering their different variations. Hereinto, the sea-ice extent of Weddell Sea and Ross Sea regions extends and area increases, while the sea-ice extent of the other three regions contracts and area decreases. They are all of obvious 2-4 years and 5-7 years significant oscillation periods. It is of significance for further understanding the sea-ice-air interaction in Antarctica region and discussing the relationship between sea-ice variation and atmospheric circulation.

Spatial-Temporal Variations in January Precipitation over the Period of 1950-2000 in Pakistan and Possible Links with Global Teleconnections: Geographical Perspective

Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.

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.

Spatial-Temporal Variations in January Temperature in Pakistan and Their Possible Links with SLP and 500-hPa Levels over the Period of 1950-2000: A Geographical Approach

By making use of Empirical Orthogonal Function (EOF) analysis the spatial and temporal variability was investigated in January over the period of 1950 to 2000 in Pakistan. The analysis is based on the combination of ground observed mean monthly temperature data and National Centre for Environmental Prediction (NCEP) reanalysis data of sea level pressure (SLP) and 500-hPa fields. The results reasonably reveal that the variation in January temperature have links with global teleconnections at SLP and 500-hPa pressure heights. The analysis shows variability at interannual to interdecadal time scale. The interannual variation is more prominent than the interdecadal signal of temperature anomaly. The simulated coefficient patterns show reasonable variation with regional detail from south (north) to north (south) in the study area. The study could be useful as baseline information for climate change studies in Pakistan.

Community structure and spatial-temporal variation of netzphytoplankton in the Bering Sea in summer

Marine biodiversity is changing in response to altered physical environment, subsequent ecological changes as well as anthropogenic disturbances. In this study, phytoplankton samples in situ collected in the Bering Sea in July of 1999 and 2010 were analyzed to obtain phytoplankton community structure and spatial-temporal variation between the beginning and end of this decade, and the correlation of phytoplankton community dynamics and environmental factors was investigated. A total of 5 divisions, 58 genera and 153 species of phytoplankton belonging to 3 ecological groups were identified. The vast majority of phytoplankton consisted of diatoms accounting for 66.7% of the total species and 95.2% of the total abundance. Considering differentiation in spatial extent and phytoplankton sample types, there were subtle changes in species composition, large altering in abundance and significant variation in spatial distribution between two surveys. The abundance peak area was located at the Bering Strait while sub peak was found at the Bering Sea Basin. The boreal-temperate diatom was the dominant flora, which was subsequently replaced by eurythermal and frigid-water diatom. Phytoplankton community in the Bering Sea was not a simplex uniform community but composed of deep-ocean assemblage and neritic assemblage. The deep-ocean assemblage was located in the northwestern Pacific Ocean and Bering Sea Basin, dominated by boreal-temperate species（Neodenticula seminae, Thalassiothrix longissima, Amphiprora hyperborean, Chaetoceros atlanticus, Thalassiosira trifulta, etc.） and eurychoric species（Thalassionema nitzschioides, Ch. compressus, Rhizosolenia styliformis, etc.）, and characterized by low abundance, even interspecies abundance allocations, diverse dominant species and high species diversity. The neritic assemblage was distributed on the continental shelf and slope of Bering Sea and was mainly composed of frigid-water species（Th.nordenski？ldii, Ch. furcellatus, Ch. socialis, Bacteriosira fragilis, etc.） and eurythermal and euryhaline species（L.danicus, Ch. curvisetus, Coscinodiscus curvatulus, etc.）, and it was characterized by high abundance, uneven interspecies allocations, prominent dominant species and low species diversity. Spatial-temporal variation of species composition and abundance of phytoplankton in the Bering Sea was directly controlled by surface circulation,nutrient supply and ice edge.

Insights into genetic diversity and phenotypic variations in domestic geese through comprehensive population and pan-genome analysis

Background Domestic goose breeds are descended from either the Swan goose(Anser cygnoides)or the Greylag goose(Anser anser),exhibiting variations in body size,reproductive performance,egg production,feather color,and other phenotypic traits.Constructing a pan-genome facilitates a thorough identification of genetic variations,thereby deepening our comprehension of the molecular mechanisms underlying genetic diversity and phenotypic variability.Results To comprehensively facilitate population genomic and pan-genomic analyses in geese,we embarked on the task of 659 geese whole genome resequencing data and compiling a database of 155 RNA-seq samples.By constructing the pan-genome for geese,we generated non-reference contigs totaling 612 Mb,unveiling a collection of 2,813 novel genes and pinpointing 15,567 core genes,1,324 softcore genes,2,734 shell genes,and 878 cloud genes in goose genomes.Furthermore,we detected an 81.97 Mb genomic region showing signs of genome selection,encompassing the TGFBR2 gene correlated with variations in body weight among geese.Genome-wide association studies utilizing single nucleotide polymorphisms(SNPs)and presence-absence variation revealed significant genomic associations with various goose meat quality,reproductive,and body composition traits.For instance,a gene encoding the SVEP1 protein was linked to carcass oblique length,and a distinct gene-CDS haplotype of the SVEP1 gene exhibited an association with carcass oblique length.Notably,the pan-genome analysis revealed enrichment of variable genes in the“hair follicle maturation”Gene Ontology term,potentially linked to the selection of feather-related traits in geese.A gene presence-absence variation analysis suggested a reduced frequency of genes associated with“regulation of heart contraction”in domesticated geese compared to their wild counterparts.Our study provided novel insights into gene expression features and functions by integrating gene expression patterns across multiple organs and tissues in geese and analyzing population variation.Conclusion This accomplishment originates from the discernment of a multitude of selection signals and candidate genes associated with a wide array of traits,thereby markedly enhancing our understanding of the processes underlying domestication and breeding in geese.Moreover,assembling the pan-genome for geese has yielded a comprehensive apprehension of the goose genome,establishing it as an indispensable asset poised to offer innovative viewpoints and make substantial contributions to future geese breeding initiatives.

Origin of the presence/absence variation of the LTIA1/LTIA2 mini-ribonucleaseⅢgenes required for low-temperature growth in rice

A rice low temperature-induced albino variant was determined by the recessive ltia1 and ltia2 genes.LTIA1 and LTIA2 encode highly conserved mini-ribonucleasesⅢlocated in chloroplasts and expressed in aerial parts of the plant.At low temperature,LTIA1 and LTIA2 redundantly affect chlorophyll levels,non-photochemical quenching,photosynthetic quantum yield of PSⅡand seedling growth.LTIA1 and LTIA2 proteins are involved in splicing of atp F and the biogenesis of 16S and 23S rRNA in chloroplasts.Presence/absence variation of LTIA1,the ancestral copy,was found only in japonica but that of LTIA2 in all rice subgroups.Accessions with LTIA2 presence tended to be distributed more remote from the equator compared to those with LTIA2 absence.LTIA2 duplicated from LTIA1 at the early stage of divergence of the AA genome Oryza species but deleted againin O.nivara.In cultivated rice,absence of LTIA2 is derived from O.nivara.LTIA1 absence occurred more recently in japonica.

Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

Variations in evaporation from water surfaces along the margins of the Badain Jaran Desert over nearly 60 years and influencing factors

Based on meteorological data collected over nearly 60 years(1960-2017)from four national meteorological stations along the margins of the Badain Jaran Desert,this study analyzed the spatiotemporal variations in evaporation from water surfaces and identified the dominant controlling factors.Methods used included linear trend analysis,linear tendency estimation,the departure method,the rank correlation coefficient-based method,and Multiple Linear Regression(MLR).Results indicate notable spatiotemporal differences in evaporation distribution and evolution.Spatially,average annual evaporation exhibited a pronounced altitude effect,decreasing at a rate of about 8.23 mm/m from east to west with increasing altitude.Temporally,annual evaporation showed significant upward trends after 1996 at the northeastern(Guaizi Lake)and western(Dingxin)margins,with rates of 132 mm/10a and 105 mm/10a,respectively.Conversely,along the northwestern(Ejina Banner)and southern(Alxa Right Banner)margins of the desert,an evaporation paradox was observed,with annual evaporation trending downward at rates of 162 mm/10a and 187 mm/10a,respectively,especially after 1987.The dominant factors controlling evaporation varied spatially:Average annual temperature and relative humidity influended the western margin(Dingxin),average annual temperature was the key factor for the northeastern margin(Guaizi Lake),and average wind speed was crucial for the northern(Ejina Banner)and southern(Alxa Right Banner)margins.

A new two-step variational model for multiplicative noise removal with applications to texture images

Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

A Study to Observe Pulse Pressure Variation after Induction with Propofol for General Anesthesia

Background and Aims: Pulse pressure variation (PPV) is a reliable and predictive dynamic parameter presently being utilized for fluid responsiveness. In the operating room, fluid administration based on PPV monitoring helps the physician in deciding whether to volume resuscitate or use interventions in patients undergoing surgery. Propofol is an intravenous induction agent which lowers blood pressure. There are multiple causes such as depression in cardiac output, and peripheral vasodilatation for hypotension. We undertook this study to observe the utility of PPV as a guide to fluid therapy after propofol induction. Primary outcome of our study was to monitor PPV as a marker of fluid responsiveness for the hypotension caused by propofol induction. Secondary outcome included the correlation of PPV with other hemodynamic parameters like heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP);after induction with propofol at regular interval of time. Methods: A total number of 90 patients were recruited. Either of the radial artery was then cannulated under local anaesthesia with 20G VygonLeadercath arterial cannula and invasive monitoring transduced. A baseline recording of heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and PPV was then recorded. Patients were then induced with predetermined doses of propofol (2 mg/kg) and recordings of HR, SBP, DBP, and PPV were taken at 5, 10 and 15 minutes. Results: Intraoperatively, PPV was significantly higher at 5 minutes and significantly lower at 15 minutes after induction. It was observed that there were no statistically significant correlations between PPV and SBP or DBP. PPV was strongly and directly associated with HR. Conclusion: We were able to establish that PPV predicts fluid responsiveness in hypotension caused by propofol induction;and can be used to administer fluid therapy in managing such hypotension. However, PPV was not directly correlated with hypotension subsequent to propofol administration.