Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China’s aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model(STIRPAT) model to analyze the time series evolution of China’s aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index(LDMI) model to analyze the influencing characteristics and degree of four factors on China’s aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors’ influencing direction and evolution trend of 31 provinces’ aviation carbon emissions in China(not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling.The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent.

Spatial and temporal heterogeneity of the impact of per capita income on household indirect carbon emissions in western China

With China entering the stage of high-quality development,the issue of carbon emission has become a hot research topic.This paper analyzes the different temporal and spatial effects of per capita income on household indirect carbon emissions in western China.Based on the data of Chinese Family Panel Studies(CFPS)in 2016 and 2018 in the western China,this paper uses Regression analysis and Bayesian correlation analysis to study the relationship between per capita income and household indirect carbon emissions.The results showed that the indirect carbon emissions generated by the expenditure on food,housing and household equipment in the household consumption structure in the western China were relatively high.In 2016-2018,the per capita income and per capita household consumption indirect carbon emissions in the western China showed an increasing trend.There was a positive correlation between per capita income and indirect carbon emissions of per capita household consumption,and its correlation was gradually enhanced in time dimension.In the spatial dimension,the household indirect carbon emissions in Yunnan,Qinghai,Guangxi Zhuang and Ningxia in the western China were greatly affected by per capita income,while the household indirect carbon emissions in Guizhou was least affected by per capita income.Finally,the paper puts forward some problems that we should consider in the process of facing the per capita income growth and climate change:the collection of carbon tax,the optimization of household consumption structure,the research and development of low-carbon products,and the differentiated carbon reduction.

Analysis of Land Use and Land Cover Changes and Evaluation of Natural Generation and Potential Restoration Areas in the Mexican Huasteca Region

This study aims to analyze temporal changes in land use and land cover change (LUCC) as well as identify areas for natural regeneration and potential areas for forest restoration in the Huasteca region for the period from 1976 to 2007. Changes were quantified in numbers and, additionally, cartography was used to identify and map the main affected areas. Different models based on Geographic Information Systems (GIS) demonstrated that LUCC have occurred on an area of 11718.82 km2, representing 17.84% of the region’s surface. Agriculture and the growth of pasture could be identified as the main human-induced activities that have led to landscape modification. In addition, forest cover is affected by a deforestation rate which is higher than the national average. Further important changes include a change from natural land cover to non-original land cover, affecting an area of 4911.88 km2 in the period from 1976-1993, and an area of 1892.5 km2 in the period from 1993-2002. Smaller changes could be observed for the period from 1993-2002 with an affected area of 1029.78 km2. At the same time, a natural regeneration from nonoriginal to original land cover took place from 1976-1993 on an area of 1318.68 km2, and also on an area of 974.18 km2 between 1993 an 2002. The surface that underwent a natural regeneration of forest cover made up 1932.07 km2. At the same time, an area of 5739.29 km2 for potential forest restoration was identified. Drawing on GIS methods and techniques, the development of thematic maps for land use, land use and land cover changes for the years of analysis (1976-1993-2002-2007) proved to be very adequate for the evaluation and analysis of the land cover and land use change, in particular with regard to the decrease of natural vegetation cover.

Time Slice Analysis Method Based on OTCA Used in fMRI Weak Signal Function Extraction

The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.

New approaches to quantify progressive damage and associated dynamic rock mass blockiness

In the past decade, numerical modelling has been increasingly used for simulating the mechanical behaviour of naturally fractured rock masses. In this paper, we introduce new algorithms for spatial and temporal analyses of newly generated fractures and blocks using an integrated discrete fracture network (DFN)-finite-discrete element method (FDEM) (DFN-FDEM) modelling approach. A fracture line calculator and analysis technique (i.e. discrete element method (DEM) fracture analysis, DEMFA) calculates the geometrical aspects of induced fractures using a dilation criterion. The resultant two-dimensional (2D) blocks are then identified and characterised using a graph structure. Block tracking trees allow track of newly generated blocks across timesteps and to analyse progressive breakage of these blocks into smaller blocks. Fracture statistics (number and total length of initial and induced fractures) are then related to the block forming processes to investigate damage evolution. The combination of various proposed methodologies together across various stages of modelling processes provides new insights to investigate the dependency of structure's resistance on the initial fracture configuration.

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

Remote Sensing Applied to NDVI and LST in Urban Streets in Southern Brazil

Urban evolution becomes a space-transforming agent capable of changing Normalised Difference Vegetation Index(NDVI)and Land Surface Temperature(LST)emissivity reflectance on the land surface.Therefore,this study identifies NDVI and LST variations locally,but it can also be applied in other cities on a global scale.In general,it aims to analyse NDVI and LST on surface areas of avenues and urban streets of Sector 1,located in downtown Passo Fundo,in the southern region of Brazil,from 2013 to 2020.Methodologically,treated Landsat 8 satellite images were used to obtain NDVI and LST variations in this study.Subsequently,data of 80 points in the avenues and streets of Sector 1 were collected over a distance of 100 meters spaced from each point.These data were statistically analysed concerning the dispersion of NDVI and LST from 2013 to 2020.The results revealed a strong interaction between NDVI and LST,demonstrating the need to conserve urban vegetation to improve temperature,which positively favours urban ambience indexes.

Towards Foreign Tourist at Heritage Sites Using GIS: A Case Study of Lahore

Pakistan is potentially a good tourist destination for cultural and heritage overs because the Indus and Gandhara Civilizations, and a great Mughals heritage also exists in Pakistan particularly in Lahore city. Tourism is the fastest growing industry in modern world. This study is based on the number of foreign tourist who visited the heritage sites of Lahore. The purpose of the study is to examine the number of foreign tourist who visited the heritage sites of the Lahore at Shalimar Gardens, Lahore Fort and Lahore Museum and to find the tourism trend in Lahore whether it is declining or growing high. The secondary source of data was used and data was collected from archeology department, Lahore Museum and Pakistan Year Book of Statics 2014. Tools used for research purpose were Microsoft excel, Microsoft word and ARC GIS. The number of foreign tourists that visited Lahore fort were highest in 2006 i.e. 21,178 this is the highest number of tourists who visited Lahore Fort in 15 years and 2906 lowest number of foreign tourist who visited Lahore fort, at Shalimar Gardens highest number of foreign tourists was 8558 in 2000 and the lowest number was 309 in 2013 and at Lahore Museum in 7242 tourists is the highest number of Foreign tourist who visited museum in 2000 and the lowest number of foreign tourists that visited Lahore Museum was 1825 in 2010. The trend of foreign tourism in Lahore is declining after the incident of nine eleven due to terrorism. But now with the efforts of government and work of Walled city authority foreign tourism industry is growing in Lahore as it is the Queen of cities having major and attractive heritage places for tourist.

Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.

ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing

This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.

Temporal sequential correlation analysis on compoundaction potentials of the auditory nerve versus the stimulating AM-sound envelope

Compound action potentials of the auditory nerve in response to amplitude modulating tones were recorded in guinea pigs with electrode implanted to the exit of the internal auditory meatus and temporal sequential correlation between the responses and the modulators was studied in a paradigm of systematically changing acoustic parameters. Three kinds of modulators were used. continuous or burst sinusoids of fixed frequency (in the range of 40 Hz-5 kHz), short bursts of sinusoids with changing frequency and short segments of speech signal. Ranges of parametric variation were 500 Hz-20 kHz for carrier frequency, 5%-95% for modulation depth and 20 dB-90 dB SPL for intensity. For continuous or burst sinusoidal modulators of fixed frequencies, the correlation coefficient (r) remained quite high in most parametric conditions, ranging from 0.80 to 0.95. It became smaller mainly in instances of decreased response amplitude on account of unfavourable parameters. For burst modulators of changing frequency, r varied around 0.66-0.86. When segments of speech signal served as the modulators, significant correlation (r around 0.50 ) also existed, indicating the validity of the timing mode of information encoding for speech sound at the cochlear nerve level. Some theoretical and technical points in studying the timing mechanism of audition is discussed.

Planning for selective amygdalohippocampectomy involving less neuronal fiber damage based on brain connectivity using tractography

Temporal lobe resection is an important treatment option for epilepsy that involves removal of potentially essential brain regions. Selective amygdalohippocampectomy is a widely performed temporal lobe surgery. We suggest starting the incision for selective amygdalohippocampectomy at the inferior temporal gyrus based on diffusion magnetic resonance imaging（MRI） tractography. Diffusion MRI data from 20 normal participants were obtained from Parkinson＇s Progression Markers Initiative（PPMI） database（www.ppmi-info.org）. A tractography algorithm was applied to extract neuronal fiber information for the temporal lobe, hippocampus, and amygdala. Fiber information was analyzed in terms of the number of fibers and betweenness centrality. Distances between starting incisions and surgical target regions were also considered to explore the length of the surgical path. Middle temporal and superior temporal gyrus regions have higher connectivity values than the inferior temporal gyrus and thus are not good candidates for starting the incision. The distances between inferior temporal gyrus and surgical target regions were shorter than those between middle temporal gyrus and target regions. Thus, the inferior temporal gyrus is a good candidate for starting the incision. Starting the incision from the inferior temporal gyrus would spare the important（in terms of betweenness centrality values） middle region and shorten the distance to the target regions of the hippocampus and amygdala.

A geocomputational analysis of Twitter activity around different world cities

The penetration and use of social media services differs from city to city.This paper is aimed to provide a comparison of the use of Twitter between different cities of the world.We present a temporal analysis of activity on Twitter in 15 cities.Our study consists of two parts:First,we created temporal graphs of the activity in the 15 cities,through which hours of high and low activity could be identified.Second,we created heat map visualizations of the Twitter activities during the period of 19 September 2012–25 September 2013.The heat map visualizations make the periods of intense and sparse activity apparent and provide a snapshot of the activity during the whole year.

A hybrid model for high spatial and temporal resolution population distribution prediction

The accurate prediction of population distribution is crucial for numerous applications,from urban planning to epidemiological modelling.Using one-week data collected from open and multiple sources,including telecommunication activity,weather,point of interest,buildings,roads,and land use in Milan,Italy,we develop a hybrid method combining cellular automata(CA)and long short-term memory(LSTM)to predict population distribution with fine temporal and spatial granularity.Specifically,the convolutional autoencoder and LightGBM are applied to identify missing building types based on the pedestrian shed.The LSTM learns the transition rules of CA and Shapley additive explanations value is used for variable importance analysis.Results demonstrate that the combination of convolutional autoencoder and LightGBM is effective in building type prediction.The proposed model for population distribution prediction outperforms LSTM,the combination of CA and neural network,and the combination of CA and LightGBM by at least 5–10%.A variable importance analysis reveals that temporal variables are the most significant for prediction,followed by spatial and natural variables.The order of hour,housing-related variables,and types of precipitation are the most important variables in each category.

On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications

As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.

Characterization of spatial relationships between three remotely sensed indirect indicators of biodiversity and climate:a 21years’data series review across the Canadian boreal forest

Climate drives ecosystem processes and impacts biodiversity.Biodiversity patterns over large areas,such as Canada’s boreal,can be monitored using indirect indicators derived from remotely sensed imagery.In this paper,we characterized the historical space–time relationships between climate and a suite of indirect indicators of biodiversity,known as the Dynamic Habitat Index(DHI)to identify where climate variability is co-occurring with changes in biodiversity indicators.We represented biodiversity using three indirect indicators generated from 1987 to 2007 National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer images.By quantifying and clustering temporal variability in climate data,we defined eight homogeneous climate variability zones,where we then analyzed the DHI.Results identified unique areas of change in climate,such as the Hudson Plains,that explain significant variations in DHI.Past variability in temperatures and growing season index had a strong influence on observed vegetation productivity and seasonality changes throughout Canada’s boreal.Variation in precipitation,for most of the area,was not associated with DHI changes.The methodology presented here enables assessment of spatial–temporal relationships between biodiversity and climate variability and characterizes distinctive zones of variation that may be used for prioritization and planning to ensure long-term biodiversity conservation in Canada.

Monitoring travel patterns in German city regions with the help of mobile phone network data

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning.Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes,a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access.Concepts need to serve the evaluation of policy objectives,for example in regional or local area plans.In this study,we,therefore,extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns.To accomplish this,we link mobile phone records with urban classifications and transport network data,using both visual and computational approaches to mine the data.The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany,testing hypotheses of transit-oriented regional development,as well as testing for congestion risks in the transport network.The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

The distribution of benthic foraminiferal assemblages in the north-west coastal region of Malacca Straits,Malaysia

Objective:To investigate the spatial and temporal distribution of benthic foraminiferal assemblages in relation with environmental parameters in Penang Island(the northern part of Malacca Straits,west coast of Peninsula Malaysia).Methods:Foraminifera samples were obtained from 144 sediment samples collected bimonthly throughout a one year sampling period using Ponar grab.These samples were then fixed with 4%buffered formalin stained with Rose Bengal.Temperature,salinity,dissolved oxygen and pH were detected in-situ at six sampling points within each transect approximately one metre above the seafloor.Sediment samples collected was also used to determine particle size.Results:A total of fourteen benthic foraminiferal genera obtained from two major groups belonging to the calcareous and agglutinated groups have been identified at all four sampling locations throughout the sampling period.The abundance of 13 out of 14 species were significantly affected by different sampling sites and times(P<0.05).Physicochemical variables comprising temperature,salinity,dissolved oxygen and pH differed among sites and sampling months(P<0.05).Conclusions:The distribution of foraminifera in terms of abundance and presence of species indicated dominance by calcareous genera of foraminifera contributed by significantly great abundances of Ammonia sp.and relatively low abundance of agglutinated taxa.This pattern of distribution could indicate a close association between foraminifera and physicochemical parameters.