Geographical Accessibility to Basic Infrastructures and Services in Rural Senegal: The Case of the Niakhar Area

Access to basic infrastructure and services is a factor in economic development and an important aspect in combatting social and spatial disparities. But this access is often subject to several constraints, including geographical accessibility. In this article, we aim to analyze the geographical accessibility to basic infrastructure and services in the Niakhar area, using the improved two step floating catchment area method and local spatial association indicators. The results reveal that the areas with high accessibility to health and education infrastructures and services are mainly located along the south-east and north-west gradient, while those with low accessibility are found in the south-west and north-east center. They also show high accessibility to trade services in the center of the study area.

Local spatial properties based image interpolation scheme using SVMs

Image interpolation plays an important role in image process applications. A novel support vector machines （SVMs） based interpolation scheme is proposed with increasing the local spatial properties in the source image as SVMs input patterns. After the proper neighbor pixels region is selected, trained support vectors are obtained by training SVMs with local spatial properties that include the average of the neighbor pixels gray values and the gray value variations between neighbor pixels in the selected region. The support vector regression machines are employed to estimate the gray values of unknown pixels with the neighbor pixels and local spatial properties information. Some interpolation experiments show that the proposed scheme is superior to the linear, cubic, neural network and other SVMs based interpolation approaches.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

An Effective Image Retrieval Mechanism Using Family-based Spatial Consistency Filtration with Object Region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.

A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and Spark.An important step for any parallel clustering algorithm is the distribution of data amongst the cluster nodes.This step governs the methodology and performance of the entire algorithm.Researchers typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the algorithm.However,these strategies are generic and are not tailor-made for any specific parallel clustering algorithm.In this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they employ.We also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution strategy.All of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load balancing.The experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

Modulating magnetism of nitrogen-doped zigzag graphene nanoribbons

We present a study of electronic properties of zigzag graphene nanoribbons （ZGNRs） substitutionally doped with nitrogen atoms at a single edge by first principle calculations. We find that the two edge states near the Fermi level sepa- rate due to the asymmetric nitrogen-doping. The ground states of these systems become ferromagnetic because the local magnetic moments along the undoped edges remain and those along the doped edges are suppressed. By controlling the charge-doping level, the magnetic moments of the whole ribbons are modulated. Proper charge doping leads to interest- ing half-metallic and single-edge conducting ribbons which would be helpful for designing graphene-nanoribbon-based spintronic devices in the future.

Delimiting the consolidation area for supplementing prime farmland with GIS technology

In view of the fact that consolidation area for supplementing prime farmland （CASPF） is a new concept proposed in the latest round of general land use planning in China, this paper proposes a new and integrated GIS- based technology for delimiting CASPF. Taking the advantage of the features of CASPF, a comprehensive evaluation indicator system for defining ordinary farmland as CASPF is established in this study, which covers 11 indicators, including slope, land plots connectivity, altitude, surface soil texture, and so on. With the help of the ideal-point method, ordinary farmland plots are sorted according to their evaluation scores in descending order. Based on the space analysis technique of GIS, high-ranking land plots are defined as CASPF reasonably.

Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE:a case study of the neon flying squid (Ommastrephes bartramii)in the northwest Pacific Ocean

We examined the scale impacts on spatial hot and cold spots of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. The original fishery data were tessellated to 18 spatial scales from 5′×5′ to 90′×90′ with a scale interval of 5′ to identify the local clusters. The changes in location, boundaries, and statistics regarding the Getis-Ord Gi＊ hot and cold spots in response to the spatial scales were analyzed in detail. Several statistics including Min, mean, Max, SD, CV, skewness, kurtosis, first quartile（Q1）, median, third quartile（Q3）, area and centroid were calculated for spatial hot and cold spots. Scaling impacts were examined for the selected statistics using linear, logarithmic, exponential, power law and polynomial functions. Clear scaling relations were identified for Max, SD and kurtosis for both hot and cold spots. For the remaining statistics, either a difference of scale impacts was found between the two clusters, or no clear scaling relation was identified. Spatial scales coarser than 30′ are not recommended to identify the local spatial patterns of fisheries because the boundary and locations of hot and cold spots at a coarser scale are significantly different from those at the original scale.

Facial expression recognition with contextualized histograms

A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.

Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots

Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive ability.As a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment adaptability.To improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation method.First,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition.And then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short distance.Moreover,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence level.Finally,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes number.The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese mainland

Background Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Koppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese mainland and assess the feasibility of developing an early warning system.Methods Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran’s/ and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.Results All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran’s/ showed that average IR had significant clustered trend (z = 53.69,P < 0.001), with local Moran’s/ identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old:F = 26.80,P < 0.001;15-64 years old:F = 25.04,P < 0.001;Above 65 years old:F = 5.27,P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR= 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.Conclusions Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Electron localization enhanced photon absorption for the missing opacity in solar interior

The internal solar structure predicted by the standard solar model disagrees with the helioseismic observations even by utilizing the most updated physical inputs, such as the opacity and element abundances. By increasing the Rosseland mean, the decade-old open problem of the missing opacity can be resolved. Herein, we propose that the continuum electrons in the radiative processes lose phases and coherence as matter waves, giving rise to a phenomenon of transient spatial localization. It not only enhances the continuum opacity but also increases the line widths of the bound-bound transitions. We demonstrate our theoretical formulation by investigating the opacity of solar mixtures in the interior. The Rosseland mean demonstrates an increase of 10%-26% in the range of 0.3 R⊙-0.75 R⊙. The results are compared with the recent experimental data and the existing theoretical models. Our findings provide novel clues to the open problem of the missing opacity in the solar interior and new insight on the radiative opacity in the hot dense-plasma regime.

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

A New Quantitative Method for Studying the Vulnerability of Civil Aviation Network System to Spatially Localized Hazards

As an important infrastructure system, civil aviation network system can be severely affected by natural hazards. Although a natural hazard is usually local, its impact, through the network topology, can become global.Inspired by Wilkinson's work in 2012, this article proposes a new quantitative spatial vulnerability model for network systems, which emphasizes the spreading impact of spatially localized hazards on these systems. This model considers hazard location and area covered by a hazard,and spatially spreading impact of the hazard(including direct impact and indirect impact through network topology) and proposes an absolute spatial vulnerability index and a relative spatial vulnerability index to reflect the vulnerability of a network system to local hazards. The model is then applied to study the spatial vulnerability of the Chinese civil aviation network system. The simulation results show that(1) the proposed model is effective and useful to study spatial vulnerability of civil aviation network systems as the results well explain the general situation of the Chinese civil aviation system; and(2) the Chinese civil aviation network system is highly vulnerable to local hazards when indirect impacts through network connections are considered.

Propagation of local spatial solitons in power-law nonlinear PT-symmetric potentials based on finite difference

We consider the(2+1)-dimensional nonlinear Schrodinger equation with power-law nonlinearity under the parity-time-symmetry potential by using the Crank-Nicolson alternating direction implicit difference scheme,which can also be used to solve general boundary problems under the premise of ensuring accuracy.We use linear Fourier analysis to verify the unconditional stability of the scheme.To demonstrate the effectiveness of the scheme,we compare the numerical results with the exact soliton solutions.Moreover,by using the scheme,we test the stability of the solitons under the small environmental disturbances.

Stochastic maximum principle for systems driven by local martingales with spatial parameters

We consider the stochastic optimal control problem for the dynamical system of the stochastic differential equation driven by a local martingale with a spatial parameter.Assuming the convexity of the control domain,we obtain the stochastic maximum principle as the necessary condition for an optimal control,and we also prove its sufficiency under proper conditions.The stochastic linear quadratic problem in this setting is also discussed.

Semitopological Systems

The aim of this paper is to establish the theory of semitopological systems,which has general topological spaces, fuzzy topological spaces, topological molecular lattices, and topological systems as special cases.