Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Territorial Planning for Coastal Zones in Chile:The Need for Geographical-Environmental and Natural Risk Indicators for Spatial Decision Support Systems

Coastal zones are very dynamic and fragile environments, constituting a landscape ever more heterogeneous, fragmented and with increasing levels of complexity due to the changing relationship between man and nature. Integrated coastal zone management therefore requires detailed knowledge of the system and its components, based—to a large extent—on technical and scientific information. However, the information generated must be in line with the political requirements necessary for decision-making and planning. Thus the use of indicators to give a simplified view of the many components of the territory, and at the same time to provide important information about patterns or trends, becomes a tool of the utmost importance. These indicators can be understood as measurable characteristics of the environment, which facilitate comprehension of the processes occurring at different scales and serve as a reference to inform the population and support decision-making. The aim of the present note is to demonstrate briefly the need to develop geographical-environmental and natural risk indicators to facilitate comprehension of the dynamic of spatial and temporal landscape patterns, particularly in coastal environments. This approach offers an historical summary of the natural, socio-economic and political processes which currently make up the territory, and which without doubt will continue to influence it in the future. At the same time, it is proposed that information should be integrated on the basis of this framework with a view to generating spatial decision support systems in a context of planning and integrated management of the coastal zones of Chile.

Strength failure of spatial reticulated structures under multi-support excitation

Under strong earthquakes, long-span spatial latticed structures may collapse due to dynamic instability or strength failure. The elasto-plastic dynamic behaviors of three spatial latticed structures, including two double-layer cylindrical shells and one spherical shell constructed for the 2008 Olympic Games in Beijing, were quantitatively examined under multi-support excitation （MSE） and uniform support excitation （USE）. In the numerical analyses, several important parameters were investigated such as the peak acceleration and displacement responses at key joints, the number and distribution of plastic members, and the deformation of the shell at the moment of collapse. Analysis results reveal the features and the failure mechanism of the spatial latticed structures under MSE and USE. In both scenarios, the double-layer reticulated shell collapses in the ＂overflow＂ mode, and the collapse is governed by the number of invalid plastic members rather than the total number of plastic members, beginning with damage to some of the local regions near the supports. By comparing the numbers and distributions of the plastic members under MSE to those under USE, it was observed that the plastic members spread more sufficiently and the internal forces are more uniform under MSE, especially in cases of lower apparent velocities in soils. Due to the effects of pseudo-static displacement, the stresses in the members near the supports under MSE are higher than those under USE.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

High-Speed Rail Route and Regional Mobility with a Ras-ter-Based Decision Support System: The Texas Urban Triangle Case

This study addresses sustainable transportation in the Texas Urban Triangle at the regional scale. Its aim is to determine the most suitable corridor for new transport infrastructure by employing a spatial decision support system proposed in this project. The system is being tested through its application to a prototype corridor parallel to Interstate 35 between San Antonio and Austin. The basic research questions asked are spatial in nature, so accordingly the geographic information system is the primary method of data analysis. The overall modeling approach is devoted to answering the following questions: What are the considerations to support sustainable growth? What scale or type of infrastructure is necessary? And how to adequately model the transportation corridors to meet the demands and to sustain the living environment at the same time?

A new spatial coherence model and analytical coeffi cients for multi-support response spectrum combination

In this paper, a new spatial coherence model of seismic ground motions is proposed by a fitting procedure. The analytical expressions of modal combination （correlation） coefficients of structural response are developed for multi-support seismic excitations. The coefficients from both the numerical integration and analytical solutions are compared to verify the accuracy of the solutions. It is shown that the analytical expressions of numerical modal combination coefficients are of high accuracy. The results of random responses of an example bridge show that the analytical modal combination coefficients developed in this paper are accurate enough to meet the requirements needed in practice. In addition, the computational efficiency of the analytical solutions of the modal combination coefficients is demonstrated by the response computation of the example bridge. It is found that the time required for the structural response analysis by using the analytical modal combination coefficients is less than 1/20 of that using numerical integral methods.

Spatial Decision Support Applications Based on Three-Dimensional City Models

The basic mathematic models, such as the statistic model, the time\|serial model, the spatial dynamic model etc., and some typical analysis methods based on 3DCM are proposed and discussed. A few typical spatial decision making methods integrating the spatial analysis and the basic mathematical models are also introduced, e.g. visual impact assessment, dispersion of noise immissions, base station plan for wireless communication. In addition, a new idea of expectation of further applications and add\|in\|value service of 3DCM is promoted. As an example, the sunshine analysis is studied and some helpful conclusions are drawn.

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.

Finite element model updating for large span spatial steel structure considering uncertainties

In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.

A Method of Identifying Electromagnetic Radiation Sources by Using Support Vector Machines

Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

Application of geographical information system technology to epidemiological surveillance and prevention and cure decision-making for SARS

Objective:To expound geographical information system (GIS) technology is a very important tool when it was employed to assist to present the distribution by time and place and the model of transmission of infectious disease. Methods: We illustrated the assistant decision-making support function of GIS with an example of the spatial decision support system for SARS controlling in Shaanxi province of China which was developed by us. Results: The spatial decision support system established by applying GIS technology fulfilled the needs of real-time collection and management and dissemination SARS information and of surveillance and analysis the epidemic situation of SARS. Conclusion: Occurrence and epidemic of diseases, implement prevention and intervention measures and collocation hygienic resources are all with the characteristic of the variation of time and space, therefore, GIS technology has become a powerful tool for identifying risk factors of diseases, providing clues of causation of diseases , evaluating the effects of intervention measures and drawing a health management plan.

Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers

The purpose of this study is to examine optical spatial frequency spectroscopy analysis(SFSA)combined with visible resonance Raman(VRR)spectroscopic method,for thefirst time,to discriminate human brain metastases of lung cancers adenocarcinoma(ADC)and squamous cell carcinoma(SCC)from normal tissues.A total of 31 label-free micrographic images of three type of brain tissues were obtained using a confocal micro-Raman spectroscopic system.VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532 nm from the same sites of the tissues.Using SFSA method,the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian functionfitting.The standard deviations,calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine(SVM)classifier.The key VRR biomolecularfingerprints of carotenoids,tryptophan,amide II,lipids and proteins(methylene/methyl groups)were also analyzed using SVM classifier.All three types of brain tissues were identified with high accuracy in the two approaches with high correlation.The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues,which are on-site,real-time and label-free and may improve the accuracy of brain biopsy.

Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India

Multi-criteria spatial modeling is one of the important components of spatial decision support system (SDSS). Multi-criteria spatial modeling often requires a common scale of values for diverse and dissimilar inputs to create an integrated analysis. Weighted overlay function is most commonly used for site suitability analysis which identifies the most preferred locations for a specific phenomenon. However, weighted overlay function gives inconsistent and erroneous results for highly dissimilar inputs as it assumes that most favorable factors result in the higher values of raster, while identifying the best sites. This paper conveys the effectiveness of fuzzy overlay function for multi-criteria spatial modeling. It is based on the principle of fuzzy logic theory which defines membership using Gaussian function on each of the input rasters instead of giving individual rank to them like in weighted overlay function. A case study on preparation of land resources map for Mawsynram block of East Khasi Hills district of Meghalaya, India is presented here. It was observed that fuzzy overlay function has given more satisfactory output in terms of site suitability while comparing with the result of weighted overlay function.

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.

基于碳汇潜力的碳排放空间关联网络结构特征及影响因素——以长江中游城市群为例

考虑碳汇潜力的碳排放空间关联是促进城市群协同减排的重要基础,更是实现碳中和的有力抓手。以长江中游城市群为研究区,对传统引力模型进行修正,构建兼具碳汇潜力的碳排放空间关联网络,采用社会网络分析(SNA)和二次指派程序方法(QAP)探究长江中游城市群碳排放空间关联网络的时空特征与影响因素。结果表明:①研究期间长江中游城市群碳排放网络关联关系数、网络密度和网络关联度呈增长趋势,网络效率和网络等级度不断下降,且网络等级度在2010年后始终为0,表明碳排放空间关联网络日益稠密、网络通达性较强,存在多重叠加的溢出渠道,但等级结构并不森严。基于碳汇潜力的长江中游城市群碳排放空间关联网络从“双核化”向“多极化、多线程”网络形态发展,核心城市的扩散作用不断增强。此外,以抚州、宜昌、鹰潭、上饶和吉安等为代表的高碳生态承载力地区,在碳排放空间关联网络中的受益关联性大于溢出关联性。②2020年,长江中游城市群碳排放空间关联网络形成四大板块,相较于板块内部成员间的碳排放空间关联效应,板块间的碳排放空间关联效应更为明显,即板块间的“碳排放转移”效应较为普遍。净溢出板块主要分布在武汉都市圈、宜荆荆都市圈和长株潭城市群外围城市,处于网络核心圈层位置;净受益板块主要分布在环鄱阳湖城市群东部,“碳排放避难所”效应显著。经纪人板块集中在环鄱阳湖城市群西部,板块间表现出“净溢出板块→经纪人板块→净受益板块”的碳排放传递路径,呈现出明显的“梯度转移”特征。双向溢出板块主要位于长江中游城市群西部,对净溢出和净受益板块都存在碳排放的空间溢出。研究期内,净溢出板块的成员虽然有所变化但总数保持不变,经纪人板块的成员有所增加,而净受益和双向溢出板块的成员均减少,环鄱阳湖城市群的“碳排放避难所”效应有所弱化。③地理邻近关系能够促进城市间碳排放关联关系的建立,而城市间的经济集聚程度、土地利用强度和经济发展水平上的互补性,促使碳排放空间关联网络随着城市间分工与协作的加强而呈现出千里“碳缘”一线牵的特点。

中国乡村创新创业发展:指数构建与测度分析

鼓励乡村创新创业是推进乡村振兴战略的重要抓手,但尚未有科学严谨的研究全面揭示中国乡村创新创业的发展程度和时空特征。通过构建包括2个一级指标、7个二级指标和21个三级指标的乡村创新创业指标体系,计算2014—2021年中国县、市、省级三个层面的乡村创新创业指数,并对其发展趋势、空间特征等展开分析。研究发现:中国乡村创新创业总体上蓬勃发展,但区域间存在差异;乡村振兴示范县的创新创业程度要显著高于其他县,国家重点帮扶县的创新创业增长速度要显著高于非重点帮扶县;乡村创新创业活动存在明显的空间集聚现象,同时也表现出区域收敛的趋势。乡村创新创业指数已向社会开放,将为各界研究中国乡村创新创业提供基础性数据。

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.

大口径反射镜Bipod支撑结构的装调与检测方法

随着我国遥感卫星对地观测分辨率的不断提升,空间遥感相机的有效口径逐步增大,为有效应对相机在地表装调时的重力误差干扰,大口径遥感相机的装调方式逐步从光轴水平形式转变为光轴竖直形式,与之对应的,相机主反射镜支撑结构与装配需求也在不断革新。为满足反射镜支撑与减重需求,在1 m及以上口径的反射镜支撑形式中,Bipod结构是较为常见的一种结构形式,不同于传统的框式支撑形式,它在光机结构粘接与结构位置标定需求上都有了巨大变革。为保证Bipod支撑结构下反射镜的装配定位精度和面形变化满足系统指标要求,提出了一种结合多目标空间位置转换和Stewart结构运动反演的大口径反射镜组件装调方法,该方法可在有效保证光机结构粘接点精度的同时,仅依靠反射镜自身支撑结构,实现反射镜与主承力板之间的高精度六维调节,系统装调误差可控制在0.04 mm左右。在遥感相机的实际装调过程中,通过分析主反射镜在系统中的装调误差区间,并以此为标准制定了反射镜Bipod结构的定位、粘接、调整和检测方案,实践结果表明该方法可有效控制反射镜的装配定位精度和面形误差,装调结果能够满足大口径遥感相机的系统成像需求。

考虑土性参数空间变异性的珠三角软土地区土钉支护结构可靠度分析

如何定量定性描述软土土性参数空间变异性对准确确定软土土性参数和合理评价软土地区土钉支护结构稳定性至关重要。采用既有统计分析方法定量描述空间变异性通常需要大量数据,难以满足。本文基于随机场理论模拟软土土性参数空间变异性,建立了一套考虑土性空间变异性的软土地区土钉支护结构可靠度设计方法,最后以MATLAB软件包为平台进行计算程序的开发与工程实例研究。分析结果表明,本文采用随机场方法模拟软土土体固有空间变异性及其在土钉支护结构可靠度分析中的应用具有合理性与普适性。

海州湾及邻近海域金乌贼的空间分布特征及其与环境因子的关系

研究根据2013—2020年在海州湾及其邻近海域进行的渔业资源和栖息环境综合调查数据,采用支持向量机(Support vector machine,SVM)模型,通过交叉验证,结合赤池信息准则(Akaike information criterion,AIC)等评判指标,构建了金乌贼(Sepia esculenta)的空间分布模型,并分析环境因子对其空间分布的影响,旨在探究海州湾金乌贼的空间分布特征及其与环境因子的关系。结果表明,水深对金乌贼空间分布的影响最大,底层盐度次之,其次是底层水温和离岸距离。金乌贼的相对资源密度随水深的增大呈现先升高后降低的趋势,随盐度的升高而增大,随底层水温的升高而减小。在海州湾水深约27 m,离岸距离约40 km的海域,金乌贼的相对资源密度较高,其分布格局呈现出在东部和中北部海域丰度高,而西南部近岸海域丰度低的特点。研究将有助于深入了解海州湾金乌贼的空间分布规律,为其资源的合理利用和科学养护提供参考依据。