Control of the interparticle spacing in superparamagnetic iron oxide nanoparticle clusters by surface ligand engineering

Polymer-mediated self-assembly of superparamagnetic iron oxide（SPIO） nanoparticles allows modulation of the structure of SPIO nanocrystal cluster and their magnetic properties. In this study, dopamine-functionalized polyesters（DApolyester） were used to directly control the magnetic nanoparticle spacing and its effect on magnetic resonance relaxation properties of these clusters was investigated. Monodisperse SPIO nanocrystals with different surface coating materials（poly（ε-caprolactone）, poly（lactic acid）） of different molecular weights containing dopamine（DA） structure（DA-PCL2k,DA-PCL1k, DA-PLA1k）） were prepared via ligand exchange reaction, and these nanocrystals were encapsulated inside amphiphilic polymer micelles to modulate the SPIO nanocrystal interparticle spacing. Small-angle x-ray scattering（SAXS）was applied to quantify the interparticle spacing of SPIO clusters. The results demonstrated that the tailored magnetic nanoparticle clusters featured controllable interparticle spacing providing directly by the different surface coating of SPIO nanocrystals. Systematic modulation of SPIO nanocrystal interparticle spacing can regulate the saturation magnetization（Ms） and T2 relaxation of the aggregation, and lead to increased magnetic resonance（MR） relaxation properties with decreased interparticle spacing.

Detection of Residual Yarn in Bobbin Based on Odd Partial Gabor Filter and Multi-Color Space Hierarchical Clustering

In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.

Determination of the Proper Motions and Membership of the Globular Cluster M3 and of its Orbit in the Galaxy

We examine 14 plates of the globular cluster M3 (NGC 5272) taken with the 40 cm refractor at the Sheshan station of Shanghai Astronomical Observatory. The plates span over a period of about 77 years. The positions and absolute proper motions of eight stars in the Hipparcos Catalogue and of 49 stars in the Tycho-2 Catalogue are used as the reference frame. The astrometric reduction is made with the central overlapping principle. The absolute proper motions of 534 stars in a region of about 100' × 100' around the cluster are measured. With the new proper motion data the membership probabilities of the stars are determined. The average absolute proper motion obtained for the cluster is -0.06±0.30 mas yr-1 in R.A. and -2.6±0.30 mas yr-1 in Decl. By combining this result with the known distance and radial velocity of the cluster, we also obtained the Galactic orbit of M3 for a chosen three-component Galactic potential.

The FAM(Fuzzy Asociative Memory)neural network model and its application in earthquake prediction

FAM(Fuzzy Associative Memory) Network Model, FAM Adaptive Learning Algorithm and Principal of FAM Inference Machine are introduced, and successfully application to ″New Generation Expert System for Earthquake Prediction″ (NGESEP). This system has good function for knowledge learning without disadvantages of neural network, which the learned knowledge implied in network is difficult to be understood or interpreted by expert system.

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

Formation Mechanism of the Cluster Space of Wuhan Metropolitan Area amid Evolution of Power Relationship

With the urban regionalization and regional urbanization, the spatial structure of cluster appears in some metropolitan areas, among which Wuhan can be taken as a typical case. This paper analyzes the formation of Wuhan metropolitan cluster space from the perspective of power relationship. A "twin-city" approach is developed in the peripheral area of Wuhan caused by the game between local governments at different levels. With the equal development opportunities in all directions and the alliances between different district governments and capital, the balancedly embedded cluster space is formed in Wuhan metropolitan area. And if there are no huge internal and external changes, the cluster space in Wuhan will continue to develop.

Similarity Search Algorithm over Data Supply Chain Based on Key Points

In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points,which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.

Social interactions in striped hyena inferred from camera trap data:is it more social than previously thought?

Understanding the drivers promoting sociality over solitariness in animal species is imperative for predicting future population trends and informing conservation and management.In this study we investigate the social structure of a desert dwelling population of striped hyena Hyaena hyaena.This species is historically regarded as strictly solitary albeit being the least studied of the extant Hyaenids.Accumulating evidence regarding the frequency of social interactions sug-gests a revision of striped hyena social structure is required.We hypothesized that striped hyena has a social structure that is more complex than expected for a strictly solitary species.For that end,we deployed an array of camera-traps in a remote desert region in Israel,and compared observed frequencies of striped hyena co-occurrence against null models to test whether hyena co-occurred more than expected by chance.Seven adults were(re)captured by our camera-traps in 49 different instances over 83 tracking days.Of these,6 exhibited shared space-use around a scarce,isolated perennial water source.Five of them,co-occurred with other hyena(in 3 instan-ces)significantly more frequent than expected by chance(and that timing suggests reproduction is unlikely to be the driving factor).Our findings substantiate evidence of complex social structure in striped hyena,highlight the importance of a scarce resource in space use and sociality,and provide a baseline for future research of striped hyena social structure.We suggest that similar methods be employed to evaluate social structure in other"solitary species"to better under-stand their social dynamics.