Preparation and properties of PAn/ATTP/PE conductive composites

Polyaniline/Attapugite/ PE(PAn-ATTP/PE)composites containing particles with core-shell structure were obtained via the two-step blending processs. The experimental condition is as follows: Organo-attapulgite and PAn was obtained by modifying attapulgite with laury benzenesulfonic acid sodium salt and, then added to PE. The electrical conductivity, structure and properties of the composites were studied. Under the function of shear stress, core-shell structure particles with ATTP as the core and PAn as the shell were formed in the composites. The structure of PAn-ATTP/PE composites were characterized by FTIR,XRD,SEM, etc, respectively. The effects of concentration of doping agent on the conductivity and mechanical property of the composites were investigated. The mechanical properties and impact fracture surface of the ternary composites were studied by means of the tensile tester, SEM, etc. The results show that polyaniline encapsulated ATTP enhances the strength of the PE. And the conductivity of PAn-ATTP/PE composites of is improved effectively when polyaniline encapsulated ATTP is added. The composite have good conductivity when 10% polyaniline encapsulated ATTP is added.

Synthesis and application of bilayer-surfactant-enveloped Fe_3O_4 nanoparticles: water-based bilayer-surfactant-enveloped ferrofluids

Superparamagnetic carbon-coated Fe3O4 nanoparticles with high magnetization（85 emu·g-（-1）） and high crystallinity were synthesized using polyethylene glycol-4000（PEG（4000）） as a carbon source.Fe3O4 water-based bilayer-surfactant-enveloped ferrofluids were subsequently prepared using sodium oleate and PEG（4000） as dispersants.Analyses using X-ray photoelectron spectroscopy,X-ray diffraction,and Fourier-transform infrared spectroscopy indicate that the Fe3O4 nanoparticles with a bilayer surfactant coating retain the inverse spinel-type structure and are successfully coated with sodium oleate and PEG（4000）.Transmission electron microscopy,vibrating sample magnetometry,and particle-size analysis results indicate that the coated Fe3O4 nanoparticles also retain the good saturation magnetization of Fe3O4（79.6 emu·g^-1） and that the particle size of the bilayer-surfactant-enveloped Fe3O4 nanoparticles is 42.97 nm,which is substantially smaller than that of the unmodified Fe3O4 nanoparticles（486.2 nm）.UV-vis and zeta-potential analyses reveal that the ferrofluids does not agglomerate for 120 h at a concentration of 4 g·L^-1,which indicates that the ferrofluids are highly stable.

A Study on the Wing Characteristics of Flies

This paper is concerned with the aerodynamic functions of fly wings. The free and tethered flight analyses were performed by using a digital high-speed video camera system. A liquid droplet impacting with a wing surface of fly was conducted to examine the wing characteristics. Microscopic observation of fly＇s wings were also conducted by using a laser beam microscope. The results of a series of observation and measurement revealed the flight characteristics of flies, such as the wing tip velocity, wing path, wing flexibility, wing structure, resistance to rain drops, and so forth.

Research Advance in Swarm Robotics

The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.

Immune based computer virus detection approaches

The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide.The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective.Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness,mainly classified into three categories: static,dynamic and heuristics techniques.As the natural similarities between the biological immune system(BIS),computer security system(CSS),and the artificial immune system(AIS) were all developed as a new prototype in the community of anti-virus research.The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses.In this paper,a variety of classic computer virus detection approaches were introduced and reviewed based on the background knowledge of the computer virus history.Next,a variety of immune based computer virus detection approaches were also discussed in detail.Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates,which have paved a new way for the anti-virus research.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

Effect of DOPO-containing Flame Retardants on Poly（lactic acid）： Non-flammability, Mechanical Properties and Thermal Behaviors

The flame retardancies of three kinds of 9,10-dihydro-9-oxa-10-phosphaphenan-threne 10-oxide（DOPO）- containing flame retardant（A1, A2, A3）/poly（lactic acid）（PLA） composites[PA-n/（Ax-y）, n= 1--12; x= l, 2, 3, denoting three kinds of flame retardants; y= 10%, 20%, 30%, 40%, denoting the mass fraction of Ax] were greatly enhanced by melt blending of flame retardant Ax with PLA, including twin-screw extrusion and injection-molding processes. With only 10%（mass fraction） of Ax added to PLA, good flame retardancy with limiting oxygen index（LOI） values of more than 33% was achieved. As the Ax mass fraction was further increased to 20%, PA-n/（Ax-20%） composites showed much better flame retardancy（LOI~〉35% and UL-94 V-0 rating）. Moreover, the thermal degradation behaviors and mechanical properties of PA-n/（Ax-y） composites were investigated via thermogravimetric analysis（TGA）, differen- tial thermal analysis（DTA）, tensile testing, notched impact-bar testing, and dynamic mechanical analysis（DMA）. TGA results show that PA-n/（Ax-y） composites have slower rate of mass loss and much higher char yield, compared to neat PLA. With the addition of Ax to PLA, the DTA and DMA results indicate slight variations in glass transition tcmpe- ratures（Tg） of PA-n/（Ax-y） composites. Based on TGA results under nonisothermal conditions, the thermal degrada- tion kinetics of PA-n/（Ax-y） composites were studied by KAssinger＇s and Ozawa＇s methods. These thermal degrada- tion dynamic analyses show lower activation energies（EK or Eo） for PA-n/（Ax-y） composites, corresponding to higher mass fractions of Ax（from 10% to 40%）. The PA-n/（Ax-y） composites with good flame retardancy and good mecha- nical properties obtained in this study could be potential candidates for fire- and heat-resistant applications in auto- motive engineering and building fields with more safety and excellent performance.

An immune local concentration based virus detection approach

Along with the evolution of computer viruses, the number of file samples that need to be analyzed has constantly increased. An automatic and robust tool is needed to classify the file samples quickly and efficiently. Inspired by the human immune system, we developed a local concentration based virus detection method, which connects a certain number of two-element local concentration vectors as a feature vector. In contrast to the existing data mining techniques, the new method does not remember exact file content for virus detection, but uses a non-signature paradigm, such that it can detect some previously unknown viruses and overcome the techniques like obfuscation to bypass signatures. This model first extracts the viral tendency of each fragment and identifies a set of statical structural detectors, and then uses an information-theoretic preprocessing to remove redundancy in the detectors’ set to generate ‘self’ and ‘nonself’ detector libraries. Finally, ‘self’ and ‘nonself’ local concentrations are constructed by using the libraries, to form a vector with an array of two elements of local concentrations for detecting viruses efficiently. Several standard data mining classifiers, including K -nearest neighbor (KNN), radial basis function (RBF) neural networks, and support vector machine (SVM), are leveraged to classify the local concentration vector as the feature of a benign or malicious program and to verify the effectiveness and robustness of this approach. Experimental results show that the proposed approach not only has a much faster speed, but also gives around 98% of accuracy.

Unsupervised random forest for affinity estimation

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data.The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness.The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees.A pseudo-leaf-splitting(PLS)algorithm is introduced to account for spatial relationships,which regularizes affinity measures and overcomes inconsistent leaf assign-ments.The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences.The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence.Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.