Characterization of functionalized multiwalled carbon nanotubes and application as an effective filter for heavy metal removal from aqueous solutions

Filtration efficiency of Ni(II) from aqueous solution using pristine and modified MWCNTs filters was investigated as a function of Ni(II) ion concentration, p H, and filter mass. MWCNTs were synthesized by CVD method and modified using two complementary treatments, purification(using a mixture of hydrochloric acid and hydrogen peroxide) and functionalization(using nitric acid). The effect and mechanism of each treatment on the structural integrity of pristine MWCNTs has been studied. Morphology of the pristine and modified filters was investigated by Raman Spectrometry(RS), Scanning Electron Microscopy(SEM), Energy Dispersive X-ray Spectroscopy(EDS),Fourier Transform Infrared(FTIR) spectrometry and Thermogravimetric analysis. It was found from Raman spectra that the ratio of the intensity of D-band to that of G-band decreased by purification process, and increased by functionalization process. The adsorption mechanism of Ni(II) onto the surface functional groups of modified MWCNTs was confirmed by FTIR spectrum. The filtration results showed that the removal efficiency of Ni(II) is strongly dependent on pH and could reach 85% at pH = 8. Also, modified MWCNT filters can be reused through many cycles of regeneration with high performance. Functionalized MWCNTs filters may be a promising adsorbent candidate for heavy metal removal from wastewater.

The invariant of the stiffness filter function with the weight filter function of the power function form

Based on the independent, continuous and map- ping （ICM） method and homogenization method, a research model is constructed to propose and deduce a theorem and corollary from the invariant between the weight filter func- tion and the corresponding stiffness filter function of the form of power function. The efficiency in searching for op- timum solution will be raised via the choice of rational filter functions, so the above mentioned results are very important to the further study of structural topology optimization.

Independent continuous mapping for topological optimization of frame structures

Based on the Independent Continuous Mapping method （ICM）, a topological optimization model with continuous topological variables is built by introducing three filter functions for element weight, element allowable stress and element stiffness, which transform the 0-1 type discrete topological variables into continuous topological variables between 0 and 1. Two methods for the filter functions are adopted to avoid the structural singularity and recover falsely deleted elements： the weak material element method and the tiny section element method. Three criteria （no structural singularity, no violated constraints and no change of structural weight） are introduced to judge iteration convergence. These criteria allow finding an appropriate threshold by adjusting a discount factor in the iteration procedure. To improve the efficiency, the original optimization model is transformed into a dual problem according to the dual theory and solved in its dual space. By using MSC/Nastran as the structural solver and MSC/Patran as the developing platform, a topological optimization software of frame structures is accomplished. Numerical examples show that the ICM method is very efficient for the topological optimization of frame structures.

A NEW METHOD FOR STRUCTURAL TOPOLOGICAL OPTIMIZATION BASED ON THE CONCEPT OF INDEPENDENT CONTINUOUS VARIABLES AND SMOOTH MODEL

A concept of the independent-continuous topological variable is proposed to establish its corresponding smooth model of structural topological optimization. The method can overcome difficulties that are encountered in conventional models and algorithms for the optimization of the structural topology. Its application to truss topological optimization with stress and displacement constraints is satisfactory, with convergence faster than that of sectional optimizations.

IMAGE RECONSTRUCTION AND OBJECT CLASSIFICATION IN CT IMAGING SYSTEM

By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms.

Delta-Davidson method for interior eigenproblem in many-spin systems

Many numerical methods,such as tensor network approaches including density matrix renormalization group calculations,have been developed to calculate the extreme/ground states of quantum many-body systems.However,little attention has been paid to the central states,which are exponentially close to each other in terms of system size.We propose a delta-Davidson(DELDAV)method to efficiently find such interior(including the central)states in many-spin systems.The DELDAV method utilizes a delta filter in Chebyshev polynomial expansion combined with subspace diagonalization to overcome the nearly degenerate problem.Numerical experiments on Ising spin chain and spin glass shards show the correctness,efficiency,and robustness of the proposed method in finding the interior states as well as the ground states.The sought interior states may be employed to identify many-body localization phase,quantum chaos,and extremely long-time dynamical structure.

An Efficient Method of N-D FIR Digital Filter Designs and Implementation

All efficient method of N-D FIR digital filter designs and implementation is presented in the peper.The most interesting aspects of the work in the paper are divided into three parts:First,an efficient transformation functions which have good properties are proposed.Second,the essential properties for spherically or hyperspherically symmetric filters are given.Finally,the most efficient implementatiou which exploits the structure inherent in the design is discussed.

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.

Experimental demonstration of concatenated composite pulses robustness to non-static errors

We examine the robustness of composite pulses to non-static classic noise in the NMR facility. Though these pulses are proposed to eliminate static, systematic errors, they are shown to be robust to time-varying noise with high-cut frequency up to near 10% of the Rabi frequency in a recent theoretical work. The CP pulses that are only robust to one kind of error have been implemented in various experiment platforms. In this paper we have compared the performance of reduced CinSK rc pulse, which is designed to cancel both the pulse length error and the off resonance error, to that of the primitive n pulse in a hybrid error environment. In order to achieve this target we have simultaneously injected noise to the RF output wave. The filter function formalism is employed to predict the evolution fidelity decay, which shows great consistency with the experiment data. Our work not only validates the superiority of reduced CinSK pulse but also indicates its sensitivity to the initial states, providing practical guidance in the noise suppression domain.

Improved Flight Conflict Detection Algorithm Based on Gauss-Hermite Particle Filter

In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.

Periodic Topology Optimization of Crane Boom Based on Improved Soft Kill Option Method

An improved soft kill option (SKO) method is proposed to achieve the optimization design of tower crane boom. For a better optimization performance, a parabolic weight coefficient is suggested and the displacement constraint is taken into consideration. In order to eliminate the numerical instability phenomena like checkerboard and mesh dependence, element temperature filter function is added into SKO method. Through the optimization data comparison of rectangular cantilever plate, it is verified that the improved SKO method can achieve a better result with more uniform stress and higher efficiency. Based on the dimension and load parameters of QTZ63 tower crane boom, an optimization model is established, and a periodic SKO method is put forward to optimize this model. The optimization result of the crane boom can provide a new thought for tower crane boom design. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.

Correlation Analysis on the SGS Velocity between Phases in an Isotropic Gasparticle Two-phase Flow with FDF Model

Correlation of the SGS (sub-grid scale) velocity between the two phases in an isotropic gas-particle two-phase flow was numerically investigated with FDF model. The results show the SGS gas velocity seen by the particles varies with the relative velocities between particle phase and gas phase. The relative velocity between the two phases produces the effect of the anisotropic turbulence on the particles. The variation of Stokes number influences the magnitude of the interaction between the two phases.

Editor＇s note

The rapid progress in information technology has provided huge opportunities to fully utilize all kinds of information resource. Materials, energy, and information are the three indispensable strategic resources of human society. Information is also one of the most active factors of social production. Vision is an essential approach for human beings to cognize the world： among all the information that human beings can perceive, 83% come from vision. Visual media, with image, video and digital geometry as their representatives, have already become a hot research area.