Cycloparaphenylene and their radicals anchored to a metal−organic framework

Cycloparaphenylene (CPP) shows modulated photophysical and electronic properties due to its strained structure and radially oriented π-electron system. Incorporation of CPP into metal-organic frameworks (MOFs) could transfer its extensive properties in solution to porous solids. Moreover, with the unique arrangement of the macrocycles and their interactions with the framework, emerging characteristics are anticipated. As an example of “robust dynamics”, we synthesized the first MOF structure (FDM-1001) with CPP precisely anchored to the ordered framework by employing a [8]CPP-containing linear dicarboxylate linker. Metric relationship between the dynamic macrocycles and the robust backbone creates ideal π-π interactions between them, which leads to an essentially directional arrangement of [8]CPP in the three-dimensional space. Furthermore, the MOF with [8]CPP could be successfully oxidized to generate an infinite array of radicals that show enhanced air stability compared to its molecular analogue.     

Ultrasound-Directed Symmetry Breaking and Spin Filtering of Supramolecular Assemblies from only Achiral Building Blocks

Herein we describe the self-assembly of an achiral molecule into macroscopic helicity as well as the emergent chiral-selective spin-filtering effect. It was found that a benzene-1,3,5-tricarboxamide (BTA) motif with an aminopyridine group in each arm could coordinate with Ag^I and self-assemble into nanospheres. Upon sonication, symmetry breaking occurred and the nanospheres transferred into helical nanofibers with strong CD signals. Although the sign of the CD signals appeared randomly, it could be controlled by using the as-made chiral assemblies as a seed. Furthermore, it was found that the charge transport of the helical nanofibers was highly selective with a spin-polarization transport of up to 45 %, although the chiral nanofibers are composed exclusively from achiral building blocks. This work demonstrates symmetry breaking under sonication and the chiral-selective spin-filtering effect.     

Poly(N-isopropylacrylamide)-grafted dual stimuli-responsive filter paper for protein separation

Thermal and salt dual stimuli-responsive filter-paper-based membranes were prepared by UV-induced grafting of NIPAM-based polymers on paper surface. The grafting ratio could be controlled by monomer concentration during grafting polymerization. The results from pressure drop measurement of the mobile phase flowed cross the membrane demonstrate that an appropriate grafting ratio would be 8%-10%. Protein adsorption on the membrane through hydrophobic interaction could be promoted by increasing temperature and lyotropic salt concentration. The effect of grafted polymer structure on protein binding performance was studied. Filter paper grafted with NIPAM-based branched copolymer consisting of hydrophobic monomer moieties shows ten times higher protein binding capacity than that of the original filter paper. The separation of plasma proteins using the dual stimuli-responsive membrane was examined to demonstrate feasible application for hydrophobic interaction chromatographic separation of proteins.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

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.The project supported by the National Natural Science Foundation of China (10472003) and Beijing Natural Science Foundation (3042002) The English text was polished by Yunming Chen.     

Adhesion model of side contact for an extensible elastic fiber

For accurately predicted adhesion laws of fibrillar structures contribute to the rational design of high-performance biomimetic adhesives, an adhesion model is proposed to study the directional adhesion behavior of an extensible elastic fiber that contacts a rigid smooth surface with its side surface under the coupling effect of normal and shear forces, based on the extensible Euler Bernoulli beam theory and the surface energy concept. The deformed configuration of the fiber is obtained analytically, and on the basis of this result, the detachment mode and the normal pull-off force of the fiber for a given shear force are predicted directly. It is also found that, due to the extensibility of the fiber, there exists a maximum normal pull-off force (MNPF) when an optimal shear force is applied. The MNPF will be enhanced by increasing the axial stiffness, and reduced by increasing the bending stiffness. In addition, generating an optimal pre-tension in the adhered part of the fiber will maximize the MNPF. The derived adhesion law is expected to contribute to the optimal design and applications of single-level fibrillar adhesives.     

Profitability in the car industry: New measures for estimating targets and target directions

In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes.     

Fingerprint image enhancement using data driven Directional Filter Bank

A new method based upon data driven tool, principal component analysis (PCA), for fingerprint enhancement is proposed in this paper. PCA is a very useful statistical technique that has found application in many different fields like image compression, face recognition and is commonly used for finding patterns in data of high dimension. In the proposed method, the input image is first decomposed into directional images using decimation free Directional Filter Bank (DDFB). Then these directional images are normalized. A data driven technique PCA is applied to these normalized directional fingerprint images, which gives the PCA filtered images. These are basically directional images. Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.     

Plate/shell topological optimization subjected to linear buckling constraints by adopting composite exponential filtering function

In this paper, a model of topology optimization with linear buckling constraints is established based on an independent and continuous mapping method to minimize the plate/shell structure weight. A composite exponential function (CEF) is selected as filtering functions for element weight, the element stiffness matrix and the element geomet-ric stiffness matrix, which recognize the design variables, and to implement the changing process of design variables from“discrete”to“continuous”and back to“discrete”. The buck-ling constraints are approximated as explicit formulations based on the Taylor expansion and the filtering function. The optimization model is transformed to dual programming and solved by the dual sequence quadratic programming algo-rithm. Finally, three numerical examples with power function and CEF as filter function are analyzed and discussed to demonstrate the feasibility and efficiency of the proposed method.     

Implementation of a nonlinear filter for online nuclear counting

Nuclear counting is a challenging task for nuclear instrumentation because of the stochastic nature of radioactivity. Event counting has to be processed and filtered to determine a stable count rate value and perform variation monitoring of the measured event. An innovative approach for nuclear counting is presented in this study, improving response time and maintaining count rate stability. Some nonlinear filters providing a local maximum likelihood estimation of the signal have been recently developed, which have been tested and compared with conventional linear filters. A nonlinear filter thus developed shows significant performance in terms of response time and measurement precision. The filter also presents the specificity of easy embedment into digital signal processor (DSP) electronics based on field-programmable gate arrays (FPGA) or microcontrollers, compatible with real-time requirements. © 2001 Elsevier Science. All rights reserved.