To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

Artificial control of cell adhesion on smart surface is an on-demand technique in areas ranging from tissue engineering, stem cell differentiation, to the design of cell-based diagnostic system. In this paper, we report an electrochemical system for dynamic control of cell catch-and-release, which is based on the redox-controlled host-guest interaction. Experimental results reveal that the interaction between guest molecule (ferrocene, Fc) and host molecule (β-cyclodextrin, β-CD) is highly sensitive to electrochemical stimulus. By applying a reduction voltage, the uncharged Fc can bind to β-CD that is immobilized at the electrode surface. Otherwise, it is disassociated from the surface as a result of electrochemical oxidation, thus releasing the captured cells. The catch-and-release process on this voltage-responsive surface is noninvasive with the cell viability over 86%. Moreover, because Fc can act as an electrochemical probe for signal readout, the integration of this property has further extended the ability of this system to cell detection. Electrochemical signal has been greatly enhanced for cell detection by introducing branched polymer scaffold that are carrying large quantities of Fc moieties. Therefore, a minimum of 10 cells can be analyzed. It is anticipated that such redox-controlled system can be an important tool in biological and biomedical research, especially for electrochemical stimulated tissue engineering and cell-based clinical diagnosis.

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<span style="font-family: Times New Roman;"><span style="font-size: 9pt; color: black;" lang="EN-US"><span style="font-size: 14pt; color: black; mso-font-kerning: 0pt;" lang="EN-US"><p class="MsoNormal" style="margin: 0cm 0cm 0pt; text-align: left; mso-pagination: widow-orphan;" align="left"><span style="font-size: 12pt; color: black; mso-font-kerning: 0pt;" lang="EN-US">The paper proposes an algorithm based on particle swarm optimization (PSO) and intuitionistic fuzzy sets theory. It fuses multiple different neural networks and applies it to the comprehensive assessment of battlefield target damage effect. It adopts PSO algorithm to improve and optimize multiple neural networks of different structure, then confirms the weights of different neural networks, and synthesizes their assessment results as the final output result according to the weight. Apply the algorithm to instance simulation, the result shows its validity and rationality.</span></p></span><p class="MsoNormal" style="margin: 0cm 0cm 0pt;"> </p><span style="font-size: 9pt; mso-font-kerning: 0pt;" lang="EN-US">.</span></span></span><p class="MsoNormal" style="margin: 0cm 0cm 0pt;"> </p>