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When femtosecond laser pulses pass through a trapped polystyrene bead, water breakdown is induced even though the energy of laser pulse is much lower compared to the threshold value of breakdown when the femtosecond laser directly irradiates in water. This mechanism is assigned to the secondary convergence of the laser by the trapped bead. [ABSTRACT FROM AUTHOR]

Although the digital micromirror device (DMD) has been considered to be a spatial light modulator (SLM) for a long time, it is seldom utilized for holographic tweezers practically because of its energy loss. In this work, a multi-trapping system built with a DMD is demonstrated. But a problem of dispersion is found when the DMD is applied to femtosecond laser, and its mechanism is studied. [ABSTRACT FROM AUTHOR]

In an optical tweezers system, the output signal of a photodiode quadrant detector and the temperature in a sample cell are two key factors for the quantitative measurements of mechanical properties of living biological objects such as cells, organelles and macro-molecules. In order to enhance the output of a quadrant detector and effectively control the temperature in a sample cell, the dependence of the temperature in the sample cell and the output of the quadrant detector for different illumination conditions are studied. The results show that appropriate illumination conditions can ensure both nearly constant temperatures in the cell and the desired output signal, which provides for the possibility of high precision and damage-free analysis of living biological objects. [ABSTRACT FROM AUTHOR]

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation. [ABSTRACT FROM AUTHOR]