Search

The ocean covers a much larger area of the earth than the land, in the case of diminishing land resources, the development of seabed resources is imperative. However, compared with the land, the deep-sea environment is more complex, risers and joints are important components in the mining process. In order to ensure the smooth operation process, it is necessary to study its fatigue life. The method is to estimate the fatigue life of the tested component by S-N curve. The device for measuring S-N curve is a resonance fatigue testing machine. According to its structural design, the actual constraints at both ends are not completely hinge constraints, and the mass distribution is mainly concentrated at both ends of the tested part. In order to further analyze its vibration response, the formula is derived again, so as to obtain the response in line with the actual work situation.

Precise nanomedicine has been extensively explored for efficient cancer imaging and targeted cancer therapy, as evidenced by a few breakthroughs in their preclinical and clinical explorations. Here, we demonstrate the recent advances of intelligent cancer nanomedicine, and discuss the comprehensive understanding of their structure-function relationship for smart and efficient cancer nanomedicine including various imaging and therapeutic applications, as well as nanotoxicity. In particular, a few emerging strategies that have advanced cancer nanomedicine are also highlighted as the emerging focus such as tumor imprisonment, supramolecular chemotherapy, and DNA nanorobot. The challenge and outlook of some scientific and engineering issues are also discussed in future development. We wish to highlight these new progress of precise nanomedicine with the ultimate goal to inspire more successful explorations of intelligent nanoparticles for future clinical translations.

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

A surface micromachined miniature switch has been made on silicon substrate using an electroplated gold micro-beam as the cantilevered arm, a chromium-to-gold electrical contact, and electrostatic actuation as the switching mechanism. The switch has an electrical isolation of -30dB in the 'off' state and an insertion loss of 4-7dB form 1 to 10 Ghz with a return loss of -15dB in the 'on' state. The high insertion loss has attributed to generation of parasitic current in low resistivity of the silicon substrate.