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A new microelectromechanical system ͑MEMS͒-based tensile testing stage ͑with integrated actuator, direct load sensing beam, and electrodes for controlled assembly of an individual nanostructure͒ was developed and used for in situ tensile loading of a templated carbon nanotube ͑T-CNT͒ inside a scanning electron microscope ͑SEM͒. Specifically, an increasing tensile load was applied to the T-CNT by actuating the device and high-resolution scanning electron microscopy images were acquired at different loads. The load ͑from the bending of the direct force-sensing beam͒, the elongation of the specimen during loading, and the specimen geometry were all obtained from analysis of SEM images. The stress versus strain curve and Young's modulus were thus obtained. A model is presented for the tensile loading experiment, and the fit value of Young's modulus from this model is compared to values obtained by an independent method. The results of this experiment on a T-CNT suggest the use of this device for loading other nanostructures and also for designing other MEMS-based systems, such as a compressive testing stage.

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis.

A new analytical model is developed for interpreting tensile loading data on "templated carbon nanotubes" ͑T-CNTs, amorphous carbon nanotubes made by pyrolysis with the channels of nanopores in anodized alumina nanopore arrays͒ obtained with a microelectromechanical-system ͑MEMS͒-based mechanical testing stage. It is found that the force output from the actuation unit of the testing stage depends on the stiffness of the force sensing beam and the nanostructure being loaded, as well as the power input. A superposition method is used to treat the mechanics of the device structure in the linear elasticity response regime. To our knowledge this is a new approach for solving the mechanical response of MEMS structures with variable force output and of the configuration described herein. An in situ mechanical testing of individual T-CNTs was undertaken in a scanning electron microscope ͑LEO1525͒ using a new device fabricated with integrated electrodes for controlled deposition of T-CNTs by electric-field guided assembly in a liquid. The T-CNT was subsequently tensile loaded to the point of fracture. The calculated modulus of the T-CNT using the new model based on the experimentally measured displacement of the moving platform with and without the T-CNT attached falls within the range expected for amorphous carbon. The new model corrects the treatment in a previously presented model ͓S.

Epoxy and glass matrix Terfenol-D composites have been produced by using a cold and hot compression-molding technique, respectively. The static and dynamic magnetic and magnetomechanical properties of samples with a volume fraction Vf in the range of 10% to 82% have been investigated as functions of bias field, frequency, and ac drive field. The epoxy composites have a greater saturation magnetostrain than the glass composite with the same Vf. The elastic modulus of the epoxy composites is dominated by that of the matrix, Em. Increasing the value of Em reduces the measured magnetostriction lambdac of the composites. A model, based on the strain related energy equilibrium, has been developed to describe the experimental results for Vf and Em dependences of lambdac and the magnetomechanical coupling coefficient k33. It is concluded that an optimum k33 value can be obtained by choosing a matrix with an appropriate elastic modulus. ©2001 American Institute of Physics

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