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The electricity trading market promotes the frequency and scale of power exchange in the distribution network, the uncertainty of trading volume, trading duration, and trading partners affect the total supply capability (TSC) of the distribution network (DN). The paper proposed a TSC model of DN with electric trading market. Firstly, the load clearing model of electricity trading is established. Then, the least square method is used to fit the growth curve functions of conventional load and power trading load. TSC model of DN is developed based on the growth functions of two loads. Finally, the TSC model is solved by the improved repetitive power flow algorithm (RPA). The simulation results of the improved IEEE 54-bus test system verify the feasibility and applicability of the proposed TSC model.

This paper focuses on the study of the induction heating process of a camshaft in a marine diesel engine. A three-dimensional finite element model for dynamic induction heating is established using the finite element method of multi-physical field coupling, aiming to investigate the temperature uniformity of the cam during this process. Three elements are analyzed in this study: the moving speed, the gap between the induction coil and the workpiece, and the width of the induction coil. These factors allow for an analysis of the temperature distribution in the thickness direction and contour line direction of the cam under various conditions. On this basis, an equivalent parameter about the temperature uniformity in the thickness direction of the cam is proposed to guide the selection of the camshaft induction heating process parameters. [ABSTRACT FROM AUTHOR]

A new cell analysis method, differential mobility cytometry (DMC), was developed to monitor cells spatially and temporally or to separate cells based on affinity interactions. DMC combines an oscillation system with open-tubular capillary cell affinity chromatography (OT-CAC), although any separation volume (capillaries, channels, etc.) can be used. This unique separation approach uses oscillating flow and differential imaging to analyze cells as they retard and adhere to an affinity surface. Three main factors of the oscillation system were studied: the pump speed, oscillation frequency, and cell velocity at different oscillation speeds. The optimized oscillation frequency and intensity were determined. Cell-surface interactions were used to estimate the number of bonds formed during cell capture. An average of 200 bonds (standard deviation of 150 bonds) were formed during cell capture. The variability was due to differences in cell-capture times (0.8 [+ or -] 0.6 s). Cells expressing the target protein on the surface oscillated slower and were captured by the corresponding ligand on the capillary surface. Cells were detected by differential imaging of a charge-coupled device camera. DMC measurements were optimized with respect to the camera frame difference. Cells were observed to slow as they reached the surface and could be observed to sway in the oscillating flow as they were tethered to the surface by a capture antibody. With the advantage of high cell-capture efficiency and temporally monitoring cell adhesion by the differential mobility of cells, DMC has proven to be a useful tool in cell analysis for basic biological studies and biomedical research.

In this paper, an open-tubular capillary cell affinity chromatography (OT-CAC) method to enrich and separate target cells is described. Open tubular capillaries coated with anti-CD4, anti-CD14, or anti-CD19 antibodies were used as affinity chromatography columns to separate target blood cells. Cells were eluted using either shear force or bubbles. Bubbles were used to elute the captured cells without diluting the captured cells appreciably, while maintaining viability (the viability of the recovered cells was 85.83 [+ or -] 7.34%; the viability of the cells was 90.41 [+ or -] 3.49% before separation). Several aspects of the OT-CAC method were studied, such as the affinity of one antibody between two different cell lines, the effect of shear force, and the recovery of captured cells. Single-and multicell type separations were demonstrated by isolating CD4+ cells with antiCD4 coated capillary and isolating CD4+ and CD19+ cells with two capillaries in tandem from blood samples. In the one cell type isolation test, an average of 87.7% of the recovered cells from antiCD4 capillary were lymphocytes and an average of 97.7% of those lymphocytes were CD4+ cells. In the original blood sample, only 14.2% of the leukocytes were CD4+ cells. Two capillary columns were also run in tandem, separating two blood cell types from a single sample with high purity. The use of different elution shear forces was demonstrated to selectively elute one cell type. This method is an inexpensive, rapid, and effective method to separate target cells from blood samples.

Abstract Background MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions. Results Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments. Conclusions Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.