An analytical method is described for profiling lactate production in single cells via the use of coupled enzyme reactions on surface-grafted resazurin molecules. The immobilization of the redox-labile probes was achieved through chemical modifications on resazurin, followed by bio-orthogonal click reactions. The lactate detection was demonstrated to be sensitive and specific. The method was incorporated into a single-cell barcode chip for simultaneous quantification of aerobic glycolysis activities and oncogenic signaling phosphoproteins in cancer. The interplay between glycolysis and oncogenic signaling activities was interrogated on a glioblastoma cell line. Results revealed a drug-induced oncogenic signaling reliance accompanying shifted metabolic paradigms. A drug combination that exploits this induced reliance exhibited synergistic effects in growth inhibition.
A new hybrid electric tracked bulldozer composed of an engine generator, two driving motors, and an ultracapacitor is put forward, which can provide high efficiencies and less fuel consumption comparing with traditional ones. This paper first presents the terramechanics of this hybrid electric tracked bulldozer. The driving dynamics for this tracked bulldozer is then analyzed. After that, based on analyzing the working characteristics of the engine, generator, and driving motors, the power train system model and control strategy optimization is established by using MATLAB/Simulink and OPTIMUS software. Simulation is performed under a representative working condition, and the results demonstrate that fuel economy of the HETV can be significantly improved.
Abstract. Hybrid power systems, formed by two motors and high-energy-density batteries in appropriate ways, provide high-performances and high-efficiency power for electric bulldozer. This paper firstly constructs the mathematical model of driving motors system based on the design of dual-motor-driving propulsion system for bulldozer. The requirements of several main parameters are analyzed. Secondly, by using the setting values from a bulldozer, simulation work is implemented, and parameters of the hybrid power system are matched, and finally, based on MATLAB/Simulink, dynamic models for the dual-motor-driving hybrid power system is established, and the simulation results are discussed.
IntroductionIn recent years, the number of construction machinery is increasing significantly with the social development. The negative effects of the ever-increasing number of construction machinery have adversely impacted both energy sustainability and overall air quality. Even worse, the energy generated from oil yields the lowest energy efficiency, with an approximate rate of only 20%. Consequently, many studies have been undertaken to address these energy issues, especially regarding the utilization of electricity as a viable replacement for oil. Caterpillar in the United States produced a revolutionary D7E electric drive track-type bulldozer with electric-drive system in March, 2008. Compared with internal-combustion driving track-type bulldozers, D7E can yield greater fuel efficiency by nearly 10% to 30%, and at the same time enhance drive train efficiency and take lower life-cycle maintenance costs.Structure of duel-motor-drive electric bulldozer. As showed in fig.1, many parts of mechanical transmission can be cut down for dual-motor-driving electric bulldozer. The paths of electric power and mechanical power are tandem to each other in the series configurations, respectively.
News story segmentation is an important aspect for news video analysis. This paper presents a method for news video story segmentation. Different form prior works, which base on visual features transform, the proposed technique uses audio features as baseline and fuses visual features with it to refine the results. At first, it selects silence clips as audio features candidate points, and selects shot boundaries and anchor shots as two kinds of visual features candidate points. Then this paper selects audio feature candidates as cues and develops different fusion method, which effectively using diverse type visual candidates to refine audio candidates, to get story boundaries. Experiment results show that this method has high efficiency and adaptability to different kinds of news video.
This paper presents a novel scene classification method using low-level feature and intermediate feature. The purpose of the proposed method is to improve the performance of scene classification and reduce the labeled data required using the complementary information between low-level and intermediate feature. The proposed method uses the co-training algorithm to classify scenes, in which the low-level feature and intermediate feature are two views of co-training algorithm. For low-level feature, Block Based Gabor Texture (BBGT) feature is extracted to describe the texture property of images incorporating the spatial layout information. For intermediate feature, Bag Of Word (BOW) feature is extracted to describe the distribution of local semantic concepts in images based on quantized local descriptors. Experiment results show that this proposed method has satisfactory classification performances on a large set of 13 categories of complex scenes.
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