The present work describes the phenomenological approach to automatically determine the frequency range for positive and negative dielectrophoresis (DEP)—an electrokinetic force that can be used for massively parallel micro- and nano-assembly. An experimental setup consists of the microfabricated chip with gold microelectrode array connected to a function generator capable of digitally controlling an AC signal of 1 V (peak-to-peak) and of various frequencies in the range between 10 kHz and 1 MHz. The suspension of latex microbeads (3-μm diameter) is either attracted or repelled from the microelectrodes under the influence of DEP force as a function of the applied frequency. The video of the bead movement is captured via a digital camera attached to the microscope. The OpenCV software package is used to digitally analyze the images and identify the beads. Positions of the identified beads are compared for successive frames via Artificial Intelligence (AI) algorithm that determines the cloud behavior of the microbeads and algorithmically determines if the beads experience attraction or repulsion from the electrodes. Based on the determined behavior of the beads, algorithm will either increase or decrease the applied frequency and implement the digital command of the function generator that is controlled by the computer. Thus, the operation of the study platform is fully automated. The AI-guided platform has determined that positive DEP (pDEP) is active below 500 kHz frequency, negative DEP (nDEP) is evidenced above 1 MHz frequency and the crossover frequency is between 500 kHz and 1 MHz. These results are in line with previously published experimentally determined frequency-dependent DEP behavior of the latex microbeads. The phenomenological approach assisted by live AI-guided feedback loop described in the present study will assist the active manipulation of the system towards the desired phenomenological outcome such as, for example, collection of the particles at the electrodes, even if, due to the complexity and plurality of the interactive forces, model-based predictions are not available.
The objective of this paper is to theoretically and empirically identify the effects of hedging and systematic fluctuation on banking stability in China. First, theoretical propositions indicate that the impact of credit derivative hedging and systematic fluctuation on banking stability in China is derived on the basis of a newly established theoretical model. Then, empirical research based on one-stage and two-stage GMM methods suggests that ascending hedging degrees leads to a linearly improving condition for banking stability with respect to overnight lending swap hedging, an improving-then-worsening condition for compensation swaps and an improving–worsening–improving condition for deposit swap hedging; at the same time, the ascending level of systematic fluctuation associated with hedging improves banking stability. Moreover, the trade-off between loan expansion and the stability maintenance of banking sectors can be managed by hedging compensation swaps and overnight lending swaps. In general, the empirical results support the applicability of the theoretical model, and the hedging of certain swaps can be used as a tool for stability maintenance purposes.
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