In this paper, a rapid bonding method is reported to fabricate a mixing microfluidic chip. The chip is composed of a glass upper plat and a polyurethane bottom plat. The upper plat with a microchannel is obtained through micromachining technology. And the bottom plat is manufactured via mixing the solution with the component of isocyanic acid and polyether polyol. The influence of the solution’s proportion on the contact angle and bonding force is investigated to enhance the hydrophilicity and the bonding strength. Furthermore, a mixing experiment is implemented to verify the chip’s bonding effect. The experimental results demonstrate that with increasing the proportions of isocyanic acid and polyether polyol, the contact angle and the bonding force increase at first and then decrease gradually. That is attributed to the variation of internal porous structure for different proportions. Considering the contact angle and the bonding force synthetically, the optimal proportion of 3.7 w/w is confirmed. The mixing efficiency is increased from 0.157 to 0.824. Compared with other bonding methods, the method in this paper has the advantages of high efficiency and high bonding strength.
“Customer retention” is an important real-world problem in many sales and services related industries today. This work illustrates how we can integrate the various techniques of data-mining, such as decision-tree induction, deviation analysis and multiple concept-level association rules to form an intuitive and novel approach to gauging customer's loyalty and predicting their likelihood of defection. Immediate action taken against these “early-warnings” is often the key to the eventual retention or loss of the customers involved.
Kernel regression is widely used in biology and economy, because it is more adaptable to complex laws than linear regression, and it has better interpretability than many methods in deep learning. In highdimensional area, l1-norm penalization is a common method for variable selection, which may be derived from the excellent performance of the lasso algorithm. Although it seems natural to generalize from consistency in variable selection to consistency in kernel selection, there are still many details that need to be taken seriously, e.g. the lower eigenvalue condition. The consistence condition of kernel selection in l1-norm regular linear kernel regression is given, including the prediction error. In simulation study, the consistency of different levels of λn and dimension of features is carefully checked. Finally, the kernel selection method was applied to high-risk area exploration of Covid-19, with the dataset provided by the US Centers for Disease Control and Prevention(CDC). Declarations of interest:The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
With the long-term outbreak of the Covid-19 around the world, identifying high-risk areas is becoming a new research boom. In this paper, we propose a novel regression method namely Regular Linear Kernel Regression(RLKR) for Covid-19 high-risk areas Exploration. We explain in detail how the canonical linear kernel regression method is linked to the identification of high-risk areas for Covid-19. Further more, The consistence condition of Kernel Selection, which is closely related to the identification of high-risk areas, is given with two mild assumptions. Finally, the RLKR method was verified by simulation experiments and applied for Covid-19 high-risk area Exploration.
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