Mixing in microfluidic devices presents a challenge because of the laminar flow in microchannels. Here, we present an oscillatory-flow-based microfluidic mixer (OFBMM) in which fluids are subjected to a back-and-forth motion by utilizing a periodically changed pressure and an on-chip gas bubble. We developed a low-cost Arduino-based pneumatic system to apply pressure and an OpenCV-based image-processing algorithm to quantitatively analyze the mixing effect. We found that the oscillatory motion of fluids is similar to the charging and discharging process of an RC circuit used in electronics. We investigated the mixing effect by varying the oscillation frequencies, initial bubble volumes, and applied pressures. The results indicated that the increase in both the applied pressure and the initial volume of the gas bubble could promote fluid mixing. A large-scale serial dilution microfluidic chip based on OFBMM was also developed to demonstrate its biochemical potential. We believe that OFBMM will provide a new choice for mixing two or more solutions in a valves-integrated microfluidic system.

Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

Lightweight, flexible and distributed-pixel piezoelectric sensors are desired in activity monitoring and human–machine interaction (HMI). In this work, a flexible P(VDF-TrFE) piezoelectric sensor array using ITO-coated PET substrate as the shared bottom electrode is demonstrated. The traditional array fabrication, which connects an individual sensor unit into an array, could easily lead to the signal discrepancy due to fabrication and assembly errors. To this end, this work introduces the shared ITO-coated-PET substrate and proposes a synchronous-fabrication method for generating the same thickness of every P(VDF-TrFE) sensor unit through a single spin coating. The designed Au top electrodes were sputtered on the spin-coated P(VDF-TrFE) to form the sensor array at one time without additional assembly step, further ensuring unit consistency. The performance of the cross-shaped sensor array was tested under cyclic compressing–releasing agitation. The results of the positive compression test show that our sensor array has a high consistency. Then, the cross-shaped array design that covers the central position is put forward, which realizes tactile sensing ability with a small number of units. Moreover, the fabricated flexible multi-pixel sensor has the advantage of sensitive identification of different contact scenes, and a recognition accuracy of 95.5% can be obtained in different types of hand touch through the machine learning technology.