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In nanoscale devices, bio-memristor has attracted more and more attention as a promising candidate due to its simple structure, fast switching speed, and low power consumption. However, the randomness of the growth and breakage of the conductive filaments will result into dispersed distribution of the switching voltage, and the performance of the memristor will be weakened. Here, we have improved the performance of Ag-doped bovine serum albumin (BSA)-based device by inserting one ZrO2 layer, such as reduced switching voltage, uniform distribution of set and reset voltage, robust retention, fast switching speed, and low switching power. Bendable memristors were fabricated on polydimethylsiloxane (PDMS) flexible substrate, which can be bent more than 500 times, and the electrical characteristics of different bending degrees have been compared. Furthermore, the device can degrade after 36 h in deionized (DI) aqueous solution. The overall performance of the memristor with the Ag-doped BSA device by inserted oxide ZrO2 layer has the potential to open up a new way to improve the reliability of oxides-based memristor and lays the foundation for flexible artificial synapses. [ABSTRACT FROM AUTHOR]

This article considers the problem of modelling the lead-time demand for multiple slow-moving inventory items in the case when the available demand history is very short, and a large percentage of items has only zero records. The Bayesian approach is used to overcome these problems with the past demand data: it is proposed to use the beta-binomial model to predict the lead-time demand probability distribution for each item. Further, an extension of this model is developed which incorporates the prior information regarding the maximum expected probability of demand per period. Parameter estimation and Bayesian forecasting routines are derived for the new model. The performance of the proposed techniques is evaluated by a simulation study. [ABSTRACT FROM AUTHOR]