A highly sensitive film bulk acoustic resonator (FBAR) mass sensor in liquid environments is described in this paper. A transmission line model is used to theoretically predict the dependence of the FBAR's resonant frequency on added mass. FBAR performance in a liquid environment is experimentally characterized for the first time and the effects of the liquid nature and conductivity on the FBAR series and parallel resonant frequencies are investigated. A TiO2-coated FBAR is developed for sensitive mass sensing of metal ions in a liquid environment.

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This study aims to explore how cross-border e-commerce (CBEC) enterprises achieve a desired balance state of supply chain resilience and vulnerability. This study proposes the concept of the balance state of supply chain resilience and vulnerability, which refers to the proximity of the current balance state of the supply chain to the ideal one and the anti-ideal one. To evaluate the balance state, a model is constructed using the integrating fuzzy Analytic Hierarchy Process (AHP) and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which holistically consider the drivers of the resilience and vulnerability of the cross-border e-commerce supply chain (CBECSC). An empirical study is included in this paper to demonstrate how the proposed model works. The empirical results suggest that the CBECSC should maintain resilience and vulnerability in an appropriate balance state instead of pursuing either (high) resilience or (low) vulnerability without considering the other.

This paper studies a repeated game between a manufacturer and two competing suppliers with imperfect monitoring. We present a principal-agent model for managing long-term supplier relationships using a unique form of measurement and incentive scheme. We measure a supplier's overall performance with a rating equivalent to its continuation utility (the expected total discounted utility of its future payoffs), and incentivize supplier effort with larger allocations of future business. We obtain the vector of the two suppliers' ratings as the state of a Markov decision process, and solve an infinite horizon contracting problem in which the manufacturer allocates business volume between the two suppliers and updates their ratings dynamically based on their current ratings and the current performance outcome.Our contributions are both theoretical and managerial: We propose a repeated principal-agent model with a novel incentive scheme to tackle a common, but challenging incentive problem in a multi-period supply chain setting. Assuming binary effort choices and performance outcomes by the suppliers, we characterize the structure of the optimal contract through a novel fixed-point analysis.Our results provide a theoretical foundation for the emergence of "business-as-usual" (low effort) trapping states and tournament competition (high effort) recurrent states as the long-run incentive drivers for motivating critical suppliers.

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Task selection (picking an appropriate labeling task) and worker selection (assigning the labeling task to a suitable worker) are two major challenges in task assignment for crowdsourcing. Recently, worker selection has been successfully addressed by the bandit-based task assignment (BBTA) method, while task selection has not been thoroughly investigated yet. In this paper, we experimentally compare several task selection strategies borrowed from active learning literature, and show that the least confidence strategy significantly improves the performance of task assignment in crowdsourcing.

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