Here, a multichannel organic electrochemical transistor (OECT) array is reported for electrophysiological monitoring and mapping of action potential propagation of a wide range of cardiac cells, including cell lines, primary cell lines, and human‐sourced stem cell derivatives in 2D and 3D structures. The results suggest that the ability to exploit this OECT‐based platform to map 2D action potential propagation provides a viable strategy to better characterize cardiac cells in response to various chronotropic drugs. The effects of chronotropic agents Isoproterenol and Verapamil on cardiac tissues validate the utility of OECT for drug screening capability, and a preliminary demonstration of a 64‐channel OECT array to monitor the cardiac action potentials for better spatial resolution is presented. The study demonstrates that OECT will be a viable and versatile platform for applications in medical and pharmacological industries.

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Artificial Intelligence (AI) / Machine Learning (ML)-based systems are widely sought-after commercial solutions that can automate and augment core business services. Intelligent systems can improve the quality of services offered and support scalability through automation. In this paper we describe our experience in engineering an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error, making models for analysis hard to build. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems. To address these challenges, we experimented with and exploited a number of cloud-based machine learning models and composed them into an application-specific processing pipeline. This design allows for modification of the underlying algorithms as more data and improved techniques become available. We describe our design, and the main challenges we faced, namely keeping a check on the quality control of the models, testing the software and deploying the computationally expensive ML models on the cloud.