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Abstract Digital transformation of manufacturing industry, Smart Manufacturing, leverages continuous measurement of machines on the shop floor to make effective decisions and improve productivity metrics such as machine uptime and overall equipment efficiency (OEE). However, despite the declining sensor cost, the initial financial and technological skill requirements of digital transformation pose significant barriers for the overwhelming majority (90%) of the manufacturers who are classed as small and medium enterprises (SMEs). To lower this barrier, here we demonstrate an inexpensive (~ $40 per machine), data-efficient solution that extracts part-level productivity metrics of a CNC machine from its total current consumption alone. We introduce the concept of a part’s “fingerprint” and develop a set of methods that allows one to extract the fingerprints and utilize them to monitor each individual manufactured part and their cycle times. Testing on actual production data of over 3 three months in a part-counting task, the algorithms show a good match (96.2% overall accuracy) with manually logged production data is achieved. The presented fingerprint framework is general: it can be extended to multi-sensors, and multi-modal analytics. We expect that such a simple, yet cost-effective, solution will be accessible for a wide range of discrete manufacturers, facilitating the beginning of their digital transformation journey. 

Successful rehabilitation of oropharyngeal swallowing disorders (i.e., dysphagia) requires frequent performance of head/neck exercises that primarily rely on expensive biofeedback devices, often only available in large medical centers. This directly affects treatment compliance and outcomes, and highlights the need to develop a portable and inexpensive remote monitoring system for the telerehabilitation of dysphagia. Here, we present the development and preliminarily validation of a skin-mountable sensor patch that can fit on the curvature of the submental (under the chin) area noninvasively and provide simultaneous remote monitoring of muscle activity and laryngeal movement during swallowing tasks and maneuvers. This sensor patch incorporates an optimal design that allows for the accurate recording of submental muscle activity during swallowing and is characterized by ease of use, accessibility, reusability, and cost-effectiveness. Preliminary studies on a patient with Parkinson’s disease and dysphagia, and on a healthy control participant demonstrate the feasibility and effectiveness of this system. 

We present a design strategy for fabricating ultrastable phase-pure films of formamidinium lead iodide (FAPbI₃) by lattice templating using specific two-dimensional (2D) perovskites with FA as the cage cation. When a pure FAPbI₃precursor solution is brought in contact with the 2D perovskite, the black phase forms preferentially at 100°C, much lower than the standard FAPbI₃annealing temperature of 150°C. X-ray diffraction and optical spectroscopy suggest that the resulting FAPbI₃film compresses slightly to acquire the (011) interplanar distances of the 2D perovskite seed. The 2D-templated bulk FAPbI₃films exhibited an efficiency of 24.1% in a p-i-n architecture with 0.5–square centimeter active area and an exceptional durability, retaining 97% of their initial efficiency after 1000 hours under 85°C and maximum power point tracking.