Managing the dynamic behavioral changes of a production plant process to keep to a production plan is a challenge and requires the ability to predict the dynamic behavior of processes and alter any controls, as needed, to adhere as closely as possible to the plan. This paper presents a novel solution (called Cognitive Plant Advisor) based on the use of advanced machine learning to learn complex dynamics from sensor data coupled with mathematical programming to optimize the operations of a production plant. The Cognitive Plant Advisor provides set point recommedations for a 12-72 hour horizon to (i) improve throughput, or (ii) provide optimal recovery plan for a disruption. This advisory system has the potential to improve throughput by upto 1% of total production.

High-resolution in vivo microscopy approaches can reveal subtle information and fine details inside the model animalCaenorhabditis elegans(C. elegans) but requires strong animal immobilization. Unfortunately, most current immobilization techniques require substantial manual effort, rendering high-resolution imaging low-throughput. We greatly simplify theC. elegansimmobilization procedure by using a cooling approach that can easily immobilize entire populations ofC. elegansdirectly on their cultivation plates. The cooling stage can establish and maintain a wide range of temperatures on the cultivation plate and ensures even temperature distribution across the agarose surface. In this article, we document the whole process of building the cooling stage from scratch. We envision that a typical researcher can build an operational cooling stage in their lab following our protocol without difficulty. We show how to utilize the cooling stage under three protocols, which have advantages for different experiments. We also show an example cooling profile of the stage as it approaches its final temperature and include some helpful tips in using cooling immobilization.

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