This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image‐based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.
Evacuation systems have always played a crucial part when designing a transport system. The cornerstone of these systems is to get people to safety in the quickest and safest way possible. When it comes to marine systems, the requirements greatly differ in comparison to those on land and in air. On a day with highly inclement and fierce weather, in the middle of the ocean, getting the crew to safety through a chute or a slide would expose the crew to ferocious danger. Thence, the proposed Puffle-Pod Evacuation System introduces a more protected and secure evacuation without putting the lives of the crew at a high risk.
In recent years, technology of processors and smaller in size. This to be merged to form a circuit called tween SoC and microcontrollers, microprocessors single chip, doing all the things the are computers just by themselves. Being a computer just by themselves also as a Server-on-Chip (Davis, 2012).
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