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A desktop NMR spectrometer was used to qualitatively analyze samples in drug-related cases in order to enhance the accuracy of the results and identify new drugs. Twelve known drugs and their derivatives were used to establish the parameters, conditions, and procedures for the methods and validate the feasibility and reliability of the methods. First, 1-D and 2-D NMR data for these 12 drugs and their derivatives were obtained in detail using a 600-MHz NMR spectrometer to create a data library. Next, some of these 12 drugs were analyzed using a Picospin 80 MHz desktop NMR spectrometer to set up the analytical procedure and method. With the procedure and method established, real case samples were analyzed and the data were compared to those obtained by a standard method. The results indicate that the desktop NMR spectrometer is a reliable and promising approach that can be used in criminology to quickly identify whether or not samples contain illegal drugs.

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This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice.