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Parenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients' health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial importance. Since most of these drugs appear as turbid liquids, they cannot be easily discriminated simply by means of basic optical analyses. To overcome this limitation, in this work, we demonstrate that the combination of speckle pattern (SP) imaging and artificial intelligence can provide precise classifications of commercial pharmaceutical suspensions for PAN. Towards this aim, we acquired SP images of each sample and extracted several statistical parameters from them. By training two machine learning algorithms (a Random Forest and a Multi-Layer Perceptron Network), we were able to identify the drugs with accurate performances. The novelty of this work lies in the smart combination of SP imaging and machine learning for realizing an optical sensing platform. For the first time, to our knowledge, this approach is exploited to identify PAN drugs.

In fisheries management, ex-ante analysis of fishermen's preferences can provide reliable insights into specific characteristics of regulatory alternatives that are desirable, objectionable, or important, in the judgement of fishermen. This knowledge could facilitate consideration by fishery managers of additional regulatory alternatives with high likelihoods of meeting program objectives, minimal disruption to fishing operations and lifestyles, and high levels of acceptance and compliance from the fishing fleet. In this case study, we interviewed Pacific halibut fishermen (n = 76) in four communities across Southeast Alaska, to document their preferences about different types of data collection methods on their vessels. We demonstrate how to use interviewing to gather preference data from a relatively small group of fishermen and get a reliable snapshot of preferences across an entire region. Pairwise comparisons from interviews were analyzed using a three-stage analytic hierarchy process model. Results characterize the variability of fishermen's preferences about data collection methods., Competing Interests: The authors have declared that no competing interests exist.