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Prior to immobilization of biomolecules or cells onto biosensor surfaces, the surface must be physically or chemically activated for further functionalization. Organosilanes are a versatile option as they facilitate the immobilization through their terminal groups and also display self-assembly. Incorporating hydroxyl groups is one of the important methods for primary immobilization. This can be done, for example, with oxygen plasma treatment. However, this treatment can affect the performance of the biosensors and this effect is not quite well understood for surface functionalization. In this work, the effect of O2 plasma treatment on EIS sensors was investigated by means of electrochemical characterizations: capacitance–voltage (C–V) and constant capacitance (ConCap) measurements. After O2 plasma treatment, the potential of the EIS sensor dramatically shifts to a more negative value. This was successfully reset by using an annealing process.
This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.