ACCURATE MACHINE LEARNING-BASED GERMINATION DETECTION, PREDICTION AND QUALITY ASSESSMENT OF THREE GRAIN CROPS

Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

Abstract Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance Algicides of seeds.Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process.Classical image analyses methods are not well suited for large-scale germination experiments

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Unraveling a three-step spatiotemporal mechanism of triggering of receptor-induced Nipah virus fusion and cell entry.

Membrane fusion is essential for entry of the biomedically-important paramyxoviruses into their host cells (viral-cell fusion), and for syncytia formation (cell-cell fusion), often induced by paramyxoviral infections [e.g.those of the Algicides deadly Nipah virus (NiV)].For most paramyxoviruses, membrane fusion requires two viral glycoproteins.Upon

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Design and Development of a Diagnostic System for a Non-Intercepting Direct Measure of the SPIDER Ion Source Beamlet Current

Stable and uniform beams with low divergence are required in particle accelerators; therefore, beyond the accelerated current, measuring the beam current spatial uniformity and stability over time is necessary to assess the beam performance, since these parameters affect the perveance and thus the beam optics.For high-power beams operating with FME

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