The main risks to bees include wide-spread pesticide use in agriculture, parasites, disease and climate change, and crucially - the loss of valuable biodiversity which poses a further threat to bees and other wild pollinators.
One way to help boost their numbers is by planting the correct wildflowers, providing a better habitat for pollinators to disperse, nest and breed.
However, it is unclear which plant species are the most preferred between different pollinators, including bees, and how this might change over time and in different environmental conditions.
Historically, scientists used light microscopy to identify individual bee-collected pollen grains, which was a time-consuming and impractical method.
To obtain a more accurate understanding without the need for laborious manual inspection of pollen, scientists have developed a rapid analysis method called 'Reverse Metagenomics' (RevMet) that can identify the plants that individual bees visit using the MinION, a portable DNA sequencer from Oxford Nanopore Technologies.
The portability of the equipment involved means that this type of analysis could be performed on-site where bees are collected and sampled - vastly increasing our understanding of where bees look for pollen on a national scale.
Thin films made of carbon nanotubes hold a lot of promise for advanced optoelectronics, energy and medicine, however with their manufacturing process subject to close supervision and stringent standardization requirements, they are unlikely to become ubiquitous anytime soon.
"A major hindrance to unlocking the vast potential of nanotubes is their multiphase manufacturing process which is extremely difficult to manage.
We have suggested using artificial neural networks (ANN) to analyze experimental data and predict the efficiency of single-walled carbon nanotubes synthesis," explains one of the authors of the study and Skoltech researcher, Dmitry Krasnikov.
In their work published in the prestigious Carbon journal, the authors show that machine learning methods, and, in particular, ANN trained on experimental parameters, such as temperature, gas pressure and flow rate, can help monitor the properties of the carbon nanotube films produced.
"The development of human civilization and the advancement of the materials manufacturing and application technologies are closely interlinked in the era of information and technology, with both materials and computational algorithms and their applications shaping our day-to-day life.
This is equally true for ANN that have evolved into an indispensable tool for dealing with multi-parameter tasks, which run the gamut from object recognition to medical diagnosis.
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