The Power of STEM Toys: A Guide to Engaging 10-Year-Olds in Science, Technology, Engineering, and Math

Refund policyOur policy lasts 3 days.

If 3 days have gone by since your purchase, unfortunately we can’t offer you a refund or exchange.To be eligible for a return, your item must be unused and in the same condition that you received it.

It must also be in the original packaging.To complete your return, we require a receipt or proof of purchase.Please do not send your purchase back to the manufacturer.Refunds (if applicable)Once your return is received and inspected, we will send you an email to notify you that we have received your returned item.

We will also notify you of the approval or rejection of your refund.If you are approved, then your refund will be processed, and a credit will automatically be applied to your credit card or original method of payment, within a certain amount of days.Late or missing refunds (if applicable)If you haven’t received a refund yet, first check your bank account again.Then contact your credit card company, it may take some time before your refund is officially posted.Next contact your bank.

There is often some processing time before a refund is posted.If you’ve done all of this and you still have not received your refund yet, please contact us at [email protected] items (if applicable)Only regular priced items may be refunded, unfortunately sale items cannot be refunded.

Exchanges (if applicable)We only replace items if they are defective or damaged.

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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A majority of people are interested in choosing B.Tech in Computer Science as a professional degree course.

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