The main purpose is to slow the spread, so that health care infrastructure can keep up with the demand. The quality of care also improves over time, since health practitioners learn more and more about how best to manage the disease. (In the extreme case, if we can slow the spread enough then some people will get the vaccine before getting the real virus.)

This visualizes this in graphical form.

There's a difference between "works" and "works well". I was recently scheduled to teach a 2-day short course recently; the meeting was cancelled (due to COVID19) so we switched to giving the lectures through video-conferencing and doing Q&A using a chat channel. It worked okay, but was not nearly as engaging as an in-person meeting. When courses are run well, the back-and-forth between instructor and students helps make the content more relevant and memorable. (E.g. the instructor can read body language and know when a concept needs to be re-explained.)

Overall, there are certainly lessons to be learned in terms of leveraging online education models to improve efficiency. And I'm not defending the dated "professor droning in front of bored students" teaching model, which could indeed be improved in numerous ways (including by leveraging online components). However currently there is no online experience that can replicate the advantages of in-person discussion, and thus a purely online course will not be as effective as a properly run in-person lecture+discussion.

The base-pair sequence of DNA determines its biological function. As you say, this sequence determines what kinds of proteins get made, including their exact shape (and more broadly how they behave).

But TFA is talking about the conformation (shape) of the DNA strand itself, not the protein structures that the DNA strand is used to make.

In living organisms, the long DNA molecule always forms a double-helix, irrespective of the base-pair sequence within the DNA. DNA double helices do actually twist and wrap into larger-scale structures: specifically by wrapping around histones, and then twisting into larger helices that eventually form chromosomes. There are hints that the DNA sequence itself is actually important in controlling how this twisting/packing happens (with ongoing research about how (innapropriately-named) "junk DNA" plays a crucial role). However, despite this influence between sequence and super-structure, DNA strands essentially are just forming double-helices at the lowest level: i.e. two complementary DNA strands are pairing up to make a really-long double-helix.

What TFA is talking about is a field called "DNA nanotechnology", where researchers synthesize non-natural DNA sequences. If cleverly designed, these sequences will, when they do their usual base-pairing, form a structure more complex than the traditional "really-long double-helix". The structures that are designed do not occur naturally. People have created some really complex structures, made entirely using DNA. Again, these are structures made out of DNA (not structures that DNA generates). You can see some examples by searching for "DNA origami". E.g. one of the famous structures was to create a nano-sized smiley face; others have 3D geometric shapes, nano-boxes and bottles, gear-like constructs, and all kinds of other things.

The 'trick' is to violate the assumptions of DNA base-pairing that occur in nature. In living cells, DNA sequences are created as two long complementary strands, which pair up with each other. The idea in DNA nanotechnology is to create an assortment of strands. None of the strands are perfectly complementary to each other, but 'sub-regions' of some strands are complementary to 'sub-regions' on other strands. As they start pairing-up with each other, this creates cross-connections between all the various strands. The end result (if your design is done correctly) is that the strands spontaneously form a ver well-defined 3D structure, with nanoscale precision. The advantage of this "self-assembly" is that you get billions of copies of the intended structure forming spontaneously and rapidly. Very cool stuff.

This kind of thing has been ongoing since 2006 at least. TFA erroneously implies that this most recent publication invented the field. Actually, this most recent publication is some nice work about how the design process can be made more robust (and software-automated). So, it's a fine paper, but certainly not the first demonstration of artificial 3D DNA nano-objects.

Human sorting tends to be rather ad-hoc, and this isn't necessarily a bad thing. Yes, if someone is sorting a large number of objects/papers according to a simple criterion, then they are likely to be implementing a version of some sort of formal searching algorithm... But one of the interesting things about a human sorting things is that they can, and do, leverage some of their intellect to improve the sorting. Examples:
1. Change sorting algorithm partway through, or use different algorithms on different subsets of the task. E.g. if you are sorting documents in a random order and suddenly notice a run that are all roughly in order, you'll intuitively switch to a different algorithm for that bunch. In fact, humans very often sub-divide the problem at large into stacks, and sub-sort each stack using a different algorithm, before finally combining the result. This is also relevant since sometimes you actually need to change your sorting target halfway through a sort (when you discover a new category of document/item; or when you realize that a different sorting order will ultimately be more useful for the high-level purpose you're trying to achieve; ...).
2. Pattern matching. Humans are good at discerning patterns. So we may notice that the documents are not really random, but have some inherent order (e.g. the stack is somewhat temporally ordered, but items for each given day are reversed or semi-random). We can exploit this to minimizing the sorting effort.
3. Memory. Even though humans can't juggle too many different items in their head at once, we're smart enough that we encounter an item, we can recall having seen similar items. Our visual memory also allows us to home-in on the right part of a semi-sorted stack in order to group like items.

The end result is a sort that is rather non-deterministic, but ultimately successful. It isn't necessarily optimal for the given problem space, but conversely their human intellect is allowing them to generate lots of shortcuts during the sorting problem. (By which I mean, a machine limited to paper-pushing at human speed, but implementing a single formal algorithm, would take longer to finish the sort... Of course in reality mechanized/computerized sorting is faster because each machine operation is faster than the human equivalent.)

This is a nice hack, but in the end, he just build a receiver for the 2.4Ghz band. Big deal.

There has been a much nicer hack to convert a nRF24L01 into a promiscuous listening device:

http://travisgoodspeed.blogspo...

This achieves a very similar goal, but much cheaper.

> 99% of the computing needs of 99% of the people can be met by the existing

You know, this phrase has been uttered so many times it became completely meaningless. Please define "Computing needs"?

If you had asked someone in the 50ies, they would have told you that the average person needs some help with adding the numbers for checkbook balancing. So a simple calculator should be enough, right? Nobody would have considered that people of 2000ies would deem it a worthwhile endeavour to use processing power that exceeds the computing power of the entire civilization in the 50ies by orders of magnitudes, simply for gaming.

Today people use a different frame of reference based on the applications they know today. The mistake is still the same.

Why do we need more of moores law?
- Right now everything is about the internet of things. Moores law is not only about transistor density, it is also about power. We need extremely low power computing. The trillion sensor revolution is not a joke. It is happening right now.
- We are still orders of magnitude away in computing power from anything required to make truely intelligent system. There are huge research projects right now, pushing the understanding of the human brain (the EU human brain project for example). If you want your fridge to be as intelligent as a dog, you may want it to have more computing power than your current pc or smartphone.

Please recommend good applications. I bought a Nexus 5, because I recently dropped my previous smartphone. But what do I do with it?

>Every one of the inventions is being pulled forward. It is clear you have no idea what's available out there. Thin film is beginning to dominate commercial installation, >in fact it's so much better that it's very difficult to even purchase thin films any more because all the production is allocated to commercial installations. Other

Bullshit. Most thin film technologies are DOA. There are two technologies that seem to suceed in the market: CdTe and CIGS. However, due to their low conversion efficiency they are only used in big projects. You are not going to see thin film in residential anytime soon. In fact First Solar, thin film market leader, recently acquired a crystalline silicon company to introduce a product in that sector.

>techniques are out there and being used, the better the cell the more likely it'll be relegated to commercial installation. Most of what's available for retail purchase >is the output of older cell lines that are no longer competitive on the commercial side.

It would be nice if market consolidation were drive by technological differentiation, but sadly that is not the case. Older cell lines are only uncompetitive due to bad scaling effects (low throughput, low degree of automation etc.), not due to their cell technology.

>They do, all the time.

In fact, they never do at all! If you look at the market statistics, you will notice that >80% of the market is crystalline silicon. And while there are different ways to manufacture crystalline silicon solar cells, companies have been extremely reluctant to introduce new technologies. In fact, almost all solar cells today are still made with the same manufacturing process steps as 10 years ago. Conversion efficiencies have improved simply by tweaking these process steps.

>Why do you think the cost of solar has decreased by 90% over the last 30 years?

I know why the cost has decreased
- Manufacturing cost reduction by scaling effects
- Very significant cost reduction in raw materials
- Reduction of material consumption by process optimization
- And to a smaller part, improvement of conversion efficiencies by process optimization.

News about surface plasmonic effects, black silicon and the like are surface every other weak. However they have not inched any closer to production than they were 5 years ago.

Photovoltaic modules are a commodity. The technology and science behind it is of limited depth and not comparable to the semiconductor industry. Look elsewhere if you want to innovate in technology.

What is needed is innovation on the system level, products and marketing.
 

Right, I was so appalled that I even overlooked this basic fact:

Why are the no booth babes in the video?

WTF is this shit? I don't even know where to start...

1) Bad audio quality
2) Bad video quality
3) Cat background?
4) Some guy who never in his life got close to a woman who would qualify as a "booth babe" talking about "booth babes"?

This is just pathetic.

Know your limits. Know your audience. Even if Slashdot is dieing, please do it with dignity!

Man I really wish for Slashdot going down with a story about how 2017 is finally the year of Linux on the Desktop.

Some people do not seem to understand the term "quantum efficiency" (QE).

The quantum efficiciency measures the fraction of photons that are actually detected by the camera.
An external quantum efficiency of 50% means that 50% of all incident photons are converted into electron-hole pairs and can be detected.
There are, however, loss mechanisms that prevent all e-h pairs to be collected. But this is not off by a factor of 1000x from the theoretical limit.

As already stated by the original poster. This figure is probebably for some other wavelengths, like far infrared, where silicon is "blind" due to its band gap.
Since humans are very blind to this wavelengths as well, the relevance in the cameras is questionable.

The semiconductor industry is a bit different. No fab is ever build without massive government subsidies. Just google about globalfoundries in new york.

They are still there. As well as several Infineon Fabs in Germany (Dresden, Regensburg, Warstein) and Austria (Villach), a massive fab by Globalfoundries in Dresden and a large fab by Intel in Ireland.

I am pretty sure IBM did not leave due to any reason directly related to the location. Semiconductor fabs can have a relatively short lifetime, depending on the technology. The IBM fab had been in operation for decades, if I am not mistaken.

If you want a leading edge fab, it is quite possible that some technology changes (e.g. wafer size conversion) make it uneconomical to upgrade an existing fab. In that case you need to build a new shell. Locations for new fabs are often significantly influenced by incentive payments from the local government. For example the new globalfoundries fab in new york state got billions of incentive payments. IBM most likely decided to discontinue the site after moving the products to a more modern fab that was build somewhere where they got more money...