An anonymous reader quotes a report from Ars Technica: Bitcoin's massive power consumption is the cryptocurrency's dirty secret. To mine bitcoin, computers across the globe chew through enough electricity to power a medium size country, somewhere on the order of the Netherlands or Poland depending on the estimate. In fact, electricity has become such a significant factor that one private equity firm owns a power plant to mine bitcoin. The company, Greenidge Generation, said at one point that they could mine one bitcoin for less than $3,000. Even today -- at $40,000 per bitcoin, some 30 percent off its peak -- the potential for profit is real. Which is why an investor-owned utility has dropped a containerized data center outside a coal-fired power plant 10 miles north of St. Louis. Ameren, the utility, was struggling to keep the 1,099 MW power plant running profitably when wholesale electricity prices dropped. But it wasn't well suited to running only when demand was high, so-called peaker duty. Instead, they're experimenting with running it full-time and using the excess electricity to mine bitcoin.

Ameren executives reportedly blame wind and solar power for the load variability that taxes the 55-year-old power plant. The utility claims that mining bitcoin could reduce its carbon footprint by allowing it to run its plants more consistently rather than ramping them up and down, which they say can increase emissions. "We have pretty dramatic changes in load minute by minute, second by second at times," Warren Wood, the utility's vice president of regulatory and legislative affairs, told E&E News. But when it's running full-time, they only have to take power away from the mining operations. Wood said it takes about 20 seconds to divert power back to the grid.

Ameren attempted to get rate payers to foot a portion of the bill for its experiment, but Missouri's consumer advocate pushed back. "If Ameren Missouri wants to enter into speculative commodities, like virtual currencies, then it should do so as a non-regulated service where ratepayers are unexposed to the economics of them," Geoff Marke, chief economist for the Missouri Office of the Public Counsel, wrote in a filing. "This endeavor is beyond the scope of intended electric utility regulation, and, if allowed, creates a slippery slope where ratepayers could be asked to put up capital for virtually anything." The utility says that if its bitcoin experiment pans out, it could attach similar containerized data centers to wind and solar farms to soak up excess electricity profitably in times of high supply or low demand. The coal-fired power plant that's being used in the experiment is scheduled to be shut down in 2028. Ameren says that so far it's pleased with the project, which has mined 20 coins and mints a new one at a rate of one every 15 days or so. Whether the math continues to work depends largely on the cost of running the plant and the price of bitcoin, which is highly volatile. Based on today's prices, the company has made about $800,000 since it switched on the miners in April.

Americans are habitually unattuned to the massive and profoundly human apparatus that brings us basically everything in our lives. Much of the country's pandemic response has treated us as somehow separate from the rest of the world and the challenges it endures, but unpredictably empty shelves, rising prices, and long waits are just more proof of how foolish that belief has always been. The Atlantic: When I called up Dan Hearsch, a managing director at the consulting firm AlixPartners who specializes in supply-chain management, I described the current state of the industry to him as a little wonky. He laughed. "'A little wonky' is one way to say it," he said. "'Everything's broken' is another way." Hearsch told me about a friend whose company imports consumer goods -- stuff that's normally available in abundance at any Walmart or Target -- from China. Before the pandemic, according to the friend, shipping a container of that merchandise to the U.S. would have cost the company $2,000 to $5,000. Recently, though, the number is more like $30,000, at least for anything shipped on a predictable timeline. You can get it down to $20,000 if you're willing to deal with the possibility of your stuff arriving in a few months, or whenever space on a ship eventually opens up that's not already accounted for by companies willing to pay more.

Such severe price hikes aren't supposed to happen. Wealthy Western countries offloaded much of their manufacturing to Asia and Latin America precisely because container shipping has made moving goods between hemispheres so inexpensive. When that math tips into unprofitability, either companies stop shipping goods and wait for better rates, or they start charging you a lot more for the things they ship. Both options constrain supply further and raise prices on what's available. "You look at the price of cars, you look at the price of food -- the price of practically anything is up significantly from one year ago, from two years ago," Hearsch told me. "The differences are really, really quite shocking." The Bureau of Labor Statistics estimates that as of July, consumer prices had grown almost 5 percent since before the pandemic, with some types of goods showing much larger increases.

Overseas shipping is currently slow and expensive for lots of very complicated reasons and one big, important, relatively uncomplicated one: The countries trying to meet the huge demands of wealthy markets such as the United States are also trying to prevent mass-casualty events. Infection-prevention measures have recently closed high-volume shipping ports in China, the country that supplies the largest share of goods imported to the United States. In Vietnam and Malaysia, where workers churn out products as varied as a third of all shoes imported to the U.S. and chip components that are crucial to auto manufacturing, controlling the far more transmissible [...] Domestically, things aren't a whole lot better. Offshoring has systematically decimated America's capacity to manufacture most things at home, and even products that are made in the United States likely use at least some raw materials or components that need to be imported or are in short supply for other reasons.

An anonymous reader quotes a report from Phys.Org: Researchers have developed a mathematical model that can predict the optimum exercise regime for building muscle. The researchers, from the University of Cambridge, used methods of theoretical biophysics to construct the model, which can tell how much a specific amount of exertion will cause a muscle to grow and how long it will take. The model could form the basis of a software product, where users could optimize their exercise regimes by entering a few details of their individual physiology. The results, reported in the Biophysical Journal, suggest that there is an optimal weight at which to do resistance training for each person and each muscle growth target. Muscles can only be near their maximal load for a very short time, and it is the load integrated over time which activates the cell signaling pathway that leads to synthesis of new muscle proteins. But below a certain value, the load is insufficient to cause much signaling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely to depend on the particular physiology of the individual.

In 2018, the Cambridge researchers started a project on how the proteins in muscle filaments change under force. They found that main muscle constituents, actin and myosin, lack binding sites for signaling molecules, so it had to be the third-most abundant muscle component -- titin -- that was responsible for signaling the changes in applied force. Whenever part of a molecule is under tension for a sufficiently long time, it toggles into a different state, exposing a previously hidden region. If this region can then bind to a small molecule involved in cell signaling, it activates that molecule, generating a chemical signal chain. Titin is a giant protein, a large part of which is extended when a muscle is stretched, but a small part of the molecule is also under tension during muscle contraction. This part of titin contains the so-called titin kinase domain, which is the one that generates the chemical signal that affects muscle growth. The molecule will be more likely to open if it is under more force, or when kept under the same force for longer. Both conditions will increase the number of activated signaling molecules. These molecules then induce the synthesis of more messenger RNA, leading to production of new muscle proteins, and the cross-section of the muscle cell increases.

This realization led to the current work. [The researchers] set out to constrict a mathematical model that could give quantitative predictions on muscle growth. They started with a simple model that kept track of titin molecules opening under force and starting the signaling cascade. They used microscopy data to determine the force-dependent probability that a titin kinase unit would open or close under force and activate a signaling molecule. They then made the model more complex by including additional information, such as metabolic energy exchange, as well as repetition length and recovery. The model was validated using past long-term studies on muscle hypertrophy. "Our model offers a physiological basis for the idea that muscle growth mainly occurs at 70% of the maximum load, which is the idea behind resistance training," said [one of the paper's authors]. "Below that, the opening rate of titin kinase drops precipitously and precludes mechanosensitive signaling from taking place. Above that, rapid exhaustion prevents a good outcome, which our model has quantitatively predicted." [...] The model also addresses the problem of muscle atrophy, which occurs during long periods of bed rest or for astronauts in microgravity, showing both how long can a muscle afford to remain inactive before starting to deteriorate, and what the optimal recovery regime could be.

Long-time Slashdot reader Pieroxy is working on a new open source project, a web-based version of the system-monitoring software Conky.

The ultimate goal is send the data to an HTML interface "to find some use for the old iPads/tablets/laptops we all have lying around. You can put them next to your screen and have your metrics displayed there...!"

There's just one problem: "I had to come up with a way for users to format a number." I needed a small string the user could write to describe exactly what they want to do with their number. Some examples can be: write it as a 3-digit number suffixed by SI prefixes when the numbers are too big or too small, display a timestamp as HH:MM string, or just the day of week, eventually cut to the first three characters, do the same with a timestamp in milliseconds, or nanoseconds, display a nice string out of a number of seconds to express a duration ("3h 12mn 17s"), pad the number with spaces so that all numbers are aligned (left or right), force a fixed number of digits after the decimal point, etc.

In other words, I was looking for a "universal" way of formatting numbers and failed to find any kind of standard online.

Do Slashdot readers know of such a thing or should I create my own?

An anonymous reader quotes a report from Oregon Live: For the next five years, an Oregon high school diploma will be no guarantee that the student who earned it can read, write or do math at a high school level. Gov. Kate Brown had demurred earlier this summer regarding whether she supported the plan passed by the Legislature to drop the requirement that students demonstrate they have achieved those essential skills. But on July 14, the governor signed Senate Bill 744 into law. Through a spokesperson, the governor declined again Friday to comment on the law and why she supported suspending the proficiency requirements. Charles Boyle, the governor's deputy communications director, said the governor's staff notified legislative staff the same day the governor signed the bill.

Boyle said in an emailed statement that suspending the reading, writing and math proficiency requirements while the state develops new graduation standards will benefit "Oregon's Black, Latino, Latina, Latinx, Indigenous, Asian, Pacific Islander, Tribal, and students of color." "Leaders from those communities have advocated time and again for equitable graduation standards, along with expanded learning opportunities and supports," Boyle wrote. The requirement that students demonstrate freshman- to sophomore-level skills in reading, writing and, particularly, math led many high schools to create workshop-style courses to help students strengthen their skills and create evidence of mastery. Most of those courses have been discontinued since the skills requirement was paused during the pandemic before lawmakers killed it entirely. The state's four-year graduation rate is 82.6%, up more than 10 points from six years ago. However, it still lags behind the national graduation rate averages, which is 85 percent.

Oregon's graduation rates currently rank nearly last in the country. But it's complicated because states use different methodologies to calculate their graduation rates, making some states appear better than others.

"Dallas-based Texas Instruments' latest generation of calculators is getting a modern-day update with the addition of programming language Python," reports the Dallas Morning News: The goal is to expand students' ability to explore science, technology, engineering and math through the device that's all-but-required in the nation's high schools and colleges...

Though most of the company's $14 billion in annual revenue comes from semiconductors, its graphing calculator remains its most recognized consumer product. This latest TI-84 model, priced between $120 to $160 depending on the retailer, was made to accommodate the increasing importance of programming in the modern world.
Judging by photos in their press release, an "alpha" key maps the calculator's keys to the letters of the alphabet (indicated with yellow letters above each key). One page on its web site also mentions "Menu selections" that "help students with discovery and syntax." (And the site confirms the calculator will "display expressions, symbols and fractions just as you write them.")

There's even a file manager that "gives quick access to Python programs you have saved on your calculator. From here, you can create, edit, run and manage your files." And one page also mentions something called TI Connect CE software application, which "connects your computer and graphing calculator so they can talk to each other. Use it to transfer data, update your operating system, download calculator software applications or take screenshots of your graphing calculator."

I'm sure Slashdot's readers have some fond memories of their first calculator. But these new models have a full-color screen and a rechargeable battery that can last up to a month on a single charge. And Texas Instruments seems to think they could even replace computers in the classroom. "By adding Python to the calculators many students are already familiar with and use in class, we are making programming more accessible and approachable for all students," their press release argues, "eliminating the need for teachers to reserve separate computer labs to teach these important skills.

storagedude writes: Alan Paller, founder of the cybersecurity training SANS Technology Institute, has launched an initiative aimed at finding and developing cybersecurity talent at the community college and high school level — through a game developed by their CTO James Lyne. A similar game was already the basis of a UK government program that has reached 250,000 students, and Paller hopes the U.S. will adopt a similar model to help ease the chronic shortage of cybersecurity talent. And Paller's own Cyber Talent Institute (or CTI) has already reached 29,000 students, largely through state-level partnerships.

But playing the game isn't the same as becoming a career-ready cybersecurity pro. By tapping high schools and community colleges, the group hopes to "discover and train a diverse new generation of 25,000 cyber stars by the year 2025," Paller told eSecurity Planet. "SANS is an organization that finds people who are already in the field and makes them better. What CTI is doing is going down a step in the pipeline, to the students, to find the talent earlier, so that we don't lose them. Because the way the education system works, only a few people seem to go into cybersecurity. We wanted to change that.

"You did an article earlier this month about looking in different places for talent, looking for people who are already working. That's the purpose of CTI. To reach out to students. It's to go beyond the pipeline that we automatically come into cybersecurity through math, computer science, and networking and open the funnel much wider. Find people who have not already found technology, but who have three characteristics that seem to make superstars — tenacity, curiosity, and love of learning new things. They don't mind being faced with new problems. They like them. And what the game does is find those people. So CTI is just moving to earlier in the pipeline."

Long-time Slashdot reader fahrbot-bot quotes LiveScience's exploration of the math: To start out 'small,' there are around 7 octillion, or 7x10^27 (7 followed by 27 zeros), atoms in an average human body, according to The Guardian. Given this vast sum of atoms in one person alone, you might think it would be impossible to determine how many atoms are in the entire universe. And you'd be right: Because we have no idea how large the entire universe really is, we can't find out how many atoms are within it.

However, it is possible to work out roughly how many atoms are in the observable universe — the part of the universe that we can see and study — using some cosmological assumptions and a bit of math.

[...]

Doing the math

To work out the number of atoms in the observable universe, we need to know its mass, which means we have to find out how many stars there are. There are around 10^11 to 10^12 galaxies in the observable universe, and each galaxy contains between 10^11 and 10^12 stars, according to the European Space Agency. This gives us somewhere between 10^22 and 10^24 stars. For the purposes of this calculation, we can say that there are 10^23 stars in the observable universe. Of course, this is just a best guess; galaxies can range in size and number of stars, but because we can't count them individually, this will have to do for now.

On average, a star weighs around 2.2x10^32 pounds (10^32 kilograms), according to Science ABC, which means that the mass of the universe is around 2.2x10^55 pounds (10^55 kilograms). Now that we know the mass, or amount of matter, we need to see how many atoms fit into it. On average, each gram of matter has around 10^24 protons, according to Fermilab, a national laboratory for particle physics in Illinois. That means it is the same as the number of hydrogen atoms, because each hydrogen atom has only one proton (hence why we made the earlier assumption about hydrogen atoms).

This gives us 10^82 atoms in the observable universe. To put that into context, that is 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 atoms.

This number is only a rough guess, based on a number of approximations and assumptions. But given our current understanding of the observable universe, it is unlikely to be too far off the mark.

The New York Times tells the story of Fayçal Ziraoui, a 38-year-old French-Moroccan business consultant who "caused an online uproar" after saying he'd cracked the last two unsolved ciphers of the four attributed to the Zodiac killer in California "and identified him, potentially ending a 50-year-old quest." Maybe because he said he cracked them in just two weeks. Many Zodiac enthusiasts consider the remaining ciphers — Z32 and Z13 — unsolvable because they are too short to determine the encryption key. An untold number of solutions could work, they say, rendering verification nearly impossible.

But Mr. Ziraoui said he had a sudden thought. The code-crackers who had solved the [earlier] 340-character cipher in December had been able to do so by identifying the encryption key, which they had put into the public domain when announcing their breakthrough. What if the killer used that same encryption key for the two remaining ciphers? So he said he applied it to the 32-character cipher, which the killer had included in a letter as the key to the location of a bomb set to go off at a school in the fall of 1970. (It never did, even though police failed to crack the code.) That produced a sequence of random letters from the alphabet. Mr. Ziraoui said he then worked through a half-dozen steps including letter-to-number substitutions, identifying coordinates in numbers and using a code-breaking program he created to crunch jumbles of letters into coherent words...

After two weeks of intense code-cracking, he deciphered the sentence, "LABOR DAY FIND 45.069 NORT 58.719 WEST." The message referred to coordinates based on the earth's magnetic field, not the more familiar geographic coordinates. The sequence zeroed in on a location near a school in South Lake Tahoe, a city in California referred to in another postcard believed to have been sent by the Zodiac killer in 1971.

An excited Mr. Ziraoui said he immediately turned to Z13, which supposedly revealed the killer's name, using the same encryption key and various cipher-cracking techniques. [The mostly un-coded letter includes a sentence which says "My name is _____," followed by a 13-character cipher.] After about an hour, Mr. Ziraoui said he came up with "KAYR," which he realized resembled the last name of Lawrence Kaye, a salesman and career criminal living in South Lake Tahoe who had been a suspect in the case. Mr. Kaye, who also used the pseudonym Kane, died in 2010.

The typo was similar to ones found in previous ciphers, he noticed, likely errors made by the killer when encoding the message. The result that was so close to Mr. Kaye's name and the South Lake Tahoe location were too much to be a coincidence, he thought. Mr. Kaye had been the subject of a report by Harvey Hines, a now-deceased police detective, who was convinced he was the Zodiac killer but was unable to convince his superiors. Around 2 a.m. on Jan. 3, an exhausted but elated Mr. Ziraoui posted a message entitled "Z13 — My Name is KAYE" on a 50,000-member Reddit forum dedicated to the Zodiac Killer.

The message was deleted within 30 minutes.

"Sorry, I've removed this one as part of a sort of general policy against Z13 solution posts," the forum's moderator wrote, arguing that the cipher was too short to be solvable.

Proof-assistant software handles an abstract concept at the cutting edge of research, revealing a bigger role for software in mathematics. From a report: Mathematicians have long used computers to do numerical calculations or manipulate complex formulas. In some cases, they have proved major results by making computers do massive amounts of repetitive work -- the most famous being a proof in the 1970s that any map can be coloured with just four different colours, and without filling any two adjacent countries with the same colour. But systems known as proof assistants go deeper. The user enters statements into the system to teach it the definition of a mathematical concept -- an object -- based on simpler objects that the machine already knows about.

A statement can also just refer to known objects, and the proof assistant will answer whether the fact is 'obviously' true or false based on its current knowledge. If the answer is not obvious, the user has to enter more details. Proof assistants thus force the user to lay out the logic of their arguments in a rigorous way, and they fill in simpler steps that human mathematicians had consciously or unconsciously skipped. Once researchers have done the hard work of translating a set of mathematical concepts into a proof assistant, the program generates a library of computer code that can be built on by other researchers and used to define higher-level mathematical objects. In this way, proof assistants can help to verify mathematical proofs that would otherwise be time-consuming and difficult, perhaps even practically impossible, for a human to check. Proof assistants have long had their fans, but this is the first time that they had a major role at the cutting edge of a field, says Kevin Buzzard, a mathematician at Imperial College London who was part of a collaboration that checked Scholze and Clausen's result. "The big remaining question was: can they handle complex mathematics?" says Buzzard. "We showed that they can."

Pie charts and scatter plots seem like ordinary tools, but they revolutionized the way we solve problems. From a report: John Carter has only an hour to decide. The most important auto race of the season is looming; it will be broadcast live on national television and could bring major prize money. If his team wins, it will get a sponsorship deal and a chance to start making some real profits for a change. There's just one problem. In seven of the past twenty-four races, the engine in the Carter Racing car has blown out. An engine failure live on TV will jeopardize sponsorships -- and the driver's life. But withdrawing has consequences, too. The wasted entry fee means finishing the season in debt, and the team won't be happy about the missed opportunity for glory. As Burns's First Law of Racing says, "Nobody ever won a race sitting in the pits."

One of the engine mechanics has a hunch about what's causing the blowouts. He thinks that the engine's head gasket might be breaking in cooler weather. To help Carter decide what to do, a graph is devised that shows the conditions during each of the blowouts: the outdoor temperature at the time of the race plotted against the number of breaks in the head gasket. The dots are scattered into a sort of crooked smile across a range of temperatures from about fifty-five degrees to seventy-five degrees. The upcoming race is forecast to be especially cold, just forty degrees, well below anything the cars have experienced before. So: race or withdraw?

This case study, based on real data, and devised by a pair of clever business professors, has been shown to students around the world for more than three decades. Most groups presented with the Carter Racing story look at the scattered dots on the graph and decide that the relationship between temperature and engine failure is inconclusive. Almost everyone chooses to race. Almost no one looks at that chart and asks to see the seventeen missing data points -- the data from those races which did not end in engine failure.

Jeff Bezos will go to space next month when his company, Blue Origin, launches its first passenger-carrying mission. From a report: The 57-year-old, who plans to travel alongside his brother, Mark, made the announcement in an Instagram post Monday. The scheduled launch next month will be about two weeks after the billionaire plans to step down as chief executive officer of Amazon.com. "Ever since I was five years old, I've dreamed of traveling to space," Bezos said in the post. "On July 20th, I will take that journey with my brother. The greatest adventure, with my best friend."

Blue Origin is one of several high-profile space-tourism companies backed by a wealthy entrepreneur, alongside Elon Musk's Space Exploration Technologies and Richard Branson-backed Virgin Galactic Holdings. Both of those companies are making plans to carry paying customers. Blue Origin is auctioning off a seat on its New Shepard rocket for the July 20 flight, an 11-minute trip to suborbital space that will reach an altitude of about 100 kilometers (62 miles). The spot will be the only one available for purchase on the flight, and the proceeds will go to a Blue Origin foundation that promotes math and science education.

A plan to reimagine math instruction for 6 million California students has become ensnared in equity and fairness issues -- with critics saying proposed guidelines will hold back gifted students and supporters saying it will, over time, give all kindergartners through 12th-graders a better chance to excel. From a report: The proposed new guidelines aim to accelerate achievement while making mathematical understanding more accessible and valuable to as many students as possible, including those shut out from high-level math in the past because they had been "tracked" in lower level classes. The guidelines call on educators generally to keep all students in the same courses until their junior year in high school, when they can choose advanced subjects, including calculus, statistics and other forms of data science.

Although still a draft, the Mathematics Framework achieved a milestone Wednesday, earning approval from the state's Instructional Quality Commission. The members of that body moved the framework along, approving numerous recommendations that a writing team is expected to incorporate. The commission told writers to remove a document that had become a point of contention for critics. It described its goals as calling out systemic racism in mathematics, while helping educators create more inclusive, successful classrooms. Critics said it needlessly injected race into the study of math. The state Board of Education is scheduled to have the final say in November.

Slashdot reader 4wdloop shared this report from NVIDIA's blog, joking that maybe this is where all NVIDIA's chips are going: It will help piece together a 3D map of the universe, probe subatomic interactions for green energy sources and much more. Perlmutter, officially dedicated Thursday at the National Energy Research Scientific Computing Center (NERSC), is a supercomputer that will deliver nearly four exaflops of AI performance for more than 7,000 researchers. That makes Perlmutter the fastest system on the planet on the 16- and 32-bit mixed-precision math AI uses. And that performance doesn't even include a second phase coming later this year to the system based at Lawrence Berkeley National Lab.

More than two dozen applications are getting ready to be among the first to ride the 6,159 NVIDIA A100 Tensor Core GPUs in Perlmutter, the largest A100-powered system in the world. They aim to advance science in astrophysics, climate science and more. In one project, the supercomputer will help assemble the largest 3D map of the visible universe to date. It will process data from the Dark Energy Spectroscopic Instrument (DESI), a kind of cosmic camera that can capture as many as 5,000 galaxies in a single exposure. Researchers need the speed of Perlmutter's GPUs to capture dozens of exposures from one night to know where to point DESI the next night. Preparing a year's worth of the data for publication would take weeks or months on prior systems, but Perlmutter should help them accomplish the task in as little as a few days.

"I'm really happy with the 20x speedups we've gotten on GPUs in our preparatory work," said Rollin Thomas, a data architect at NERSC who's helping researchers get their code ready for Perlmutter. DESI's map aims to shed light on dark energy, the mysterious physics behind the accelerating expansion of the universe.

A similar spirit fuels many projects that will run on NERSC's new supercomputer. For example, work in materials science aims to discover atomic interactions that could point the way to better batteries and biofuels. Traditional supercomputers can barely handle the math required to generate simulations of a few atoms over a few nanoseconds with programs such as Quantum Espresso. But by combining their highly accurate simulations with machine learning, scientists can study more atoms over longer stretches of time. "In the past it was impossible to do fully atomistic simulations of big systems like battery interfaces, but now scientists plan to use Perlmutter to do just that," said Brandon Cook, an applications performance specialist at NERSC who's helping researchers launch such projects. That's where Tensor Cores in the A100 play a unique role. They accelerate both the double-precision floating point math for simulations and the mixed-precision calculations required for deep learning.

"As a neuroscientist long experienced in the field, I recently completed a painstaking analysis of 30 years of research on human brain sex differences..." reports Lise Eliot in a recent article on The Conversation. "[T]here's no denying the decades of actual data, which show that brain sex differences are tiny and swamped by the much greater variance in individuals' brain measures across the population."

Bloomberg follows up: In 2005, Harvard's then president Lawrence Summers theorized that so few women went into science because, well, they just weren't inherently good at it. "Issues of intrinsic aptitude," Summers said, such as "overall IQ, mathematical ability, scientific ability" kept many women out of the field... "I would like nothing better than to be proved wrong," Summers said back in 2005. Well, sixteen years later, it appears his wish came true.

In a new study published in in the June edition of Neuroscience & Behavioral Reviews, Lise Eliot, a professor of neuroscience at Rosalind Franklin University, analyzed 30 years' worth of brain research (mostly fMRIs and postmortem studies) and found no meaningful cognitive differences between men and women. Men's brains were on average about 11% larger than women's — as were their hearts, lungs and other organs — because brain size is proportional to body size. But just as taller people aren't any more intelligent than shorter people, neither, Eliot and her co-authors found, were men smarter than women. They weren't better at math or worse at language processing, either.

In her paper, Eliot and her co-authors acknowledge that psychological studies have found gendered personality traits (male aggression, for example) but at the brain level those differences don't seem to appear.
"Another way to think about it is every individual brain is a mosaic of circuits that control the many dimensions of masculinity and femininity, such as emotional expressiveness, interpersonal style, verbal and analytic reasoning, sexuality and gender identity itself," Eliot's original article had stated.

"Or, to use a computer analogy, gendered behavior comes from running different software on the same basic hardware."

An anonymous reader quotes a report from Phys.Org: A trio of researchers from Monash University and Deakin University has found that teaching children to play chess can reduce their aversion to risk. In their paper published in Journal of Development Economics, Asad Islam, Wang-Sheng Lee and Aaron Nicholas describe studying the impact of learning chess on 400 children in the U.K. The researchers found that most of the children experienced a decrease in risk aversion in a variety of game playing scenarios. They also noticed that playing chess also led to better math scores for some of the students and improvements in logic or rational thinking.

The researchers note that the game of chess is very well suited to building confidence in risk taking when there is reason to believe it might improve an outcome. In contrast, students also learned to avoid taking risks haphazardly, finding that such risks rarely lead to a positive outcome. They [...] line between good and poor risk-taking is especially evident in chess, which means that the more a person plays, the sharper their skills become. The researchers also found that the skills learned during chess playing appeared to be long lasting -- most of the children retained their decrease in risk aversion a full year after the end of their participation in the study. The researchers [...] did not find any evidence of changes in other cognitive skills, such as improvements in grades other than math or general creativity.

An anonymous reader quotes a report from Engadget: Verizon will shut down its 3G services on December 31st, 2022, VP of network engineering Mike Haberman announced today. According to Haberman, less than 1 percent of Verizon customers still access the 3G network, with 99 percent on 4G LTE or 5G. Verizon has roughly 94 million customers, so by the company's own math, as many as 940,000 people are still using Verizon's 3G network.

"Customers who still have a 3G device will continue to be strongly encouraged to make a change now," Haberman wrote. "As we move closer to the shut-off date customers still accessing the 3G network may experience a degradation or complete loss of service, and our service centers will only be able to offer extremely limited troubleshooting help on these older devices." Verizon has been teasing a shut-off of its 3G CDMA services for years. [...] The delay to 2022 is final — there will be no more extensions, Haberman said. He noted that this will be "months after our competitors have shut off their networks completely."

ZDNet reports: Scientists from quantum computing company D-Wave have demonstrated that, using a method called quantum annealing, they could simulate some materials up to three million times faster than it would take with corresponding classical methods.

Together with researchers from Google, the scientists set out to measure the speed of simulation in one of D-Wave's quantum annealing processors, and found that performance increased with both simulation size and problem difficulty, to reach a million-fold speedup over what could be achieved with a classical CPU... The calculation that D-Wave and Google's teams tackled is a real-world problem; in fact, it has already been resolved by the 2016 winners of the Nobel Prize in Physics, Vadim Berezinskii, J. Michael Kosterlitz and David Thouless, who studied the behavior of so-called "exotic magnetism", which occurs in quantum magnetic systems....

Instead of proving quantum supremacy, which happens when a quantum computer runs a calculation that is impossible to resolve with classical means, D-Wave's latest research demonstrates that the company's quantum annealing processors can lead to a computational performance advantage... "What we see is a huge benefit in absolute terms," said Andrew King, director of performance research at D-Wave. "This simulation is a real problem that scientists have already attacked using the algorithms we compared against, marking a significant milestone and an important foundation for future development. This wouldn't have been possible today without D-Wave's lower noise processor."

Equally as significant as the performance milestone, said D-Wave's team, is the fact that the quantum annealing processors were used to run a practical application, instead of a proof-of-concept or an engineered, synthetic problem with little real-world relevance. Until now, quantum methods have mostly been leveraged to prove that the technology has the potential to solve practical problems, and is yet to make tangible marks in the real world.
Looking ahead to the future, long-time Slashdot reader schwit1 asks, "Is this is bad news for encryption that depends on brute-force calculations being prohibitively difficult?"

An anonymous reader shares a report: Three decades ago, the U.S. passed an infinitesimal milestone: solar and wind power generated one-tenth of one percent of the country's electricity. It took 18 years, until 2008, for solar and wind to reach 1% of U.S. electricity. It took 12 years for solar and wind to increase by another factor of 10. In 2020, wind and solar generated 10.5% of U.S. electricity. If this sounds a bit like a math exercise, that's because it is. Anything growing at a compounded rate of nearly 18%, as U.S. wind and solar have done for the past three decades, will double in four years, then double again four years after that, then again four years after that, and so on. It gets confusing to think in so many successive doublings, especially when they occur more than twice a decade. Better, then, to think in orders of magnitude -- 10^10.

There are a number of reasons why exponential consideration matters. The first is that U.S. power demand isn't growing, and hasn't since wind and solar reached that 1% milestone in the late 2000s. That means that the growth of wind and solar -- and that of natural gas-fired power -- have come entirely at the expense of coal-fired power. That replacement of coal with either natural gas (half the emissions of coal) or with wind and solar (zero emissions) is certainly an environmental achievement. Coupled with last year's massive drop in emissions, that power shift also makes it much easier for the U.S. to meet its Paris Agreement obligations.

An anonymous reader quotes a report from Motherboard: [A] group of researchers from the Technion in Israel and Google in Tel Aviv presented an automated conjecturing system that they call the Ramanujan Machine, named after the mathematician Srinivasa Ramanujan, who developed thousands of innovative formulas in number theory with almost no formal training. The software system has already conjectured several original and important formulas for universal constants that show up in mathematics. The work was published last week in Nature.

One of the formulas created by the Machine can be used to compute the value of a universal constant called Catalan's number more efficiently than any previous human-discovered formulas. But the Ramanujan Machine is imagined not to take over mathematics, so much as provide a sort of feeding line for existing mathematicians. As the researchers explain in the paper, the entire discipline of mathematics can be broken down into two processes, crudely speaking: conjecturing things and proving things. Given more conjectures, there is more grist for the mill of the mathematical mind, more for mathematicians to prove and explain. That's not to say their system is unambitious. As the researchers put it, the Ramanujan Machine is "trying to replace the mathematical intuition of great mathematicians and providing leads to further mathematical research." In particular, the researchers' system produces conjectures for the value of universal constants (like pi), written in terms of elegant formulas called continued fractions. Continued fractions are essentially fractions, but more dizzying. The denominator in a continued fraction includes a sum of two terms, the second of which is itself a fraction, whose denominator itself contains a fraction, and so on, out to infinity.

The Ramanujan Machine is built off of two primary algorithms. These find continued fraction expressions that, with a high degree of confidence, seem to equal universal constants. That confidence is important, as otherwise, the conjectures would be easily discarded and provide little value. Each conjecture takes the form of an equation. The idea is that the quantity on the left side of the equals sign, a formula involving a universal constant, should be equal to the quantity on the right, a continued fraction. To get to these conjectures, the algorithm picks arbitrary universal constants for the left side and arbitrary continued fractions for the right, and then computes each side separately to a certain precision. If the two sides appear to align, the quantities are calculated to higher precision to make sure their alignment is not a coincidence of imprecision. Critically, formulas already exist to compute the value of universal constants like pi to an arbitrary precision, so that the only obstacle to verifying the sides match is computing time.