Category: Academic Analysis

Source: The Conversation – USA – By Olga Lazareva, Professor of Psychology, Drake University

Can a monkey, a pigeon or a fish reason like a person? It’s a question scientists have been testing in increasingly creative ways – and what we’ve found so far paints a more complicated picture than you’d think.

Imagine you’re filling out a March Madness bracket. You hear that Team A beat Team B, and Team B beat Team C – so you assume Team A is probably better than Team C. That’s a kind of logical reasoning known as transitive inference. It’s so automatic that you barely notice you’re doing it.

It turns out humans are not the only ones who can make these kinds of mental leaps. In labs around the world, researchers have tested many animals, from primates to birds to insects, on tasks designed to probe transitive inference, and most pass with flying colors.

As a scientist focused on animal learning and behavior, I work with pigeons to understand how they make sense of relationships, patterns and rules. In other words, I study the minds of animals that will never fill out a March Madness bracket – but might still be able to guess the winner.

Logic test without words

The basic idea is simple: If an animal learns that A is better than B, and B is better than C, can it figure out that A is better than C – even though it’s never seen A and C together?

In the lab, researchers test this by giving animals randomly paired images, one pair at a time, and rewarding them with food for picking the correct one. For example, animals learn that a photo of hands (A) is correct when paired with a classroom (B), a classroom (B) is correct when paired with bushes (C), bushes (C) are correct when paired with a highway (D), and a highway (D) is correct when paired with a sunset (E). We don’t know whether they “understand” what’s in the picture, and it is not particularly important for the experiment that they do.

One possible explanation is that the animals that learn all the tasks create a mental ranking of these images: A > B > C > D > E. We test this idea by giving them new pairs they’ve never seen before, such as classroom (B) vs. highway (D). If they consistently pick the higher-ranked item, they’ve inferred the underlying order.

What’s fascinating is how many species succeed at this task. Monkeys, rats, pigeons – even fish and wasps – have all demonstrated transitive inference in one form or another.

The twist: Not all tasks are easy

But not all types of reasoning come so easily. There’s another kind of rule called transitivity that is different from transitive inference, despite the similar name. Instead of asking which picture is better, transitivity is about equivalence.

In this task, animals are shown a set of three pictures and asked which one goes with the center image. For example, if white triangle (A1) is shown, choosing red square (B1) earns a reward, while choosing blue square (B2) does not. Later, when red square (B1) is shown, choosing white cross (C1) earns a reward while choosing white circle (C2) does not. Now comes the test: white triangle (A1) is shown with white cross (C1) and white circle (C2) as choices. If they pick white cross (C1), then they’ve demonstrated transitivity.

The change may seem small, but species that succeed in those first transitive inference tasks often stumble in this task. In fact, they tend to treat the white triangle and the white cross as completely separate things, despite their common relationship with the red square. In my recently published review of research using the two tasks, I concluded that more evidence is needed to determine whether these tests tap into the same cognitive ability.

Small differences, big consequences

Why does the difference between transitive inference and transitivity matter? At first glance, they may seem like two versions of the same ability – logical reasoning. But when animals succeed at one and struggle with the other, it raises an important question: Are these tasks measuring the same kind of thinking?

The apparent difference between the two tasks isn’t just a quirk of animal behavior. Psychology researchers apply these tasks to humans in order to draw conclusions about how people reason.

For example, say you’re trying to pick a new almond milk. You know that Brand A is creamier than Brand B, and your friend told you that Brand C is even waterier than Brand B. Based on that, because you like a thicker milk, you might assume Brand A is better than Brand C, an example of transitive inference.

But now imagine the store labels both Brand A and Brand C as “barista blends.” Even without tasting them, you might treat them as functionally equivalent, because they belong to the same category. That’s more like transitivity, where items are grouped based on shared relationships. In this case, “barista blend” signals the brands share similar quality.

Researchers often treat these types of reasoning as measuring the same ability. But if they rely on different mental processes, they might not be interchangeable. In other words, the way scientists ask their questions may shape the answer – and that has big implications for how they interpret success in animals and in people.

This difference could affect how researchers interpret decision-making not only in the lab, but also in everyday choices and in clinical settings. Tasks like these are sometimes used in research on autism, brain injury or age-related cognitive decline.

If two tasks look similar on the surface, then choosing the wrong one might lead to inaccurate conclusions about someone’s cognitive abilities. That’s why ongoing work in my lab is exploring whether the same distinction between these logical processes holds true for people.

Just like a March Madness bracket doesn’t always predict the winner, a reasoning task doesn’t always show how someone got to the right answer. That’s the puzzle researchers are still working on – figuring out whether different tasks really tap into the same kind of thinking or just look like they do. It’s what keeps scientists like me in the lab, asking questions, running experiments and trying to understand what it really means to reason – no matter who’s doing the thinking.

Olga Lazareva does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Humans and animals can both think logically − but testing what kind of logic they’re using is tricky – https://theconversation.com/humans-and-animals-can-both-think-logically-but-testing-what-kind-of-logic-theyre-using-is-tricky-253001

Source: The Conversation – USA – By Anna Erickson, Professor of Nuclear and Radiological Engineering, Georgia Institute of Technology

When U.S. forces attacked Iran’s nuclear facilities on June 21, 2025, the main target was metal tubes in laboratories deep underground. The tubes are centrifuges that produce highly enriched uranium needed to build nuclear weapons.

Inside of a centrifuge, a rotor spins in the range of 50,000 to 100,000 revolutions per minute, 10 times faster than a Corvette engine’s crankshaft. High speeds are needed to separate lighter uranium-235 from heavier uranium-238 for further collection and processing. Producing this level of force means the rotor itself must be well balanced and strong and rely on high-speed magnetic bearings to reduce friction.

Over the years, Iran has produced thousands of centrifuges. They work together to enrich uranium to dangerous levels – close to weapons-grade uranium. Most of them are deployed in three enrichment sites: Natanz, the country’s main enrichment facility, Fordow and Isfahan. Inside of these facilities, the centrifuges are arranged into cascades – series of machines connected to each other. This way, each machine yields slightly more enriched uranium, feeding the gas produced into its neighbor to maximize production efficiency.

As a nuclear engineer who works on nuclear nonproliferation, I track centrifuge technology, including the Iranian enrichment facilities targeted by the U.S. and Israel. A typical cascade deployed in Iran is composed of 164 centrifuges, working in series to produce enriched uranium. The Natanz facility was designed to hold over 50,000 centrifuges.

Iran’s early intentions to field centrifuges on a very large scale were clear. At the peak of the program in the early 2010s it deployed over 19,000 units. Iran later scaled down the number of its centrifuges in part due to international agreements such as the since scrapped Joint Comprehensive Plan of Action signed in 2015.

Legacy of enrichment

Iran has a long history of enriching uranium.

In the late 1990s, it acquired a Pakistani centrifuge design known as P-1. The blueprints and some components were supplied via the A.Q. Khan black market network – the mastermind of the Pakistani program and a serious source of nuclear proliferation globally. Today, the P-1 design is known as IR-1. IR-1 centrifuges use aluminum and a high-strength alloy, known as maraging steel.

About one-third of the centrifuges that were deployed at the sites of the recent strike on June 21 are IR-1. Each one produces on the order of 0.8 separative work units, which is the unit for measuring the amount of energy and effort needed to separate uranium-235 molecules from the rest of the uranium gas. To put this in perspective, one centrifuge would yield about 0.2 ounces (6 grams) of 60%-enriched uranium-235 per year.

A typical uranium-based weapon requires 55 pounds (25 kilograms) of 90%-enriched uranium. To get to weapons-grade level, a single centrifuge would produce only 0.14 ounces (4 grams) per year. It requires more work to go higher in enrichment. While capable of doing the job, the IR-1 is quite inefficient.

More and better centrifuges

Small yields mean that over 6,000 centrifuges would need to work together for a year to get enough material for one weapon such as a nuclear warhead. Or the efficiency of the centrifuges would have to be improved. Iran did both.

Before the strike by U.S. forces, Iran was operating close to 7,000 IR-1 centrifuges. In addition, Iran designed, built and operated more efficient centrifuges such as the IR-2m, IR-4 and IR-6 designs. Comparing the IR-1 with the latest designs is like comparing a golf cart with the latest electric vehicles in terms of range and payload.

Iran’s latest centrifuge designs contain carbon fiber composites with exceptional strength and durability and low weight. This is a recipe for producing light and compact centrifuges that are easier to conceal from inspections. According to the international nuclear watchdog International Atomic Energy Agency, before the strike Iran was operating 6,500 IR-2m centrifuges, close to 4,000 IR-4 centrifuges and over 3,000 IR-6 centrifuges.

With each new generation, the separative work unit efficiency increased significantly. IR-6 centrifuges, with their carbon fiber rotors, can achieve up to 10 separative work units per year. That’s about 2.8 ounces (80 grams) of 60%-enriched uranium-235 per year. The International Atomic Energy Agency verified that the IR-6 cascades have been actively used to ramp up production of 60%-enriched uranium.

The most recent and advanced centrifuges developed by Iran, known as IR-9, can achieve 50 separative work units per year. This cuts down the time needed to produce highly enriched uranium for weapon purposes from months to weeks. The other aspect of IR-9 advanced centrifuges is their compactness. They are easier to conceal from inspections or move underground, and they require less energy to operate.

Advanced centrifuges such as the IR-9 drive up the risk of nuclear weapons proliferation significantly. Fortunately, the International Atomic Energy Agency reports that only one exists in testing laboratories, and there is no evidence Iran has deployed them widely. However, it’s possible more are concealed.

Bombs or talks?

Uranium enrichment of 60% is far beyond the needs of any civilian use. The International Atomic Energy Agency confirmed that Iran stockpiled about 880 pounds (400 kilograms) of highly enriched uranium before the attack, and it might have escaped intact. That’s enough to make 10 weapons. The newer centrifuges – IR-2m, IR-4 and IR-6 – would need a bit over eight months to produce that much.

It’s not clear what the U.S. attack has accomplished, but destroying the facilities targeted in the attack and hindering Iran’s ability to continue enriching uranium might be a way to slow Iran’s move toward producing nuclear weapons. However, based on my work and research on preventing nuclear proliferation, I believe a more reliable means of preventing Iran from achieving its nuclear aims would be for diplomacy and cooperation to prevail.

Anna Erickson receives funding from Department of Energy National Nuclear Security Administration (NNSA) related to nuclear nonproliferation technologies. She has previously served on the Board of Directors of the American Nuclear Society.

– ref. Why the US bombed a bunch of metal tubes − a nuclear engineer explains the importance of centrifuges to Iranian efforts to build nuclear weapons – https://theconversation.com/why-the-us-bombed-a-bunch-of-metal-tubes-a-nuclear-engineer-explains-the-importance-of-centrifuges-to-iranian-efforts-to-build-nuclear-weapons-259883

Source: The Conversation – USA – By Kassem Fawaz, Associate Professor of Electrical and Computer Engineering, University of Wisconsin-Madison

You wake up in the morning and, first thing, you open your weather app. You close that pesky ad that opens first and check the forecast. You like your weather app, which shows hourly weather forecasts for your location. And the app is free!

But do you know why it’s free? Look at the app’s privacy settings. You help keep it free by allowing it to collect your information, including:

• What devices you use and their IP and Media Access Control addresses.
• Information you provide when signing up, such as your name, email address and home address.
• App settings, such as whether you choose Celsius or Fahrenheit.
• Your interactions with the app, including what content you view and what ads you click.
• Inferences based on your interactions with the app.
• Your location at a given time, including, depending on your settings, continuous tracking.
• What websites or apps that you interact with after you use the weather app.
• Information you give to ad vendors.
• Information gleaned by analytics vendors that analyze and optimize the app.

This type of data collection is standard fare. The app company can use this to customize ads and content. The more customized and personalized an ad is, the more money it generates for the app owner. The owner might also sell your data to other companies.

You might also check a social media account like Instagram. The subtle price that you pay is, again, your data. Many “free” mobile apps gather information about you as you interact with them.

As an associate professor of electrical and computer engineering and a doctoral student in computer science, we follow the ways software collects information about people. Your data allows companies to learn about your habits and exploit them.

It’s no secret that social media and mobile applications collect information about you. Meta’s business model depends on it. The company, which operates Facebook, Instagram and WhatsApp, is worth US$1.48 trillion. Just under 98% of its profits come from advertising, which leverages user data from more than 7 billion monthly users.

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Read more:
How Internet of Things devices affect your privacy – even when they’re not yours

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What your data is worth

Before mobile phones gained apps and social media became ubiquitous, companies conducted large-scale demographic surveys to assess how well a product performed and to get information about the best places to sell it. They used the information to create coarsely targeted ads that they placed on billboards, print ads and TV spots.

Mobile apps and social media platforms now let companies gather much more fine-grained information about people at a lower cost. Through apps and social media, people willingly trade personal information for convenience. In 2007 – a year after the introduction of targeted ads – Facebook made over $153 million, triple the previous year’s revenue. In the past 17 years, that number has increased by more than 1,000 times.

Five ways to leave your data

App and social media companies collect your data in many ways. Meta is a representative case. The company’s privacy policy highlights five ways it gathers your data:

First, it collects the profile information you fill in. Second, it collects the actions you take on its social media platforms. Third, it collects the people you follow and friend. Fourth, it keeps track of each phone, tablet and computer you use to access its platforms. And fifth, it collects information about how you interact with apps that corporate partners connect to its platforms. Many apps and social media platforms follow similar privacy practices.

Your data and activity

When you create an account on an app or social media platform, you provide the company that owns it with information like your age, birth date, identified sex, location and workplace. In the early years of Facebook, selling profile information to advertisers was that company’s main source of revenue. This information is valuable because it allows advertisers to target specific demographics like age, identified gender and location.

And once you start using an app or social media platform, the company behind it can collect data about how you use the app or social media. Social media keeps you engaged as you interact with other people’s posts by liking, commenting or sharing them. Meanwhile, the social media company gains information about what content you view and how you communicate with other people.

Advertisers can find out how much time you spent reading a Facebook post or that you spent a few more seconds on a particular TikTok video. This activity information tells advertisers about your interests. Modern algorithms can quickly pick up subtleties and automatically change the content to engage you in a sponsored post, a targeted advertisement or general content.

Your devices and applications

Companies can also note what devices, including mobile phones, tablets and computers, you use to access their apps and social media platforms. This shows advertisers your brand loyalty, how old your devices are and how much they’re worth.

Because mobile devices travel with you, they have access to information about where you’re going, what you’re doing and who you’re near. In a lawsuit against Kochava Inc., the Federal Trade Commission called out the company for selling customer geolocation data in August 2022, shortly after Roe v Wade was overruled. The company’s customers, including people who had abortions after the ruling was overturned, often didn’t know that data tracking their movements was being collected, according to the commission. The FTC alleged that the data could be used to identify households.

Kochava has denied the FTC’s allegations.

Information that apps can gain from your mobile devices includes anything you have given an app permission to have, such as your location, who you have in your contact list or photos in your gallery.

If you give an app permission to see where you are while the app is running, for instance, the platform can access your location anytime the app is running. Providing access to contacts may provide an app with the phone numbers, names and emails of all the people that you know.

Cross-application data collection

Companies can also gain information about what you do across different apps by acquiring information collected by other apps and platforms.

This is common with social media companies. This allows companies to, for example, show you ads based on what you like or recently looked at on other apps. If you’ve searched for something on Amazon and then noticed an ad for it on Instagram, it’s probably because Amazon shared that information with Instagram.

This combined data collection has made targeted advertising so accurate that people have reported that they feel like their devices are listening to them.

Companies, including Google, Meta, X, TikTok and Snapchat, can build detailed user profiles based on collected information from all the apps and social media platforms you use. They use the profiles to show you ads and posts that match your interests to keep you engaged. They also sell the profile information to advertisers.

Meanwhile, researchers have found that Meta and Yandex, a Russian search engine, have overcome controls in mobile operating system software that ordinarily keep people’s web-browsing data anonymous. Each company puts code on its webpages that used local IPs to pass a person’s browsing history, which is supposed to remain private, to mobile apps installed on that person’s phone, de-anonymizing the data. Yandex has been conducting this tracking since 2017, while Meta began in September 2024, according to the researchers.

What you can do about it

If you use apps that collect your data in some way, including those that give you directions, track your workouts or help you contact someone, or if you use social media platforms, your privacy is at risk.

Aside from entirely abandoning modern technology, there are several steps you can take to limit access – at least in part – to your private information.

Read the privacy policy of each app or social media platform you use. Although privacy policy documents can be long, tedious and sometimes hard to read, they explain how social media platforms collect, process, store and share your data.

Check a policy by making sure it can answer three questions: what data does the app collect, how does it collect the data, and what is the data used for. If you can’t answer all three questions by reading the policy, or if any of the answers don’t sit well with you, consider skipping the app until there’s a change in its data practices.

Remove unnecessary permissions from mobile apps to limit the amount of information that applications can gather from you.

Be aware of the privacy settings that might be offered by the apps or social media platforms you use, including any setting that allows your personal data to affect your experience or shares information about you with other users or applications.

These privacy settings can give you some control. We recommend that you disable “off-app activity” and “personalization” settings. “Off-app activity” allows an app to record which other apps are installed on your phone and what you do on them. Personalization settings allow an app to use your data to tailor what it shows you, including advertisements.

Review and update these settings regularly because permissions sometimes change when apps or your phone update. App updates may also add new features that can collect your data. Phone updates may also give apps new ways to collect your data or add new ways to preserve your privacy.

Use private browser windows or reputable virtual private networks software, commonly referred to as VPNs, when using apps that connect to the internet and social media platforms. Private browsers don’t store any account information, which limits the information that can be collected. VPNs change the IP address of your machine so that apps and platforms can’t discover your location.

Finally, ask yourself whether you really need every app that’s on your phone. And when using social media, consider how much information you want to reveal about yourself in liking and commenting on posts, sharing updates about your life, revealing locations you visited and following celebrities you like.

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This article is part of a series on data privacy that explores who collects your data, what and how they collect, who sells and buys your data, what they all do with it, and what you can do about it.

Kassem Fawaz receives funding from the National Science Foundation. In the past, his research group has received unrestricted gifts from Meta and Google.

Jack West does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. The hidden cost of convenience: How your data pulls in hundreds of billions of dollars for app and social media companies – https://theconversation.com/the-hidden-cost-of-convenience-how-your-data-pulls-in-hundreds-of-billions-of-dollars-for-app-and-social-media-companies-251698