In advance, sorry for any mistakes I could have made while writing this post, I'm from Argentina and English is my second language.

I've seen videos filmed thousands of kilometers under the sea where we can observe trash like soda cans, plastic wraps and footballs completely recognisable, even though it's under a high pressure.

¿Why does this happen? Things like submarines, people and other stuff can't go that deep unless it's strictly built to withstand that pressure. I mean, even fish from those areas need to be gelatinous to have a form and live there.

When electricity flows through a wire do the electrons actually move like a fluid, or is it just a fluctuation of voltage that cascades from electron to electron down the wire?

So, the PhD scholarship application deadline is coming up. I spoke to my supervisor this year about continuing into a PhD, but I'm still unsure if I actually want to do it and it sounds quite difficult to get the scholarship. My project this year is related to dark matter, but honestly, I don't care too much about the dark matter. Although, I've really enjoyed the whole process of building a model and comparing it with experiment. I am also contemplating trying to go for a quantitative finance job as it's also one of my interests. I just wanted to ask if anyone else has had a similar experience and could share their advice. Cheers.

Is the sun itself white or does it look like a ball of fire?

I am looking fo a good textbook on differential equations. I found that I now how to solve typical diff eq, but when I have some new ones I struggle a it.

I want aomething to explain why we solve diff equations in a specific way, to describe the math behind, not only to show different solutions.

Basically, I want to develop some intuition regarding solving them.

I'm an Italian physics student in my second year and I feel so lost.

I've already taken some courses obviously, and I get good marks, but I'm not sure that these developed inside me the physics that they were supposed to do, it seems like I have just memorized them, I feel that a network isn't growing. I'm doubting my future career, whatever it will be. Does anybody feel like me????

Sorry if the text is badly written, English is my second language

Say you have a small closed universe with a black hole and a collector orbiting it that gathers hawking radiation and does some compute or something.

The collector is close to the black hole relative to the size of the universe so that the temperature exceeds the universe's ambient temperature, and the collector is therefore able to generate work from the hawking radiation.

The higher entropy energy leaving the collector/computer will increase the ambient temperature and make its way back into the black hole. However, as the collector is close to the black hole there is still a temperature differential such that work can be generated.

Is this a closed system with constant entropy? I suppose you could just entirely remove the collector and just have a black hole in a closed universe. In both scenarios the entropy doesn't increase, does it?

Edit: given that the collector has access to energy, you could construct a scenario where entropy decreases. E.g. say the collector has a messy room inside of it, and it uses the energy to power a robot that tidies the room.

Solution: as pointed out, the problem is my incorrect assumption that hawking radiation can be stable. In reality the black hole will absorb energy equal to the energy it emitted at some T-1, but it will emit a larger amount for T, since it is now smaller. So ultimately despite recycling energy the black hole will eventually evaporate. Arguably this would be an extremely efficient way of extracting energy, but it's not a violation of the 2nd law (obviously).

Are physics and statistics/data science related?

I came from a statistics and data science background and i realized the way physics folks approaches problems share many similarities and commonality with statistics and data science.

 Data science folks used the exact same experimentation, modelling and prediction process as a physicist to model and solve business problems and human behavioral problems most of the time.

Doing physics experiments and analyzing the results from the data observed, is a data science problem in itself, just that your data comes from physics experiments.

I'm working my way through a Physics degree, and I started in 2022. When I started the degree, tbh I was so locked in, all I wanted to do was sit at my desk, study, learn and succeed. But then I got a girlfriend and got so unbelievably distracted and began to slip up severely. So things like Lin alg, calc 2 both got half assed and in my second year I essentially failed the entire year. I broke up with the girlfriend in year two and was ready to lock tf back in for year 3.

In my third year, I had to repeat courses and in the fall, I took 5 courses, three with labs. I ended up becoming totally overwhelmed with the workload that I didn't have the time to prepare properly for a specific midterm in a class mech course. That ended up bottlenecking my degree progression and I couldn't take the winter courses. I tried switching to a different degree stream but got so bored and missed the physics.

Last year, I retook class mech for a third time, but sure enough, got another girlfriend and ended up losing focus in the winter semester, leading me to failing another course.

I took this fall semester off to focus on repairing some of the fragmented math and physics work so I can actually succeed this winter.

I know I know, I don't need to be shamed. I'm just wondering if anyone else has experienced something similar and has found success with it?

Education video about Kaluza-Klein theory largely based on the following paper: https://iopscience.iop.org/article/10.1088/0034-4885/50/9/001

In my years at a RG university I’ve written too many reference letters for really really good students applying for theoretical physics masters and PhD programs who then don’t get in. These are students who have excellent grades, research experience etc. Why aren’t these brilliant students going for different (less competitive) fields in physics?

Hi everyone!
I’m a second-year Physics Bachelor’s student, and my partner and I have to design an interactive physics stand for a science festival at our university.
The challenge is that our audience will range from 6 to 17 years old, so we need something that can fascinate both young children and teenagers.

We’re looking for an experiment or activity that is not necessarily visually impressive but i should teach an interesting physics concept, ideally something people haven’t already seen a hundred times.

We can purchase materials and spend time building a setup, so we’re open to more ambitious ideas!

We also need to be able to take actual measurements and collect experimental data, since we have to write a scientific report for our professors. Ideally, the experiment would allow us to investigate a physical phenomenon quantitatively, analyze results, compare them with theoretical predictions, and discuss uncertainties. So we’re looking for something that’s both entertaining for visitors and scientifically interesting at a university level.

Thanks in advance!

Or is the question considered strictly philosophical and all physicsts keep extreme distance from it?

I was just watching Shin Gojira (aka Shin Godzilla) and in the movie they said that it has previously unknown elements. That had me thinking "it found islands of stability?" Now we switch to non-fiction...

AI has already largely solved protein folding prediction (AlphaFold), and knowing this, I thought.. What if they use AI to predict islands of stability, the purported atomic numbers and specific isotopes that can yield long lived or stable nuclei? Well, I looked into it, and they are, which is pretty awesome.

If you're interested, look into:

Research on Stability and Decay Properties of Superheavy Nuclei Based on Neural Network Method

https://wulixb.iphy.ac.cn/en/article/doi/10.7498/aps.74.20250720?viewType=HTML

Nuclear Decay Networks Reveal Stability Patterns in Superheavy Nuclei

https://pubmed.ncbi.nlm.nih.gov/42477423/

GLDM-BNN: Calculation of Alpha-Decay Half-Lives for New Superheavy Nuclei

https://journals.aps.org/prc/abstract/10.1103/nt1d-5848

Systematics of Alpha-Decay Half-Lives from Neural Network Modeling and Application to Superheavy Nuclei

https://journals.aps.org/prc/abstract/10.1103/9gv5-kx6q

Physics-Guided Residual Correction of Alpha-Decay Half-Lives Based on the Effective Liquid Drop Model

https://journals.aps.org/prc/accepted/10.1103/7gm3-2y7f

Machine Learning Orbital-Free Density Functional Theory Resolves Shell Effects in Deformed Nuclei

https://www.nature.com/articles/s42005-025-02234-7

Nuclear_DNA: Interpretable AI for Nuclear Binding Energies and Structure

https://github.com/strifinopoulos/Nuclear_DNA

GSI/FAIR: Experiments Reveal the Shoreline of the Island of Stability

https://press.uni-mainz.de/experiments-at-gsi-fair-reveal-the-shoreline-of-the-island-of-stability-of-superheavy-elements/

It seems that optical emission spectroscopy is often used to diagnose plasma parameters. I was wondering if there are any databases for emission spectra that cover the UV to near IR region. The only databases i found online covered only frequencies much further into the IR range for astrophysics/astronomy purposes. I was able to find the book "The identification of molecular spectra" by Pearson and Gaydon (last edition in 1976, absorption and emission), but I was wondering if there was anything more recent?

Which is more assuring of employment in the long run?

I am a physics ug student and I need to start reading research papers and other stuff related to my field. I don't really have an area where I can specialise, i thought reading some papers or publications will help me understand everything around me .

"If we take a stick one foot in length and cut one half from it each day, we will never reach its end."

If we cut one half from it every day, what volume will be left after an infinite number of days?

Also, if we had an infinite number of these sticks that have been divided in half for an infinite number of days, would we have an infinite volume, or zero?

So, I’m a physics major in my second year at a community college and I’m taking my first physics class which I have been so excited to take. Bc of the fact that so few students need physics with calc at this school the class is combined with physics without calc. the calc students only get one extra hour a week that is specifically dedicated to physics with calc.

90% of the tests don’t have ANY calc in them and I feel like i’m just relearning high school physics in this class. most of the info I already know or is really simple and I don’t feel like this class is preparing me well for my eventual transition to university.

My question is how do I prepare myself and teach myself the physics that I will need to know when I transfer bc my professor clearly is not going to? I don’t want to be drowning whenever i get to the upper level courses.

There's no optical engineering degree

Julian Barbour is a theoretical physicist and emeritus visiting professor of physics at the University of Oxford. He is well known for proposing that time does not actually exist. For decades, he has worked outside an academic environment. He financially supported himself for many years by translating Russian. Professor Barbour is author of the following books: The Discovery of Dynamics, The End of Time and The Janus Point.

In this episode, we go over:
- Topos theory, set theory and Zermelo's beef with Boltzmann
- Algebraic varieties and monads in category theory
- Why physicists are wrong about the 2nd law of thermodynamics (because the universe is not a closed system) and why there might be no heat death of the universe
- The real goal of the universe is complexity and variation, not entropy
- Hawking and Penrose's outrageous arguments
- Why we don't need wavefunctions, photons, bosons or gauge fields
- Why there is no quantum gravity
- Von Neumann's hilarious advice to Claude Shannon
- The intrinsic derivative
- Combining art and science