Intrinsically disordered proteins (IDPs) do not attain a stable secondary or tertiary structure and rapidly change their conformation, making structure prediction particularly challenging. Although these proteins exhibit chaotic and “disordered” structures, they still perform essential functions.
IDPs comprise approximately 30% of the human proteome and play important functional roles in transcription, translation, and signaling. Many mutations linked to neurological diseases, including amyotrophic lateral sclerosis (ALS), are located in intrinsically disordered protein regions (IDRs).
Powerful machine-learning algorithms, including AlphaFold and RoseTTAFold, cannot provide realistic representations of these ‘disordered’ and ‘chaotic’ protein regions as a whole. This is because they have not been trained on such data and because these proteins exhibit inherent dynamic behavior, adopting a range of conformations rather than a single stable one.
Many physicists and engineers have recently been trying to demonstrate the potential of quantum computers for tackling some problems that are particularly demanding and are difficult to solve for classical computers. A task that has been found to be challenging for both quantum and classical computers is finding the ground state (i.e., lowest possible energy state) of systems with multiple interacting quantum particles, called quantum many-body systems.
When one of these systems is placed in a thermal bath (i.e., an environment with a fixed temperature that interacts with the systems), it is known to cool down without always reaching its ground state. In some instances, a quantum system can get trapped at a so-called local minimum; a state in which its energy is lower than other neighboring states but not at the lowest possible level.
Researchers at California Institute of Technology and the AWS Center for Quantum Computing recently showed that while finding the local minimum for a system is difficult for classical computers, it could be far easier for quantum computers.
A fundamental goal of physics is to explain the broadest range of phenomena with the fewest underlying principles. Remarkably, seemingly disparate problems often exhibit identical mathematical descriptions.
For instance, the rate of heat flow can be modeled using an equation very similar to that governing the speed of particle diffusion. Another example involves wave equations, which apply to the behavior of both water and sound. Scientists continuously seek such connections, which are rooted in the principle of the “universality” of underlying physical mechanisms.
In a study published in the journal Royal Society Open Science, researchers from Osaka University uncovered an unexpected connection between the equations for defects in a crystalline lattice and a well-known formula from electromagnetism.
In human engineering, we design systems to be predictable and controlled. By contrast, nature thrives on systems where simple rules generate rich, emergent complexity. The computational nature of the universe explains how simplicity can generate the complexity we see in natural phenomena. Imagine being able to understand everything about the universe and solve all its mysteries by a computational approach that uses very simple rules. Instead of being limited to mathematical equations, using very basic computational rules, we might be able to figure out and describe everything in the universe, like what happened at the very beginning? What is energy? What’s the nature of dark matter? Is traveling faster than light possible? What is consciousness? Is there free will? How can we unify different theories of physics into one ultimate theory of everything?
This paradigm goes against the traditional notion that complexity in nature must arise from complicated origins. It claims that simplicity in fundamental rules can produce astonishing complexity in behavior. Entering the Wolfram’s physics project: The computational universe!
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Chapters: 00:00 Intro. 01:48 Fundamentally computational. 08:51 Computational irreducibility. 13:14 Causal invariance. 16:16 Universal computation. 18:44 Spatial dimensions. 21:36 Space curvature. 23:52 Time and causality. 27:12 Energy. 29:38 Quantum mechanics. 31:31 Faster than light travel. 34:56 Dark matter. 36:30 Critiques. 39:15 Meta-framework. 41:19 The ultimate rule. 44:21 Consciousness. 46:00 Free will. 48:02 Meaning and purpose. 49:09 Unification. 55:14 Further analysis. 01:02:30 Credits.
Although Navier–Stokes equations are the foundation of modern hydrodynamics, adapting them to quantum systems has so far been a major challenge. Researchers from the Faculty of Physics at the University of Warsaw, Maciej Łebek, M.Sc. and Miłosz Panfil, Ph.D., Prof., have shown that these equations can be generalized to quantum systems, specifically quantum liquids, in which the motion of particles is restricted to one dimension.
This discovery opens up new avenues for research into transport in one-dimensional quantum systems. The resulting paper, published in Physical Review Letters, was awarded an Editors’ Suggestion.
Liquids are among the basic states of matter and play a key role in nature and technology. The equations of hydrodynamics, known as the Navier–Stokes equations, describe their motion and interactions with the environment. Solutions to these equations allow us to predict the behavior of fluids under various conditions, from the ocean currents and the blood flow in blood vessels, to the dynamics of quark-gluon plasma on subatomic scales.
Light was long considered to be a wave, exhibiting the phenomenon of interference in which ripples like those in water waves are generated under specific interactions. Light also bends around corners, resulting in fringing effects, which is termed diffraction. The energy of light is associated with its intensity and is proportional to the square of the amplitude of the electric field, but in the photoelectric effect, the energy of emitted electrons is found to be proportional to the frequency of radiation.
This observation was first made by Philipp Lenard, who did initial work on the photoelectric effect. In order to explain this, in 1905, Einstein suggested in Annalen der Physik that light comprises quantized packets of energy, which came to be called photons. It led to the theory of the dual nature of light, according to which light can behave like a wave or a particle depending on its interactions, paving the way for the birth of quantum mechanics.
Although Einstein’s work on photons found broader acceptance, eventually leading to his Nobel Prize in Physics, Einstein was not fully convinced. He wrote in a 1951 letter, “All the 50 years of conscious brooding have brought me no closer to the answer to the question: What are light quanta?”
Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible.
Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (Ctsm). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate Ctsm. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance.
