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AI systems could identify math anxiety from student inputs and change feedback

Math anxiety is a significant challenge for students worldwide. While personalized support is widely recognized as the most effective way to address it, many teachers struggle to deliver this level of support at scale within busy classrooms. New research from Adelaide University shows how artificial intelligence (AI) could help address challenges such as math anxiety by using a student’s inputs and identifying signs of anxiety or disengagement during learning.

Published in npj Science of Learning, the study suggests that when AI systems are designed to use the right data and goals, they can adapt their responses to help counteract negative emotional experiences associated with math, before these feelings escalate.

Lead researcher Dr. Florence Gabriel says AI has the potential to transform how math anxiety is supported, by offering timely, tailored interventions that step through learning and build student well-being.

Using duality to construct and classify new quantum phases

A team of theoretical researchers has found duality can unveil non-invertible symmetry protected topological phases, which can lead to researchers understanding more about the properties of these phases, and uncover new quantum phases. Their study is published in Physical Review Letters.

Symmetry is one of the most fundamental concepts for understanding phases of matter in modern physics—in particular, symmetry-protected topological (SPT) phases, whose quantum mechanical properties are protected by symmetries, with possible applications in quantum computing and other fields.

Over the past few years, non-invertible symmetries, which extend the framework of conventional symmetries, have attracted significant attention in high energy physics and condensed matter physics. However, their complex mathematical structures have made it difficult to understand their corresponding phases of matter, or SPT phases.

Mathematical Innovation Advances Complex Simulations for Science’s Toughest Problems

Berkeley researchers have developed a proven mathematical framework for the compression of large reversible Markov chains—probabilistic models used to describe how systems change over time, such as proteins folding for drug discovery, molecular reactions for materials science, or AI algorithms making decisions—while preserving their output probabilities (likelihoods of events) and spectral properties (key dynamical patterns that govern the system’s long-term behavior).

While describing the dynamics of ubiquitous physical systems, Markov chains also allow for rich theoretical and computational investigation. By exploiting the special mathematical structure behind these dynamics, the researchers’ new theory delivers models that are quicker to compute, equally accurate, and easier to interpret, enabling scientists to efficiently explore and understand complex systems. This advance sets a new benchmark for efficient simulation, opening the door to scientific explorations once thought computationally out of reach.

Background.

SOME PHYSICISTS SUGGEST GRAVITY ISN’T A FORCE AT ALL — BUT A QUANTUM ECHO OF ENTANGLEMENT

Gravity is the most familiar force in human experience, yet it remains the least understood at a fundamental level. Despite centuries of study—from Newton’s law of universal gravitation to Einstein’s general theory of relativity—gravity stubbornly resists unification with quantum mechanics. In recent decades, this tension has led some physicists to propose a radical rethinking of gravity’s nature. According to these ideas, gravity may not be a fundamental force at all, but instead an emergent effect arising from quantum entanglement and the flow of information in spacetime.

This perspective represents a profound conceptual shift. Rather than treating gravity as something particles “exert” on one another, these theories suggest it emerges statistically, much like temperature arises from the collective motion of atoms. This article examines the scientific foundations of this idea, the key theoretical frameworks supporting it, and the evidence—both suggestive and incomplete—that motivates such claims. By analyzing gravity through quantum, thermodynamic, and informational lenses, we gain insight into one of the most ambitious research directions in modern theoretical physics.

The Standard Model of particle physics successfully describes three of the four fundamental interactions: electromagnetism, the weak force, and the strong force. Gravity, however, remains outside this framework. Attempts to quantize gravity using the same methods applied to other forces lead to mathematical infinities that cannot be renormalized.

Exploration of exoplanets: A mathematical solution for investigating their atmospheres

Dr. Leonardos Gkouvelis, researcher at LMU’s University Observatory Munich and member of the ORIGINS Excellence Cluster, has solved a fundamental mathematical problem that had obstructed the interpretation of exoplanet atmospheres for decades. In a paper published in The Astrophysical Journal, Gkouvelis presents the first closed-form analytical theory of transmission spectroscopy that accounts for how atmospheric opacity varies with pressure—an effect that is crucial in the scientific exploration of real atmospheres but had until now been considered mathematically intractable.

For more than 30 years, analytical models were based on a “simplified” atmosphere, as the full mathematical treatment requires solving a complex geometric integral in the presence of altitude-dependent opacity—a problem that could only be tackled using expensive numerical simulations. However, this limitation concealed how the true vertical structure of an atmosphere alters the signals observed by telescopes.

The new model provides key insights into why many exoplanet atmospheres display “muted” spectral features, directly links laboratory molecular-physics data with astronomical observations, and significantly improves agreement with real data—both for Earth’s atmosphere and for high-precision observations of exoplanets.

Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more energy-efficient computation. In this computing method, input data are encoded as a set of temperatures using the waste heat already present in a device.

The flow and distribution of heat through a specially designed material forms the basis of the calculation. Then the output is represented by the power collected at the other end, which is a thermostat at a fixed temperature.

The researchers used these structures to perform matrix vector multiplication with more than 99% accuracy. Matrix multiplication is the fundamental mathematical technique machine-learning models like LLMs utilize to process information and make predictions.

Mapping cell development with mathematics-informed machine learning

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions via a process called cell fate determination. The fate of individual cells, or in other words, what type of cells they will become, is influenced both by predictable biological signals and random physiological fluctuations.

Over the past decades, medical researchers and neuroscientists have been able to study these processes in greater depth, using a technique known as single-cell RNA sequencing (scRNA-seq). This is an experimental tool that can be used to measure the gene activity of individual cells.

To better understand how cells develop over time, researchers also rely on mathematical models. One of these models, dubbed the drift-diffusion equation, describes the evolution of systems as the combination of predictable changes (i.e., drift) and randomness (i.e., diffusion).

Elon Musk Holds Surprise Talk At The World Economic Forum In Davos

The musk blueprint: navigating the supersonic tsunami to hyperabundance when exponential curves multiply: understanding the triple acceleration.

On January 22, 2026, Elon Musk sat down with BlackRock CEO Larry Fink at the World Economic Forum in Davos and delivered what may be the most important articulation of humanity’s near-term trajectory since the invention of the internet.

Not because Musk said anything fundamentally new—his companies have been demonstrating this reality for years—but because he connected the dots in a way that makes the path to hyperabundance undeniable.

[Watch Elon Musk’s full WEF interview]

This is not visionary speculation.

This is engineering analysis from someone building the physical infrastructure of abundance in real-time.

Scientists Uncover Hidden Weakness in Quantum Encryption

Quantum key distribution (QKD) is a next generation method for protecting digital communications by drawing on the fundamental behavior of quantum particles. Instead of relying on mathematical complexity alone, QKD allows two users to establish a shared secret key in a way that is inherently resistant to interception, even if the communication channel itself is not private.

When an unauthorized observer attempts to extract information, the quantum states carrying the data are unavoidably altered, creating telltale disturbances that signal a potential security breach.

The real-world performance of QKD systems, however, depends on precise control of the physical link between sender and receiver. One of the most influential factors is pointing error, which occurs when the transmitted beam does not perfectly align with the receiving device.

Neuropsychiatric symptoms in cognitive decline and Alzheimer’s disease: biomarker discovery using plasma proteomics

Placental toxicology progress!

Commonly used in vitro and in vivo placental models capture key placental functions and toxicity mechanisms, but have significant limitations.

The physiological relevance of placental models varies, with a general hierarchy of simple in vitro complex in vitro/ organ-on-chip in vivo, but species-of origin considerations may alter their relevance to human physiology.

Cellular, rodent, human, and computational modeling systems provide insights into placental transport, physiology, and toxicology linked to maternal–fetal health.

Recent advances in 3D culture and microfluidic technologies offer more physiologically relevant models for studying the placenta.

Mathematical modeling approaches can integrate mechanistic physiological data and exposure assessments to define key toxicokinetic parameters.

Environmental chemical concentrations and omic data obtained from placental tissues can link toxicant influences on placental function to adverse birth outcomes.

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