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Quantum crystals offer a blueprint for the future of computing and chemistry

Imagine industrial processes that make materials or chemical compounds faster, cheaper, and with fewer steps than ever before. Imagine processing information in your laptop in seconds instead of minutes or a supercomputer that learns and adapts as efficiently as the human brain. These possibilities all hinge on the same thing: how electrons interact in matter.

A team of Auburn University scientists has now designed a new class of materials that gives scientists unprecedented control over these tiny particles. Their study, published in ACS Materials Letters, introduces the tunable coupling between isolated-metal molecular complexes, known as solvated electron precursors, where electrons aren’t locked to atoms but instead float freely in open spaces.

From their key role in energy transfer, bonding, and conductivity, electrons are the lifeblood of chemical synthesis and modern technology. In , electrons drive redox reactions, enable bond formation, and are critical in catalysis. In technological applications, manipulating the flow and interactions between electrons determines the operation of electronic devices, AI algorithms, photovoltaic applications, and even . In most materials, electrons are bound tightly to atoms, which limits how they can be used. But in electrides, electrons roam freely, creating entirely new possibilities.

The Role of Artificial Intelligence in Early Cancer Diagnosis

Diagnosing cancer at an early stage increases the chance of performing effective treatment in many tumour groups. Key approaches include screening patients who are at risk but have no symptoms, and rapidly and appropriately investigating those who do. Machine learning, whereby computers learn complex data patterns to make predictions, has the potential to revolutionise early cancer diagnosis. Here, we provide an overview of how such algorithms can assist doctors through analyses of routine health records, medical images, biopsy samples and blood tests to improve risk stratification and early diagnosis. Such tools will be increasingly utilised in the coming years.

China’s AI Hospital with 14 Robotic Doctors — The Future of Medicine!

In a monumental leap for healthcare innovation, China has opened the world’s first fully AI-powered hospital, staffed by 14 artificial intelligence “doctors” capable of diagnosing, treating, and managing up to 10,000 virtual patients per day.

This revolutionary facility, developed by Tsinghua University, is called the Smart Hospital of the Future — and it may represent the most advanced experiment in AI-driven medicine the world has ever seen.

Designed as a testbed for AI medical systems, the hospital blends robotics, machine learning, natural language processing, and big data analytics to simulate full-spectrum care at lightning speed — with zero fatigue, no paperwork errors, and real-time updates from global medical databases.

AI Breakthrough Finally Cracks Century-Old Physics Problem

An AI framework now computes once-impossible physics equations within seconds. The breakthrough redefines how scientists study the behavior of materials. Researchers at the University of New Mexico and Los Alamos National Laboratory have created an advanced computational framework that solves a m

When mathematics meets aesthetics: Tessellations as a precise tool for solving complex problems

In a recent study, mathematicians from Freie Universität Berlin have demonstrated that planar tiling, or tessellation, is much more than a way to create a pretty pattern. Consisting of a surface covered by one or more geometric shapes with no gaps and no overlaps, tessellations can also be used as a precise tool for solving complex mathematical problems.

This is one of the key findings of the study, “Beauty in/of Mathematics: Tessellations and Their Formulas,” authored by Heinrich Begehr and Dajiang Wang and recently published in the scientific journal Applicable Analysis. The study combines results from the field of complex analysis, the theory of partial differential equations, and geometric function theory.

A central focus of the study is the “parqueting-reflection principle.” This refers to the use of repeated reflections of geometric shapes across their edges to tile a plane, resulting in highly symmetrical patterns. Aesthetic examples of planar tessellations can be seen in the work of M.C. Escher. Beyond its visual appeal, the principle has applications in mathematical analysis—for example, as a basis for solving classic boundary value problems such as the Dirichlet problem or the Neumann problem.

Michael Freedman | The Poincaré Conjecture and Mathematical Discovery

Millennium Prize Problems Lecture 9/17/2025
Speaker: Michael Freedman, Harvard CMSA and Logical Intelligence.

Title: the poincaré conjecture and mathematical discovery.

Abstract: The AI age requires us to re-examine what mathematics is about. The Seven Millenium Problems provide an ideal lens for doing so. Five of the seven are core mathematical questions, two are meta-mathematical – asking about the scope of mathematics. The Poincare conjecture represents one of the core subjects, manifold topology. I’ll explain what it is about, its broader context, and why people cared so much about finding a solution, which ultimately arrived through the work of R. Hamilton and G. Perelman. Although stated in manifold topology, the proof requires vast developments in the theory of parabolic partial differential equations, some of which I will sketch. Like most powerful techniques, the methods survive their original objectives and are now deployed widely in both three-and four-dimensional manifold topology.

A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

In this section, the authors reveal the findings of this review. The findings are categorized based on data modalities, showcasing the effectiveness of AI models in terms of evaluation metrics. Figure 1 summarizes the extraction process, providing a clear representation of the progression from article identification to final selection of studies. The initial search yielded a substantial total of 1,175 articles. Based on the inclusion and exclusion criteria, the subsequent screening process excluded irrelevant articles. By meticulously filtering the literature, 25 studies were deemed suitable for inclusion into this review.

A chronology of research studies on the uses of AI in DS diagnosis is shown in Figure 2. This timeline highlights a considerable growth in academic interest over the course of the years. A single study was published per year between the years 2013 and 2017. Technical restrictions and the availability of datasets restricted the early attempts to integrate AI into DS diagnoses. Advancements in deep learning and machine learning technologies have been driven by continuous growth in research, representing a milestone in 2021. These developments are signs of increasing confidence in the ability of artificial intelligence to identify and resolve challenging diagnostic problems. The year 2021 reaches a high with four studies, indicating a surge of innovation. This may result from improved computing tools and a more extensive understanding of the usefulness of artificial intelligence in the medical field. However, the minor decline in 2022 and 2023, with three studies, may indicate difficulties in maintaining the rapid pace of research. These challenges may include restricted access to different datasets or limitations to clinical adoption.

In 2024, there was a significant increase in DS diagnostics approaches, achieving a total of seven studies. This increase is a result of developments in AI algorithms, collaborations across diverse fields, and the significant role of AI in medical diagnosis. It demonstrates the increased academic and multidisciplinary interest in developing effective AI-powered DS detection models. In addition, an increasing trajectory highlights the importance of maintaining research efforts in order to overcome current challenges in implementing AI applications in the healthcare sector.

Novel AI tool opens 3D modeling to blind and low-vision programmers

Blind and low-vision programmers have long been locked out of three-dimensional modeling software, which depends on sighted users dragging, rotating and inspecting shapes on screen.

Now, a multiuniversity research team has developed A11yShape, a new tool designed to help blind and low-vision programmers independently create, inspect and refine three-dimensional models. The study is published on the arXiv preprint server.

The team consists of Anhong Guo, assistant professor of electrical engineering and computer science at the University of Michigan, and researchers from the University of Texas at Dallas, University of Washington, Purdue University and several partner institutions—including Gene S-H Kim of Stanford University, a member of the blind and low-vision community.

Algorithm reveals ‘magic sizes’ for assembling programmable icosahedral shells at minimal cost

Over the past decade, experts in the field of nanotechnology and materials science have been trying to devise architectures composed of small structures that spontaneously arrange themselves following specific patterns. Some of these architectures are based on so-called icosahedral shells, structures with 20 different triangular phases that are symmetrically organized.

2025 Nobel Prize in Physics Peer Review

Introduction.

Grounded in the scientific method, it critically examines the work’s methodology, empirical validity, broader implications, and opportunities for advancement, aiming to foster deeper understanding and iterative progress in quantum technologies. ## Executive Summary.

This work, based on experiments conducted in 1984–1985, addresses a fundamental question in quantum physics: the scale at which quantum effects persist in macroscopic systems.

By engineering a Josephson junction-based circuit where billions of Cooper pairs behave collectively as a single quantum entity, the laureates provided empirical evidence that quantum phenomena like tunneling through energy barriers and discrete energy levels can manifest in human-scale devices.

This breakthrough bridges microscopic quantum mechanics with macroscopic engineering, laying foundational groundwork for advancements in quantum technologies such as quantum computing, cryptography, and sensors.

Overall strengths include rigorous experimental validation and profound implications for quantum information science, though gaps exist in scalability to room-temperature applications and full mitigation of environmental decoherence.

Framed within the broader context, this award highlights the enduring evolution of quantum mechanics from theoretical curiosity to practical innovation, building on prior Nobel-recognized discoveries like the Josephson effect (1973) and superconductivity mechanisms (1972).

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