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Association of Perinatal HIV Exposure and HIV Disease Severity With BP in Youth

RESEARCH ARTICLE: association of perinatal HIV exposure and HIV disease severity with BP in youth.

BACKGROUND: HIV infection is associated with cardiovascular events in adults. We compared mean blood pressure (BP) obtained at study visits between youth with/without perinatally acquired HIV infection and evaluated whether HIV disease severity was associated with BP. METHODS: BP was compared between participants with/without HIV in the Adolescent Master Protocol of the Pediatric HIV/AIDS Cohort Study. Marginal repeated measures analyses using generalized estimating equations evaluated the association of HIV disease severity with BP index (mean BP/95th percentile BP) and abnormal BP. RESULTS: 447 youth with HIV and 226 youth without HIV were included. Youth with HIV were more often Black non-Hispanic (66% versus 54%), had greater household income (54% versus 35%), and lower measures of adiposity than those without.

Critical flaw in wolfSSL library enables forged certificate use

A critical vulnerability in the wolfSSL SSL/TLS library can weaken security via improper verification of the hash algorithm or its size when checking Elliptic Curve Digital Signature Algorithm (ECDSA) signatures.

Researchers warn that an attacker could exploit the issue to force a target device or application to accept forged certificates for malicious servers or connections.

WolfSSL is a lightweight TLS/SSL implementation written in C, designed for embedded systems, IoT devices, industrial control systems, routers, appliances, sensors, automotive systems, and even aerospace or military equipment.

Striatal Dopamine Transporter and Rest Tremor in Parkinson DiseaseA Clinical Validation

【】 Full article: (Authored by Nader Butto, from Petah Tikva, Israel.)

This work presents a vortex-based geometric interpretation of atomic structure, in which electrons are described as localized vortex excitations embedded in a structured vacuum, offering a physically intuitive framework for understanding shells, subshells, orbitals, quantum numbers, and electron configurations without altering the formal structure of quantum mechanics. QUANTUM_NUMBERS vortex_geometry ElectronConfiguration.

1. Introduction

The atomic structure of matter represents one of the foundational achievements of modern physics and chemistry. Early experimental investigations by Rutherford established the nuclear model of the atom [1], while Bohr introduced the concept of discrete electronic energy levels to explain atomic spectra [2]. Sommerfeld subsequently extended this picture by incorporating angular momentum quantization and relativistic corrections [3]. These developments paved the way for the formulation of quantum mechanics, which replaced classical electron orbits with a wave-based description of electronic states.

The quantum-mechanical framework, formalized through the work of Schrödinger, Pauli, Born, and Dirac, provides a mathematically rigorous and highly successful description of atomic behavior [4]-[7]. Within this formalism, electrons are described by wavefunctions whose squared modulus gives the probability density of finding an electron in a given region of space. Atomic orbitals arise as solutions of the Schrödinger equation and are characterized by a set of quantum numbers that determine their energy, angular momentum, spatial orientation, and spin. This approach accurately predicts atomic spectra, selection rules, and chemical periodicity.

Math Professor Wrote Wrong Equation on the Board to Test a Black Student—But He Was a Genius Student

What if creativity wasn’t magic—but math?
In this video, we explore the mathematics of creativity through psychology, philosophy, and science. From Dean Keith Simonton’s law of large numbers, Margaret Boden’s theory of combinational creativity, Zipf’s Law, Malcolm Gladwell’s 10,000-hour curve, and even cellular automata—we break down how imagination follows hidden equations.

Whether you’re a student, teacher, scientist, engineer, or philosopher, this video will change how you think about art, science, and human innovation.

Chapters:
00:00 – Intro: Is Creativity Random?
00:34 – The Law of Large Numbers
01:42 – Zipf’s Law of Ideas
02:33 – Combinational Creativity (Boden)
03:15 – Time & Growth (Gladwell)
03:58 – Edge of Chaos (Complexity Theory)
04:48 – The Formula for Creativity.

📌 Subscribe for more videos on philosophy, science, and abstract thinking.

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Physics-Informed LSTM for Fatigue Life Prediction of Rubber Isolators under Thermo-Mechanical Coupling

【】 Full article: (Authored by Shen Liu and Fei Meng, from University of Shanghai for Science and Technology, China.)

Rubber supports are essential in automotive, heavy machinery, and aerospace engineering. They offer excellent hyper elasticity, viscoelastic dissipation, and noise reduction. However, their fatigue evolution under coupled thermo-mechanical loading is exceptionally complex. This study develops an LSTM-Physics-Informed Neural Network (PINN) framework that integrates prior physical knowledge transfer with Partial Differential Equation (PDE) constraints, to address the challenge of predicting the fatigue life of rubber_isolators under thermo-mechanical-damage coupling.

Abstract

Rubber supports are ubiquitous in modern vibration isolation systems. Their fatigue evolution under coupled thermo-mechanical loading is exceptionally complex. Traditional life prediction methods rely heavily on empirical formulas. These methods often lack accuracy and extrapolation capabilities under varying temperatures. To address this, we propose a novel LSTM-PINN architecture. This framework integrates physical constitutive relations and temperature effects into a neural network. We used transfer learning to extract baseline physical data across wide temperature ranges. Long Short-Term Memory (LSTM) layers capture sequential loading features. We embedded partial differential equations (PDEs) into the loss function. These PDEs are based on strain energy density (SED) and Arrhenius thermodynamics. This approach ensures strict adherence to physical laws. Results demonstrate that LSTM-PINN achieves high precision even with small datasets. It also exhibits superior out-of-distribution (OOD) generalization. This framework provides a new paradigm for evaluating the reliability of rubber components.

Rubber Isolator, Fatigue Life, PINN, LSTM, Thermo–Mechanical Coupling

Universal surface-growth law confirmed in two dimensions after 40 years

Crystals, bacterial colonies, flame fronts: the growth of surfaces was first described in the 1980s by the Kardar–Parisi–Zhang equation. Since then, it has been regarded as a fundamental model in physics, with implications for mathematics, biology, and computer science.

Now—40 years later—a Würzburg-based research team from the Cluster of Excellence ctd.qmat has achieved the first experimental demonstration of KPZ behavior on 2D surfaces in space and time.

This was made possible by sophisticated materials engineering and a bold experimental approach: researchers injected polaritons—hybrid particles composed of light and matter—into the material. The results have been published in Science.

Predicting cardiovascular events from routine mammograms using machine learning

Background Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises. Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk. We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images.

Methods Lifepool is a cohort of women with at least one screening mammogram linked to hospitalisation and death databases. A deep learning model based on DeepSurv architecture was developed to predict major cardiovascular events from mammography images. Model performance was compared against standard risk prediction models using the concordance index, comparative to the Harrells C-statistic.

Results There were 49 196 women included, with a median follow-up of 8.8 years (IQR 7.7–10.6), among whom 3,392 experienced a first major cardiovascular event. The DeepSurv model using mammography features and participant age had a concordance index of 0.72 (95% CI 0.71 to 0.73), with similar performance to modern models containing age and clinical variables including the New Zealand ‘PREDICT’ tool and the American Heart Association ‘PREVENT’ equations.

AI trained like a Rubik’s Cube solver simplifies particle physics equations

For years, Rutgers physicist David Shih solved Rubik’s Cubes with his children, twisting the colorful squares until the scrambled puzzle returned to order. He didn’t expect the toy to connect to his research, but recently he realized the logic behind the puzzle was exactly what he needed to solve a problem involving particle physics.

That idea led to a new artificial intelligence (AI) method that can simplify some of the extremely complex equations used in particle physics. Shih described the method in a study posted to the arXiv preprint server, a widely used site where scientists share new research.

“In reaching our solutions, we found that an analogy between mathematical simplification and solving Rubik’s Cubes was key,” said Shih, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences. “Both can be viewed as scrambling and unscrambling problems.”

Quantum computing without interruptions

Mid-circuit measurements are one of the biggest practical hurdles in quantum error correction on encoded qubits. Researchers in Innsbruck and Aachen have now proposed and experimentally demonstrated that a universal fault-tolerant quantum algorithm can be executed without such measurements. Using a trapped-ion quantum processor, the team successfully ran Grover’s quantum search algorithm on three logical qubits.

A key bottleneck in today’s leading approaches to quantum error correction is the need to repeatedly pause and measure the quantum processor mid-computation, a process that is slow, technically demanding, and itself a significant source of errors.

Now, a joint team from the University of Innsbruck, RWTH Aachen University, Forschungszentrum Jülich and spin-off Alpine Quantum Technologies (AQT) has demonstrated fault-tolerant quantum computation without any such interruptions.

A new equation may help baristas produce the perfect espresso shot every time

Everyone’s idea of the perfect cup of coffee is different. Whether you have yours black, with a splash of milk or extra sweet, you like it your way. But is there a universal law that governs how that flavor gets into your cup? According to new research published in the journal Royal Society Open Science, part of the answer lies in the permeability of the puck, the name for the bed of tightly packed coffee grains through which water passes under high pressure.

To make a really good espresso is essentially trial and error. No matter the coffee type, baristas must constantly adjust how finely the coffee is ground and how much is packed into the puck to achieve the right flow rate. This is the volume of liquid passing through the puck over a specific amount of time and determines how long the water stays in contact with the grounds. This new research helps take some of the guesswork out of the process.

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