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Solar Wind Waves Are Stripping Mars’ Atmosphere into Space

Dual-spacecraft observations reveal that Kelvin-Helmholtz waves at Mars’s ionospheric boundary trigger sudden, violent bursts that strip away atmospheric plasma far faster than steady escape routes. [http://labroots.com/trending/space/30856/solar-wind-waves-st…re-space-2](http://labroots.com/trending/space/30856/solar-wind-waves-st…re-space-2)

How has Mars lost its atmosphere over time? This is what a recent study published in Science Advances hopes to address as a team of scientists investigated a connection between the solar wind the Mars’ atmospheric loss. This study has the potential to help scientists better understand how Mars went from a potentially habitable world to the cold and dry planet today, along with gaining insight into other worlds.

For the study, the researchers analyzed a combination of data obtained from NASA’s Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft and China’s Tianwen-1 mission. The primary motivation behind the study was to address a longstanding knowledge gap regarding the physical processes occurring when the solar wind strikes Mar’s atmosphere and how this results in atmospheric loss. While Tianwen-1 data was used to track the incoming solar wind, MAVEN data was used to observe how this incoming solar wind impacted atmospheric loss.

In the end, the researchers found a known phenomenon called Kelvin–Helmholtz (KH) waves that are produced as the incoming solar wind collides with Mars’ atmosphere. KH waves are produced when two fluids or gases of different speeds and densities slied past each other and can often be seen in Earth’s clouds. The researchers concluded that these KH waves are the reason behind the spikes in atmospheric ions being lost to space.

Automated Speech Analysis to Identify Clinical, Anatomical, and Pathological Variants of Primary Progressive Aphasia

This cross-sectional study investigates if automated analysis of connected speech can distinguish primary progressive aphasia variants and reflect neuroanatomical and neuropathologic substrates.

New strategy for designing ultra-fast charging batteries could prevent hazardous lithium plating

A redesigned lithium-ion anode retained 86% of its initial capacity at a demanding 10C charge rate and stayed stable for more than 250 cycles, while aiming to reduce hazardous lithium plating during fast charging.

The rapid progress in electric vehicles and high-power electronics has increased the demand for ultra-fast-charging lithium-ion (Li-ion) batteries. However, during fast charging, current Li-ion rechargeable batteries suffer from severe degradation in power and potential catastrophic failure, increasing safety risks. This is mainly due to electrochemical instability at the anode–electrolyte interface, causing hazardous Li metal plating on their surface and poor thermal stability.

Recently, high-voltage anode materials have emerged as promising alternatives because they prevent excessive lithium plating and the formation of unstable solid-electrolyte interface layers. Despite these advantages, current state-of-the-art materials are limited by poor ionic conductivity and thermal stability, reducing power output and long-term reliability.

To address these issues, a research team led by associate professor Dongwook Han from Seoul National University of Science and Technology in South Korea developed a novel strategy.

🏰 The Only Moat That Survives AI

Christian Catalini’s central argument is that AI is commoditizing intelligence itself, so the traditional sources of competitive advantage (“moats”) are becoming much less durable. Instead, the scarce resource shifts from *generating answers* to *verifying which answers are actually correct*.

So what is actually left? Today’s guest has spent his career answering that question with models rather than vibes. Christian Catalini co-founded Lightspark, co-created Libra at Meta, founded the MIT Cryptoeconomics Lab, and is a Research Associate and Senior Lecturer at MIT. His answer is uncomfortable and, I think, correct: most of the network effects we treat as permanent are far weaker than they look, and exactly one kind gets stronger every time the models improve. He calls it a verification-grade network effect, and if he is right about it, the money in this industry lands somewhere very different from where most of us are pointing.

I will stay out of the way and let the work speak for itself.

If you also think you have an exciting contribution, apply at the following link: [CLICK THIS].

Texture Analysis of 68GaDOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors

Objectives: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). Methods: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log10, GLCM Entropy-log10, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. Results: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (p = 0.0079), grading (p = 0.0130), SUVmax (p = 0.0017), SUVmean (p = 0.0011), CoV (p = 0.0037), HISTO Entropy-log10 (p = 0.0039), GLCM Entropy-log10 (p = 0.0044), and GLCM Dissimilarity (p = 0.0063). At multivariate analysis, only GLCM Entropy-log10 was retained in the model (χ2 = 7.7120, p = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log10 >1.28 had a significantly better OS than patients with GLCM Entropy-log10 ≤1.28 (χ2 = 10.6063, p = 0.0011). Conclusions: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients.

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