A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental capacity and probability density methods, it improves ...

A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...

The evidence is solid but not definitive, as the conclusions rely on the absence of changes in spatial breadth and would benefit from clearer statistical justification and a more cautious ...

Abstract: Many statistic based machine learning methods depend on the estimation of probability density function from observations. Non-parametric density estimation algorithms based on minimizing ...

This study provides important insights into how working memory shapes perceptual decisions, using a dual-task design, continuous mouse tracking, and hierarchical Bayesian modeling. By dissociating ...

Evan Soltas and Jonathan Gruber have published a paper titled, “How Costly Is Permitting in Housing Development?”. They ...

Abstract: In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of ...

The year 2025 will be remembered for the ascendance of young Chinese innovators to the forefront of the AI world. It began with DeepSeek’s R1 model release and ended with AI agent Manus being acquired ...

Learn how to select reliable rigid box manufacturers for ecommerce. Evaluate quality, customization, scalability, and pricing ...

This paper examines whether Chinese development finance is associated with faster progress toward Millennium Development Goal style targets in low- and middle-income countries. We combine AidData’s ...

Estimating things that exist is generally easy, but when it comes to estimating things that do not exist, it's more difficult. This is something physicists from Poland and the UK are well aware of. To ...