Tootfinder

Amid US public media funding cuts, a look at how the model of private donations can lead to an audience that's older, whiter, and richer than average Americans (Sarah Scire/Nieman Lab)
https://www.niemanlab.org/2025/11/funding-c…

Funding cuts may make public radio more reliant on old, rich, white donors
After years of interviews, research, and writing, Laura Garbes submitted the final draft of her book on public radio in December 2024. By the time Listeners Like Who? Exclusion and Resistance in the Public Radio Industry published last month, a lot had changed. Following Trump’s election and fede…

Gravity-Induced Modulation of Negative Differential Thermal Resistance in Fluids
Qiyuan Zhang, Juncheng Guo, Juchang Zou, Rongxiang Luo
https://arxiv.org/abs/2510.08909 https://…

Gravity-Induced Modulation of Negative Differential Thermal Resistance in Fluids
We investigate how gravity influences negative differential thermal resistance (NDTR) in fluids modeled by multiparticle collision dynamics. In the integrable case, we derive the heat flux formula for the system exhibiting the NDTR effect, and show that by introducing a gravity along the direction of the thermodynamic force, the temperature difference required for the occurrence of NDTR can be greatly reduced. Meanwhile, we also demonstrate that the heat-bath-induced NDTR mechanism -- originall…

Well-posedness of stationary 2D and 3D convective Brinkman-Forchheimer extended Darcy Hemivariational inequalities
Manil T. Mohan
https://arxiv.org/abs/2509.09335 https://

Well-posedness of stationary 2D and 3D convective Brinkman-Forchheimer extended Darcy Hemivariational inequalities
This study addresses the well-posedness of a hemivariational inequality derived from the convective Brinkman-Forchheimer extended Darcy (CBFeD) model in both two and three dimensions. The CBFeD model describes the behavior of incompressible viscous fluid flow through a porous medium, incorporating the effects of convection, damping, and nonlinear resistance. The mathematical framework captures steady-state flow conditions under a no-slip boundary assumption, with a non-monotone boundary conditi…

Estimation of Resistance Training RPE using Inertial Sensors and Electromyography
James Thomas, Johan Walhstr\"om
https://arxiv.org/abs/2510.03197 https://

Estimation of Resistance Training RPE using Inertial Sensors and Electromyography
Accurate estimation of rating of perceived exertion (RPE) can enhance resistance training through personalized feedback and injury prevention. This study investigates the application of machine learning models to estimate RPE during single-arm dumbbell bicep curls, using data from wearable inertial and electromyography (EMG) sensors. A custom dataset of 69 sets and over 1000 repetitions was collected, with statistical features extracted for model training. Among the models evaluated, a random f…

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Linfeng Wang, Susana Campino, Taane G. Clark, Jody E. Phelan
https://arxiv.org/abs/2510.08703

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Positive selection drives the emergence of adaptive mutations in Mycobacterium tuberculosis, shaping drug resistance, transmissibility, and virulence. Phylogenetic trees capture evolutionary relationships among isolates and provide a natural framework for detecting such adaptive signals. We present a phylogeny-guided graph attention network (GAT) approach, introducing a method for converting SNP-annotated phylogenetic trees into graph structures suitable for neural network analysis. Using 500 M…

Assessment of Hybrid RANS-LES and WALE Formulations for Wake and Resistance Prediction of the BB2 Submarine
Noh Zainal Abidin (GeM), Frederic Grondin (GeM), Pol Muller (Naval Expert), Jean-Fran\c{c}ois Sigrist (Naval Expert)
https://arxiv.org/abs/2510.03383

Assessment of Hybrid RANS-LES and WALE Formulations for Wake and Resistance Prediction of the BB2 Submarine
Submarine hydrodynamics presents unique challenges in accurately predicting flow separation, wake structure, and resistance due to complex geometry and turbulent behaviour at high Reynolds (Re) numbers. Traditional Reynolds-Averaged Navier-Stokes (RANS) approaches are often limited in resolving unsteady flow structures and turbulence in the near and far regions. To address these limitations, hybrid RANS-LES models such as Detached Eddy Simulation (DES) and Large Eddy Simulation (LES) offer impr…

Machine Learning for Polymer Chemical Resistance to Organic Solvents
Shogo Kunieda, Mitsuru Yambe, Hiromori Murashima, Takeru Nakamura, Toshiaki Shintani, Hitoshi Kamijima, Yoshihiro Hayashi, Yosuke Hanawa, Ryo Yoshida
https://arxiv.org/abs/2509.05344

Machine Learning for Polymer Chemical Resistance to Organic Solvents
Predicting the chemical resistance of polymers to organic solvents is a longstanding challenge in materials science, with significant implications for sustainable materials design and industrial applications. Here, we address the need for interpretable and generalizable frameworks to understand and predict polymer chemical resistance beyond conventional solubility models. We systematically analyze a large dataset of polymer solvent combinations using a data-driven approach. Our study reveals th…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…