Tootfinder

It's time for building work. Uh ho.
The bedroom that has remained undecorated and mostly dark since I moved in more than 20 years ago will finally be stripped and rebuilt according to my specifications.
What is my specifications? Humm. Well. I live alone and nobody can tell me what to do so I'm doing it Tardis themed.
Mostly I built the shape of it in Blender last year and the tardis theme only popped out when I readdressed that design this spring with an eye to actual colors. Adding the frosted glass to the windows on the top cupboards is the main thing that sells it.
Here's the video I made for the builder.
#diy #interiorDesign #rebuild #bedroom #doctorWho #tardis

🚜 One-time nitrogen application boosts ammonia emissions in maize fields
#nitrogen

One-time nitrogen application boosts ammonia emissions in maize fields
Recently, a research team in the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has revealed that one-time nitrogen (N) application, a labor-saving practice favored by many farmers, increases both soil and canopy ammonia (NH3) emissions in maize fields, while reducing grain yield and nitrogen use efficiency.

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks
Yanbing Chen, Jonathan Nelson, Bing Zhou, Ryan Zhenqi Zhou, Shan Ye, Haokun Liu, Zhining Gu, Armita Kar, Hoeyun Kwon, Pengyu Chen, Maoran Sun, Yuhao Kang
https://arxiv.org/abs/2508.09043

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks
Academia is profoundly influenced by faculty hiring networks, which serve as critical conduits for knowledge dissemination and the formation of collaborative research initiatives. While extensive research in various disciplines has revealed the institutional hierarchies inherent in these networks, their impacts within GIScience remain underexplored. To fill this gap, this study analyzes the placement patterns of 946 GIScience faculty worldwide by mapping the connections between PhD-granting ins…

Russian bots pose as The Insider to spread disinfo about Moldova’s President Maia Sandu, alleging she uses orphans to win votes
https://theins.press/en/news/283467

Russian bots pose as The Insider to spread disinfo about Moldova’s President Maia Sandu, alleging she uses ...
The Kremlin-linked Matryoshka bot network, named after the famous Russian nesting doll, is spreading disinformation on Twitter and Bluesky while posing as The Insider. The false narratives in the network’s latest campaign target Moldovan President Maia Sandu and her ruling Action and Solidarity Party (PAS), as revealed by the bot-monitoring project Bot Blocker (@antibot4navalny).

Teaching LLMs to Speak Spectroscopy
Nesar Ramachandra, Yuan-Sen Ting, Zechang Sun, Azton Wells, Salman Habib
https://arxiv.org/abs/2508.10075 https://arxiv…

Teaching LLMs to Speak Spectroscopy
Pre-trained Large Language Models (LLMs) have revolutionized text processing, yet adapting Transformer-based neural networks to non-textual scientific modalities typically requires specialized architectures and extensive computational resources. We demonstrate that LLaMA-3.1-8B can be efficiently repurposed to predict galaxy redshifts from spectroscopic data through Low-Rank Adaptation (LoRA), achieving competitive performance while preserving its linguistic capabilities. Using only 16 GPU-hour…

Journal of Ecumenical Studies: Religious Pluralism, Freedom, and the Founding of the Nation https://networks.h-net.org/group/announcements/20121201/journal-ecumenical-studies-religious-pluralism-freedom-and-founding…

Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference
Sichao Shan, Han Ye, Zhengmei Yang, Junpeng Hou, Zhitong Li
https://arxiv.org/abs/2510.00767

Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference
Deep learning (DL) has revolutionized many fields such as materials design and protein folding. Recent studies have demonstrated the advantages of DL in the inverse design of structural colors, by effectively learning the complex nonlinear relations between structure parameters and optical responses, as dictated by the physical laws of light. While several models, such as tandem neural networks and generative adversarial networks, have been proposed, these methods can be biased and are difficul…