Tootfinder

‘Chemical cocktail’ of pharmaceuticals found in Djibouti coastal waters https://news.mongabay.com/short-article/2026/06/chemical-cocktail-of-pharmaceuticals-found-in-djibouti-coastal-waters/

‘Chemical cocktail’ of pharmaceuticals found in Djibouti coastal waters
Common medications that billions of people take for ailments like pain, fever and infections were detected in several sites along Djibouti’s Gulf of Tadjourah in East Africa, according to a recent study. Researchers found that untreated urban wastewater contained dangerous concentrations of anti-inflammatory medicine like ibuprofen, caffeine, and the antiepileptic drug carbamazepine, which were contaminating […]

California is making waves with a jaw-dropping plan to settle a 175-year-old score
— returning stewardship of a staggering 7.5 million acres of land and coastal waters to Native tribes.
That’s 7% of the Golden State
— and not by accident.
It matches exactly what the federal government promised indigenous tribes back in the 1850s,
before quietly backing out.

California plots return of 7.5 million acres of land and coastal waters to Indigenous tribes
California is making waves with a jaw-dropping plan to settle a 175-year-old score — handing back a staggering 7.5 million acres of land and coastal waters to Native tribes.

Regret Minimization in Single-Dimensional Contract-Design with Binary Actions
Riccardo Poiani, Martino Bernasconi, Andrea Celli
https://arxiv.org/abs/2606.06125 https://arxiv.org/pdf/2606.06125 https://arxiv.org/html/2606.06125
arXiv:2606.06125v1 Announce Type: new
Abstract: We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme (i.e., a contract) in order to induce an agent to take a costly action leading to a favorable outcome. We consider the online extension of the classical (one-shot) principal-agent problem, in which the principal repeatedly interacts with agents by proposing contracts over multiple rounds. The principal has no information about the agents and, crucially, does not observe their actions. As a result, the principal must learn an optimal contract using only the realized outcomes observed at each round. We focus on the setting with binary actions and single-dimensional agent types, where the agent's private type represents their cost per unit-of-effort. For adversarial-type sequences, we provide tight $\Theta(T^{2/3})$ regret guarantees. Remarkably, this rate is completely independent of the number of outcomes $m$. The upper bound is based on two key components: 1) a reduction to a one-dimensional threshold optimization problem and 2) a non-uniform discretization to handle the non-Lipschitz nature of the problem. Moreover, in the case of a single (fixed) hidden type, we show that it is possible to improve the rates and provide a tight $\widetilde{\Theta}(\sqrt{T})$ regret bound. Our algorithm is based on an explore-then-commit strategy where we first approximately learn the hidden type via a stochastic binary search, and then we commit to a ``robustified'' near-optimal contract.
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True to the promise of
“drill, baby, drill,”
leaders in Washington are doubling down on attacks
against Alaska’s wildlands
and the clean water, wildlife and ways of life they sustain.
Here’s how the administration is advancing drilling and mining attacks on public lands:
->> Opened every acre of the Arctic National Wildlife Refuge’s fragile coastal plain  to oil and gas leasing
->> Approved and reissued permits to move forward on the cont…

Homepage | The Wilderness Society
Our Mission: United people to protect our wild places. Our Work: Climate Solutions, Landscape Connectivity, Community Led Conservation Each day thousands of acres of wild places are lost to mining, drilling, logging and other development. But we don't have to give it all away. Together, we can save our wildest lands for future generations.

Volador 1.0: A Data-Driven Air-Sea Full-Coupling Regional Forecast Model with Submesoscale-Permitting Based on MOE-Swin-Transformer Framework
Yuhang Zhu, Jianxin Wang, Yu-kun Qian, Yineng Li, Yahui Liu, Yankun Gong, Shilin Tang, Shiqiu Peng, Tao Song
https://arxiv.org/abs/2605.24032 https://arxiv.org/pdf/2605.24032 https://arxiv.org/html/2605.24032
arXiv:2605.24032v1 Announce Type: new
Abstract: A data-driven air-sea full-coupling regional forecast model with submesoscale-permitting, named "Volador 1.0", is developed for the South China Sea (SCS). The model features a Swin-Transformer framework integrated with a Mixture-of-Experts (MoE) system, a latent space interaction architecture based on Cross-Grid Bidirectional Cross-Attention, and a fast-slow dual-branch architecture. Both the three-month hindcast test and the 15-day operational real-time forecasting demonstrate that Volador 1.0 has a very encouraging and promising performance in 0-72h forecasting of temperature and salinity in the 0-500m upper ocean as well as the sea surface height with root-mean-square-error (RMSE) or mean absolute error (MAE) smaller than or at least comparable to those from the reanalysis datasets REDOS V2.0 and GLORYS12 and the state-of-the-art regional numerical model Regional Ocean Modeling System (ROMS). In particular, Volador 1.0 demonstrates its capability of capturing/forecasting submesoscale processes including internal waves, with an energy spectrum well representing sub- to mesoscale energy cascade as expected by the classical turbulence theory. Further analysis based on ablation experiments shows that the air-sea full-coupling framework, which takes into account the dynamic exchanges of momentum and heat fluxes between the atmosphere and the ocean, indeed helps improve the model's performance compared to the non-full-coupling one. Volador 1.0, though still subject to refinement in the coming future with a large space for improvement, blazes a path for an accurate, fine and fast marine environment forecasting, and thus could help promote our capability of disaster prevention and mitigation in the SCS as well as in other coastal regions where these innovative techniques can be applied.
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Princess Bedtime Stories With Eva And Gigi
Eva and Gigi are two little princesses who live in a magical castle with their mum and dad, the King and Queen of Storyland...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/princess-bedtime-sto…

Customs and Border Protection (CBP) is seeking permission from the California city of San Clemente
to install an Anduril Industries surveillance tower on a cliff that would allow for constant monitoring of entire coastal neighborhoods. 
The proposed tower is Anduril's Sentry, part of the "Autonomous Surveillance Tower" (AST) program.
While CBP says it will primarily monitor the coastline for boats carrying migrants,
it will actually be installed 1.5 mile…

California Coastal Community Must Reject CBP's AI-Powered Surveillance Tower
Customs and Border Protection (CBP) is seeking permission from the California city of San Clemente to install an Anduril Industries surveillance tower on a cliff that would allow for constant monitoring of entire coastal neighborhoods. The proposed tower is Anduril's Sentry, part of the Autonomous...