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In a closed-door simulation, insurers played out their response to a mass disruption by China's Volt Typhoon hackers--and found a nightmare scenario. It's around an hour and 10 minutes into the role-playing game I've been invited to observe, a simulated catastrophic cyberattack on US water utilities, when the whole thing begins to feel less like a fun afternoon playing Dungeons & Dragons and more like a plausible threat to civilization. A full 24 hours of in-game time have passed since hackers disrupted 5,000 water utilities across the United States in this imagined scenario. Joshua Corman, the former Cybersecurity and Infrastructure Security Agency strategist serving as our dungeon master, stands at the front of a conference space in an office tower high above Times Square, narrating the latest updates to the game's participants, a few dozen insurance executives set up in six teams. All of them have gone disturbingly silent. It's about to get harder," Corman says. "I'm going to share a few things, and it's going to hurt." It is, of course, still the same April afternoon as when we started--but in game time, the second-order effects of widespread water outages have started to become clear. Food refrigeration systems are failing at cold storage warehouses. Water-dependent drug and chemical manufacturing has been bottlenecked, leading to insulin shortages. Data centers' cooling systems are failing, causing outages of cloud services. Most critically, 2,000 hospitals are without water, hampering patient care and in some cases leading to evacuations as HVAC systems shut down and the July heat--the game takes place just before Independence Day in 2027--bakes facilities. Worse yet, Corman is playing a looping video onscreen, at the front of the room, showing a burst water main: The hackers have managed to trigger not just IT disruption but also, in at least some cases, real physical destruction that will take far longer to fix. "Everyone downstream is without water pressure," Corman says. "There are no breaks in real incident response," Corman explains just before the giant water pipe starts gushing onscreen. "If you have to go to the bathroom, go to the bathroom.

'We're up against forces that have all the money in the world': Erin Brockovich on her battle against AI datacentres In 1993, she squeezed a $333m settlement from a Californian energy company in a scandal over contaminated water. Three decades later, she has a new target in her sights - and it's global When Erin Brockovich woke to find 30 emails from people from the same town, she realised something was going on. People email Brockovich all the time because of what happened in 1993, when she was instrumental in suing Pacific Gas and Electric Company (PG&E) on behalf of residents of the town of Hinkley, California, whose groundwater had been contaminated. The case resulted in a settlement of $333m - then the largest ever payout for a direct-action lawsuit. When she was immortalised by Julia Roberts in the 2000 film Erin Brockovich, she became the hero we didn't know we needed, a modern day Joan of Arc.

The deals are almost feral. We are still here in the trenches digging up the deals and trends. It is the final day of Amazon Prime Day--the final countdown, the time of FOMO dread, the last official day of the best deals. The fourth day is the day when we bring you the sun, the moon, and the stars. After today, many things will cost a little bit more for the foreseeable future-- especially laptops. But new deals are still rolling out all day today. Friday is the day when a lot of you get your paychecks, and so Amazon is filling up the virtual racks with impulse buys.

Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchmarks, detecting only fragments of anomalous segments and overstating performance. In this paper, we introduce OracleAD, a simple and interpretable unsupervised framework for multivariate time series anomaly detection. OracleAD encodes each variable's past sequence into a single causal embedding to jointly predict the present time point and reconstruct the input window, effectively modeling temporal dynamics. These embeddings then undergo self-attention mechanism to project them into a shared latent space and capture spatial relationships.

The lack of fine-grained, large-scale datasets on water availability presents a critical barrier to applying machine learning (ML) for agricultural water management. Since there are multiple natural and anthropogenic factors that influence water availability, incorporating diverse multimodal features can significantly improve modeling performance. However, integrating such heterogeneous data is challenging due to spatial misalignments, inconsistent formats, semantic label ambiguities, and class imbalances. To address these challenges, we introduce IRRISIGHT, a large-scale, multimodal dataset spanning 20 U.S. states. It consists of 1.4 million pixel-aligned 224 224 patches that fuse satellite imagery with rich environmental attributes. We develop a robust geospatial fusion pipeline that aligns raster, vector, and point-based data on a unified 10m grid, and employ domain-informed structured prompts to convert tabular attributes into natural language. With irrigation type classification as a representative problem, the dataset is AI-ready, offering a spatially disjoint train/test split and extensive benchmarking with both vision and vision-language models. Our results demonstrate that multimodal representations substantially improve model performance, establishing a foundation for future research on water availability.

The lack of fine-grained, large-scale datasets on water availability presents a critical barrier to applying machine learning (ML) for agricultural water management. Since there are multiple natural and anthropogenic factors that influence water availability, incorporating diverse multimodal features can significantly improve modeling performance. However, integrating such heterogeneous data is challenging due to spatial misalignments, inconsistent formats, semantic label ambiguities, and class imbalances. To address these challenges, we introduce IRRISIGHT, a large-scale, multimodal dataset spanning 20 U.S. states. It consists of 1.4 million pixel-aligned 224 224 patches that fuse satellite imagery with rich environmental attributes. We develop a robust geospatial fusion pipeline that aligns raster, vector, and point-based data on a unified 10m grid, and employ domain-informed structured prompts to convert tabular attributes into natural language. With irrigation type classification as a representative problem, the dataset is AI-ready, offering a spatially disjoint train/test split and extensive benchmarking with both vision and vision-language models.

Science Biology Your next sunscreen could be made from E. coli A chemical compound inside the bacterium may offer an eco-friendly way to block harmful UV rays. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Scientists are turning to nature for eco-friendly sunscreens. Breakthroughs, discoveries, and DIY tips sent six days a week. Let's face it, sunscreen is important to our health, but can really be a drag.

Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination.