After a year of logs, buckets, bins, and bark chips, I've found that growing mushrooms is tough. If you're a novice who wants to guarantee success, there are only two methods I'd recommend. Mushroom hunting is cool and fun, but you know what's not cool and fun? Having slugs or other mushroom hunters discover your secret spot. Or, worst of all, accidentally collecting the wrong kind of mushroom that could make you quite sick--or worse.

From the best air fryer to frying pans to knife sharpeners anyone can use, these ideas will keep the curious home chef in your life tinkering away. A Lightweight Cast-Iron Skillet Field Company No. 8 Cast Iron Skillet The Best gifts for cooks can be a tough prospect, especially because chances are the cook in your life is cooking for you. You want it to feel special, thoughtful, something luxuriant that they wouldn't buy for themselves and will be excited to have. This is a guide to the best kitchen gifts that mix classic beauty and technological innovation, geared to home chefs who love not just food but the act of cooking. We've gathered up some of our favorite gift ideas for every budget. For more inspiration, be sure to check out our guides to the best air fryers, the best cast iron pans, the best blenders, chef knives, and pizza ovens . One could plausibly argue for a 5-quart Le Creuset. But otherwise, when you picture a classic and beautiful cast-iron Dutch oven, what you picture is likely this: the 4-quart Staub La Cocotte. It argues for itself, sitting majestically baroque in its heft and with its trademark self-basting cones on the underside of its lid, its classic shape, and its elegant sense of proportion. It is welcome in any home.

Why has British Steel been nationalised? British Steel has been taken into public ownership after years of uncertainty over the future of the steelworks. It comes months after the UK government took control of the company's plant in Scunthorpe, Lincolnshire, though it was still owned by China's Jingye Group. What is British Steel and why is it important? British Steel's Scunthorpe plant employs 2,700 people, about three-quarters of the company's workforce.

Agriculture is ready for AI, but its data isn't Data accuracy, structure, and governance are foundational components required for agricultural AI. Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%. However, what AI vendors usually won't tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide-we've seen it first hand.

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Samples of rare earth luminescent materials displayed at an exhibition on China's manufacturing achievements at the National Museum in Beijing in March | REUTERS China's tightening export controls on dual-use materials and strategically important rare earths are beginning to disrupt Japanese industry -- including the defense sector. Chinese customs data tell the sharpest part of the story. Exports of dysprosium oxide to Japan ceased after October 2025, and shipments of terbium oxide ended a month later. No shipments of either material have been recorded since. The halt matters because dysprosium and terbium -- both heavy rare earth elements -- are among the most critical inputs for high-performance permanent magnets used in advanced military systems, electric vehicle motors, aerospace applications and industrial robotics.

Estimating causal effects in industrial time series requires handling temporal dynamics, time-varying treatments, and unobserved confounders. Existing causal foundation models (CausalPFN, CausalFM) operate only on static cross-sectional data; neural temporal methods (CRN, G-Net) require per-dataset training; and concurrent temporal-PFN proposals have not been demonstrated at industrial scale. None output explicit per-pair reliability signals alongside their CATE estimates. We introduce Temporal Causal Prior-Data Fitted Networks (TCPFN), a foundation model for zero-shot temporal causal discovery with learned reliability signals. TCPFN makes four contributions: (1) a Causal Judgment Head that jointly predicts null-effect probability, confounding strength, identifiability, mediation fraction, and causal regime; (2) a mixed training prior covering six causal regimes (independent, direct, confounded, mediated, time-varying confounded, feedback) plus CausalFM-style front-door and instrumental-variable priors; (3) a discrete-token panel-data architecture with cross-attention masking that prevents inter-horizon leakage; (4) zero-shot inference at industrial scale via FAISS-based context selection and one-step posterior correction. On 19 benchmark datasets across five domains, TCPFN achieves competitive zero-shot causal discovery: AUROC 0.96 on Tennessee Eastman, 0.93 on SWaT, 0.98 on Causal Rivers, 0.97 on CAUSRCA. The null detector reaches NullF1 0.94, AUROC 0.99. TCPFN scales to V=1,275 on a proprietary Kraft pulp-and-paper dataset in 6 hours on a single GPU; PCMCI, a CPU-only library, on a V=666 sub-panel of the same data took 81.5 hours, extrapolating by O(V^2) to ~12.5 days at V=1,275. TCPFN's top edges identify cross-subsystem causal relationships while PCMCI's surface within-instrument controller-measurement coupling -- a scalability case study.

Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce EnzyBind, a dataset with 11,100 experimentally validated enzyme-substrate pairs specifically curated from PDBbind. Building on this, we propose EnzyControl, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSAannotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme-substrate data. At the core of EnzyControl is EnzyAdapter, a lightweight, modular component integrated into a pretrained motifscaffolding model, allowing it to become substrate-aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyControl achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements of 13% in designability and 13% in catalytic efficiency compared to the baseline models.

In this section, we propose the detailed information during our benchmark and dataset construction821 process, including the data source description, dataset composition, filtering strategies, and the822 rationale for dataset construction. Chemical reaction data are separately collected from patent databases, including USPTO [19], Pista-828 chio [37], and Reaxys [8]. For reaction mechanism annotation, we followed the processing pipeline829 described in [26].830 A.2 Dataset Composition and Filtering Strategies831 Molecular Samples (25% of Benchmark): Although the ZINC database contains 250,000832 molecules, we observed that its molecular weight distribution is relatively concentrated. To en-833 sure diversity, we carefully selected molecules from PubChem, ChEMBL, and ZINC based on834 molecular weight and structural complexity.

While large language models (LLMs) with Chain-of-Thought (CoT) reasoning excel in mathematics and coding, their potential for systematic reasoning in chemistry, a domain demanding rigorous structural analysis for real-world tasks like drug design and reaction engineering, remains untapped. Current benchmarks focus on simple knowledge retrieval, neglecting step-by-step reasoning required for complex tasks such as molecular optimization and reaction prediction. To address this, we introduce ChemCoTBench, a reasoning framework that bridges molecular structure understanding with arithmetic-inspired operations, including addition, deletion, and substitution, to formalize chemical problem-solving into transparent, step-by-step workflows. By treating molecular transformations as modular "chemical operations", the framework enables slow-thinking reasoning, mirroring the logic of mathematical proofs while grounding solutions in real-world chemical constraints. We evaluate models on two high-impact tasks: Molecular Property Optimization and Chemical Reaction Prediction. These tasks mirror real-world challenges while providing structured evaluability. We further provide ChemCoTDataset, a pioneering 22,000-instance chemical reasoning dataset with expert-annotated chains of thought to facilitate LLM fine-tuning.