Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 °C across all phases except bainite, which shows a slightly higher MAE of 27 °C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows.

The chef's knife is the workhorse of the kitchen. We sliced, diced, and minced to find the best for every home chef. A Close Second Chef's Knife (Made From High-Carbon Stainless Steel) Zwilling Four Star 8-Inch Chef's Knife Not all knives are created equal, and a chef's knife is given that name for a reason. Like the proverbial dog to man, a chef needs their knife. Arguably the most important multipurpose tool you can find in a kitchen, it's the chef's main weapon--it can slice, dice, and chop ingredients with speed and precision. A chef's knife generally has a super-sharp end point and a curved, sloping edge. This curve is what makes the chef knife stand out, as it's designed to work with the natural rocking motion for quick chopping that also allows for finer cuts. With technology like ovens with cameras inside and AI-enabled refrigerators, the chef's knife remains the simple tool necessary for any kitchen.

Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis.

After Zohran Mamdani's victory, Republicans are fearmongering about Democrats turning socialist. Meanwhile, Donald Trump is busy taking stakes in private companies and ordering them around. With the victory of Zohran Mamdani, a self-described democratic socialist, in New York City's mayoral election last week, socialism is on the march. "This is the future House Democrats want, and your city could be next," an N.R.C.C. ad blared the day after Mamdani won. Mamdani is hardly representative of the national Democratic Party.

Increased defense spending is a boon for Germany's ailing industrial sector. Numerous companies, even those with no previous military experience, are now hoping to get in on the act. Visiting the works of Ilsenburger Grobblech GmbH is like taking a trip back in time. Way back in the 16th century, copper used to be produced at this site in the northern Harz Mountains, not far from eastern Germany' tallest peak, the Brocken. Today, slabs of steel up to 35 centimeters thick are piled up in front of the factory halls, delivered from the blast furnaces and converters of parent company Salzgitter, less than an hour's drive away. What is happening behind the factory walls, though, is part of a new hype that has gripped Germany's crisis-ridden industrial sector. A hype which many are hoping will be enough to revive it.

Steel casting processes are vulnerable to financial losses due to slag flow contamination, making accurate slag flow condition detection essential. This study introduces a novel cross-domain diagnostic method using vibration data collected from an industrial steel foundry to identify various stages of slag flow. A hybrid deep learning model combining one-dimensional convolutional neural networks and long short-term memory layers is implemented, tested, and benchmarked against a standard one-dimensional convolutional neural network. The proposed method processes raw time-domain vibration signals from accelerometers and evaluates performance across 16 distinct domains using a realistic cross-domain dataset split. Results show that the hybrid convolutional neural network and long short-term memory architecture, when combined with root mean square preprocessing and a selective embedding data loading strategy, achieves robust classification accuracy, outperforming traditional models and loading techniques. The highest test accuracy of 99.10 +/- 0.30 demonstrates the method's capability for generalization and industrial relevance. This work presents a practical and scalable solution for real-time slag flow monitoring, contributing to improved reliability and operational efficiency in steel manufacturing.

The hydrogen trapping behaviour of metallic alloys is generally characterised using Thermal Desorption Spectroscopy (TDS). However, as an indirect method, extracting key parameters (trap binding energies and densities) remains a significant challenge. To address these limitations, this work introduces a machine learning-based scheme for parameter identification from TDS spectra. A multi-Neural Network (NN) model is developed and trained exclusively on synthetic data to predict trapping parameters directly from experimental data. The model comprises two multi-layer, fully connected, feed-forward NNs trained with backpropagation. The first network (classification model) predicts the number of distinct trap types. The second network (regression model) then predicts the corresponding trap densities and binding energies. The NN architectures, hyperparameters, and data pre-processing were optimised to minimise the amount of training data. The proposed model demonstrated strong predictive capabilities when applied to three tempered martensitic steels of different compositions. The code developed is freely provided.

Müller, Vincent C. (2015), 'Which symbol grounding problem should we try to solve?', Journal of Experimental and Theoretical Artificial Intellig ence, 27 (1, ed. Which symbol grounding problem should we try to solve? October, 201 3 Floridi and Taddeo propose a condition of "zero semantic co m-mitment" for sol u tions to the grounding problem, and a solution to it . I argue briefly that their condition cannot be fulfilled, not even by their own solu tion . After a look at Luc Steel's very different competing suggestion, I suggest that w e need to rethink what the problem is and what role the'goals' in a system play in formulating the problem .

This paper presents a case study on how to process cooking recipes (and more generally, how-to instructions) in a way that makes it possible for a robot or artificial cooking assistant to support human chefs in the kitchen. Such AI assistants would be of great benefit to society, as they can help to sustain the autonomy of aging adults or people with a physical impairment, or they may reduce the stress in a professional kitchen. We propose a novel approach to computational recipe understanding that mimics the human sense-making process, which is narrative-based. Using an English recipe for almond crescent cookies as illustration, we show how recipes can be modelled as rich narrative structures by integrating various knowledge sources such as language processing, ontologies, and mental simulation. We show how such narrative structures can be used for (a) dealing with the challenges of recipe language, such as zero anaphora, (b) optimizing a robot's planning process, (c) measuring how well an AI system understands its current tasks, and (d) allowing recipe annotations to become language-independent.

The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate the steel phosphorus content at the end of the process based on input parameters. Data were collected over two years from a steel plant, focusing on the chemical composition and weight of the scrap, the volume of oxygen injected, and process duration. After preprocessing the data, several machine learning models were evaluated, with the artificial neural network (ANN) emerging as the most effective. The best ANN model included four hidden layers. The model was trained for 500 epochs with a batch size of 50. The best model achieves a mean square error (MSE) of 0.000016, a root-mean-square error (RMSE) of 0.0049998, a coefficient of determination (R2) of 99.96%, and a correlation coefficient (r) of 99.98%. Notably, the model achieved a 100% hit rate for predicting phosphorus content within +-0.001 wt% (+-10 ppm). These results demonstrate that the optimized ANN model offers accurate predictions for the steel final phosphorus content.