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.

Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments. Nonetheless, their performance is hindered by the absence of domain-specific knowledge. Methods that finetune on large-scale domain-specific data entail prohibitive development costs. This paper introduces VistaWise, a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis. It reduces the requirement for domain-specific training data from millions of samples to a few hundred. VistaWise integrates visual information and textual dependencies into a cross-modal knowledge graph (KG), enabling a comprehensive and accurate understanding of multimodal environments. We also equip the agent with a retrieval-based pooling strategy to extract task-related information from the KG, and a desktop-level skill library to support direct operation of the Minecraft desktop client via mouse and keyboard inputs. Experimental results demonstrate that VistaWise achieves state-of-the-art performance across various open-world tasks, highlighting its effectiveness in reducing development costs while enhancing agent performance.

Natural organisms utilize distributed actuation through their musculoskeletal systems to adapt their gait for traversing diverse terrains or to morph their bodies for varied tasks. A longstanding challenge in robotics is to emulate this capability of natural organisms, which has motivated the development of numerous soft robotic systems. However, such systems are generally optimized for a single functionality, lack the ability to change form or function on demand, or remain tethered to bulky control systems. To address these limitations, we present a framework for designing and controlling robots that utilize distributed actuation. We propose a novel building block that integrates 3D-printed bones with liquid crystal elastomer (LCE) muscles as lightweight actuators, enabling the modular assembly of musculoskeletal robots. We developed LCE rods that contract in response to infrared radiation, thereby providing localized, untethered control over the distributed skeletal network and producing global deformations of the robot. To fully capitalize on the extensive design space, we introduce two computational tools: one for optimizing the robot's skeletal graph to achieve multiple target deformations, and another for co-optimizing skeletal designs and control gaits to realize desired locomotion. We validate our framework by constructing several robots that demonstrate complex shape morphing, diverse control schemes, and environmental adaptability. Our system integrates advances in modular material building, untethered and distributed control, and computational design to introduce a new generation of robots that brings us closer to the capabilities of living organisms.

We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing highly accurate predictions for properties of pure components and their mixtures, and (ii) exploring the chemical space for new molecular structures. We review current state-of-the-art molecular ML models and discuss research directions that promise further advancements. This includes ML methods, such as graph neural networks and transformers, which can be further advanced through the incorporation of physicochemical knowledge in a hybrid or physics-informed fashion. Then, we consider leveraging molecular ML at the chemical process scale, which is highly desirable yet rather unexplored. We discuss how molecular ML can be integrated into process design and optimization formulations, promising to accelerate the identification of novel molecules and processes. To this end, it will be essential to create molecule and process design benchmarks and practically validate proposed candidates, possibly in collaboration with the chemical industry.

Rapid and reliable qualification of advanced materials remains a bottleneck in industrial manufacturing, particularly for heterogeneous structures produced via non-conventional additive manufacturing processes. This study introduces a novel framework that links microstructure informatics with a range of expert characterization knowledge using customized and hybrid vision-language representations (VLRs). By integrating deep semantic segmentation with pre-trained multi-modal models (CLIP and FLAVA), we encode both visual microstructural data and textual expert assessments into shared representations. To overcome limitations in general-purpose embeddings, we develop a customized similarity-based representation that incorporates both positive and negative references from expert-annotated images and their associated textual descriptions. This allows zero-shot classification of previously unseen microstructures through a net similarity scoring approach. Validation on an additively manufactured metal matrix composite dataset demonstrates the framework's ability to distinguish between acceptable and defective samples across a range of characterization criteria. Comparative analysis reveals that FLAVA model offers higher visual sensitivity, while the CLIP model provides consistent alignment with the textual criteria. Z-score normalization adjusts raw unimodal and cross-modal similarity scores based on their local dataset-driven distributions, enabling more effective alignment and classification in the hybrid vision-language framework. The proposed method enhances traceability and interpretability in qualification pipelines by enabling human-in-the-loop decision-making without task-specific model retraining. By advancing semantic interoperability between raw data and expert knowledge, this work contributes toward scalable and domain-adaptable qualification strategies in engineering informatics.

In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Random Tree Star) algorithm for collision-free trajectory generation. Further optimization of task sequencing and path efficiency is conducted using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Simulation experiments conducted in a realistic post-disaster environment demonstrate that our proposed approach significantly improves the overall efficiency of medical rescue operations compared to traditional strategies. Specifically, our method reduces the total mission completion time to 26.7 minutes for a 15-task scenario, outperforming K-Means clustering and random allocation by over 73%. Furthermore, the framework achieves a substantial 15.1% reduction in total traveled distance after CMA-ES optimization. The cooperative utilization of UAVs and UGVs effectively balances their complementary advantages, highlighting the system's scalability and practicality for real-world deployment.

More than 350 million tonnes of plastic waste are generated annually. But AI-designed enzymes can "un-bake the cake--turn [plastic] back into the chemical compounds that are used to make it--unlock[ing] infinite recycling applications for all the materials that go to landfill or incineration today," says Jacob Nathan founder and CEO of London-based Epoch Biodesign. It has designed enzymes for the three major plastic groups using machine learning, which could help break down not just textiles, but packaging, and more. The startup, which spun out of Nathan's high school project in 2019 and has since raised 18.3 million, will complete its first production-scale facility later this year, which is expected to be capable of processing 150 tons of waste annually, Nathan says. AI tools that help diagnose strokes, cancers, and other medical conditions are becoming a fixture in healthcare systems around the world.

Accurate prediction of protein-ligand interactions is essential for computer-aided drug discovery. However, existing methods often fail to capture solvent-dependent conformational changes and lack the ability to jointly learn multiple related tasks. To address these limitations, we introduce a pre-training method that incorporates ligand conformational ensembles generated under diverse solvent conditions as augmented input. This design enables the model to learn both structural flexibility and environmental context in a unified manner. The training process integrates molecular reconstruction to capture local geometry, interatomic distance prediction to model spatial relationships, and contrastive learning to build solvent-invariant molecular representations. Together, these components lead to significant improvements, including a 3.7% gain in binding affinity prediction, an 82% success rate on the PoseBusters Astex docking benchmarks, and an area under the curve of 97.1% in virtual screening. The framework supports solvent-aware, multi-task modeling and produces consistent results across benchmarks. A case study further demonstrates sub-angstrom docking accuracy with a root-mean-square deviation of 0.157 angstroms, offering atomic-level insight into binding mechanisms and advancing structure-based drug design.

Rice is a staple food of global importance in terms of trade, nutrition, and economic growth. Among Asian nations such as China, India, Pakistan, Thailand, Vietnam and Indonesia are leading producers of both long and short grain varieties, including basmati, jasmine, arborio, ipsala, and kainat saila. To ensure consumer satisfaction and strengthen national reputations, monitoring rice crops and grain quality is essential. Manual inspection, however, is labour intensive, time consuming and error prone, highlighting the need for automated solutions for quality control and yield improvement. This study proposes an automated approach to classify five rice grain varieties using Convolutional Neural Networks (CNN). A publicly available dataset of 75000 images was used for training and testing. Model evaluation employed accuracy, recall, precision, F1-score, ROC curves, and confusion matrices. Results demonstrated high classification accuracy with minimal misclassifications, confirming the model effectiveness in distinguishing rice varieties. In addition, an accurate diagnostic method for rice leaf diseases such as Brown Spot, Blast, Bacterial Blight, and Tungro was developed. The framework combined explainable artificial intelligence (XAI) with deep learning models including CNN, VGG16, ResNet50, and MobileNetV2. Explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) revealed how specific grain and leaf features influenced predictions, enhancing model transparency and reliability. The findings demonstrate the strong potential of deep learning in agricultural applications, paving the way for robust, interpretable systems that can support automated crop quality inspection and disease diagnosis, ultimately benefiting farmers, consumers, and the agricultural economy.

Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft.