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Externally solidified crystals (ESCs) can easily form during high pressure die casting (HPDC) of aluminum alloys if the process is not properly controlled, leading to significant reduction of casting quality and performance. This study explores seven machine learning (ML) models, including decision tree, random forest, logistic regression, neural network, K-nearest neighbors (KNN), support vector machine (SVM), and naïve Bayes classifier. The random forest and classification tree models showed the highest accuracy of 95

This paper addresses the challenge of developing a realistic urban-driving simulator to accurately model agent behaviors, a crucial component for self-driving car development. Most previous simulators focus on the plausibility of sensor data synthesis, whereas the plausibility of driving behaviors is poorly explored. To tackle this problem, we propose a hierarchical architecture, which comprises (i) a high-level intention simulation summarizing driving scenarios and (ii) a low-level policy trained by reinforcement algorithms to refine plans. Unlike existing simulators, our approach captures diverse behaviors, even sub-optimal ones, vital for robust policy training and evaluation. We also highlight the importance of interactive simulations over static scenarios for realistic policy development. Extensive experiments demonstrate that our approach significantly improves long-term behavior prediction and closed-loop simulation, enhancing the realism and diversity of urban-driving simulations. The videos of this work are available in our project page: href{https://sites.google.com/ucsd.edu/h-sim/home}{https:/ /sites.google.com/ucsd.edu/h-sim/home}.

Machine learning, particularly deep learning, is transforming industrial quality inspection. Yet, training robust machine learning models typically requires large volumes of high-quality labeled data, which are expensive, time-consuming, and labor-intensive to obtain in manufacturing. Moreover, defective samples are intrinsically rare, leading to severe class imbalance that degrades model performance. These data constraints hinder the adoption of machine learning-based quality inspection methods in real production environments. Synthetic data generation (SDG) offers a promising solution by enabling the creation of large, balanced, and fully annotated datasets in an efficient, cost-effective, and scalable manner. This paper presents a hybrid SDG framework that integrates simulation-based rendering, domain randomization, and real background compositing to enable annotation-free computer vision-based industrial part inspection and zero-shot deployment on real parts. In our implementation, the SDG pipeline generates 12,960 labeled, balanced images in one hour, demonstrating its efficiency and scalability in addressing manufacturing data bottlenecks. A two-stage architecture utilizing a YOLOv8n backbone for object detection and MobileNetV3-small for quality classification is trained exclusively on synthetic data and evaluated on 300 real industrial parts. The proposed approach achieves a mean Average Precision (mAP) of 0.995 at an intersection-over-union threshold of 0.5 (mAP@0.5) for object detection and 96% overall accuracy with 90.5% balanced accuracy for quality classification, indicating robust performance under severe class imbalance. Compared with few-shot baselines trained on limited real data, the SDG approach improves balanced accuracy from 50% to 91%. These results demonstrate that the proposed method enables annotation-free, scalable, and robust quality inspection for real-world manufacturing applications.

Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review. We formulate this as budgeted act-or-defer decision making. At each round, the system maps the debate prefix to a low-dimensional state, computes a k-nearest-neighbor lower confidence bound on state-conditional correctness using calibration data, and acts only when the bound exceeds a user-specified reliability threshold. The certificate controls wrong actions through the decomposition β= δ+ α+ ε_act, separating calibration failure, residual action risk, and representation gap. The guarantee is conditional, not distribution-free: it relies on a valid local bias envelope and an action-region representation-gap bound, and each assumption is paired with falsification-style diagnostics. Because the same absolute wrong-action budget has different meanings across tasks of different difficulty, we set budgets relative to each task's final-round error using training data only, and evaluate safety by normalized budget usage WA/β. On six benchmarks against nine baselines, the method uses 9–12

Active fault management of the integration of Cyber-Physical Systems (CPSs), Digital Twins (DTs), and AI-driven sensors needs a secure and uniform framework. In relevance, the paper presents a hybrid Variational Autoencoder-Generative Adversarial Network (VAE-GAN)-based model with a redesigned Generative AI-Driven Sensor Fusion (GAI-SF) architecture. The proposed architecture combines the streams of multivariate sensor data and preempts anomalies before performance levels may be detected as degraded. Experimental validation based on Tennessee Eastman Process (TEP) and NASA turbofan engine data shows that it is up to 80 times better than classic fusion techniques in F1-score, and lowers the detection latency by some 40%. In addition to predictive performance, GAI-SF is fully compatible with ISO/IEC JTC 1/ SC 42, 3GPP SA6, and ETSI MEC standards, meaning it can be used in secure, interoperable, and AI-controlled deployment in next-generation 5G/6G CPS ecosystems.