Hydrogen produces only water emissions, plus the fuel-cell trucks are quick. Breakthroughs, discoveries, and DIY tips sent six days a week. Filling up a hydrogen tank is much like filling up a gas-powered car in both the basic experience and in the time it takes. That's been a major barrier for EVs thus far; adding 20 minutes or more for each recharge on a road trip is not nearly as appealing as pulling up to a Chevron station and getting out of there in a few minutes. However, hydrogen hasn't yet caught on as a large-scale solution largely due to funding, even though even the US Department of Energy says it has "several benefits over conventional combustion-based technologies currently used in many power plants and vehicles."

Reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs), but critically depends on a key prerequisite: the LLM can already generate high-utility reasoning paths with non-negligible probability. For tasks beyond the LLM's current competence, such reasoning path can be hard to sample, and learning risks reinforcing familiar but suboptimal reasoning. We are motivated by the insight from cognitive science that Why is this the answer is often an easier question than What is the answer, as it avoids the heavy cognitive load of open-ended exploration, opting instead for explanatory reconstruction-systematically retracing the reasoning that links a question to its answer. We show that LLMs can similarly leverage answers to derive high-quality reasoning paths. We formalize this phenomenon and prove that conditioning on answer provably increases the expected utility of sampled reasoning paths, thereby transforming intractable problems into learnable ones. Building on this insight, we introduce RAVR (Reference-Answer-guided Variational Reasoning), an end-to-end framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning. Experiments in both general and math domains demonstrate consistent improvements over strong baselines. We further analyze the reasoning behavior and find that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning.

Preprint accepted by IEEE Transactions on Industrial Cyber-Physical Systems. T o appear in TICPS on IEEE Explore. Abstract --This paper presents HERO (Hierarchical T esting with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains. With the rapid development of net zero, there is a need for advanced predictive models and system integration plays a crucial role in the field of renewable energy technologies, particularly in the deployment and management of Proton Exchange Membrane Fuel Cells (PEMFC). Regarded as an integral part of future energy conversion technologies, PEMFC boast high energy conversion efficiency, low operating temperature, low emissions, and rapid startup capabilities [1].

After growing up without reliable power at home, Iwnetim Abate is working to develop a steady supply of sustainable energy. "I'm the only one who wears glasses and has eye problems in the family," Iwnetim Abate says with a smile as sun streams in through the windows of his MIT office. "I think it's because of the candles." In the small town in Ethiopia where he grew up, Abate's family had electricity, but it was unreliable. So, for several days each week when they were without power, Abate would finish his homework by candlelight. Today, Abate, 32, is an assistant professor at MIT in the department of materials science and engineering.

The oxygen reduction reaction (ORR) catalyst plays a critical role in enhancing fuel cell efficiency, making it a key focus in material science research. However, extracting structured information about ORR catalysts from vast scientific literature remains a significant challenge due to the complexity and diversity of textual data. In this study, we propose a named entity recognition (NER) and relation extraction (RE) approach using DyGIE++ with multiple pre-trained BERT variants, including MatSciBERT and PubMedBERT, to extract ORR catalyst-related information from the scientific literature, which is compiled into a fuel cell corpus for materials informatics (FC-CoMIcs). A comprehensive dataset was constructed manually by identifying 12 critical entities and two relationship types between pairs of the entities. Our methodology involves data annotation, integration, and fine-tuning of transformer-based models to enhance information extraction accuracy. We assess the impact of different BERT variants on extraction performance and investigate the effects of annotation consistency. Experimental evaluations demonstrate that the fine-tuned PubMedBERT model achieves the highest NER F1-score of 82.19% and the MatSciBERT model attains the best RE F1-score of 66.10%. Furthermore, the comparison with human annotators highlights the reliability of fine-tuned models for ORR catalyst extraction, demonstrating their potential for scalable and automated literature analysis. The results indicate that domain-specific BERT models outperform general scientific models like BlueBERT for ORR catalyst extraction.

In exploring Predictive Health Management (PHM) strategies for Proton Exchange Membrane Fuel Cells (PEMFC), the Transformer model, widely used in data-driven approaches, excels in many fields but struggles with time series analysis due to its self-attention mechanism, which yields a complexity of the input sequence squared and low computational efficiency. It also faces challenges in capturing both global long-term dependencies and local details effectively. To tackle this, we propose the Temporal Scale Transformer (TSTransformer), an enhanced version of the inverted Transformer (iTransformer). Unlike traditional Transformers that treat each timestep as an input token, TSTransformer maps sequences of varying lengths into tokens at different stages for inter-sequence modeling, using attention to capture multivariate correlations and feed-forward networks (FFN) to encode sequence representations. By integrating a one-dimensional convolutional layer into the multivariate attention for multi-level scaling of K and V matrices, it improves local feature extraction, captures temporal scale characteristics, and reduces token count and computational costs. Experiments comparing TSTransformer with models like Long Short-Term Memory, iTransformer, and Transformer demonstrate its potential as a powerful tool for advancing PHM in renewable energy, effectively addressing the limitations of pure Transformer models in data-driven time series tasks.

Machine learning (ML) models are valuable tools for analyzing the impact of technology using patent citation information. However, existing ML-based methods often struggle to account for the dynamic nature of the technology impact over time and the interdependencies of these impacts across different periods. This study proposes a multi-task learning (MTL) approach to enhance the prediction of technology impact across various time frames by leveraging knowledge sharing and simultaneously monitoring the evolution of technology impact. First, we quantify the technology impacts and identify patterns through citation analysis over distinct time periods. Next, we develop MTL models to predict citation counts using multiple patent indicators over time. Finally, we examine the changes in key input indicators and their patterns over different periods using the SHapley Additive exPlanation method. We also offer guidelines for validating and interpreting the results by employing statistical methods and natural language processing techniques. A case study on battery technologies demonstrates that our approach not only deepens the understanding of 1 technology impact, but also improves prediction accuracy, yielding valuable insights for both academia and industry.

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

Scan provided by Dr Quentin Meyer. Researchers from UNSW Sydney have developed an algorithm which produces high-resolution modelled images from lower-resolution micro X-ray computerised tomography (CT). The new process, detailed in a paper published in Nature Communications, has been tested on individual hydrogen fuel cells to accurately model the interior in precise detail and potentially improve the efficiency of them. But the researchers say it could also be used in future on human X-rays to give medical professionals a better understanding of tiny cellular structures inside the body, which could allow for better and faster diagnosis of a wide range of diseases. The team, featuring Professor Ryan Armstrong, Professor Peyman Mostaghimi, Dr Ying Da Wang, and Kunning Tang from the School of Mineral and Energy Resources Engineering and Prof Chuan Zhao and Dr Quentin Meyer from the School of Chemistry, developed the algorithm to improve the understanding of what is happening inside a Proton Exchange Membrane Fuel Cell (PEMFC).

Modeling difficulty, time-varying model, and uncertain external inputs are the main challenges for energy management of fuel cell hybrid electric vehicles. In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintain the batteries' long-term operation, and extend the lifetime of the fuel cells system. Fuzzy Q-learning is a model-free reinforcement learning that can learn itself by interacting with the environment, so there is no need for modeling the fuel cells system. In addition, frequent startup of the fuel cells will reduce the remaining useful life of the fuel cells system. The proposed method suppresses frequent fuel cells startup by considering the penalty for the times of fuel cell startups in the reward of reinforcement learning. Moreover, applying fuzzy logic to approximate the value function in Q-Learning can solve continuous state and action space problems. Finally, a python-based training and testing platform verify the effectiveness and self-learning improvement of the proposed method under conditions of initial state change, model change and driving condition change.