Engineering最新文献

{"title":"Xuanfei Baidu Formula Ameliorates Influenza A Virus-induced Lung Inflammation by Repressing the NLRP3 Inflammasome in Macrophages","authors":"Tao Liu, Yueyuan Xu, Ziwei Yan, Lin Ma, Hongda Sheng, Mingyu Ding, Jiabao Wang, Qingdi Fang, Qianru Zhao, Yu Tang, Tianyuan Zhang, Lu Chen, Rui Shao, Bin Qu, Jing Qian, Yi Wang, Junhua Zhang, Xiaohuan Guo, Yu Wang, Han Zhang","doi":"10.1016/j.eng.2025.07.039","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.039","url":null,"abstract":"The NOD-like receptor family pyrin domain-containing protein 3 (NLRP3) inflammasome is an intracellular protein complex containing a nucleotide-binding oligomerization domain, leucine-rich repeats, and a pyrin domain. It is a key regulator of inflammation in viral pneumonia (VP). Small-molecule inhibitors targeting various NLRP3 binding sites are advancing into early clinical trials, but their therapeutic utility is incompletely established. Xuanfei Baidu Formula (XF), clinically used for VP treatment, attenuates NLRP3 activation by hampering caspase-11 to impede polarization of pro-inflammatory macrophages in a model of lipopolysaccharide (LPS)-induced lung injury inmice. Herein, we demonstrate that XF attenuated influenza A virus (IAV)-induced lung inflammation as well as lung injury in immunocompetent (but not in macrophage-depleted) mice. RNA-sequencing of sorted lung macrophages from IAV-infected mice revealed that XF inhibited activation of the NLRP3 inflammation and interleukin (IL)-1β production. Quantitative nuclear magnetic resonance of XF enabled us to develop XF-Comb1, a fixed-ratio combination of five bioactive compounds that recapitulated the bioactivity of XF in suppressing NLRP3 activation in macrophages <em>in vitro</em> and <em>in vivo</em>. Interestingly, XF-Comb1 inhibited assembly of the NLRP3 inflammasome through multi-site interactions with functional residues of NLRP3, apoptosis-associated speck-like protein containing caspase recruitment domain (CARD) (ASC), and caspase-1. Taken together, this work advances the development of NLRP3 inhibitors by translating a complex herbal formula into defined bioactive compounds.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"34 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Inference: Data Security Threats in the AI Era","authors":"Zijun Wang, Ting Liu, Yang Liu, Enrico Zio, Xiaohong Guan","doi":"10.1016/j.eng.2025.08.007","DOIUrl":"https://doi.org/10.1016/j.eng.2025.08.007","url":null,"abstract":"No Abstract","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"1 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Large Language Model-Powered Autonomous Driving","authors":"Yuxuan Zhu, Shiyi Wang, Wenqing Zhong, Nianchen Shen, Yunqi Li, Siqi Wang, Zhiheng Li, Cathy Wu, Zhengbing He, Li Li","doi":"10.1016/j.eng.2025.07.038","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.038","url":null,"abstract":"Artificial intelligence (AI) plays a crucial role in autonomous driving (AD), advancing its development toward greater intelligence and efficiency. In response to persistent challenges in current AD algorithms, many researchers believe that large language models (LLMs), with their powerful reasoning capabilities and extensive knowledge, may offer promising solutions, enabling AD systems to achieve deeper understanding and more informed decision-making. Both industry and academia have actively explored the application of LLMs in AD tasks, showing early signs of progress in addressing issues such as the long-tail problem. To examine whether and how LLMs can enhance AD, this paper provides a comprehensive analysis of their potential applications, including their optimization strategies in both modular and end-to-end approaches, with a particular focus on how LLMs can address existing problems and challenges in current solutions. Furthermore, we explore an important question: Can LLM-based artificial general intelligence (AGI) serve as a key for achieving high-level AD? We also analyze the potential limitations and challenges LLMs may face in advancing AD technology and extend the discussion to societal considerations, including critical safety and security concerns. This survey aims to provide a foundational reference for cross-disciplinary researchers and help guide future research directions.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"749 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Perspective on Fault Detection and Diagnosis for Plantwide Systems in the Era of Smart Process Manufacturing","authors":"Wangyan Li, Jie Bao","doi":"10.1016/j.eng.2025.08.006","DOIUrl":"https://doi.org/10.1016/j.eng.2025.08.006","url":null,"abstract":"No Abstract","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"1 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical Review of Intelligent Coal-Fired Power Technologies and Applications","authors":"Jizhen Liu, Zhongming Du, Qinghua Wang, Kaijun Jiang, Dan Gao","doi":"10.1016/j.eng.2025.07.036","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.036","url":null,"abstract":"With the rapid expansion of renewable energy systems, particularly wind and solar energy, coal-fired power plants (CFPPs) are expected to serve as flexible and dispatchable backup resources. This evolving role imposes new demands on their operational adaptability, efficiency, and intelligence. In this context, the intelligent transformation of CFPPs has become a key enabler for achieving both flexible operations and long-term sustainability. This paper provides a comprehensive review of the latest developments in intelligent coal-fired power technologies, focusing on three critical pillars: intelligent perception, intelligent control, and intelligent operation. Key enabling technologies, such as ubiquitous sensing systems, advanced control algorithms, and automated operation platforms, are examined in detail. Additionally, two representative engineering cases are introduced to demonstrate practical applications and benefits: the intelligent control of coal-fired units coupled with novel energy-storage systems and the implementation of unmanned operation in smart power plants. These projects highlight the transformative potential of intelligent technologies in enhancing the flexibility, efficiency, and autonomy of coal-fired power units. Finally, future perspectives on intelligent technologies are presented. The findings of this study offer valuable insights into the pathway toward clean, flexible, and intelligent coal-based power generation in an evolving energy landscape.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"79 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144824843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Large Language Models Solve Complex Engineering Issues? Practical Applications in Reliability Systems Engineering","authors":"Yue Zhang, Yanjie Song, Yi Ren, Lining Xing, Qiang Feng, Ruifeng Xiang, Zili Wang, Witold Pedrycz","doi":"10.1016/j.eng.2025.07.037","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.037","url":null,"abstract":"No Abstract","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"7 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI and Deep Learning for Terahertz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models","authors":"Wentao Yu, Hengtao He, Shenghui Song, Jun Zhang, Linglong Dai, Lizhong Zheng, Khaled B. Letaief","doi":"10.1016/j.eng.2025.07.032","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.032","url":null,"abstract":"This study explored the transformative potential of artificial intelligence (AI) in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output (UM-MIMO) systems. It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design: computational complexity, modeling difficulty, and measurement limitations. The study posits that AI provides a promising solution to these challenges. Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems. The first roadmap, model-driven deep learning (DL), emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework. Four essential steps are discussed: algorithmic frameworks, basis algorithms, loss-function design, and neural architecture design. The second roadmap presents channel state information (CSI) foundation models, aimed at unifying the design of different transceiver modules by focusing on their shared foundation, that is, the wireless channel. The training of a single compact foundation model is proposed to estimate the score function of wireless channels, which serve as a versatile prior for designing a wide variety of transceiver modules. Four essential steps are outlined: general frameworks, conditioning, site-specific adaptation, joint design of CSI foundation models, and model-driven DL. The third roadmap aims to explore potential directions for applying pretrained large language models (LLMs) to terahertz UM-MIMO systems. Several application scenarios are envisioned, including LLM-based estimation, optimization, search, network management, and protocol understanding. Finally, the study highlights open problems and future research directions.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"18 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future Manufacturing with AI-Driven Particle Vision Analysis in the Microscopic World","authors":"Guangyao Chen, Fengqi You","doi":"10.1016/j.eng.2025.08.005","DOIUrl":"https://doi.org/10.1016/j.eng.2025.08.005","url":null,"abstract":"Recent advances in artificial intelligence (AI) have led to the development of sophisticated algorithms that significantly improve image analysis capabilities. This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data, simplifying complex tasks and enabling innovative experimental methods previously thought impossible. In smart manufacturing, these improvements are especially impactful, increasing precision and efficiency in production processes. This review examines the convergence of AI with particle image analysis, an area we refer to as “particle vision analysis (PVA).” We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors, where it plays a crucial role in both innovation and operational improvements. We explore four key areas of advancement—namely, particle classification, detection, segmentation, and object tracking—along with a look into the emerging field of augmented microscopy. This paper also underscores the vital role of the existing datasets and implementations that support these applications, which provide essential insights and resources that drive continuous research and development in this fast-evolving field. Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing, biomanufacturing, and pharmaceutical manufacturing. This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing, which is set to revolutionize industry standards and operational practices.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"27 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144824915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Enhanced Industrial Question-Answering Using Large Language Models","authors":"Ronghui Liu, Hao Ren, Haojie Ren, Wu Rui, Wei Cui, Xiaojun Liang, Chunhua Yang, Weihua Gui","doi":"10.1016/j.eng.2025.07.035","DOIUrl":"https://doi.org/10.1016/j.eng.2025.07.035","url":null,"abstract":"Modern industrial systems have grown increasingly extensive, complex, and hierarchical, with operations relying on numerous knowledge-based queries. These queries necessitate considerable human resources while also requiring high levels of accuracy, subjectivity, and consistency, all of which critically influence operational efficiency. To overcome these challenges, this study proposes an industrial retrieval-augmented generation (RAG) method designed to enhance large language models (LLMs) using domain-specific knowledge, thereby improving the precision of question answering. A comprehensive industrial knowledge base was constructed from diverse sources, including journal articles, theses, books, and patents. A Text classification model based on bidirectional encoder representations from transformers (BERTs) was trained to accurately classify incoming queries. Furthermore, the general text embedding–dense passage retrieval (GTE–DPR) model was employed to perform word embedding and vector similarity retrieval, facilitating the alignment of query vectors with relevant entries in the knowledge base to obtain initial responses. These initial results were subsequently refined by LLMs to produce accurate final answers. Experimental evaluations confirm the effectiveness of the proposed approach. In particular, when applied to ChatGLM2-6B, the RAG method increased the ROUGE-L score from 32.52% to 55.04% and improved accuracy from 50.52% to 73.92%. Comparable improvements were also observed with LLaMA2-7B, underscoring the RAG framework’s capability to significantly enhance the accuracy and relevance of industrial question-answering (QA) systems.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"38 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144824844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IgG Fucosylation: An Emerging Key Player in the Treatment of Severe COVID-19","authors":"Caiping Zhao, Jingrong Wang, Yuan Liu, Baoling Shang, Danna Lin, Yao Xiao, Hong Ren, Yue Li, Wen Rui, Xu Zou, Hudan Pan, Liang Liu","doi":"10.1016/j.eng.2025.08.004","DOIUrl":"https://doi.org/10.1016/j.eng.2025.08.004","url":null,"abstract":"Protein glycosylation is one of the most vital modifications. Understanding the role of protein glycosylation in coronavirus disease 2019 (COVID-19) is the key elucidating its pathogenesis and developing therapeutic strategies. We conducted a case-control study to examine the total fucosylation levels and the levels of individual immunoglobulin G (IgG) subtypes in the serum of COVID-19 patients. Notably, we identified 13 glycosyltransferase-related and glycosidase-related genes displaying differential expression among COVID-19 patients. Our findings from the detection of serum fucosylation levels in COVID-19 patients revealed a diminished degree of glycosylation. Furthermore, the analysis of the levels of different IgG subtypes revealed an increase in IgG1 fucosylation and a decrease in IgG2 fucosylation, with the latter being linked to patients’ body temperature and disease progression. The change in COVID-19 disease severity from mild to severe may be related to fucosylation. The single-cell sequencing analysis revealed the expression of members of the fucosyltransferase family in the plasma cells and plasmablasts of COVID-19 patients. We leveraged the recommended medication for severe COVID-19, <em>Fuzheng Jiedu</em> Decoction (FZJDD), to confirm the importance of fucosylation in severe COVID-19. The network pharmacology analysis of FZJDD revealed that fucosylation inhibition might contribute to its antiviral effects against COVID-19. We assessed the efficacy of this compound in septic mice, by monitoring serum fucosylation levels, and found that FZJDD significantly alleviated inflammation in lipopolysaccharide (LPS)-induced septic mice. Concurrently, the analysis of plasma fucosylation levels in septic mice indicated a marked decrease in total fucosylation. The glycan analysis revealed the involvement of α1, 6-fucosyltransferase (FUT8) and α-<em>L</em>-fucosidase 1 (FUCA1), a pair of interacting fucosidases, in COVID-19 pathogenesis. This study revealed substantial alterations in fucosylation among patients with severe COVID-19, with the primary variations observed in the IgG2 subtype. These changes are intricately coordinated by the mutual regulation of the FUT8 and FUCA1 enzymes. Furthermore, the endorsement of FZJDD as a recommended therapeutic option for severe COVID-19 underscores the promising potential of defucosylation as a viable treatment strategy for this disease.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"23 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144824919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}