Engineering最新文献

{"title":"A Multi-Scale Graph Neural Network for the Prediction of Multi-Component Gas Adsorption","authors":"Lujun Li ,&nbsp;Haibin Yu","doi":"10.1016/j.eng.2025.08.012","DOIUrl":"10.1016/j.eng.2025.08.012","url":null,"abstract":"<div><div>Metal–organic frameworks (MOFs) hold great potential for gas separation and storage, and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials. Unlike traditional molecular graphs, crystal graphs require consideration of periodic invariance and modes. In addition, MOF structures such as covalent bonds, functional groups, and global structures impact adsorption performance in different ways. However, redundant atomic interactions can disrupt training accuracy, potentially leading to overfitting. In this paper, we propose a multi-scale crystal graph for describing periodic crystal structures, modeling interatomic interactions at different scales while preserving periodicity invariance. We also propose a multi-head attention crystal graph network in multi-scale graphs (MHACGN-MS), which learns structural characteristics by focusing on interatomic interactions at different scales, thereby reducing interference from redundant interactions. Using MOF adsorption for gases as an example, we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption. We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 102-111"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157577","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":"How to Build New Productive Forces for Traditional Chinese Medicine Industry: Industrial Perception Intelligence and AI-Based Pharmaceutical Robot","authors":"Zheng Li ,&nbsp;Qilong Xue ,&nbsp;Yang Yu ,&nbsp;Yequan Yan ,&nbsp;Jingxuan Zhang ,&nbsp;YangYang Su ,&nbsp;Chenfei Li ,&nbsp;Boli Zhang ,&nbsp;Yiyu Cheng","doi":"10.1016/j.eng.2025.07.027","DOIUrl":"10.1016/j.eng.2025.07.027","url":null,"abstract":"<div><div>Extraction unit operation is the first step in traditional Chinese medicine (TCM) product manufacturing, and it is crucial in determining the quality of the produced medicine. However, due to a lack of effective multimodal monitoring and adjustment strategies, achieving high quality and efficiency remains a challenge. In this work, we proposed an artificial intelligence (AI)-based robot platform for the multi-objective optimization of the extraction process. First, a perception intelligence method for multimodal process monitoring was established to track active ingredient transfer and production changes during the extraction process. Second, a digital twin model was developed to reconstruct the field information, which interacted with real-time monitoring data. Furthermore, the model performed real-time inference to predict future production process states by using the reconstructing information. Finally, according to the predicted process states, the autonomous decision-making robot implemented multi-objective optimization, ensuring efficient process adjustments for global optimization. Experimental and industrial results demonstrated that the platform could effectively infer component transfer dynamics, monitor temperature variations, and identify boiling states, ensuring product quality while reducing energy consumption. This pharmaceutical robot could promote the integration of AI and pharmaceutical engineering, thereby accelerating the iterative development and improvement of China’s pharmaceutical industry.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 244-255"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748028","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 ,&nbsp;Fengqi You","doi":"10.1016/j.eng.2025.08.005","DOIUrl":"10.1016/j.eng.2025.08.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 68-84"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","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":"A New Perspective on Fault Detection and Diagnosis for Plantwide Systems in the Era of Smart Process Manufacturing","authors":"Wangyan Li ,&nbsp;Jie Bao","doi":"10.1016/j.eng.2025.08.006","DOIUrl":"10.1016/j.eng.2025.08.006","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 19-24"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","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":"An Exact Algorithm for Placement Optimization in Circuit Design","authors":"Binqi Zhang ,&nbsp;Lu Zhen ,&nbsp;Gilbert Laporte","doi":"10.1016/j.eng.2025.03.020","DOIUrl":"10.1016/j.eng.2025.03.020","url":null,"abstract":"<div><div>Placement optimization is a crucial phase in chip design, involving the strategic arrangement of cells within a limited region to enhance space utilization and reduce wirelength. Chip design enterprises need to optimize the placement according to design rules to meet customer demands. While mixed-cell-height circuits are widely used in modern chip design, few studies have simultaneously considered the non-overlapping cells, rails alignment, and minimum implantation area constraints in the placement optimization problems. Hence, this study involves preprocessing the non-linear parts and developing a mixed-integer linear programming model to reduce the cost of legalizing chip placements for businesses. Furthermore, this study designs and implements an exact algorithm based on Benders decomposition, utilizing dual theory to obtain an optimal cut and iteratively solve for the coordinates of cells. Numerical experiments across various scales validate the performance of the algorithm. Through a detailed analysis of the shape of the chip region division, the proportion of different types of cells, the total number of cells and bins, and their impact on the placement, we derive some potentially useful design insights that can benefit chip design enterprises.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 278-296"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157485","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":"Pressure Sensors Based on the Third-Generation Semiconductor Silicon Carbide: A Comprehensive Review","authors":"Xudong Fang ,&nbsp;Chen Wu ,&nbsp;Bian Tian ,&nbsp;Libo Zhao ,&nbsp;Xueyong Wei ,&nbsp;Zhuangde Jiang","doi":"10.1016/j.eng.2024.12.036","DOIUrl":"10.1016/j.eng.2024.12.036","url":null,"abstract":"<div><div>Microelectromechanical system (MEMS) high-temperature pressure sensors are widely used in aerospace, petrochemical industries, automotive electronics, and other fields owing to their advantages of miniaturization, lightweight design, simplified signal processing, and high accuracy. In recent years, advances in semiconductor material growth technology and intelligent equipment operation have significantly increased interest in high-temperature pressure sensors based on the third-generation semiconductor silicon carbide (SiC). This review examines the material properties of SiC single crystals and discusses several technologies influencing the performance of SiC pressure sensors, including the piezoresistive effect, ohmic contact, etching processes, and packaging methodologies. Additionally, it explores future research directions in the field. The review highlights the importance of increasing operating temperatures and advancing sensor integration as critical trends for future SiC high-temperature pressure sensor research and applications.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 183-203"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157479","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":"STROM: A Spatial–Temporal Reduced-Order Model for Zinc Fluidized Bed Roaster Temperature Field Prediction","authors":"Yunfeng Zhang ,&nbsp;Chunhua Yang ,&nbsp;Keke Huang ,&nbsp;Tingwen Huang ,&nbsp;Weihua Gui","doi":"10.1016/j.eng.2025.04.013","DOIUrl":"10.1016/j.eng.2025.04.013","url":null,"abstract":"<div><div>With the intelligent transformation of process manufacturing, accurate and comprehensive perception information is fundamental for application of artificial intelligence methods. In zinc smelting, the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry; its internal temperature field directly determines the quality of zinc calcine and other related products. However, due to its vast spatial dimensions, the limited observation methods, and the complex multiphase, multifield coupled reaction atmosphere inside it, accurately and timely perceiving its temperature field remains a significant challenge. To address these challenges, a spatial–temporal reduced-order model (STROM) is proposed, which can realize fast and accurate temperature field perception based on sparse observation data. Specifically, to address the difficulty in matching the initial physical field with the sparse observation data, an initial field construction based on data assimilation (IFC-DA) method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state, which provides a basis for constructing a high-precision computational fluid dynamics (CFD) model. Then, to address the high simulation cost of high-precision CFD models under full working conditions, a high uniformity (HU)-orthogonal test design (OTD) method with the centered <em>L</em>₂ deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component, feed, and blast parameters. Finally, to address the difficulty in real-time and accurate temperature field prediction, considering the spatial correlation between the observed temperature and the temperature field, as well as the dynamic correlation of the observed temperature in the time dimension, a spatial–temporal predictive model (STPM) is established, which realizes rapid prediction of the temperature field through sparse observation data. To verify the accuracy and validity of the proposed method, CFD model validation and reduced-order model prediction experiments are designed, and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data. Compared with the CFD model, the prediction root-mean-square error (RMSE) of STROM is less than 0.038, and the computational efficiency is improved by 3.4184  × 10⁴ times. In particular, STROM also has a good prediction ability for unmodeled conditions, with a prediction RMSE of less than 0.1089.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 112-128"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897590","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":"Targeting IGF2BP2–CEMIP Boosts Antiangiogenic Therapy in Colorectal Cancer in Mice","authors":"Weikang Chen ,&nbsp;Haojie Bai ,&nbsp;Yani Huo ,&nbsp;Yifan Wu ,&nbsp;Wei Kang ,&nbsp;Dong Zhang ,&nbsp;Yongxin Zhang ,&nbsp;Shiyan Wang ,&nbsp;Lixia Xu ,&nbsp;Chi Chun Wong ,&nbsp;Ka Fai To ,&nbsp;Xiaoxing Li ,&nbsp;Jun Yu","doi":"10.1016/j.eng.2025.06.035","DOIUrl":"10.1016/j.eng.2025.06.035","url":null,"abstract":"<div><div>Angiogenesis is essential for supporting tumor progression and metastasis. However, the potential role of the epitranscriptome in regulating angiogenesis remains unclear. Here, we identify the RNA N⁶-methyladenosine (m⁶A) reader insulin-like growth factor 2 (IGF2) messenger RNA (mRNA)-binding protein 2 (IGF2BP2) as the top enriched m⁶A regulator in hypervascular colorectal cancer (CRC), with its expression correlating with poor prognosis. Knockdown of <em>IGF2BP2</em> in CRC cells suppressed their ability to promote pro-angiogenic phenotypes in endothelial cells <em>in vitro</em>, as well as vascular abnormalization, tumor progression, and metastasis <em>in vivo</em>. Supporting these findings, intestine-specific <em>Igf2bp2</em> knock-in mice exhibited accelerated azoxymethane (AOM) plus dextran sulfate sodium (DSS)-induced CRC through enhanced angiogenesis and vascular abnormalities, whereas intestine-specific <em>Igf2bp2</em> knockout inhibited tumor growth by normalizing tumor vasculature. Mechanistically, IGF2BP2 binds to m⁶A-modified cell migration inducing and hyaluronan binding protein (<em>CEMIP</em>) mRNA and enhanced its stability, leading to increased secretion of CEMIP. Secreted CEMIP interacts with membrane glucose-regulated protein 78 (GRP78) on endothelial cells, activating pro-angiogenic signaling. Importantly, targeting <em>IGF2BP2</em> through genetic ablation, lipid nanoparticle (LNP)-encapsulated small interfering <em>IGF2BP2</em>, or the chemical inhibitor (CWI1-2) synergized with anti-angiogenic drugs to suppress tumor growth in multiple CRC models. Together, these findings suggest that targeting IGF2BP2 is a promising strategy to enhance the efficacy of anti-angiogenic therapy in CRC.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 229-243"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594704","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":"Visualization of Industrial Big Data: State-of-the-Art and Future Perspectives","authors":"Tongkang Zhang ,&nbsp;Jinliang Ding ,&nbsp;Zheng Liu ,&nbsp;Wenjun Zhang","doi":"10.1016/j.eng.2025.08.014","DOIUrl":"10.1016/j.eng.2025.08.014","url":null,"abstract":"<div><div>As industrial production progresses toward digitalization, massive amounts of data have been collected, transmitted, and stored, with characteristics of large-scale, high-dimensional, heterogeneous, and spatiotemporal dynamics. The high complexity of industrial big data poses challenges for the practical decision-making of domain experts, leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis. Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines, including data mining, information visualization, computer graphics, and human–computer interaction, providing a highly effective manner for understanding and exploring the complex industrial processes. This review summarizes the state-of-the-art approaches, characterizes them with six visualization methods, and categorizes them based on analytical tasks and applications. Furthermore, key research challenges and potential future directions are identified.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 85-101"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157398","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 Perspective on Artificial Intelligence for Process Manufacturing","authors":"Vipul Mann ,&nbsp;Jingyi Lu ,&nbsp;Venkat Venkatasubramanian ,&nbsp;Rafiqul Gani","doi":"10.1016/j.eng.2025.01.014","DOIUrl":"10.1016/j.eng.2025.01.014","url":null,"abstract":"<div><div>To achieve sustainable development goals and the requirements of a circular economy, a new class of intelligent computer-aided methods and tools is needed. Artificial intelligence (AI) techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing. However, the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed. In this perspective paper, we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing, with a focus on chemical product design, process synthesis and design, process control, and process safety and hazards.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 ","pages":"Pages 60-67"},"PeriodicalIF":11.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157396","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}