Pub Date : 2026-02-01Epub Date: 2025-07-17DOI: 10.1016/j.eng.2025.06.040
Gong Li , Ang Gao , Xin-Yi Lu , Tian-Hong Zhou , Shi-Ying Zhou , Li-Juan Xia , Lei Wan , Yu-Zhang He , Xin-Yi Chen , Wen-Ying Guo , Jia-Min Zheng , Hao Ren , Sheng-Qiu Tang , Xiao-Ping Liao , Liang Chen , Jian Sun
The global spread of antibiotic resistance genes (ARGs) continues to worsen, with plasmid-mediated conjugation serving as a major transmission route. Although developing conjugation inhibitors to block this process is a promising strategy, current options are limited by toxicity and poor in vivo efficacy. This study evaluated the effect of cinnamic acid (CA; 3-phenyl-2-acrylic acid), a widely abundant food additive found in cinnamon, on plasmid conjugation. CA effectively inhibited the conjugation of various resistance plasmids in vitro, ex vivo, and in vivo. Transcriptomic analysis indicated that CA disrupts the electron transport chain (ETC) and proton motive force (PMF) by inhibiting the tricarboxylic acid (TCA) cycle, leading to reduced intracellular adenosine triphosphate (ATP)—a critical factor for plasmid conjugation. Biocompatibility assays showed that CA maintains high biosafety while preserving gut microbiota homeostasis. Therefore, these findings provide new insights into ARG inhibition and highlight the potential of CA as a novel strategy to combat the global rise in antibiotic-resistant infections.
{"title":"Targeting Plasmid Conjugation with Cinnamic Acid: A Novel Approach to Combat Antibiotic Resistance","authors":"Gong Li , Ang Gao , Xin-Yi Lu , Tian-Hong Zhou , Shi-Ying Zhou , Li-Juan Xia , Lei Wan , Yu-Zhang He , Xin-Yi Chen , Wen-Ying Guo , Jia-Min Zheng , Hao Ren , Sheng-Qiu Tang , Xiao-Ping Liao , Liang Chen , Jian Sun","doi":"10.1016/j.eng.2025.06.040","DOIUrl":"10.1016/j.eng.2025.06.040","url":null,"abstract":"<div><div>The global spread of antibiotic resistance genes (ARGs) continues to worsen, with plasmid-mediated conjugation serving as a major transmission route. Although developing conjugation inhibitors to block this process is a promising strategy, current options are limited by toxicity and poor <em>in vivo</em> efficacy. This study evaluated the effect of cinnamic acid (CA; 3-phenyl-2-acrylic acid), a widely abundant food additive found in cinnamon, on plasmid conjugation. CA effectively inhibited the conjugation of various resistance plasmids <em>in vitro</em>, <em>ex vivo</em>, and <em>in vivo</em>. Transcriptomic analysis indicated that CA disrupts the electron transport chain (ETC) and proton motive force (PMF) by inhibiting the tricarboxylic acid (TCA) cycle, leading to reduced intracellular adenosine triphosphate (ATP)—a critical factor for plasmid conjugation. Biocompatibility assays showed that CA maintains high biosafety while preserving gut microbiota homeostasis. Therefore, these findings provide new insights into ARG inhibition and highlight the potential of CA as a novel strategy to combat the global rise in antibiotic-resistant infections.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"57 ","pages":"Pages 165-177"},"PeriodicalIF":11.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645502","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}
Pub Date : 2026-02-01Epub Date: 2025-08-12DOI: 10.1016/j.eng.2025.08.004
Caiping Zhao , Jingrong Wang , Yuan Liu , Baoling Shang , Danna Lin , Yao Xiao , Hong Ren , Yue Li , Wen Rui , Xu Zou , Hudan Pan , Liang Liu
Protein glycosylation is one of the most vital modifications. Understanding the role of protein glycosylation in coronavirus disease 2019 (COVID-19) is the key in 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, Fuzheng Jiedu 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-L-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.
{"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":"10.1016/j.eng.2025.08.004","url":null,"abstract":"<div><div>Protein glycosylation is one of the most vital modifications. Understanding the role of protein glycosylation in coronavirus disease 2019 (COVID-19) is the key in 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, Fuzheng Jiedu 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.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"57 ","pages":"Pages 72-86"},"PeriodicalIF":11.6,"publicationDate":"2026-02-01","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}
This study presents a novel self-sensing steel fiber-reinforced polymer composite bar (SFCB). The SFCB combines damage control, self-sensing, and structural reinforcement functions using distributed fiber optic sensing (DFOS) technology. By combining DFOS strains with theoretical and numerical models, a multilevel performance method for damage assessment is proposed from the perspectives of safety, suitability, and durability. Stiffness is a metric used to assess the complete service history of the reinforced concrete (RC) structure, which was used to define the damage variables. Initially, a basic correlation is created between the SFCB strain and several performance characteristics, such as moment, curvature, load, deflection, stiffness, and crack breadth, at characteristic points. The threshold values of damage variables for safety, serviceability, and durability were determined based on loading peak, mid-span deflection limits, and crack width limits corresponding to the damage variables. Then, a modified fiber damage model based on DFOS strain data is proposed to improve identification, quantification, and tracking for fiber damage. Finally, the reliability of the proposed theoretical and numerical models was verified by three-point flexural tests of SFCB–RC beams, and the test beams were analyzed using the proposed method. The results show that increasing the reinforcement ratio can lower the threshold at all levels and improve the ability of the flexural beams to control damage. This study contributes to advancing the intelligence of RC structures and offers valuable insights for the design of intelligent RC structures.
{"title":"Performance Assessment of Reinforced Concrete Structures Using Self-Sensing Steel Fiber-Reinforced Polymer Composite Bars: Theory and Test Validation","authors":"Zenghui Ye , Zhongfeng Zhu , Feng Xing , Yingwu Zhou","doi":"10.1016/j.eng.2024.11.022","DOIUrl":"10.1016/j.eng.2024.11.022","url":null,"abstract":"<div><div>This study presents a novel self-sensing steel fiber-reinforced polymer composite bar (SFCB). The SFCB combines damage control, self-sensing, and structural reinforcement functions using distributed fiber optic sensing (DFOS) technology. By combining DFOS strains with theoretical and numerical models, a multilevel performance method for damage assessment is proposed from the perspectives of safety, suitability, and durability. Stiffness is a metric used to assess the complete service history of the reinforced concrete (RC) structure, which was used to define the damage variables. Initially, a basic correlation is created between the SFCB strain and several performance characteristics, such as moment, curvature, load, deflection, stiffness, and crack breadth, at characteristic points. The threshold values of damage variables for safety, serviceability, and durability were determined based on loading peak, mid-span deflection limits, and crack width limits corresponding to the damage variables. Then, a modified fiber damage model based on DFOS strain data is proposed to improve identification, quantification, and tracking for fiber damage. Finally, the reliability of the proposed theoretical and numerical models was verified by three-point flexural tests of SFCB–RC beams, and the test beams were analyzed using the proposed method. The results show that increasing the reinforcement ratio can lower the threshold at all levels and improve the ability of the flexural beams to control damage. This study contributes to advancing the intelligence of RC structures and offers valuable insights for the design of intelligent RC structures.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"57 ","pages":"Pages 283-301"},"PeriodicalIF":11.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386701","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}
Pub Date : 2026-01-30DOI: 10.1016/j.eng.2026.01.017
Qian Chen, Zhanwei Wang, Xianhao Chen, Juan Wen, Di Zhou, Sijing Ji, Min Sheng, Kaibin Huang
The work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China under a fellowship award (HKU RFS2122-7S04), the National Natural Science Foundation of China/Research Grants Council Collaborative Research Scheme (CRS_HKU702/24), the Areas of Excellence Scheme (AoE/E-601/22-R), the Collaborative Research Fund (C1009-22G), the General Research Fund (17212423), the Shenzhen–Hong Kong–Macao Technology Research Programme (Type C; SGDX20230821091559018), and the National Natural Science Foundation of China (62461160329 and 62371368). The authors apologize for the omission of the above funding information in the originally published article.
{"title":"Corrigendum to “Space–Ground Fluid AI for 6G Edge Intelligence” [Engineering 54 (2025) 14–19]","authors":"Qian Chen, Zhanwei Wang, Xianhao Chen, Juan Wen, Di Zhou, Sijing Ji, Min Sheng, Kaibin Huang","doi":"10.1016/j.eng.2026.01.017","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.017","url":null,"abstract":"The work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China under a fellowship award (HKU RFS2122-7S04), the National Natural Science Foundation of China/Research Grants Council Collaborative Research Scheme (CRS_HKU702/24), the Areas of Excellence Scheme (AoE/E-601/22-R), the Collaborative Research Fund (C1009-22G), the General Research Fund (17212423), the Shenzhen–Hong Kong–Macao Technology Research Programme (Type C; SGDX20230821091559018), and the National Natural Science Foundation of China (62461160329 and 62371368). The authors apologize for the omission of the above funding information in the originally published article.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"38 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089277","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":"Engineering in Situ Cu Electrodeposition and Ionization in the LEEFT-Cu System with a Stainless-Steel/Cu Core–Shell Electrode for Extended Water Disinfection","authors":"Wei Wang, Feiyang Mo, Mengkun Tian, Shuai Wang, Nathan Dong, Xing Xie","doi":"10.1016/j.eng.2026.01.016","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.016","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"260 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072121","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}
Pub Date : 2026-01-24DOI: 10.1016/j.eng.2025.12.036
Xiong Zhang, Siyi Ji, Benny C.F. Cheung, Yixuan Sun, Chunming Wang, Zibin Chen, Xiaohong Zhan, Chunjin Wang
The manufacture of nanostructures on silicon (Si) has profound implications across diverse disciplines. Previous research has focused mainly on qualitative explanations based solely on evidence from electromagnetic and matter reorganization theories. This study designed experiments and explored the characteristics of nanostructures created by scanning with an ultrafast laser, in which experimental evidence supporting both electromagnetic and matter reorganization theories was simultaneously observed. Four distinct surface morphologies were identified, each with specific formation thresholds: 0.06, 0.09, 0.22, and 0.27 J⋅cm−2. The polycrystalline Si that emerged at the apex of the monocrystalline Si matrix was directly characterized, and the occurrence of SiO2 and α-Si was detailed. The simultaneous and distinct contributions of electromagnetic and reorganization theories were revealed. By examining the relationship between phase transitions and laser features, the conditions under which each mechanism operates were established. This study provides novel insights into the precise control of Si nanostructures, which could revolutionize applications in electronics, photonics, and materials science.
{"title":"Formation Mechanism of the Nanostructures and Crystallographic Features on Silicon Created by Ultrafast Laser Scanning","authors":"Xiong Zhang, Siyi Ji, Benny C.F. Cheung, Yixuan Sun, Chunming Wang, Zibin Chen, Xiaohong Zhan, Chunjin Wang","doi":"10.1016/j.eng.2025.12.036","DOIUrl":"https://doi.org/10.1016/j.eng.2025.12.036","url":null,"abstract":"The manufacture of nanostructures on silicon (Si) has profound implications across diverse disciplines. Previous research has focused mainly on qualitative explanations based solely on evidence from electromagnetic and matter reorganization theories. This study designed experiments and explored the characteristics of nanostructures created by scanning with an ultrafast laser, in which experimental evidence supporting both electromagnetic and matter reorganization theories was simultaneously observed. Four distinct surface morphologies were identified, each with specific formation thresholds: 0.06, 0.09, 0.22, and 0.27 J⋅cm<ce:sup loc=\"post\">−2</ce:sup>. The polycrystalline Si that emerged at the apex of the monocrystalline Si matrix was directly characterized, and the occurrence of SiO<ce:inf loc=\"post\">2</ce:inf> and α-Si was detailed. The simultaneous and distinct contributions of electromagnetic and reorganization theories were revealed. By examining the relationship between phase transitions and laser features, the conditions under which each mechanism operates were established. This study provides novel insights into the precise control of Si nanostructures, which could revolutionize applications in electronics, photonics, and materials science.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"30 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048207","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}
Pub Date : 2026-01-24DOI: 10.1016/j.eng.2025.11.034
Xiaoliang Yan, Zhichao Wang, Shreyes N. Melkote, David W. Rosen
Cyber manufacturing services, which aim to connect geographically distributed designers and manufacturing service providers via the internet, are emerging to address the market shift from mass production to mass personalization. Recent advances in the Internet of Things (IoT) and machine learning enable new capabilities that promise improved efficiencies across the cyber manufacturing ecosystem. In this paper, we focus on machine learning methods that facilitate cyber manufacturing services in the areas of manufacturing process planning and design for manufacturing (DFM). To enable automated manufacturing process planning, we review recent advances in manufacturing capability modeling, manufacturing process selection, and feature recognition for process planning. To facilitate DFM, data-driven tools for generative design are reviewed and new methods and results presented. In the context of the literature review, we summarize work from our research group and present some new methods and results in the DFM area. Critical summaries of research challenges are provided to set the stage for recommendations on future research directions toward realizing cyber manufacturing services.
{"title":"Machine Learning-Based Cyber Manufacturing Services: A Review of Manufacturing Process Selection, Process Planning, and Design for Manufacturing","authors":"Xiaoliang Yan, Zhichao Wang, Shreyes N. Melkote, David W. Rosen","doi":"10.1016/j.eng.2025.11.034","DOIUrl":"https://doi.org/10.1016/j.eng.2025.11.034","url":null,"abstract":"Cyber manufacturing services, which aim to connect geographically distributed designers and manufacturing service providers via the internet, are emerging to address the market shift from mass production to mass personalization. Recent advances in the Internet of Things (IoT) and machine learning enable new capabilities that promise improved efficiencies across the cyber manufacturing ecosystem. In this paper, we focus on machine learning methods that facilitate cyber manufacturing services in the areas of manufacturing process planning and design for manufacturing (DFM). To enable automated manufacturing process planning, we review recent advances in manufacturing capability modeling, manufacturing process selection, and feature recognition for process planning. To facilitate DFM, data-driven tools for generative design are reviewed and new methods and results presented. In the context of the literature review, we summarize work from our research group and present some new methods and results in the DFM area. Critical summaries of research challenges are provided to set the stage for recommendations on future research directions toward realizing cyber manufacturing services.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"395 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042692","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}
Pub Date : 2026-01-23DOI: 10.1016/j.eng.2025.11.033
Shuo Wang, Lin Zhou, Shiyu Zhong, Gan Li, Lei Zhang, Xu Wang, Zhiqiang Li, Jian Lu
{"title":"Recent Advances in Metal Additive Manufacturing: Materials Design and Artificial Intelligence Applications","authors":"Shuo Wang, Lin Zhou, Shiyu Zhong, Gan Li, Lei Zhang, Xu Wang, Zhiqiang Li, Jian Lu","doi":"10.1016/j.eng.2025.11.033","DOIUrl":"https://doi.org/10.1016/j.eng.2025.11.033","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"86 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033469","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}
Pub Date : 2026-01-22DOI: 10.1016/j.eng.2026.01.013
Zuo-Jun Max Shen, Shaochong Lin
This study introduces a novel conceptual framework to understand the transformative impact of Artificial Intelligence (AI) on global supply chains. We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness. The Three-Chain perspective examines how AI transforms the logistics chain (physical flow), information chain (data flow), and value chain (value creation) from fragmented operations to synchronized intelligent ecosystems. The Four-Intelligence pathway maps the evolutionary progression from digital connectivity to operational optimization, collaborative ecosystems, and ultimately self-evolving intelligent systems. AI serves as an orchestrating force that processes rich contextual information spanning product attributes, market dynamics, environmental conditions, and operational realities. We demonstrated the practical application of the framework through a comprehensive case study of JD.com, where AI implementation across all dimensions yielded quantifiable improvements. Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness. The paper concludes by identifying six emerging research frontiers, such as generative AI integration with decision optimization.
{"title":"AI Empowers Supply Chain Intelligence: A Three-Chain Four-Intelligence Framework","authors":"Zuo-Jun Max Shen, Shaochong Lin","doi":"10.1016/j.eng.2026.01.013","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.013","url":null,"abstract":"This study introduces a novel conceptual framework to understand the transformative impact of Artificial Intelligence (AI) on global supply chains. We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness. The Three-Chain perspective examines how AI transforms the logistics chain (physical flow), information chain (data flow), and value chain (value creation) from fragmented operations to synchronized intelligent ecosystems. The Four-Intelligence pathway maps the evolutionary progression from digital connectivity to operational optimization, collaborative ecosystems, and ultimately self-evolving intelligent systems. AI serves as an orchestrating force that processes rich contextual information spanning product attributes, market dynamics, environmental conditions, and operational realities. We demonstrated the practical application of the framework through a comprehensive case study of JD.com, where AI implementation across all dimensions yielded quantifiable improvements. Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness. The paper concludes by identifying six emerging research frontiers, such as generative AI integration with decision optimization.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"71 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022047","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}
Pub Date : 2026-01-22DOI: 10.1016/j.eng.2026.01.012
Toshiro Fujimori
{"title":"Ammonia-Fueled Power Generation for Energy Transition","authors":"Toshiro Fujimori","doi":"10.1016/j.eng.2026.01.012","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.012","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"38 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033476","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}
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