Pub Date : 2026-01-01DOI: 10.1016/j.eng.2025.10.023
Dingran Song , Feng Dai , Yi Liu , Hao Tan , Mingdong Wei
Automatic identification of microseismic (MS) signals is crucial for early disaster warning in deep underground engineering. However, three major challenges remain for practical deployment, namely limited resources, severe noise interference, and data scarcity. To address these issues, this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework (LRE-UDAF) for cross-domain MS signal classification. The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation (UDA) module utilizing a bi-classifier disparity metric and entropy regularization. The feature extractor derives high-level representations from the preprocessed signals, which are subsequently fed into two classifiers to predict class probability. Through three-stage adversarial learning, the feature extractor and classifiers progressively align the distributions of the source and target domains, facilitating knowledge transfer from the labeled source to the unlabeled target domain. Source-domain experiments reveal that the feature extractor achieves high effectiveness, with a classification accuracy of up to 97.7%. Moreover, LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness. Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals. Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules. This framework presents an efficient solution for resource-constrained, noise-prone, and data-scarce environments in deep underground engineering, offering significant promise for practical implementations in early disaster warning.
{"title":"Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning","authors":"Dingran Song , Feng Dai , Yi Liu , Hao Tan , Mingdong Wei","doi":"10.1016/j.eng.2025.10.023","DOIUrl":"10.1016/j.eng.2025.10.023","url":null,"abstract":"<div><div>Automatic identification of microseismic (MS) signals is crucial for early disaster warning in deep underground engineering. However, three major challenges remain for practical deployment, namely limited resources, severe noise interference, and data scarcity. To address these issues, this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework (LRE-UDAF) for cross-domain MS signal classification. The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation (UDA) module utilizing a bi-classifier disparity metric and entropy regularization. The feature extractor derives high-level representations from the preprocessed signals, which are subsequently fed into two classifiers to predict class probability. Through three-stage adversarial learning, the feature extractor and classifiers progressively align the distributions of the source and target domains, facilitating knowledge transfer from the labeled source to the unlabeled target domain. Source-domain experiments reveal that the feature extractor achieves high effectiveness, with a classification accuracy of up to 97.7%. Moreover, LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness. Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals. Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules. This framework presents an efficient solution for resource-constrained, noise-prone, and data-scarce environments in deep underground engineering, offering significant promise for practical implementations in early disaster warning.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 267-283"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145434643","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 : 2026-01-01DOI: 10.1016/j.eng.2025.10.015
Siqi Zhang, Yi Ma, Rahim Tafazolli
In this paper, we investigate the problem of maximizing the lifetime of robot swarms in wireless networks utilizing a multi-user edge computing system. Robots offload their computational tasks to an edge server, and our objective is to efficiently exploit the correlation between distributed data sources to extend the operational lifetime of the swarm. The optimization problem is approached by selecting appropriate subsets of robots to transmit their sensed data to the edge server. Information theory principles are used to justify the grouping of robots in the swarm network, with data correlation among distributed robot subsets modeled as an undirected graph. We introduce a periodic subset selection problem, along with related and more relaxed formulations such as a graph partitioning problem and a subgraph-level vertex selection problem, to address the swarm lifetime maximization challenge. For additive white Gaussian noise channels, we analyze the theoretical upper bound of the swarm lifetime and propose several algorithms—including the least-degree iterative partitioning algorithm and final vertex search algorithm—to approach this bound. Additionally, we consider the impact of channel diversity on subset selection in flat-fading channels and adapt the algorithm to account for variations in the base station’s channel estimation capabilities. Comprehensive simulation experiments are conducted to evaluate the effectiveness of the proposed methods. Results show that the algorithms achieve a swarm lifetime up to 650% longer than that of benchmark approaches.
{"title":"Robot Subset Selection-Based Multi-User Edge Computing for Swarm Lifetime Maximization with Correlated Data Sources","authors":"Siqi Zhang, Yi Ma, Rahim Tafazolli","doi":"10.1016/j.eng.2025.10.015","DOIUrl":"10.1016/j.eng.2025.10.015","url":null,"abstract":"<div><div>In this paper, we investigate the problem of maximizing the lifetime of robot swarms in wireless networks utilizing a multi-user edge computing system. Robots offload their computational tasks to an edge server, and our objective is to efficiently exploit the correlation between distributed data sources to extend the operational lifetime of the swarm. The optimization problem is approached by selecting appropriate subsets of robots to transmit their sensed data to the edge server. Information theory principles are used to justify the grouping of robots in the swarm network, with data correlation among distributed robot subsets modeled as an undirected graph. We introduce a periodic subset selection problem, along with related and more relaxed formulations such as a graph partitioning problem and a subgraph-level vertex selection problem, to address the swarm lifetime maximization challenge. For additive white Gaussian noise channels, we analyze the theoretical upper bound of the swarm lifetime and propose several algorithms—including the least-degree iterative partitioning algorithm and final vertex search algorithm—to approach this bound. Additionally, we consider the impact of channel diversity on subset selection in flat-fading channels and adapt the algorithm to account for variations in the base station’s channel estimation capabilities. Comprehensive simulation experiments are conducted to evaluate the effectiveness of the proposed methods. Results show that the algorithms achieve a swarm lifetime up to 650% longer than that of benchmark approaches.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 173-185"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382397","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 : 2026-01-01DOI: 10.1016/j.eng.2025.07.032
Wentao Yu , Hengtao He , Shenghui Song , Jun Zhang , Linglong Dai , Lizhong Zheng , Khaled B. Letaief
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, and the 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.
{"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":"10.1016/j.eng.2025.07.032","url":null,"abstract":"<div><div>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, and the 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.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 14-33"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","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}
Pub Date : 2026-01-01DOI: 10.1016/j.eng.2025.09.030
Zhiwen Lin , Kaien Wei , Yiqiao Wang , Chuanhai Chen , Jinyan Guo , Qiang Cheng , Zhifeng Liu
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks. These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships. The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks. Furthermore, there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency. These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data. This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration. This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes, task characteristics, dependency complexity, and resource availability, ultimately facilitating low-latency, energy-efficient, and secure task processing. A novel hybrid hyper-heuristic algorithm has been developed to address large-scale task allocation challenges in heterogeneous edge-cloud environments, enabling the flexible allocation of computational resources and performance optimization. Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36% in task processing time and 7.89% in energy efficiency when compared to state-of-the-art techniques, all while maintaining superior security performance. Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing, complex dependency task collaboration, high-computational machine learning workloads, and continuous batch task deployment scenarios, achieving a 37.03% reduction in latency and a 25.93% optimization in energy use relative to previous generation collaboration methods. These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools, thereby contributing to the evolution of next-generation smart manufacturing systems.
{"title":"An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool: Low-Latency, Energy-Efficient, and Secure","authors":"Zhiwen Lin , Kaien Wei , Yiqiao Wang , Chuanhai Chen , Jinyan Guo , Qiang Cheng , Zhifeng Liu","doi":"10.1016/j.eng.2025.09.030","DOIUrl":"10.1016/j.eng.2025.09.030","url":null,"abstract":"<div><div>Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks. These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships. The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks. Furthermore, there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency. These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data. This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration. This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes, task characteristics, dependency complexity, and resource availability, ultimately facilitating low-latency, energy-efficient, and secure task processing. A novel hybrid hyper-heuristic algorithm has been developed to address large-scale task allocation challenges in heterogeneous edge-cloud environments, enabling the flexible allocation of computational resources and performance optimization. Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36% in task processing time and 7.89% in energy efficiency when compared to state-of-the-art techniques, all while maintaining superior security performance. Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing, complex dependency task collaboration, high-computational machine learning workloads, and continuous batch task deployment scenarios, achieving a 37.03% reduction in latency and a 25.93% optimization in energy use relative to previous generation collaboration methods. These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools, thereby contributing to the evolution of next-generation smart manufacturing systems.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 201-218"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545565","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 : 2026-01-01DOI: 10.1016/j.eng.2025.03.031
Yanli Liu , Xin Chen , Jianyun Zhang , Xing Yuan , Tiesheng Guan , Junliang Jin , Guoqing Wang
Soil could represent a potentially notable source of carbon for achieving global carbon neutrality. However, how the land surface soil organic carbon (SOC) stock, which is more sensitive to climate change than other carbon stocks, will change naturally under the influence of global warming remains unknown. In this work, the global land surface SOC trends from 1981 to 2019 were explored, and the driving factors were identified. A random forest model (a type of machine learning method) was proposed to predict future global surface SOC trends integrated with climate scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The results revealed that the global surface SOC content will increase, while the temperature and precipitation are the main climate drivers at the global scale, and vegetation cover is a crucial local factor influencing the increase in SOC. However, under the 1.5 °C global warming scenario, the land SOC sink will increase by 13.0 petagram carbon (PgC) at most compared with that under the SSP2-4.5 scenario, which accounts for only 19% of the total carbon emission capacity at the current 1.1 to 1.5 °C global warming level. Moreover, this value is far from the Paris Agreement target of four out of one thousand for the annual increase in the soil carbon stock 40 cm below the surface over the next 20 years (2.72 PgC·a−1). This illustrates that overreliance on natural carbon sinks is a high-risk strategy. These findings highlight the urgency of implementing mitigation and removal strategies to reduce greenhouse gas emissions.
{"title":"Nature-Based Global Land Surface Soil Organic Carbon Indicates Increasing Driven by Climate Change","authors":"Yanli Liu , Xin Chen , Jianyun Zhang , Xing Yuan , Tiesheng Guan , Junliang Jin , Guoqing Wang","doi":"10.1016/j.eng.2025.03.031","DOIUrl":"10.1016/j.eng.2025.03.031","url":null,"abstract":"<div><div>Soil could represent a potentially notable source of carbon for achieving global carbon neutrality. However, how the land surface soil organic carbon (SOC) stock, which is more sensitive to climate change than other carbon stocks, will change naturally under the influence of global warming remains unknown. In this work, the global land surface SOC trends from 1981 to 2019 were explored, and the driving factors were identified. A random forest model (a type of machine learning method) was proposed to predict future global surface SOC trends integrated with climate scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The results revealed that the global surface SOC content will increase, while the temperature and precipitation are the main climate drivers at the global scale, and vegetation cover is a crucial local factor influencing the increase in SOC. However, under the 1.5 °C global warming scenario, the land SOC sink will increase by 13.0 petagram carbon (PgC) at most compared with that under the SSP2-4.5 scenario, which accounts for only 19% of the total carbon emission capacity at the current 1.1 to 1.5 °C global warming level. Moreover, this value is far from the Paris Agreement target of four out of one thousand for the annual increase in the soil carbon stock 40 cm below the surface over the next 20 years (2.72 PgC·a<sup>−1</sup>). This illustrates that overreliance on natural carbon sinks is a high-risk strategy. These findings highlight the urgency of implementing mitigation and removal strategies to reduce greenhouse gas emissions.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 306-316"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070755","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 : 2026-01-01DOI: 10.1016/j.eng.2025.06.027
Xu Li , Weisen Shi , Hang Zhang , Chenghui Peng , Shaoyun Wu , Wen Tong
While the complexity of fifth-generation wireless networks is being widely commented upon, there is great anticipation for the arrival of the sixth generation (6G), with its enriched capabilities and features. It can easily be imagined that, without proper design, the enrichment of 6G will further increase system complexity. To address this issue, we propose the Agentic-AI Core (A-Core), an artificial intelligence (AI)-empowered, mission-oriented core network architecture for next-generation mobile telecommunications. In A-Core, network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers. These missions are created and executed by autonomous network agents according to the customer’s intent, which may be expressed in natural language. The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission. As an open, agile system architecture, A-Core holds promise for accelerating innovation and greatly reducing standard release times. The advantages of A-Core are demonstrated through two use cases.
{"title":"The Agentic-AI Core: An AI-Empowered, Mission-Oriented Core Network for Next-Generation Mobile Telecommunications","authors":"Xu Li , Weisen Shi , Hang Zhang , Chenghui Peng , Shaoyun Wu , Wen Tong","doi":"10.1016/j.eng.2025.06.027","DOIUrl":"10.1016/j.eng.2025.06.027","url":null,"abstract":"<div><div>While the complexity of fifth-generation wireless networks is being widely commented upon, there is great anticipation for the arrival of the sixth generation (6G), with its enriched capabilities and features. It can easily be imagined that, without proper design, the enrichment of 6G will further increase system complexity. To address this issue, we propose the Agentic-AI Core (A-Core), an artificial intelligence (AI)-empowered, mission-oriented core network architecture for next-generation mobile telecommunications. In A-Core, network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers. These missions are created and executed by autonomous network agents according to the customer’s intent, which may be expressed in natural language. The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission. As an open, agile system architecture, A-Core holds promise for accelerating innovation and greatly reducing standard release times. The advantages of A-Core are demonstrated through two use cases.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 104-119"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503880","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 : 2026-01-01DOI: 10.1016/j.eng.2025.08.039
Fengyu Wang , Yuan Zheng , Wenjun Xu , Junxiao Liang , Ping Zhang , Zhu Han
Increasing demands for massive data transmission pose significant challenges to communication systems. Compared with traditional communication systems that focus on the accurate reconstruction of bit sequences, semantic communications (SemComs), which aim to deliver information connotation, are regarded as a key technology for sixth-generation (6G) mobile networks. Most current SemComs utilize an end-to-end (E2E) trained neural network (NN) for semantic extraction and interpretation, which lacks interpretability for further optimization. Moreover, NN-based SemComs assume that the application and physical layers of the protocol stack can be jointly trained, which is incompatible with current digital communication systems. To overcome those drawbacks, we propose a SemCom system that employs explicit semantic bases (Sebs) as the basic units to represent semantic connotations. First, a mathematical model of Sebs is proposed to build an explicit knowledge base (KB). Then, the Seb-based SemCom architecture is proposed, including both a communication mode and a KB update mode to enable the evolution of communication systems. Sem-codec and channel codec modules are designed specifically, with the assistance of an explicit KB for the efficient and robust transmission of semantics. Moreover, unequal error protection (UEP) is strategically implemented, considering communication intent and the importance of Sebs, thereby ensuring the reliability of critical semantics. In addition, a Seb-based SemCom protocol stack that is compatible with the fifth-generation (5G) protocol stack is proposed. To assess the effectiveness and compatibility of the proposed Seb-based SemComs, a case study focusing on an image-transmission task is conducted. The simulations show that our Seb-based SemComs outperform state-of-the-art works in learned perceptual image patch similarity (LPIPS) by over 20% under varying communication intents and exhibit robustness under fluctuating channel conditions, highlighting the advantages of the interpretability and flexibility afforded by explicit Sebs.
{"title":"Explicit Semantic-Base-Empowered Communications for 6G Mobile Networks","authors":"Fengyu Wang , Yuan Zheng , Wenjun Xu , Junxiao Liang , Ping Zhang , Zhu Han","doi":"10.1016/j.eng.2025.08.039","DOIUrl":"10.1016/j.eng.2025.08.039","url":null,"abstract":"<div><div>Increasing demands for massive data transmission pose significant challenges to communication systems. Compared with traditional communication systems that focus on the accurate reconstruction of bit sequences, semantic communications (SemComs), which aim to deliver information connotation, are regarded as a key technology for sixth-generation (6G) mobile networks. Most current SemComs utilize an end-to-end (E2E) trained neural network (NN) for semantic extraction and interpretation, which lacks interpretability for further optimization. Moreover, NN-based SemComs assume that the application and physical layers of the protocol stack can be jointly trained, which is incompatible with current digital communication systems. To overcome those drawbacks, we propose a SemCom system that employs explicit semantic bases (Sebs) as the basic units to represent semantic connotations. First, a mathematical model of Sebs is proposed to build an explicit knowledge base (KB). Then, the Seb-based SemCom architecture is proposed, including both a communication mode and a KB update mode to enable the evolution of communication systems. Sem-codec and channel codec modules are designed specifically, with the assistance of an explicit KB for the efficient and robust transmission of semantics. Moreover, unequal error protection (UEP) is strategically implemented, considering communication intent and the importance of Sebs, thereby ensuring the reliability of critical semantics. In addition, a Seb-based SemCom protocol stack that is compatible with the fifth-generation (5G) protocol stack is proposed. To assess the effectiveness and compatibility of the proposed Seb-based SemComs, a case study focusing on an image-transmission task is conducted. The simulations show that our Seb-based SemComs outperform state-of-the-art works in learned perceptual image patch similarity (LPIPS) by over 20% under varying communication intents and exhibit robustness under fluctuating channel conditions, highlighting the advantages of the interpretability and flexibility afforded by explicit Sebs.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 34-44"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025298","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 : 2026-01-01DOI: 10.1016/j.eng.2025.04.012
Hongwei Yu , He Ji , Yang Li , Jing Qi , Baiwen Ma , Chengzhi Hu , Jiuhui Qu
Historical legacy effects and the mechanisms underlying primary producer community succession are not well understood. In this study, environmental DNA (eDNA) sequencing technology and chronological sequence analysis in sediments were utilized to examine long-term changes in cyanobacterial and aquatic plant communities. The analysis results indicate that the nutritional status and productivity of aquatic ecosystems have been relatively high since 2010, which could reflect a period of eutrophication due to high long-term rates of organic matter deposition (33.22–42.08 g·kg−1). The temporal and spatial characteristics of community structure were related to environmental filtering based on trophic status between 1849 and 2020. Turnover in the primary producer community was confirmed through change-point model analyses with regime shifts toward new ecological states. On the basis of ecological data and geochronological techniques, it was determined that the quality of habitats at a local scale may affect ecological niche shifts between cyanobacterial and aquatic plant communities. These observations suggest how primary producers respond to rapid urbanization, serving as an invaluable guide for protecting freshwater biodiversity.
{"title":"Long-Term Succession in Cyanobacteria and Aquatic Plant Communities: Insights from Sediment Analysis","authors":"Hongwei Yu , He Ji , Yang Li , Jing Qi , Baiwen Ma , Chengzhi Hu , Jiuhui Qu","doi":"10.1016/j.eng.2025.04.012","DOIUrl":"10.1016/j.eng.2025.04.012","url":null,"abstract":"<div><div>Historical legacy effects and the mechanisms underlying primary producer community succession are not well understood. In this study, environmental DNA (eDNA) sequencing technology and chronological sequence analysis in sediments were utilized to examine long-term changes in cyanobacterial and aquatic plant communities. The analysis results indicate that the nutritional status and productivity of aquatic ecosystems have been relatively high since 2010, which could reflect a period of eutrophication due to high long-term rates of organic matter deposition (33.22–42.08 g·kg<sup>−1</sup>). The temporal and spatial characteristics of community structure were related to environmental filtering based on trophic status between 1849 and 2020. Turnover in the primary producer community was confirmed through change-point model analyses with regime shifts toward new ecological states. On the basis of ecological data and geochronological techniques, it was determined that the quality of habitats at a local scale may affect ecological niche shifts between cyanobacterial and aquatic plant communities. These observations suggest how primary producers respond to rapid urbanization, serving as an invaluable guide for protecting freshwater biodiversity.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 296-305"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071143","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 : 2026-01-01DOI: 10.1016/j.eng.2025.04.026
Huanyu Li , Ziwei Yang , Chuanyu Zhang , Xueyong Wei , Wenjing Wang , Ting Bai , Zhichao Deng , Bowen Gao , Manli Cui , Weixuan Jing , Mingzhen Zhang , Zhaoxiang Yu , Mingxin Zhang
Ulcerative colitis (UC) is a chronic, non-specific inflammatory disorder of the intestines whose etiology is influenced by various factors. Intestinal barrier impairment due to disturbances in the intestinal microenvironment is a key feature of UC. Current therapeutic strategies are constrained in their capacity to fully restore the intestinal barrier and achieve comprehensive resolution of inflammation in a coordinated manner. In this study, we constructed a pterostilbene (PSB)-loaded prebiotic microcapsule (PSB@MC) using a microfluidic electrospray method and characterized it using various means. Its safety, biodistribution, protective, and therapeutic effects on colitis were evaluated in various animal models. The potential mechanisms by which PSB@MC exerts its therapeutic effects were subsequently explored. The results indicated that PSB@MC exhibited favorable biocompatibility and facilitated targeted delivery of PSB to the colon. Moreover, the wrinkled morphology of PSB@MC contributed to prolonged drug retention in the colon. Oral PSB@MC administration restored intestinal microenvironment homeostasis by scavenging reactive oxygen species (ROS), decreasing pro-inflammatory cytokines, modulating gut microbiota and metabolism, and providing protective and therapeutic benefits against dextran sulfate sodium-induced colitis. Additionally, our research demonstrated that PSB@MC could activate the aryl hydrocarbon receptor/interleukin-22 (AHR/IL-22) pathway to enhance the integrity of the intestinal barrier. These results suggest that PSB@MC could be a new, secure, and efficient UC therapy option.
{"title":"Prebiotic Microcapsule-Encapsulated Pterostilbene Alleviates Ulcerative Colitis by Regulating the Intestinal Microenvironment and Activating AHR/IL-22 Pathway","authors":"Huanyu Li , Ziwei Yang , Chuanyu Zhang , Xueyong Wei , Wenjing Wang , Ting Bai , Zhichao Deng , Bowen Gao , Manli Cui , Weixuan Jing , Mingzhen Zhang , Zhaoxiang Yu , Mingxin Zhang","doi":"10.1016/j.eng.2025.04.026","DOIUrl":"10.1016/j.eng.2025.04.026","url":null,"abstract":"<div><div>Ulcerative colitis (UC) is a chronic, non-specific inflammatory disorder of the intestines whose etiology is influenced by various factors. Intestinal barrier impairment due to disturbances in the intestinal microenvironment is a key feature of UC. Current therapeutic strategies are constrained in their capacity to fully restore the intestinal barrier and achieve comprehensive resolution of inflammation in a coordinated manner. In this study, we constructed a pterostilbene (PSB)-loaded prebiotic microcapsule (PSB@MC) using a microfluidic electrospray method and characterized it using various means. Its safety, biodistribution, protective, and therapeutic effects on colitis were evaluated in various animal models. The potential mechanisms by which PSB@MC exerts its therapeutic effects were subsequently explored. The results indicated that PSB@MC exhibited favorable biocompatibility and facilitated targeted delivery of PSB to the colon. Moreover, the wrinkled morphology of PSB@MC contributed to prolonged drug retention in the colon. Oral PSB@MC administration restored intestinal microenvironment homeostasis by scavenging reactive oxygen species (ROS), decreasing pro-inflammatory cytokines, modulating gut microbiota and metabolism, and providing protective and therapeutic benefits against dextran sulfate sodium-induced colitis. Additionally, our research demonstrated that PSB@MC could activate the aryl hydrocarbon receptor/interleukin-22 (AHR/IL-22) pathway to enhance the integrity of the intestinal barrier. These results suggest that PSB@MC could be a new, secure, and efficient UC therapy option.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 219-233"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305545","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 : 2026-01-01DOI: 10.1016/j.eng.2025.10.029
Hongda Liu , Le Yang , Yu Yang , Huan Tang , Junling Ren , Hui Sun , Xin Sun , Songyuan Tang , Chong Qiu , Ye Sun , Jigang Wang , Guangli Yan , Ling Kong , Ying Han , Xijun Wang
Rheumatoid arthritis (RA) remains a therapeutic challenge because of the suboptimal efficacy and significant adverse effects of current treatments. Obakulactone (OL), a natural tetracyclic triterpenoid isolated from Phellodendri cortex, has emerged as a promising candidate for RA intervention. However, its underlying mechanism remains poorly understood. In this study, we investigated the therapeutic effects of OL and its molecular mechanisms in RA using a multifaceted approach. A complete Freund’s adjuvant (CFA)-induced RA rat model revealed that OL significantly alleviated joint swelling and restored the expression of CD3+ T cells and CD68+ macrophages in joints, and the polarization state of macrophages shifted from proinflammatory M1 (CD86) to anti-inflammatory M2 (CD206) dominant. In addition, OL alleviated pathological changes in lymphoid organs (thymus and spleen), effectively inhibited the differentiation of CD4+ T cells into T helper 17 (Th17) cells, and normalized serum levels of inflammatory cytokines (e.g., interleukin (IL)-6 and tumor necrosis factor-α (TNF-α)) and RA diagnostic markers (e.g., c-reactive protein (CRP) and rheumatoid factor (RF)). Multiomics profiling revealed that OL corrected the dysregulated biosynthesis and metabolism of unsaturated fatty acids (e.g., arachidonic acid and linolenic acid) in RA rats, with acyl coenzyme A (CoA) thioesterase 1 (ACOT1) identified as a critical regulator. In vitro studies have shown that OL significantly inhibits cell proliferation and inflammatory cytokine secretion and promotes the apoptosis of RA synovial fibroblasts (SFs). It inhibited the M1 polarization of Raw264.7 macrophages and promoted M2 polarization. Mechanistically, cellular thermal shift assays (CETSA), microscale thermophoresis (MST), surface plasmon resonance (SPR), and short hairpin RNA (shRNA) experiments revealed ACOT1 as the direct target of OL. OL enhanced ACOT1 ubiquitination-mediated proteasomal degradation, thereby reducing downstream stearoyl-CoA desaturase-1 expression and inhibiting the Janus kinase (JAK)–signal transducer and activator of transcription (STAT) and phosphoinositide 3-kinase (PI3K)–protein kinase B (AKT) signaling pathways, thus suppressing inflammation and fibrosis in SFs. This study establishes OL as a potential RA therapeutic agent and highlights ACOT1 as a novel target for RA intervention, offering insights into fatty acid metabolism reprogramming as a therapeutic strategy.
{"title":"Obakulactone Alleviates Rheumatoid Arthritis by Promotion of ACOT1 Degradation via the Ubiquitin‒Proteasome Pathway and Restoration of Unsaturated Fatty Acid Homeostasis","authors":"Hongda Liu , Le Yang , Yu Yang , Huan Tang , Junling Ren , Hui Sun , Xin Sun , Songyuan Tang , Chong Qiu , Ye Sun , Jigang Wang , Guangli Yan , Ling Kong , Ying Han , Xijun Wang","doi":"10.1016/j.eng.2025.10.029","DOIUrl":"10.1016/j.eng.2025.10.029","url":null,"abstract":"<div><div>Rheumatoid arthritis (RA) remains a therapeutic challenge because of the suboptimal efficacy and significant adverse effects of current treatments. Obakulactone (OL), a natural tetracyclic triterpenoid isolated from <em>Phellodendri cortex</em>, has emerged as a promising candidate for RA intervention. However, its underlying mechanism remains poorly understood. In this study, we investigated the therapeutic effects of OL and its molecular mechanisms in RA using a multifaceted approach. A complete Freund’s adjuvant (CFA)-induced RA rat model revealed that OL significantly alleviated joint swelling and restored the expression of CD3<sup>+</sup> T cells and CD68<sup>+</sup> macrophages in joints, and the polarization state of macrophages shifted from proinflammatory M1 (CD86) to anti-inflammatory M2 (CD206) dominant. In addition, OL alleviated pathological changes in lymphoid organs (thymus and spleen), effectively inhibited the differentiation of CD4<sup>+</sup> T cells into T helper 17 (Th17) cells, and normalized serum levels of inflammatory cytokines (e.g., interleukin (IL)-6 and tumor necrosis factor-α (TNF-α)) and RA diagnostic markers (e.g., c-reactive protein (CRP) and rheumatoid factor (RF)). Multiomics profiling revealed that OL corrected the dysregulated biosynthesis and metabolism of unsaturated fatty acids (e.g., arachidonic acid and linolenic acid) in RA rats, with acyl coenzyme A (CoA) thioesterase 1 (ACOT1) identified as a critical regulator. <em>In vitro</em> studies have shown that OL significantly inhibits cell proliferation and inflammatory cytokine secretion and promotes the apoptosis of RA synovial fibroblasts (SFs). It inhibited the M1 polarization of Raw264.7 macrophages and promoted M2 polarization. Mechanistically, cellular thermal shift assays (CETSA), microscale thermophoresis (MST), surface plasmon resonance (SPR), and short hairpin RNA (shRNA) experiments revealed ACOT1 as the direct target of OL. OL enhanced ACOT1 ubiquitination-mediated proteasomal degradation, thereby reducing downstream stearoyl-CoA desaturase-1 expression and inhibiting the Janus kinase (JAK)–signal transducer and activator of transcription (STAT) and phosphoinositide 3-kinase (PI3K)–protein kinase B (AKT) signaling pathways, thus suppressing inflammation and fibrosis in SFs. This study establishes OL as a potential RA therapeutic agent and highlights ACOT1 as a novel target for RA intervention, offering insights into fatty acid metabolism reprogramming as a therapeutic strategy.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 341-360"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145454645","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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