Engineering最新文献_第7页

Diameter-Transformed Fluidized Bed-Based Catalytic Reaction Engineering and Industrial Application 基于直径变换流化床的催化反应工程与工业应用

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 10.1016/j.eng.2025.02.024

Youhao Xu , Bona Lu , Mingyuan He , Wei Wang

In response to the critical national demand for upgrading automotive gasoline quality, the concept of dual reaction zones was developed to intensify both olefin generation and conversion. The successful large-scale implementation of this process has yielded substantial economic benefits and spurred the invention and systematic study of the diameter-transformed fluidized bed (DTFB) reactor, leading to a suite of new catalytic processes. This study begins with the conceptual origins of the DTFB reactor. By analyzing unimolecular and bimolecular mechanisms in hydrocarbon catalysis, the key conditions necessary for maximizing target products are identified. Furthermore, it elucidates the scientific and technological challenges in applying diameter variation to partition the reaction section, highlighting that the primary challenge lies in achieving precise coupling between flow and reaction multimodalities, which necessitates a generalized drag model for accurate prediction of flow regime transitions. Since flow structure is influenced by both macroscopic parameters and local dynamics, a two-way coupled energy minimization multi-scale (EMMS) drag model and a corresponding multi-scale computational fluid dynamics (CFD) approach have been proposed, laying a theoretical foundation for quantitative design of diameter-transformed sections. The subsequent development of ancillary technologies has provided the necessary engineering safeguards for flexible control of temperature, density, and gas–solid contact time in each zone, ultimately enabling the industrialization, large-scale operation, and long-term stability of DTFB-based catalytic technology. Finally, the study outlines several typical processes and their application performance, and prospects future work.

为响应国家对提高车用汽油质量的迫切需求，提出了双反应区的概念，以加强烯烃的生成和转化。该工艺的成功大规模实施产生了可观的经济效益，并刺激了直径转化流化床（DTFB）反应器的发明和系统研究，导致了一套新的催化工艺。本研究从DTFB反应器的概念起源开始。通过对单分子和双分子催化机理的分析，确定了目标产物最大化的关键条件。此外，本文还阐述了应用直径变化来划分反应截面的科学和技术挑战，强调主要挑战在于实现流动和反应多模态之间的精确耦合，这就需要一个广义的阻力模型来准确预测流型转变。针对流动结构受宏观参数和局部动力学双重影响的特点，提出了一种双向耦合能量最小化多尺度（EMMS）阻力模型和相应的多尺度计算流体动力学（CFD）方法，为径变截面的定量设计奠定了理论基础。后续配套技术的发展，为各区域温度、密度、气固接触时间的灵活控制提供了必要的工程保障，最终实现了dtfb催化技术的工业化、规模化运行和长期稳定。最后，概述了几种典型工艺及其应用性能，并对今后的工作进行了展望。

{"title":"Diameter-Transformed Fluidized Bed-Based Catalytic Reaction Engineering and Industrial Application","authors":"Youhao Xu , Bona Lu , Mingyuan He , Wei Wang","doi":"10.1016/j.eng.2025.02.024","DOIUrl":"10.1016/j.eng.2025.02.024","url":null,"abstract":"<div><div>In response to the critical national demand for upgrading automotive gasoline quality, the concept of dual reaction zones was developed to intensify both olefin generation and conversion. The successful large-scale implementation of this process has yielded substantial economic benefits and spurred the invention and systematic study of the diameter-transformed fluidized bed (DTFB) reactor, leading to a suite of new catalytic processes. This study begins with the conceptual origins of the DTFB reactor. By analyzing unimolecular and bimolecular mechanisms in hydrocarbon catalysis, the key conditions necessary for maximizing target products are identified. Furthermore, it elucidates the scientific and technological challenges in applying diameter variation to partition the reaction section, highlighting that the primary challenge lies in achieving precise coupling between flow and reaction multimodalities, which necessitates a generalized drag model for accurate prediction of flow regime transitions. Since flow structure is influenced by both macroscopic parameters and local dynamics, a two-way coupled energy minimization multi-scale (EMMS) drag model and a corresponding multi-scale computational fluid dynamics (CFD) approach have been proposed, laying a theoretical foundation for quantitative design of diameter-transformed sections. The subsequent development of ancillary technologies has provided the necessary engineering safeguards for flexible control of temperature, density, and gas–solid contact time in each zone, ultimately enabling the industrialization, large-scale operation, and long-term stability of DTFB-based catalytic technology. Finally, the study outlines several typical processes and their application performance, and prospects future work.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 244-256"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412176","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}

引用次数: 0

A Compact Millimeter-Wave, Dual-Band, Dual-Polarized, Duplex, and Scalable Phased Array Enabling B5G/6G Multi-Standard Systems 支持B5G/6G多标准系统的紧凑型毫米波、双频、双极化和双工可扩展相控阵

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 10.1016/j.eng.2025.06.017

Kai Chen , Jun Xu , Renrong Zhao , Lei Xiang , Debin Hou , Zhiqiang Yu , Jianyi Zhou , Jixin Chen , Zhang-Cheng Hao , Wei Hong

A new, compact, and dual-band dual-polarized duplex (D³) phased array architecture is proposed in this study. In contrast to studies reported previously, this design integrates four independent beamforming systems within a single printed circuit board (PCB), enabling the proposed 1 × 4 phased array to transmit or receive simultaneously vertically and horizontally polarized signals at 28 and 38 GHz, thereby supporting concurrent, dual-band, and dual-polarized four-beam operations. In addition, the exceptional frequency selectivity of the phased array facilitates frequency-division duplex operations. By adopting a brick-type architecture, the proposed phased array achieves two-dimensional scalability, which allows it to serve either as a standalone, small-scale phased array, or as a sub-block for larger-scale arrays. A novel, dual-polarized end-fire magnetoelectric dipole antenna was developed as the radiating element for the phased array. This antenna exhibits an impedance bandwidth of return loss below −10 dB across the frequency range of 24.8–40.3 GHz (47.6%), which represents one of the broadest operating bands reported for PCB-based, co-apertured, and dual-polarized end-fire antennas. Experimental validation of the fabricated phased array demonstrated that the two orthogonal polarizations could achieve beam-scanning ranges exceeding 90° and 60° at 28 and 38 GHz, respectively. The measured effective isotropic radiated power values exhibited distinct frequency selectivities between the two bands. To the best of our knowledge, this is the first demonstration of a D³ phased array that presents a promising solution for beyond fifth-generation (B5G) and sixth-generation (6G) millimeter-wave multi-standard systems.

本研究提出了一种新的、紧凑的双频双极化双工相控阵结构。与之前报道的研究相比，该设计将四个独立的波束形成系统集成在一个印刷电路板（PCB）中，使所提出的1 × 4相控阵能够同时发射或接收28 GHz和38 GHz的垂直和水平极化信号，从而支持并发、双频和双极化四波束操作。此外，相控阵的特殊频率选择性促进了分频双工操作。通过采用砖式架构，所提出的相控阵实现了二维可扩展性，这使得它既可以作为独立的小型相控阵，也可以作为大型阵列的子块。研制了一种新型的双极化端火磁电偶极子天线作为相控阵的辐射元件。该天线在24.8-40.3 GHz（47.6%）的频率范围内显示出低于- 10 dB的回波损耗阻抗带宽，这代表了基于pcb、共孔径和双极化的端火天线中最宽的工作频段之一。实验验证表明，在28 GHz和38 GHz下，两种正交极化可以实现90°和60°以上的波束扫描范围。测量的有效各向同性辐射功率值在两个波段之间表现出明显的频率选择性。据我们所知，这是D3相控阵的首次演示，为第五代（B5G）和第六代（6G）毫米波多标准系统提供了有前途的解决方案。

{"title":"A Compact Millimeter-Wave, Dual-Band, Dual-Polarized, Duplex, and Scalable Phased Array Enabling B5G/6G Multi-Standard Systems","authors":"Kai Chen , Jun Xu , Renrong Zhao , Lei Xiang , Debin Hou , Zhiqiang Yu , Jianyi Zhou , Jixin Chen , Zhang-Cheng Hao , Wei Hong","doi":"10.1016/j.eng.2025.06.017","DOIUrl":"10.1016/j.eng.2025.06.017","url":null,"abstract":"<div><div>A new, compact, and dual-band dual-polarized duplex (D<sup>3</sup>) phased array architecture is proposed in this study. In contrast to studies reported previously, this design integrates four independent beamforming systems within a single printed circuit board (PCB), enabling the proposed 1 × 4 phased array to transmit or receive simultaneously vertically and horizontally polarized signals at 28 and 38 GHz, thereby supporting concurrent, dual-band, and dual-polarized four-beam operations. In addition, the exceptional frequency selectivity of the phased array facilitates frequency-division duplex operations. By adopting a brick-type architecture, the proposed phased array achieves two-dimensional scalability, which allows it to serve either as a standalone, small-scale phased array, or as a sub-block for larger-scale arrays. A novel, dual-polarized end-fire magnetoelectric dipole antenna was developed as the radiating element for the phased array. This antenna exhibits an impedance bandwidth of return loss below −10 dB across the frequency range of 24.8–40.3 GHz (47.6%), which represents one of the broadest operating bands reported for PCB-based, co-apertured, and dual-polarized end-fire antennas. Experimental validation of the fabricated phased array demonstrated that the two orthogonal polarizations could achieve beam-scanning ranges exceeding 90° and 60° at 28 and 38 GHz, respectively. The measured effective isotropic radiated power values exhibited distinct frequency selectivities between the two bands. To the best of our knowledge, this is the first demonstration of a D<sup>3</sup> phased array that presents a promising solution for beyond fifth-generation (B5G) and sixth-generation (6G) millimeter-wave multi-standard systems.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 149-162"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329280","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}

引用次数: 0

Climate Change and Pollution Threaten Exploding Space Economy 气候变化和污染威胁着爆炸式的空间经济

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 10.1016/j.eng.2025.11.009

引用次数: 0

Template-Directed Growth of a 3D Hierarchical Structure of Well-Aligned Bimetallic MOF Arrays for High-Efficiency Electrocatalytic Air Sterilization 模板定向生长用于高效电催化空气灭菌的双金属MOF阵列的三维分层结构

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 10.1016/j.eng.2025.05.020

Liting Dong, Shougang Chen, Zhipeng Zhao, Xiao Sun, Gaojian Lv, Wei Wang, Chengcheng Ma, Chunchao Hou, Wen Li, Jiakun Wang, Jianglin Gou

To address increasing concerns regarding environmental air quality, it is highly desirable to develop low-cost and high-efficiency air-sterilization technology. Herein, as a proof-of-concept, a template-directed growth strategy is designed to fabricate a 3D hierarchical superstructure of well-aligned bimetallic metal–organic framework (MOF) arrays. Taking advantage of the designed electrode material (0.3Co-MOF/Cu@Cu), which provides a greater number of catalytically active sites, better conductivity, and water stability in comparison with pure copper mesh, the proposed strategy exhibits high electrocatalytic efficiency for air sterilization. Under an external electric field, the designed electrode can electroporate bacteria, accelerate the electrocatalytic reduction of oxygen adsorbed by oxygen vacancies, and dynamically generate more exogenous reactive oxygen species (ROS), which will increase the negative ion concentration in the air and thereby increase the comfort level for people in the room. Moreover, the free electrons and exogenous ROS on the surface of the material will disturb the physiological activities inside bacteria, resulting in the production of endogenous ROS inside the bacteria and bacterial death. The sterilization rate of 1.5 m∙s⁻¹ of airflow at 2 V (equivalent to a treatment time of 0.0026 s) is as high as 99.51%, demonstrating the great potential of the proposed strategy for practical application.

为了解决人们对环境空气质量日益关注的问题，开发低成本、高效率的空气灭菌技术是迫切需要的。在此，作为概念验证，设计了模板导向生长策略来制造排列良好的双金属金属有机框架（MOF）阵列的3D分层上层结构。利用设计的电极材料（0.3Co-MOF/Cu@Cu），与纯铜网相比，提供了更多的催化活性位点，更好的导电性和水稳定性，所提出的策略对空气杀菌具有很高的电催化效率。在外加电场作用下，所设计的电极可以使细菌电穿孔，加速氧空位吸附的氧的电催化还原，并动态产生更多的外源活性氧（ROS），从而增加空气中的负离子浓度，从而提高室内人员的舒适度。此外，材料表面的自由电子和外源性ROS会干扰细菌内部的生理活动，导致细菌内部产生内源性ROS，导致细菌死亡。1.5 m∙s−1 2 V气流（相当于处理时间0.0026 s）的灭菌率高达99.51%，显示了所提出的策略在实际应用中的巨大潜力。

{"title":"Template-Directed Growth of a 3D Hierarchical Structure of Well-Aligned Bimetallic MOF Arrays for High-Efficiency Electrocatalytic Air Sterilization","authors":"Liting Dong, Shougang Chen, Zhipeng Zhao, Xiao Sun, Gaojian Lv, Wei Wang, Chengcheng Ma, Chunchao Hou, Wen Li, Jiakun Wang, Jianglin Gou","doi":"10.1016/j.eng.2025.05.020","DOIUrl":"10.1016/j.eng.2025.05.020","url":null,"abstract":"<div><div>To address increasing concerns regarding environmental air quality, it is highly desirable to develop low-cost and high-efficiency air-sterilization technology. Herein, as a proof-of-concept, a template-directed growth strategy is designed to fabricate a 3D hierarchical superstructure of well-aligned bimetallic metal–organic framework (MOF) arrays. Taking advantage of the designed electrode material (0.3Co-MOF/Cu@Cu), which provides a greater number of catalytically active sites, better conductivity, and water stability in comparison with pure copper mesh, the proposed strategy exhibits high electrocatalytic efficiency for air sterilization. Under an external electric field, the designed electrode can electroporate bacteria, accelerate the electrocatalytic reduction of oxygen adsorbed by oxygen vacancies, and dynamically generate more exogenous reactive oxygen species (ROS), which will increase the negative ion concentration in the air and thereby increase the comfort level for people in the room. Moreover, the free electrons and exogenous ROS on the surface of the material will disturb the physiological activities inside bacteria, resulting in the production of endogenous ROS inside the bacteria and bacterial death. The sterilization rate of 1.5 m∙s<sup>−1</sup> of airflow at 2 V (equivalent to a treatment time of 0.0026 s) is as high as 99.51%, demonstrating the great potential of the proposed strategy for practical application.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"56 ","pages":"Pages 234-243"},"PeriodicalIF":11.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145478203","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}

引用次数: 0

Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning 基于双分类器对抗学习的轻量级鲁棒跨域微震信号分类框架

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

微震信号的自动识别是深埋地下工程灾害早期预警的关键。然而，实际部署仍面临三大挑战，即有限的资源、严重的噪声干扰和数据稀缺。为了解决这些问题，本研究提出了用于跨域MS信号分类的轻量级鲁棒熵正则化无监督域自适应框架（LRE-UDAF）。该框架包括一个轻量级的鲁棒特征提取器和一个利用双分类器差异度量和熵正则化的无监督域自适应（UDA）模块。特征提取器从预处理信号中提取高级表示，随后将其输入两个分类器以预测类别概率。通过三个阶段的对抗学习，特征提取器和分类器逐步对齐源域和目标域的分布，促进知识从标记的源转移到未标记的目标域。源域实验表明，该特征提取器达到了较高的分类效率，分类准确率高达97.7%。此外，LRE-UDAF在其轻量级设计和健壮性方面优于流行的工业网络。跨域实验表明，该方法能有效地减轻域漂移，且未标记信号最少。烧蚀和对比实验进一步验证了特征提取器和UDA模块设计的有效性。该框架为深部地下工程中资源受限、噪声易发和数据稀缺的环境提供了有效的解决方案，为早期灾害预警的实际实施提供了重要的前景。

{"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}

引用次数: 0

Robot Subset Selection-Based Multi-User Edge Computing for Swarm Lifetime Maximization with Correlated Data Sources 基于机器人子集选择的多用户边缘计算与相关数据源的群寿命最大化

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

在本文中，我们研究了利用多用户边缘计算系统最大化无线网络中机器人群生命周期的问题。机器人将其计算任务卸载到边缘服务器上，我们的目标是有效地利用分布式数据源之间的相关性来延长集群的运行寿命。通过选择适当的机器人子集将其感知数据传输到边缘服务器来解决优化问题。利用信息论原理来证明群网络中机器人的分组，并将分布式机器人子集之间的数据关联建模为无向图。我们引入了一个周期子集选择问题，以及相关的和更宽松的公式，如图划分问题和子图级顶点选择问题，以解决群体生命周期最大化的挑战。对于加性高斯白噪声信道，我们分析了种群寿命的理论上界，并提出了几种逼近该上界的算法，包括最小次迭代划分算法和最终顶点搜索算法。此外，我们考虑了信道分集对平坦衰落信道中子集选择的影响，并调整算法以考虑基站信道估计能力的变化。通过综合仿真实验对所提方法的有效性进行了评价。结果表明，该算法的群生存期比基准方法延长了650%。

{"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}

引用次数: 0

AI and Deep Learning for Terahertz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models 太赫兹超大规模MIMO的人工智能和深度学习：从模型驱动的方法到基础模型

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

本研究探讨了人工智能（AI）在解决太赫兹超大规模多输入多输出（UM-MIMO）系统带来的挑战方面的变革潜力。本文首先概述了太赫兹UM-MIMO系统的特点，并确定了收发器设计的三个主要挑战：计算复杂性、建模难度和测量限制。该研究认为，人工智能为这些挑战提供了一个有希望的解决方案。为开发适合太赫兹UM-MIMO系统的人工智能算法，提出了三个系统的研究路线图。第一个路线图，模型驱动深度学习（DL），强调利用可用领域知识的重要性，并主张采用人工智能仅用于增强既定信号处理或优化框架内的瓶颈模块。讨论了四个基本步骤：算法框架、基算法、损失函数设计和神经结构设计。第二个路线图给出了信道状态信息（CSI）基础模型，旨在通过关注它们的共享基础（即无线信道）来统一不同收发器模块的设计。提出了训练单个紧凑基础模型来估计无线信道分数函数的方法，为设计各种收发模块提供了一个通用的前提。概述了四个基本步骤：一般框架、条件调节、特定场地的适应、CSI基础模型的联合设计和模型驱动的深度学习。第三个路线图旨在探索将预训练大语言模型（llm）应用于太赫兹UM-MIMO系统的潜在方向。设想了几个应用场景，包括基于llm的估计、优化、搜索、网络管理和协议理解。最后，指出了研究中存在的问题和未来的研究方向。

{"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}

引用次数: 0

An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool: Low-Latency, Energy-Efficient, and Secure 面向智能机床大规模计算任务的自适应混合边缘云协同卸载方法：低延迟、节能和安全

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

在连续加工环境下运行的智能机床通常受到多部件退化和加工任务更新的耦合效应的影响。这些因素导致产生大量多源传感器数据流和具有相互依赖数据关系的大量计算任务。严格的实时约束和复杂的依赖结构对传统的单模计算框架提出了相当大的挑战。此外，智能机床对计算卸载解决方案的需求日益增长，而不仅仅是优化延迟。这些解决方案还必须解决可持续制造的能源管理问题，并确保保护敏感工业数据的安全性。介绍了一种将单边缘云协作与多边缘云协作相结合的自适应混合边缘云协同卸载机制。该机制能够根据计算节点的状态、任务特征、依赖关系复杂性和资源可用性在协作模式之间动态切换，最终促进低延迟、高能效和安全的任务处理。为了解决异构边缘云环境下的大规模任务分配挑战，开发了一种新型混合超启发式算法，实现了计算资源的灵活分配和性能优化。大量的实验表明，与最先进的技术相比，所提出的方法在任务处理时间上平均提高了27.36%，在能源效率上平均提高了7.89%，同时保持了优越的安全性能。通过对数字双龙门五轴加工中心的案例研究验证，该机制有效地协调了多源并发数据处理、复杂依赖任务协作、高计算机器学习工作负载和连续批处理任务部署场景的任务执行，与上一代协作方法相比，延迟降低了37.03%，能源使用优化了25.93%。这些结果为智能机床的可持续和安全的计算卸载提供了理论和技术支持，从而有助于下一代智能制造系统的发展。

{"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}

引用次数: 0

Nature-Based Global Land Surface Soil Organic Carbon Indicates Increasing Driven by Climate Change 基于自然的全球陆地表层土壤有机碳在气候变化的驱动下呈增加趋势

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

土壤可能是实现全球碳中和的一个潜在的显著碳来源。然而，与其他碳储量相比，对气候变化更为敏感的陆地表层土壤有机碳储量在全球变暖的影响下如何发生自然变化仍是未知的。本文对1981 - 2019年全球陆地表面有机碳变化趋势进行了分析，并对其驱动因素进行了分析。结合耦合模式比对项目第6阶段（CMIP6）模式的气候情景，提出了一种随机森林模型（一种机器学习方法）来预测未来全球地表有机碳趋势。结果表明：全球地表有机碳含量呈上升趋势，而温度和降水是全球尺度上主要的气候驱动因素，植被覆盖是影响土壤有机碳增加的重要局地因子。然而，在1.5°C全球变暖情景下，与SSP2-4.5情景相比，陆地有机碳汇最多将增加13.0千兆碳（PgC），仅占当前1.1 ~ 1.5°C全球变暖水平下总碳排放能力的19%。此外，这一数值还远未达到《巴黎协定》的目标，即在未来20年内，地表以下40厘米土壤碳储量的年增长量为千分之四（2.72 PgC·a−1）。这说明过度依赖天然碳汇是一种高风险策略。这些调查结果突出了实施减缓和消除战略以减少温室气体排放的紧迫性。

{"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}

引用次数: 0

The Agentic-AI Core: An AI-Empowered, Mission-Oriented Core Network for Next-Generation Mobile Telecommunications 代理-人工智能核心：下一代移动通信的人工智能授权、面向任务的核心网络

IF 11.6 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering

Pub Date : 2026-01-01 DOI: 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.

虽然第五代无线网络的复杂性正在被广泛评论，但人们对第六代（6G）的到来充满期待，因为它具有丰富的功能和特性。不难想象，如果没有适当的设计，6G的丰富性将进一步增加系统的复杂性。为了解决这个问题，我们提出了agent -AI核心（A-Core），这是一种基于人工智能（AI）的、面向任务的下一代移动通信核心网络架构。在A-Core中，网络功能可以随时添加和更新，并进一步编程到任务中，为客户提供各种服务。这些任务由自主的网络代理根据客户的意图创建和执行，这些意图可以用自然语言表达。通过利用大规模网络AI模型，代理将客户的意图解析为网络功能的工作流，并遵循工作流执行任务。作为一个开放、敏捷的系统架构，A-Core有望加速创新，并大大缩短标准发布时间。通过两个用例演示了A-Core的优点。

{"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}

引用次数: 0