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of Göttingen",[131,112],"{\"ror\":\"01y9bpm73\",\"openalex\":\"I74656192\"}","2026-05-14T00:27:22.216709159","2026-06-01T04:30:35.677489897","companyType","UNIVERSITY","foundedDate","headquarters","country","DE","employeeCount","fundingStage","totalFunding","valuation","website","stockSymbol","uscc","crunchbaseUrl","Georg-August-Universität Göttingen","company_I157773358","Sun Yat-sen University","中山大学",[133,134],"{\"ror\":\"0064kty71\",\"openalex\":\"I157773358\"}","openalex,graphrag","2026-05-14T00:32:16.223592967","2026-07-27T17:01:02.674986697","Guangzhou, China","CN","https://www.sysu.edu.cn","company_I55732556","Arizona State University",[144,152,153],"{\"ror\":\"03efmqc40\",\"openalex\":\"I55732556\",\"wikidata\":\"Q670897\"}","2026-05-14T00:33:06.420458624","2026-06-01T04:26:47.372030906","Tempe, United States","US","https://www.asu.edu","Universidad Estatal de Arizona","Université d'État de l'Arizona","company_I78988378","Renmin University of China","中国人民大学",[163,155,164,156],"{\"ror\":\"041pakw92\",\"openalex\":\"I78988378\",\"wikidata\":\"https://www.wikidata.org/wiki/Q1074117\"}","2026-05-14T01:09:32.28992984","2026-07-29T19:30:44.408950179","Beijing, China","https://www.ruc.edu.cn","People's University of China","Zhōngguó Rénmín Dàxué","company_I143593769","East China University of Science and Technology","华东理工大学",[166,174,167,175],"{\"ror\":\"01vyrm377\",\"openalex\":\"I143593769\",\"wikidata\":\"https://www.wikidata.org/wiki/Q2655352\"}","2026-05-14T00:48:20.386994397","2026-07-26T14:07:35.879864154","Shanghai, China","https://www2.ecust.edu.cn/_t41/main.htm","Huádōng Lǐgōng Dàxué","華東理工大學","company_I115592961","Jiangsu University","江苏大学",[177,185,178],"{\"ror\":\"03jc41j30\",\"openalex\":\"I115592961\",\"wikidata\":\"https://www.wikidata.org/wiki/Q6191740\"}","2026-05-14T00:39:37.405781966","2026-06-01T04:46:24.196704286","Zhenjiang, China","https://www.ujs.edu.cn","Jiāngsū Dàxué","company_I177721651","Oakland University",[187],"{\"ror\":\"01ythxj32\",\"openalex\":\"I177721651\",\"wikidata\":\"https://www.wikidata.org/wiki/Q782116\"}","2026-05-14T01:27:50.356825054","2026-06-01T05:41:24.006637682","Rochester, United States","https://oakland.edu",{"_15":343,"_29":344,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":345,"_39":-5,"_40":41,"_42":346,"_44":347,"_209":348,"_211":-5,"_212":349,"_48":344,"_214":350,"_215":351,"_217":352,"_219":353,"_52":354,"_222":-5,"_223":355,"_225":-5,"_226":87,"_58":344,"_59":-5},{"_15":330,"_29":331,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":332,"_39":-5,"_40":41,"_42":333,"_44":334,"_209":335,"_211":-5,"_212":336,"_48":331,"_214":337,"_215":338,"_217":254,"_219":339,"_52":340,"_222":-5,"_223":341,"_225":342,"_226":87,"_58":331,"_59":-5},{"_15":316,"_29":317,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":318,"_39":-5,"_40":41,"_42":319,"_44":320,"_209":321,"_211":322,"_212":323,"_48":317,"_214":324,"_215":325,"_217":239,"_219":326,"_52":327,"_222":-5,"_223":328,"_225":329,"_226":87,"_58":317,"_59":-5},{"_15":301,"_29":302,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":303,"_39":-5,"_40":41,"_42":304,"_44":305,"_209":306,"_211":307,"_212":308,"_48":302,"_214":309,"_215":310,"_217":254,"_219":311,"_52":312,"_222":-5,"_223":313,"_225":314,"_226":87,"_58":302,"_59":-5},{"_15":284,"_29":285,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":286,"_39":-5,"_40":41,"_42":287,"_44":288,"_209":289,"_211":-5,"_212":290,"_48":285,"_214":291,"_215":292,"_217":293,"_219":294,"_52":295,"_222":-5,"_223":296,"_225":297,"_226":87,"_58":285,"_59":-5},{"_15":271,"_29":272,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":273,"_39":-5,"_40":41,"_42":274,"_44":275,"_209":276,"_211":-5,"_212":277,"_48":272,"_214":278,"_215":279,"_217":254,"_219":240,"_52":280,"_222":-5,"_223":281,"_225":-5,"_226":87,"_58":272,"_59":-5},{"_15":258,"_29":259,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":260,"_39":-5,"_40":41,"_42":261,"_44":262,"_209":263,"_211":-5,"_212":264,"_48":259,"_214":-5,"_215":265,"_217":266,"_219":267,"_52":268,"_222":-5,"_223":269,"_225":-5,"_226":87,"_58":259,"_59":-5},{"_15":246,"_29":247,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":248,"_39":-5,"_40":41,"_42":249,"_44":250,"_209":251,"_211":-5,"_212":252,"_48":247,"_214":-5,"_215":253,"_217":254,"_219":220,"_52":255,"_222":-5,"_223":256,"_225":-5,"_226":87,"_58":247,"_59":-5},{"_15":230,"_29":231,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":232,"_39":-5,"_40":41,"_42":233,"_44":234,"_209":235,"_211":-5,"_212":236,"_48":231,"_214":237,"_215":238,"_217":239,"_219":240,"_52":241,"_222":-5,"_223":242,"_225":-5,"_226":87,"_58":231,"_59":-5},{"_15":204,"_29":205,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":206,"_39":-5,"_40":41,"_42":207,"_44":208,"_209":210,"_211":-5,"_212":213,"_48":205,"_214":-5,"_215":216,"_217":218,"_219":220,"_52":221,"_222":-5,"_223":224,"_225":-5,"_226":87,"_58":205,"_59":-5},"paper_W2788378051","Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation","{\"openalex\":\"W2788378051\",\"doi\":\"10.1016/j.apenergy.2017.12.115\"}","2026-05-19T10:51:43.274155341","2026-05-19T10:51:43.273880691","doi","10.1016/j.apenergy.2017.12.115","arxivId","openalexId","W2788378051","abstractText","publishedDate","2018-02-01","publishedYear",2018,"venue","Applied Energy",420,"authors","keywords",[227,228,229],"pdfUrl","topVenue","Photovoltaic System Optimization Techniques","Solar Radiation and Photovoltaics","Metaheuristic Optimization Algorithms Research","paper_W3037582816","HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning","{\"openalex\":\"W3037582816\",\"doi\":\"10.1109/twc.2020.3003744\"}","2026-05-19T09:50:41.205974987","2026-05-19T09:50:41.206504767","10.1109/twc.2020.3003744","W3037582816","Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with raw user data uploading. By leveraging edge servers as intermediaries to perform partial model aggregation in proximity and relieve core network transmission overhead, it enables great potentials in low-latency and energy-efficient FL. Hence we introduce a novel Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation is partially migrated to edge servers from the cloud. We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization. To solve the problem, we propose an efficient resource scheduling algorithm in the HFEL framework. It can be decomposed into two subproblems: resource allocation given a scheduled set of devices for each edge server and edge association of device users across all the edge servers. With the optimal policy of the convex resource allocation subproblem for a set of devices under a single edge server, an efficient edge association strategy can be achieved through iterative global cost reduction adjustment process, which is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our HFEL framework outperforms the proposed benchmarks in global cost saving and achieves better training performance compared to conventional federated learning.","2020-06-27",2020,"IEEE Transactions on Wireless Communications",425,[243,244,245],"Privacy-Preserving Technologies in Data","Age of Information Optimization","IoT and Edge/Fog Computing","paper_W2911001524","A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module","{\"openalex\":\"W2911001524\",\"doi\":\"10.1016/j.apenergy.2019.01.008\"}","2026-05-16T14:04:16.233706765","2026-05-19T06:44:57.183861542","10.1016/j.apenergy.2019.01.008","W2911001524","2019-01-08",2019,448,[227,228,257],"Solar Thermal and Photovoltaic Systems","paper_W2760572903","Parameters identification of photovoltaic models using an improved JAYA optimization algorithm","{\"openalex\":\"W2760572903\",\"doi\":\"10.1016/j.enconman.2017.08.063\"}","2026-05-15T20:33:09.20625827","2026-05-21T15:09:10.187850984","10.1016/j.enconman.2017.08.063","W2760572903","2017-09-23",2017,"Energy Conversion and Management",544,[227,228,270],"Power Systems and Renewable Energy","paper_W2980856918","Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","{\"openalex\":\"W2980856918\",\"doi\":\"10.1109/twc.2019.2946140\"}","2026-05-14T20:02:51.429246588","2026-05-20T20:28:38.211656395","10.1109/twc.2019.2946140","W2980856918","As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What’s worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent , a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in real-world deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regression-based prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent ’s effectiveness in enabling on-demand low-latency edge intelligence.","2019-10-18",844,[245,282,283],"Advanced Neural Network Applications","Blockchain Technology Applications and Security","paper_W4393065402","A survey on large language model based autonomous agents","{\"openalex\":\"W4393065402\",\"doi\":\"10.1007/s11704-024-40231-1\"}","2026-05-14T13:21:06.321532832","2026-05-21T17:04:37.186583874","10.1007/s11704-024-40231-1","W4393065402","Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.","2024-03-22",2024,"Frontiers of Computer Science",1033,[298,299,300],"https://link.springer.com/content/pdf/10.1007/s11704-024-40231-1.pdf","Reinforcement Learning in Robotics","Topic Modeling","Multimodal Machine Learning Applications","paper_W2962804345","In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning","{\"arxiv\":\"1809.07857\",\"openalex\":\"W2962804345\",\"doi\":\"10.1109/mnet.2019.1800286\"}","2026-05-14T13:07:05.420123876","2026-05-21T17:08:56.194817343","10.1109/mnet.2019.1800286","1809.07857","W2962804345","Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attention from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate content delivery and improve the quality of mobile services. In order to bring more intelligence to edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, caching and communication. And thus, we design the “In-Edge AI” framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. “In-Edge AI” is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of “In-Edge AI.”","2019-07-24","IEEE Network",1041,[315,245,243],"https://arxiv.org/pdf/1809.07857","Caching and Content Delivery","paper_W2962814013","Convergence of Edge Computing and Deep Learning: A Comprehensive Survey","{\"arxiv\":\"1907.08349\",\"openalex\":\"W2962814013\",\"doi\":\"10.1109/comst.2020.2970550\"}","2026-05-14T07:27:29.200388691","2026-05-21T10:22:06.187929674","10.1109/comst.2020.2970550","1907.08349","W2962814013","Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.","2020-01-01","IEEE Communications Surveys & Tutorials",1423,[245,282,243],"https://arxiv.org/pdf/1907.08349","paper_W2950865323","Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing","{\"openalex\":\"W2950865323\",\"doi\":\"10.1109/jproc.2019.2918951\"}","2026-05-14T04:22:15.241805924","2026-05-21T06:20:11.184718077","10.1109/jproc.2019.2918951","W2950865323","With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.","2019-06-12","Proceedings of the IEEE",2118,[245,282,243],"https://ieeexplore.ieee.org/ielx7/5/8789751/08736011.pdf","paper_W2195423816","Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing","{\"openalex\":\"W2195423816\",\"doi\":\"10.1109/tnet.2015.2487344\"}","2026-05-14T03:13:50.239034258","2026-05-21T05:12:04.189840169","10.1109/tnet.2015.2487344","W2195423816","Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.","2015-10-26",2015,"IEEE/ACM Transactions on Networking",2622,[243,245,356],"Mobile Crowdsensing and Crowdsourcing",{"_60":358,"_62":359},[],[360],{"_65":361,"_67":362,"_69":363,"_71":364},1785468009966,{"_74":366,"_76":77,"_78":361,"_80":-5,"_81":26,"_82":26,"_83":26,"_84":-5,"_85":-5,"_86":87,"_35":88,"_89":90},[73,365],"[\"portal\",\"industries\"]","industries",[367,368,369,370,371,372,373,374,375,376,377,378],{"_15":440,"_29":441,"_31":-5,"_32":442,"_33":443,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":444,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":441,"_59":443},{"_15":436,"_29":437,"_31":-5,"_32":-5,"_33":438,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":439,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":437,"_59":438},{"_15":430,"_29":431,"_31":-5,"_32":432,"_33":433,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":434,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":431,"_59":433},{"_15":426,"_29":427,"_31":-5,"_32":-5,"_33":428,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":429,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":427,"_59":428},{"_15":422,"_29":423,"_31":-5,"_32":-5,"_33":424,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":425,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":423,"_59":424},{"_15":416,"_29":417,"_31":-5,"_32":418,"_33":419,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":420,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":417,"_59":419},{"_15":411,"_29":412,"_31":-5,"_32":-5,"_33":413,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":414,"_391":26,"_392":415,"_393":26,"_394":-5,"_58":412,"_59":413},{"_15":407,"_29":408,"_31":-5,"_32":-5,"_33":409,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":410,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":408,"_59":409},{"_15":403,"_29":404,"_31":-5,"_32":-5,"_33":405,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":406,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":404,"_59":405},{"_15":399,"_29":400,"_31":-5,"_32":-5,"_33":401,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":402,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":400,"_59":401},{"_15":395,"_29":396,"_31":-5,"_32":-5,"_33":397,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":398,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":396,"_59":397},{"_15":379,"_29":380,"_31":-5,"_32":-5,"_33":381,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":382,"_42":-5,"_44":-5,"_383":384,"_385":-5,"_386":77,"_387":388,"_389":390,"_391":26,"_392":87,"_393":26,"_394":-5,"_58":380,"_59":381},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[421],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[435],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[445,446],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_449":24},"SITE_BASE_URL"]

Xu Chen 人物

Xu Chen · 相关事件