Query：
学者姓名：吴越钟
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
Edge computing brings computing resources closer to the Internet of Things (IoT) devices, significantly reducing transmission latency and bandwidth usage. However, the limited resources of edge servers require efficient management. Serverless computing meets this demand through its elastic resource provisioning, leading to the emergence of serverless edge computing-a promising computing paradigm. Despite its potential, realtime task dispatching and scheduling in the highly complex and dynamic environment of serverless edge computing present significant challenges. On the one hand, task execution requires not only sufficient CPU resources but also free containers; on the other hand, tasks are typically event-driven, with strong burstiness and high concurrency, and impose stringent demands on fast decision-making. To address these challenges, we propose a real-time task dispatching and scheduling method, aiming to maximize the satisfaction rate of Service Level Objectives (SLOs) for tasks. First, we design a task dispatching algorithm named Adaptive Deep Reinforcement Learning (ADRL). This algorithm can quickly decide the execution position of tasks based on coarse information and effectively adapt to the changes in available servers in dynamic environments. Second, we propose a task scheduling algorithm named Warm-aware Shortest Remaining Idle Time (WSRIT), which guides the edge servers to schedule the tasks in the request queue based on the tasks' remaining idle time and the state of the warm containers. Considering the limited storage space of the edge servers, we further introduce a container replacement algorithm named Low Priority First (LPF) to ensure smooth container launches. Extensive simulation experiments are conducted based on Azure datasets. The results show that our methodcan improve the satisfaction rate of SLOs by 12.57 similar to 41.87% and achieve the lowest cold start rate compared to existing methods.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Edge computing Edge computing Real-time tasks Real-time tasks Serverless computing Serverless computing Task scheduling Task scheduling
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Ming , Xu, Furong , Wu, Yuqin et al. Real-time task dispatching and scheduling in serverless edge computing [J]. | AD HOC NETWORKS , 2025 , 174 . |
| MLA | Li, Ming et al. "Real-time task dispatching and scheduling in serverless edge computing" . | AD HOC NETWORKS 174 (2025) . |
| APA | Li, Ming , Xu, Furong , Wu, Yuqin , Zhang, Jianshan , Xu, Weitao , Wu, Yuezhong . Real-time task dispatching and scheduling in serverless edge computing . | AD HOC NETWORKS , 2025 , 174 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Formaldehyde (FA), a known carcinogen, is occasionally used illegally as a preservative in seafood, while traditional detection methods for FA residues often fail to meet the practical needs for nondestructive detection. In this study, a approach was developed by combining a portable Raman spectrometer with the InceptionTime deep learning model without sample pretreatment. Model were trained by FA-negative and FA-positive Raman spectral data from the shrimp surface and achieved accuracies of 84.40 % and 85.17 % at detection thresholds of 5 mg/kg (the primary safety detection threshold) and 100 mg/kg (the abuse-level contamination threshold), respectively. Metabolomic analysis and weight visualization indicated that the model particularly focused on Raman peaks associated with specific amino acids and astaxanthin-binding proteins. Two amino acid metabolites, timonacic and spinacine, were also identified as direct indicators of FA addition. Our model offers a fielddeployable and practical approach for real-time and on-site FA detection scenario.
Keyword ：
Deep learning Deep learning Formaldehyde detection Formaldehyde detection Nondestructive detection Nondestructive detection Raman spectroscopy Raman spectroscopy Shrimp Shrimp
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wei, Chencheng , Zhang, Jiheng , Li, Gaozheng et al. Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy [J]. | FOOD CHEMISTRY , 2025 , 492 . |
| MLA | Wei, Chencheng et al. "Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy" . | FOOD CHEMISTRY 492 (2025) . |
| APA | Wei, Chencheng , Zhang, Jiheng , Li, Gaozheng , Zhong, Yi , Ye, Zhaoting , Wang, Handong et al. Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy . | FOOD CHEMISTRY , 2025 , 492 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The combined contamination of multiple mycotoxins (MTs) is a matter of utmost concern, posing significant threats to both the economy stability and the well-being of humans and animals. Consequently, there is an urgent demand for highly sensitive and portable multiplex detection methods to effectively identify and quantify MTs. The primary objective of this paper is to provide a comprehensive overview of the methods to detect multiple MTs in agricultural products, including instrumental and biosensor approaches published in the last five years. Specifically, we emphasize detection methods for antibody (ab)- and aptamer (apt)-based biosensors, while undertaking a comparative analysis of their performance, particularly focusing on the sensitivity. Furthermore, this review proposes several promising technologies that can be leveraged in the future and outlines the challenges and prospects associated with achieving rapid, accurate, and intelligent detection of MTs.
Keyword ：
Agriculture products Agriculture products Antibody Antibody Aptamer Aptamer Multiplex detection Multiplex detection Mycotoxin Mycotoxin
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wei, Chencheng , Wang, Handong , Li, Gaozheng et al. Multiplex detection methods for mycotoxins in agricultural products: A systematic review [J]. | FOOD CONTROL , 2023 , 158 . |
| MLA | Wei, Chencheng et al. "Multiplex detection methods for mycotoxins in agricultural products: A systematic review" . | FOOD CONTROL 158 (2023) . |
| APA | Wei, Chencheng , Wang, Handong , Li, Gaozheng , Li, Jianhua , Zhang, Fang , Wu, Yuezhong et al. Multiplex detection methods for mycotoxins in agricultural products: A systematic review . | FOOD CONTROL , 2023 , 158 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
