The purpose of this study was to investigate the effects of various exercise modalities on inflammatory factors in middle-aged and elderly patients with type 2 diabetes (MEPT2D), as lifestyle changes, such as physical activity and dietary modifications, are considered important in the prevention of type 2 diabetes. For the study methodology, Pubmed, CNKI, EBSCO, Wanfang Data, and Web of Science were selected for the search. The methodological quality of the included studies was assessed by the Cochrane Risk of Bias (ROB) tool, and statistically analyzed using the RevMan 5.4.1 analysis software, which included 18 investigations involving 853 study subjects. Meta-analysis findings indicated that aerobic training (AT), resistance training (RT), combined training (CT), and high-intensity interval training (HIIT) showed significant reductions in CRP, TNF-α, IL-6, and IL-10 levels in MEPT2D. Among them, HIIT was superior to other training modalities in reducing TNF-α levels, while CT was superior to AT, RT, and HIIT in decreasing IL-6, IL-10, and CRP in MEPT2D. Meanwhile, RT had limited effects in reducing CRP and TNF-α levels in MEPT2D. However, HIIT had no significant effect on IL-6 and IL-10 in MEPT2D. In conclusion, long-term regular AT, RT, CT, and HIIT all contributed to the reduction of inflammatory status (CRP, TNF-α, IL-6, and IL-10) in MEPT2D, while CT (for CRP, IL-6, and IL-10) and HIIT (for TNF-α) represent the best approaches to counteract the inflammatory response in MEPT2D.

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The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption.

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With the surge in the amount of data transmitted on the network, intelligent learning and other technologies have emerged to solve the problem of anomaly detection of streaming data in large data. For network security issues, based on the extraction of network traffic characteristics, network traffic classification or clustering is an important technical means to discover network faults and network attacks. In this paper, a distributed detection framework for detecting anomalous behaviors of encrypted network traffic is proposed. The intelligent router is adopted to obtain the encrypted network traffic monitoring stub, and then the neural network codec is used to adaptively learn the characteristics of the encrypted traffic and identify the abnormal behavior of the encrypted protocol traffic. A wide coverage traffic pattern extraction algorithm based on the network state sequence is designed to obtain the traffic patterns that represent the network conditions of the data center. Finally, the simulation test verifies that the model has frequent traffic patterns with priority. The performance of the network anomaly detection model is better than other detection methods, which improves the accuracy of detection and has a better recognition effect.