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832 results on '"Yao, Xiaoqiang"'

18. Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma

Author

Chu, Funing, Liu, Yun, Liu, Qiuping, Li, Weijia, Jia, Zhengyan, Wang, Chenglong, Wang, Zhaoqi, Lu, Shuang, Li, Ping, Zhang, Yuanli, Liao, Yubo, Xu, Mingzhe, Yao, Xiaoqiang, Wang, Shuting, Liu, Cuicui, Zhang, Hongkai, Wang, Shaoyu, Yan, Xu, Kamel, Ihab R., Sun, Haibo, Yang, Guang, Zhang, Yudong, and Qu, Jinrong

Published
2022
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21. Construction Technology of Open Command and Control Platform Based on Cloud Native

Author

Fan Liangyou, Yao Xiaoqiang, Wang Gang, Liu Wei, He Sheng

Subjects
command and control system, cloud native, ooda, bpmn, devops, microservices, operational process, tactics, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Aiming at the problems of traditional command and control system, such as closed system, low efficiency of man-machine cooperation and poor scalability, an open command and control system based on cloud native technology is proposed. Firstly, the expression model of commander’s operational intention is studied based on the combination of Boyd’s loop (OODA) and business process modeling notation (BPMN), which realizes multi-intention parallel execution and multi-strategy conflict resolution, and makes commander’s operational intention deeply couple with command and control system operation. Then, based on micro-services, service mesh, message middleware and other technologies, the basic framework based on cloud native theory is built to realize the rapid and safe expansion of basic services. In order to rapidly expand the atomic services that constitute the commander’s decision, a rapid development, deployment and delivery system of services is designed based on DevOps. Finally, the feasibility and reliability of the system are verified by the case of fire-concentrated shooting. Compared with the traditional command and control system, the open command and control platform can realize rapid and flexible system expansion, support flexible editing of operational processes and tactics. This technology can provide a solution for the command and control system from closed to open.

Published
2022
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28. Using neural data to forecast aggregate consumer behavior in neuromarketing: Theory, metrics, progress, and outlook.

Author

Yao, Xiaoqiang and Wang, Yiwen

Subjects
CONSUMER behavior, NEUROMARKETING, CONSUMER preferences, FORECASTING, RESEARCH personnel, MACHINE learning
Abstract

The field of using neural data to forecast aggregate consumer choice has garnered attention in the past decade, holding substantial promise for both researchers and practitioners. However, a comprehensive understanding of this emerging field is lacking. This paper aims to bridge that gap by summarizing existing research, encompassing relevant theories, metrics, progress, and future directions. We begin by introducing the concept of neuroforecasting within the field of neuromarketing. We then delve into theories that leverage neural data for forecasting aggregate choice, including affect‐integration‐motivation framework, frontal asymmetry, and inter‐subject correlation. Subsequently, we review various metrics, including self‐reported, behavioral, and neural metrics employed to forecast market‐level behavior, presenting key findings from relevant studies. Furthermore, we examine the strengths and weaknesses of this field. Advantages of this approach include its ability to offer effective predictions of consumer behavior and provide enhanced insights into consumer preferences and choices, while its weaknesses encompass relatively high cost, sample size constraints, issues of ecological validity, and challenges related to reverse inference. In conclusion, future research should prioritize integrating diverse data types with machine learning techniques to forecast the outcomes of marketing campaigns in advance. Additionally, a deeper exploration of the psychological and cognitive processes underlying successful predictions can augment predictive accuracy and effectiveness. This review provides a systematic overview for researchers and practitioners in this field, offering valuable insights and guidance for future research endeavors and industry applications. [ABSTRACT FROM AUTHOR]

Published
2024
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