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805 results on '"Lu, Xinxin"'

9. Research on Nanliu River Integrated Information Management System Based on Land and Sea Integration

Author

PAN Jie, LI Shushi, YANG Xialing, YU Chongxi, and LU Xinxin

Subjects
information management system, land and sea coordination, small and medium-sized rivers, nanliu river, Oceanography, GC1-1581
Abstract

The construction of integrated basin information management model based on land and sea coordination is the key content to realize the coordinated development of basin environmental governance and coastal economic and social development. Taking Nanliu River Basin as an example, based on the analysis of the key problems in the current basin environmental management process, this paper discusses the implementation of comprehensive basin management from the perspective of land-sea coordination, including the design of comprehensive information database, data requirement analysis, system function research, etc., and then puts forward specific strategies for building an integrated information management system for Nanliu River. In order to provide scientific theory and decision-making basis for regional economic and social development and disaster prevention and reduction.

Published
2024

41. Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer

Author

Wang, Siwei, Li, Ming, Zhang, Jingyuan, Xing, Peng, Wu, Min, Meng, Fancheng, Jiang, Feng, Wang, Jie, Bao, Hua, Huang, Jianfeng, Ren, Binhui, Yu, Mingfeng, Qiu, Ninglei, Li, Houhuai, Yuan, Fangliang, Zhang, Zhi, Jia, Hui, Lu, Xinxin, Zhang, Shuai, Wang, Xiaojun, Xu, Youtao, Xia, Wenjia, Liu, Tongyan, Xu, Weizhang, Xu, Xinyu, Sun, Mengting, Wu, Xue, Shao, Yang, Wang, Qianghu, Dai, Juncheng, Qiu, Mantang, Wang, Jinke, Zhang, Qin, Xu, Lin, Shen, Hongbing, and Yin, Rong

Published
2022
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46. Direct Cost Analysis of Microbial Keratitis in North China: A Hospital-Based Retrospective Study.

Author

Shi, Qingquan, Peng, Bo, Cheng, Zhen, Zhang, Zijun, Wei, Zhenyu, Wang, Zhiqun, Zhang, Yang, Chen, Kexin, Xu, Xizhan, Lu, Xinxin, Cao, Kai, Wei, Xueyao, and Liang, Qingfeng

Subjects
ACANTHAMOEBA keratitis, FUNGAL keratitis, COST analysis, CORNEAL ulcer, DIRECT costing
Abstract

Microbial keratitis (MK) is the fourth leading cause of blindness globally, imposing a substantial burden on the healthcare system. This study aims to determine the cost composition of MK patients and explore factors influencing these expenses. We analyzed the demographics, clinical features, and costs of 602 MK patients treated at Beijing Tongren Hospital from June 2021 to October 2023. The analysis revealed the average total cost of treating MK was USD 1646.8, with a median of USD 550.3 (IQR: 333.3–1239.1). Patients with Acanthamoeba keratitis (AK) incurred the highest median total costs at USD 706.2 (IQR: 399.2–3370.2). Additionally, AK patients faced the highest costs for ophthalmic exams and laboratory tests (both p < 0.001), while patients with fungal keratitis (FK) and viral keratitis (VK) experienced higher medication costs. Costs varied significantly with the severity of MK, especially for outpatients at severity level 4, which was markedly higher than levels 1–3 (USD 1520.1 vs. USD 401.0, p < 0.001). Delayed presentation also resulted in increased costs (USD 385.2 vs. USD 600.3, p < 0.001). Our study highlights the financial burden associated with MK treatment and underscores the importance of timely and accurate diagnosis and intervention. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Wave-CapNet: A Wavelet Neuron-based Wi-Fi Sensing Model for Human Identification.

Author

Zhou, Zhiyi, Wang, Lei, Lu, Xinxin, Tian, Yu, Fang, Jian, and Lu, Bingxian

Subjects
WIRELESS Internet, GAIT in humans, ELECTRONIC data processing, WEARABLE technology, INTERNET of things
Abstract

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

Published
2024
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49. AutoDLAR: A Semi-supervised Cross-modal Contact-free Human Activity Recognition System.

Author

Lu, Xinxin, Wang, Lei, Lin, Chi, Fan, Xin, Han, Bin, Han, Xin, and Qin, Zhenquan

Subjects
SUPERVISED learning, HUMAN activity recognition, EMOTION recognition, SMART homes, BLENDED learning
Abstract

WiFi-based human activity recognition (HAR) plays an essential role in various applications such as security surveillance, health monitoring, and smart home. Existing HAR methods, though yielding promising performance in indoor scenarios, highly depend on a massive labeled dataset for training which is extremely difficult to acquire in practical applications. In this paper, we present an automatic data labeling and HAR system, termed AutoDLAR. Taking a semi-supervised cross-modal learning framework with a hybrid loss function as the core, AutoDLAR transfers rich visual information to automatically label WiFi signals for WiFi-based HAR. Specifically, we devise a lightweight and multi-view WiFi sensing model with a parallel feature embedding method to accurately identify activities and accelerate recognition speed. Then, we exploit the video data to fine-tune a well-established visual HAR model, generating effective pseudo-labels for guiding the WiFi model's training. We also build a synchronized Video-WiFi dataset with seven types of human activities under different scenarios to enable training and validating the semi-supervised HAR system. Extensive experiments on our collected activity dataset and the emotion recognition benchmark demonstrate that AutoDLAR attains an average accuracy of over 95.89% without manual labeling and only spends the inference time of 3.35 ms, outperforming the state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

Published
2024
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