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The data of traditional video surveillance is very large and complex, and cannot detect the abnormal behaviors of human in a timely and effective manner.In response to these problems, this paper presents an improved YOLOv3 algorithm (YOLOv3-MSSE) for the detection of human abnormal behavior.This algorithm can improve the detection accuracy of large targets for it is based on the traditional YOLOv3 network model, and a multi-scale feature extraction network is constructed by the residual mo-dules.At the same time, by incorporating the attention mechanism into different positions of the network structure, and the importance of the features in each channel of the feature map can be weighted, which effectively improves the detection performance of the model for abnormal human behavior.Experimental results show that compared with the traditional YOLOv3 algorithm, the mAP of YOLOv3-MSSE is increased by 20.8%, and F1-scores is increased by 11.3%.The proposed algorithm can not only detect the specific abnormal behavior of the human in the monitoring scene effectively, but also can balance the relationship between the detection accuracy rate and the recall rate well.In addition, It is more suitable for the detection of human abnormal behavior in actual monitoring scenarios than similar methods.

The growing utilization of critical care echocardiography (CCE) by clinicians necessitates a meticulous review of clinical conditions in critically ill patients, both before and during the examination. The reviewing process of clinical conditions minimizes the risk of overlooking or misinterpreting crucial findings. This article proposes a comprehensive strategy, namely BILL strategy, to integrate into the CCE protocol, where "B" represents baseline respiratory and hemodynamic support, "I" signifies information gleaned from invasive monitoring, including central venous pressure and thermodilution-derived cardiac output, the first "L" denotes laboratory results such as central venous oxygen saturation, troponin, and brain natriuretic peptide, and the second "L" refers to lung ultrasound data. Combining the BILL strategy with CCE can enhance comprehensive understanding of critical conditions, potentially leading to improved diagnostic accuracy and patient outcomes. [ABSTRACT FROM AUTHOR]

Geometrical imaging models and calculations of orientation parameters are the main factors affecting the positioning of stereo Synthetic Aperture Radar (SAR) images. For accurate positioning with squint stereo SAR images and less Ground Control Points (GCPs), a positioning algorithm with one GCP is designed. In this algorithm, the position and velocity of the radar antenna phase center are derived using orbit parameters, and the close range and Doppler centroid of the SAR images are compensated by one GCP. Thus, accurate orientation parameters are obtained and accurate positioning with stereo SAR images is completed. Airborne SAR images acquired by the Chinese Academy of Surveying and Mapping are used in experiments. The positioning errors of the checkpoints are calculated and analyzed, and it verified the accuracy and effectiveness of the proposed method.

Purpose The primary objective was to demonstrate the relationship between lung ultrasound (LUS) manifestations and the outcomes of intensive care unit (ICU) patients. The secondary objective was to determine the characteristics of LUS manifestations in different subgroups of ICU patients. Methods: This prospective multi‐center cohort study was conducted in 17 ICUs. A total of 1702 patients admitted between August 31, 2017 and February 16, 2019 were included. LUS was performed according to the bedside lung ultrasound in emergency (BLUE)‐plus protocol, and LUS scores were calculated. Data on the outcomes and oxygenation indices were analyzed and compared between different primary indication groups. Results: The LUS scores were significantly higher for non‐survivors than for survivors and were significantly different between the oxygenation index groups, with higher scores in the lower oxygenation index groups. The LUS score was an independent risk factor for the 28‐day mortality. The area under the receiver operating characteristic curve was 0.663 for prediction of the 28‐day mortality and 0.748 for prediction of an oxygenation index ≤100. Conclusions: The LUS score based on the BLUE‐plus protocol was an independent risk factor for the 28‐day mortality and was important for the prediction of an oxygenation index ≤100. An early LUS score within 24 hours of ICU admission helps predicting the outcome of ICU patients. [ABSTRACT FROM AUTHOR]