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Neonatal biliary atresia is one of the most common fatal diseases in neonates, with higher incidence rates in Asia than in other parts of the world. Early detection and treatment of neonatal biliary atresia are crucial, yet the lack of professional pediatricians and auxiliary diagnostic and treatment methods can cause parents to miss the best treatment window. To address this issue, this paper develops a predictive algorithm with practical application value that uses neonatal stool pictures to predict whether the newborn has neonatal biliary atresia and reminds parents to visit a doctor in time. To achieve higher recognition rates in practical scenarios, the algorithm in this paper is developed based on a real-scene dataset of newborn fecal images. First, we designed a self-attention network model BANet (Biliary Atresia Network), which will combine shallow features and deep features of pictures to get better classification. To address issues as dark or overexposed images, we developed an automatic brightness adjust-ment algorithm by analyzing the brightness distribution of the dataset. Furthermore, we added a shadow data enhancement method duiring training to mitigate the inference of shadows on recognition results. In order to verify the effectiveness of the algorithm proposed in this paper, we design a comparison test with doctors. The results show that BANet out performed doctors in objective evaluation indicators such as the recognition rate of four classifications, the recognition rate of two classifications, specificity and sensitivity. The proposed BANet can effectively use the color, abnormal points and other information in the picture, by compensating the brightness of the picture, the accuracy and robustness of the overall algorithm are improved and good results have been achieved in practical application scenarios. [ABSTRACT FROM AUTHOR]

Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing highway construction monitoring systems are independently deployed. This paper proposes an intelligent monitoring system for highway construction based on remote wide area networks. By employing the tracking devices equipped with inertial measurement units and global positioning systems, the workers' activity space is monitored. People and equipment entering the site are registered by radio frequency identification tags with protective measures. Motion sensors are employed to monitor the changes in scaffolding, building structures, and highway low-grade inclinations. In addition, a light-weight object detection (LOD) algorithm is proposed to detect construction workers who fail to wear safety helmets. The LOD algorithm replaces the standard convolutional networks with depthwise separable convolution networks. It introduces the receptor field module, employs the LOD-NMS algorithm and Mish activation function, and sets an appropriate prior box according to the characteristics and ratios of people's heads in Asia, thus well balancing the precision and prediction efficiency. Experiments are conducted on the WIDER FACE open source data set and the real data sets. The results show LOD algorithm not only detects workers' safety helmets in real time with high precision, but also achieves higher recognition accuracy than traditional algorithms despite the low-resolution input images. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)