高含盐工业废水处理技术研究进展

现代工业生产的工业废水往往含有超量的有机危害物或过量的盐、酸碱等,在对此类废水进行处理时,传统的方法将无法起到有效的作用。且由于此类废水同样不适宜微生物的生长,利用微生物进行污水处理的方式也受到很大的局限。对于这种工业排污废水,在实现有机物的降解之外,还需要实现无机盐和废水的分离来达到排污标准。对现在针对高含盐废水的成熟处理技术进行了总结,介绍了几种常见处理技术的特点及应用,以及他们在实际高盐工业废水处理中的作用和一些问题。通过对现有高盐废水的处理技术的分析和了解,可以针对不同类型的高盐废水使用不同的处理方式以达到最大的经济效应,并且对实现排放量最小化具有重要意义。     

Detection of crowdedness in bus compartments based on ResNet algorithm and video images

Multimedia Tools and Applications - The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding...     

Augmented reality instructions for construction toys enabled by accurate model registration and realistic object/hand occlusions

Virtual Reality - BRICKxAR is a novel augmented reality (AR) instruction method for construction toys such as LEGO®. With BRICKxAR, physical LEGO construction is guided by virtual bricks....     

公共建筑水系统监测体系的构建

我国公共建筑用水现状动态变化范围大,供水设备综合能效低,并存在漏损难发现、设备选型性能过剩等问题。构建公共建筑用水精细化计量监测体系,可通过收集详尽的供用水数据,形成针对不同工况的供水策略,实现节能降耗,以及对建筑供水系统设计及设备选型优化的数据支撑。阐述公共建筑水系统监测体系,包括监测指标、监测实现方法、监测位置、监测条件的设置等,探讨监测数据的应用价值,指出公共建筑水系统监测体系是实现建筑全面精细化节水的重要技术路径。     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

Study on Runoff Control Effect of Different Drainage Schemes in Sponge Airport

Water Resources Management - The low impact development (LID) concept aims to control storm runoff and pollution through decentralized, small-scale source control to bring the development area as...