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The substitution patterns in fluorinated tetraphenylethenes affect the emission properties, mechano- and thermofluorochromism, compared to those of the parent TPE.

The current situation of coronavirus disease 2019 (COVID-19) is rapidly evolving. Radiation therapy facilities are places of concentrated patient interactions. Oncology patients with immunosuppression are at a higher risk for contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and easily developing severe postinfection events during the SARS-CoV-2 outbreaks. This review aimed to provide some guidance and be a reference to medical professionals in radiation oncology so that they may provide oncology patients with safe and high-quality care. Methods and Materials: This paper discussed how radiation therapy departments or centers can most effectively respond to this public health emergency through summarizing the procedures and protocols implemented at hospitals in ShenZhen, China. Results: The impact of the virus in radiation therapy facilities can be mitigated and managed with appropriate and timely implementation of infection control procedures and protocols. Conclusions: In the face of acute infectious disease, it is critical to maintain strict infectious disease control procedures and to create a clear clinical workflow protocol to best protect medical staff and patients from the effect of acute infectious diseases.

Featured Application: Using polarimetric synthetic aperture radar (SAR) remote sensing to detect and classify sea surface oil spills, for the early warning and monitoring of marine oil spill pollution.Abstract: Polarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks (ANN) with similar parameter settings, especially when the number of training data samples is limited.

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Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.