• Coronavirus pandemic have made facemasks worldwide healthcare essentials • Shortage of masks exposes medical personnel and the public to the risk of infection • Utilisation of sustainable raw materials to develop bio-based masks is needed • Electrospun and compression moulded gluten can be used to develop biobased masks • Gluten masks can be made flame retardant by adding b10 wt% of lanosol.

The relationship between spatial density and size of plants is an important topic in plant ecology. The self‐thinning rule suggests a −3/2 power between average biomass and density or a −1/2 power between stand yield and density. However, the self‐thinning rule based on total leaf area per plant and density of plants has been neglected presumably because of the lack of a method that can accurately estimate the total leaf area per plant. We aimed to find the relationship between spatial density of plants and total leaf area per plant. We also attempted to provide a novel model for accurately describing the leaf shape of bamboos. We proposed a simplified Gielis equation with only two parameters to describe the leaf shape of bamboos one model parameter represented the overall ratio of leaf width to leaf length. Using this method, we compared some leaf parameters (leaf shape, number of leaves per plant, ratio of total leaf weight to aboveground weight per plant, and total leaf area per plant) of four bamboo species of genus Indocalamus Nakai (I. pedalis (Keng) P.C. Keng, I. pumilus Q.H. Dai and C.F. Keng, I. barbatus McClure, and I. victorialis P.C. Keng). We also explored the possible correlation between spatial density and total leaf area per plant using log‐linear regression. We found that the simplified Gielis equation fit the leaf shape of four bamboo species very well. Although all these four species belonged to the same genus, there were still significant differences in leaf shape. Significant differences also existed in leaf area per plant, ratio of leaf weight to aboveground weight per plant, and leaf length. In addition, we found that the total leaf area per plant decreased with increased spatial density. Therefore, we directly demonstrated the self‐thinning rule to improve light interception.

Carbon based fillers have attracted a great deal of interest in polymer composites because of their ability to beneficially alter properties at low filler concentration, good interfacial bonding with polymer, availability in different forms, etc. The property alteration of polymer composites makes them versatile for applications in various fields, such as constructions, microelectronics, biomedical, and so on. Devastations due to building fire stress the importance of flame-retardant polymer composites, since they are directly related to human life conservation and safety. Thus, in this review, the significance of carbon-based flame-retardants for polymers is introduced. The effects of a wide variety of carbon-based material addition (such as fullerene, CNTs, graphene, graphite, and so on) on reaction-to-fire of the polymer composites are reviewed and the focus is dedicated to biochar-based reinforcements for use in flame retardant polymer composites. Additionally, the most widely used flammability measuring techniques for polymeric composites are presented. Finally, the key factors and different methods that are used for property enhancement are concluded and the scope for future work is discussed.

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance.

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