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Abstract The development of ultra-low near-infrared reflectivity coatings with outstanding engineering properties remains a challenge in laser stealth materials research. Herein, we reported a laser stealth coating with outstanding mechanical properties, super-hydrophobicity, and an ultra-low near-infrared reflectivity for 1.06 μm wavelength. The effects of the mass ratio of graphene to nano-SiO2, the proportion of total filler, the addition of KH560, the mass ratio of Polydimethylsiloxane (PDMS) to acrylic-modified polyurethane (APU), and the addition of dioctyl phthalate (DOP) on the coating properties were thoroughly discussed. The coating can achieve a low reflectivity of 9.3% at 1.06 μm and a high water contact angle of 152° at a mass ratio of 7:3 for PDMS to APU and 6:4 for graphene to nano-SiO2 with a total filler amount of 40 wt%. KH560 can play a bridging role between the blended resin matrix and nano-SiO2, which can significantly improve the impact strength of the coating. The DOP, which contains a polar ester group and a non-polar carbon chain structure, can be inserted between the molecular chains of the resin to weaken the intermolecular force of the resin, so that the flexibility of the coating can be significantly improved. Adding KH560 at 4 wt% and DOP at 1 wt%, resulted in a coating with ultra-low near-infrared reflectivity of 1.06 μm (9.3%), super-hydrophobic properties, outstanding adhesion strength (grade 2), flexibility (2 mm), and impact strength (50 kg cm). The above super-hydrophobic ultra-low near-infrared reflectivity coating has significant potential for use in the field of laser stealth equipment, and it can serve as a useful reference for optimizing the mechanical properties of super-hydrophobic functional coatings.

Abstract Lockdown is a common policy used to deter the spread of COVID-19. However, the question of how our society comes back to life after a lockdown remains an open one. Understanding how cities bounce back from lockdown is critical for promoting the global economy and preparing for future pandemics. Here, we propose a novel computational method based on electricity data to study the recovery process, and conduct a case study on the city of Hangzhou. With the designed Recovery Index, we find a variety of recovery patterns in main sectors. One of the main reasons for this difference is policy; therefore, we aim to answer the question of how policies can best facilitate the recovery of society. We first analyze how policy affects sectors and employ a change-point detection algorithm to provide a non-subjective approach to policy assessment. Furthermore, we design a model that can predict future recovery, allowing policies to be adjusted accordingly in advance. Specifically, we develop a deep neural network, TPG, to model recovery trends, which utilizes the graph structure learning to perceive influences between sectors. Simulation experiments using our model offer insights for policy-making: the government should prioritize supporting sectors that have greater influence on others and are influential on the whole economy.

A social recommendation system has attracted a lot of attention recently in the research communities of information retrieval, machine learning, and data mining. Traditional social recommendation algorithms are often based on batch machine learning methods which suffer from several critical limitations, e.g., extremely expensive model retraining cost whenever new user ratings arrive, unable to capture the change of user preferences over time. Therefore, it is important to make social recommendation system suitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. In this paper, we present a new framework of online social recommendation from the viewpoint of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-item relationship as well as item content features into an unified preference learning process. We further develop an efficient iterative procedure, OGRPL-FW which utilizes the Frank-Wolfe algorithm, to solve the proposed online optimization problem. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (in terms of both RMSE and MAE) than the state-of-the-art online recommendation methods when receiving the same amount of training data in the online learning process. [ABSTRACT FROM PUBLISHER]

Online interaction with 3D facial animation & an alternative way of face-to-face communication for distance education. 3D facial modeling is essential for virtual educational environments establishment. This article presents a novel 3D facial modeling solution that facilitates quasi-facial communication for online learning. Our algorithm builds 3D facial models from a single image, with support of a 3D face database. First from the image, we extract a set of feature points, which are then used to automatically estimate the head pose parameters using the 3D mean face in our database as a reference model. After the pose recovery, a similarity measurement function is proposed to locate the neighborhood for the given image in the 3D face database. The scope of neighborhood can be determined adaptively using our cross-validation algorithm. Furthermore, the individual 3D shape is synthesized by neighborhood interpolation. Texture mapping is achieved based on feature points. The experimental results show that our algorithm can robustly produce 3D facial models from images captured in various scenarios to enhance the lifelikeness in distant learning. [ABSTRACT FROM AUTHOR]

Chinese brush calligraphy is a valuable civilization legacy and a high art of scholarship. It is still popular in Chinese banners, newspaper mastheads, university names, and celebration gifts. There are Web sites that try to help people enjoy and learn Chinese calligraphy. However, there lacks advanced services such as content-based retrieval or vivid writing process simulation for calligraphy learning. This article proposes a novel Chinese calligraphy learning approach: First, the scanned calligraphy pages were segmented into individual calligraphy characters using minimum-bounding box. Second, the individual character's feature information was extracted and kept. Then, a corresponding database was built to serve as a map between the feature data and the original raw data. Finally, a retrieval engine was constructed and a dynamic writing process was simulated to help learners get the calligraphy character they were interested in and watch how it was generated step by step. [ABSTRACT FROM AUTHOR]

A preface for the January 2006 issue of the "International Journal of Image and Graphics" is presented.