Spawns Structure of Rod-Like ZnO Wrapped in Cellulose Nanofibers for Electromagnetic Wave Absorption

Spawns structure of rod-like ZnO wrapped in the cellulose nanofibers was successfully fabricated through a facile one-step hydrothermal method, and their electromagnetic wave absorption properties were investigated. The structure and properties of the composite aerogel were characterized. The enlarged morphology images showed that the as-prepared cellulose nanofiber/ZnO samples were spawns structure of rod-like ZnO wrapped in the cellulose nanofibers. The composite aerogel in a wax matrix exhibited excellent electromagnetic wave absorption performance over 2–18 GHz. The widest absorption bandwidth of 30 wt% contained with reflection loss values less than −10 dB was up to 12 GHz (6–18 GHz) at the thickness of 5.5 mm and the minimum reflection loss value reached −26.32 dB at 15.2 GHz when the thickness of the absorber was 3 mm

Digital twin-based multi-level task rescheduling for robotic assembly line

Abstract Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL

Fabrication of Cellulose Nanofiber/AlOOH Aerogel for Flame Retardant and Thermal Insulation

Cellulose nanofiber/AlOOH aerogel for flame retardant and thermal insulation was successfully prepared through a hydrothermal method. Their flame retardant and thermal insulation properties were investigated. The morphology image of the cellulose nanofiber/AlOOH exhibited spherical AlOOH with an average diameter of 0.5 μm that was wrapped by cellulose nanofiber or adhered to them. Cellulose nanofiber/AlOOH composite aerogels exhibited excellent flame retardant and thermal insulation properties through the flammability test, which indicated that the as-prepared composite aerogels would have a promising future in the application of some important areas such as protection of lightweight construction materials

Hydrothermal Synthesis of Nanooctahedra MnFe2O4 onto the Wood Surface with Soft Magnetism, Fire Resistance and Electromagnetic Wave Absorption

In this study, nanooctahedra MnFe2O4 were successfully deposited on a wood surface via a low hydrothermal treatment by hydrogen bonding interactions. As-prepared MnFe2O4/wood composite (MW) had superior performance of soft magnetism, fire resistance and electromagnetic wave absorption. Among them, small hysteresis loops and low coercivity (<±5 Oe) were observed in the magnetization-field curve of MW with saturation magnetization of 28.24 emu/g, indicating its excellent soft magnetism. The MW also exhibited a good fire-resistant property due to its initial burning time at 20 s; while only 6 s for the untreated wood (UW) in combustion experiments. Additionally, this composite revealed good electromagnetic wave absorption with a minimum reflection loss of −9.3 dB at 16.48 GHz. Therefore, the MW has great potential in the fields of special decoration and indoor electromagnetic wave absorbers

Preparation and Characterization of Mo Doped in BiVO4 with Enhanced Photocatalytic Properties

Molybdenum (Mo) doped BiVO4 was fabricated via a simple electrospun method. Morphology, structure, chemical states and optical properties of the obtained catalysts were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV-vis diffuse reflectance spectroscopy (DRS), N2 adsorption–desorption isotherms (BET) and photoluminescence spectrum (PL), respectively. The photocatalytic properties indicate that doping Mo into BiVO4 can enhance the photocatalytic activity and dark adsorption ability. The photocatalytic test suggests that the 1% Mo-BiVO4 shows the best photocatalytic activity, which is about three times higher than pure BiVO4. Meanwhile, 3% Mo-BiVO4 shows stronger dark adsorption than pure BiVO4 and 1% Mo-BiVO4. The enhancement in photocatalytic property should be ascribed to that BiVO4 with small amount of Mo doping could efficiently separate the photogenerated carries and improve the electronic conductivity. The high concentration doping would lead the crystal structure transformation from monoclinic to tetragonal phase, as well as the formation of MoO3 nanoparticles on the BiVO4 surface, which could also act as recombination centers to decrease the photocatalytic activity

S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms