The VO2‐based smart windows can dynamically and reversibly adjust the indoor solar irradiation to reduce the energy consumption for building glasses. This paper reports the first successful preparation of Mg‐doped VO2@ZrO2 core−shell nanocrystals with promoted thermochromic performance as well as tunable appearance and wettability. The achieved combination of Tlum of 52.4% and ∆Tsol of 7.1% is comparable to the best‐reported results of VO2‐based core−shell rod structures. The thickness of ZrO2 shell is tunable between 3 and 14 nm, resulting in a slightly hydrophobic surface and a slight color change toward blue‐green and brighter produced films. The preparation method based on hydrothermal and UV‐curing methods is facile. This work may inspire future development of VO2 synthesis, their structure−property, and the application the thermochromic films for energy‐saving purposes in buildings or automobiles.
The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability.
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