Three types of self-assembled ZnOx quantum dots (QDs) or islands on silicon substrates with distinctive morphologies were successfully synthesized using various growth schemes in a simple magnetron sputter. If hydrogen/argon was employed as the sputtering gas under negative substrate bias, the growth started from surface pit formation, leading to self-aligned cone shaped ZnOx QDs with composition, x, being linearly dependent on substrate bias, providing an ideal platform for defect engineering and related application. Intriguingly, if there is no substrate bias applied, the most energetically favorable ZnOx hexagonal pyramids were formed on the surface, in quasi-epitaxy with the Si substrate. Spherical stoichimetric ZnO QDs in a narrow size range were synthesized when oxygen was particularly introduced, and these exhibited true quantum confinement effects, evidenced by a blue shift of the UV emission in the photoluminescence spectrum. This work facilitates the development of controllable ZnO QDs, and, most importantly, sheds light on the quick implantation of ZnO QDs into devices by an industrially compatible sputter.

viation position is determined by a token replay method. Then, some algorithms are designed to repair the process models based on logical Petri nets. Finally, the effectiveness of the proposed method is illustrated by some experiments, and the proposed method has relatively high fitness and precision compared with its peers.

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While the computing power of mobile devices has been quickly evolving in recent years, the growth of mobile storage capacity is, however, relatively slower. A common problem shared by budget-phone users is that they frequently run out of storage space. This article conducts a deep inspection of file usage of mobile applications and their potential implications on user experience. Our major findings are as follows: First, mobile applications could rapidly consume storage space by creating temporary cache files, but these cache files quickly become obsolete after being re-used for a short period of time. Second, file access patterns of large files, especially executable files, appear highly sparse and random, and therefore large portions of file space are never visited. Third, file prefetching brings an excessive amount of file data into page cache but only a few prefetched data are actually used. The unnecessary memory pressure causes premature memory reclamation and prolongs application launching time. Through the feasibility study of two preliminary optimizations, we demonstrated a high potential to eliminate unnecessary storage and memory space consumption with a minimal impact on user experience.

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.