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Due to the huge volume and complex structure, simplification of point clouds is an important technique in practical applications. However, the traditional algorithms often lose geometric information and have no dynamic expanding structure. In this paper, a new simplification algorithm is proposed based on conformal geometric algebra. First of all, a multiresolution subdivision is constructed by the sphere tree, which computes the minimal bounding spheres with the help of k‐means clustering, and then 2 kinds of simplification methods with full advantages of distance computing convenience are applied to carry out self‐adapting simplification. Finally, several comparisons with original data or other algorithms are implemented from visualization to parameter contrast. The results show that the proposed algorithm has good effect not only on the local details but also on the overall error rate.

The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond. Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas: (1) the defect detectability evaluation, (2) the diverse optical inspection systems, and (3) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.

Academic supervisors plays a significant role in the cultivation of postgraduate students, but little is known about how academic supervisor feedback affects their creativity. This study hypothesizes and tests a moderated mediation model to explore how and when academic supervisor developmental feedback (ASDF) affects postgraduate student creativity (PSC), including the mediating effect of intrinsic motivation and the moderating effect of creative self-efficacy. After collecting three-wave time-lagged data from 374 postgraduate students and their academic supervisors, SPSS and Amos software were used to test the research hypotheses and the whole model. The results show that ASDF is positively related to intrinsic motivation and PSC. Intrinsic motivation not only has a positive effect on PSC, but it also plays a mediating role in the relationship between ASDF and PSC. Creative self-efficacy plays a moderating role in the relationships between ASDF, intrinsic motivation, and PSC, that is, ASDF can cause postgraduate students with high creative self-efficacy to develop higher levels of intrinsic motivation than those with low creative self-efficacy, which ultimately leads to more PSC. These findings not only enrich the literature on feedback, motivation, and creativity research in the field of education, but also provide some suggestions for promoting PSC from the perspective of universities, academic supervisors, and postgraduate students.

Objective: To explore the effects of programmed intermittent epidural bolus (PIEB) combined with patient-controlled epidural analgesia (PCEA) at different intervals on body temperature and serum CRP, TNF-α, IL-6 levels in parturient women receiving analgesia.