Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling. Using the word embedding techniques, we reveal the constructed emotional dependency of female characters on male characters in stories. We call this narrative structure “Cinderella complex”, which assumes that women depend on men in the pursuit of a happy, fulfilling life. Our analysis covers a substantial portion of narratives that shape the modern collective memory, including 7,226 books, 6,087 movie synopses, and 1,109 movie scripts. The “Cinderella complex” is observed to exist widely across periods and contexts, reminding how gender stereotypes are deeply rooted in our society. Our analysis of the words surrounding female and male characters shows that the lives of males are adventure-oriented, whereas the lives of females are romantic-relationship oriented. Finally, we demonstrate the social endorsement of gender stereotypes by showing that gender-stereotypical movies are voted more frequently and rated higher.

In this paper, by introducing a new frame on spacelike curves lying in lightcone 3-space, we investigate the geometric properties of the lightlike surface of the Darboux-like indicatrix and the lightlike surface of the binormal indicatrix generated by spacelike curves in lightcone 3-space. As an extension of our previous work and an application of the singularity theory, the singularities of the lightlike surfaces of the Darboux-like indicatrix and the lightlike surface of the binormal indicatrix are classified, several new invariants of spacelike curves are discovered to be useful for characterizing these singularities, meanwhile, it is found that the new invariants also measure the order of contact between spacelike curves or principal normal indicatrixes of spacelike curves located in lightcone 3-space and two-dimensional lightcone whose vertices are at the singularities of lightlike surfaces. One concrete example is provided to illustrate our results. KEYWORDS Darboux-like indicatrix, lightcone helix, lightlike surfacesMath Meth Appl Sci. 2020; :5-34.wileyonlinelibrary.com/journal/mmawhere (e 1 , e 2 , e 3 , e 4 ) is the canonical basis of R 4 1 . One can easily show that ⟨a, x ∧ y ∧ z⟩ = det(a, x, y, z). We say that a vector x ∈ R 4 1 ∖{0} is spacelike, lightlike, or timelike if ⟨x, x⟩ is positive, zero, or negative, respectively. The norm of a vector x ∈ R 4 1 is defined by || x|| = √ |⟨x, x⟩|, we call x the unit vector if || x|| = 1. Let ∶ I → R 4 1 be a regular curve in R 4 1 (ie, ′ (t) ≠ 0 for any t ∈ I), where I is an open interval. For any t ∈ I, the curve is called spacelike curve, lightlike curve, or timelike curve if all its velocities are ⟨ ′ (t), ′ (t)⟩ > 0, ⟨ ′ (t), ′ (t)⟩ = 0, or ⟨ ′ (t), ′ (t)⟩ < 0, respectively. We call the nonlightlike curve if is a timelike curve or a spacelike curve. The arc-length of a nonlightlike curve , measured from (t 0 ) (t 0 ∈ I), is s(t) = ∫ t t 0 || ′ (t)||dt. Then, the parameter s is determined such that || ′ (s)|| = 1 for the nonlightlike curve, where ′ (s) = d ds (s). Therefore, we say that a nonlightlike

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision‐making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

COVID-19 pandemic started in late 2019 and scientists contributed to the unexpected coronavirus research promptly. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021. Our paper emphasizes on the dynamic evolution of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a pure new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight over time. Even though this pandemic provides equal opportunities for every scientist to start a study, collaboration disparity still exists. Only a few top authors are highly connected with other authors, following the scale-free distribution. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases.

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