FACE‐R—A 3D Database of 400 Living Individuals' Full Head CT‐ and Face Scans and Preliminary GMM Analysis for Craniofacial Reconstruction

In the past, improvements in craniofacial reconstructions (CFR) methodology languished due to the lack of adequate 3D databases that were sufficiently large and appropriate for 3‐dimensional shape statistics. In our study, we created the “FACE‐R” database from CT records and 3D surface scans of 400 clinical patients from Hungary, providing a significantly larger sample that was available before. The uniqueness of our database is linking of two data types that makes possible to investigate the bone and skin surface of the same individual, in upright position, thus eliminating many of the gravitational effects on the face during CT scanning. We performed a preliminary geometric morphometric (GMM) study using 3D data that produces a general idea of skull and face shape correlations. The vertical position of the tip of the (soft) nose for a skull and landmarks such as rhinion need to be taken into account. Likewise, the anterior nasal spine appears to exert some influence in this regard.     

What caused fertility variations by settlement in Transylvania at the time of industrialization?

There is an ongoing debate in the literature regarding the causes behind fertility transition. Especially, the relative importance of economic modernization versus cultural adaptation is hotly debated. The paper takes Transylvania, the eastern part of the Austro-Hungarian Monarchy as an example. The period of 1880–1910 was a time of fast modernization and industrialization in Transylvania, and it created large territorial differences in economic development. The ethnic and religious composition of the area is versatile; mainly Orthodox Romanians, Catholic Hungarians, and Protestant Germans populated the area.DataA cross-sectional database has been created by matching census and vital statistics records for 4112 settlements, for the 1900–1910 period.MethodOLS regression is used to model crude birth rates by settlement. The factors affecting fertility are modeled using the Easterlin–Crimmins framework.ResultsAn explanation placing economic factors (demand and supply) in first place, but accepting the secondary role of innovation factors as barriers to implement fertility regulation, fits the data about Transylvania well.DiscussionPrevious research results regarding Hungary could not show the effect of some socio-economic variables on fertility, due to the high level of aggregation. They favoured cultural explanations, and shown Hungary as an exception to the rules of demographic transition. In contrast, this paper shows that the classic explanatory factors like infant mortality, migration, literacy, and secularization do explain fertility differentials in Transylvania at the turn of the 20th century.