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Recent progress in modelling individual growth has been achieved by combining the principal component analysis and the maximum likelihood principle. This combination models growth even in incomplete sets of data and in data obtained at irregular intervals. We re-analysed late 18th century longitudinal growth of German boys from the boarding school Carlsschule in Stuttgart. The boys aged 6-23 years, were measured at irregular 3-12 monthly intervals during the period 1771-1793. At the age of 18 years, mean height was 1652 mm, but height variation was large. The shortest boy reached 1474 mm, the tallest 1826 mm. Measured height closely paralleled modelled height, with mean difference of 4 mm, SD 7 mm. Seasonal height variation was found. Low growth rates occurred in spring and high growth rates in summer and autumn. The present study demonstrates that combining the principal component analysis and the maximum likelihood principle enables growth modelling in historic height data also.
Twenty-two scientists met at Krobielowice, Poland, to discuss the impact of the social environment, spatial proximity, migration, poverty, but also psychological factors such as body perception and satisfaction, and social stressors such as elite sports, and teenage pregnancies, on child and adolescent growth. The data analysis included linear mixed effects models with different random effects, Monte Carlo analyses, and network simulations. The work stressed the importance of the peer group, but also included historic material, some considerations about body proportions, and growth in chronic liver, and congenital heart disease.
The metaverse is envisioned as a virtual shared space facilitated by emerging technologies such as virtual reality (VR), augmented reality (AR), the Internet of Things (IoT), 5G, artificial intelligence (AI), big data, spatial computing, and digital twins (Allam et al., 2022; Dwivedi et al., 2022; Ravenscraft, 2022; Wiles, 2022). While still a nascent concept, the metaverse has the potential to “transform the physical world, as well as transport or extend physical activities to a virtual world” (Wiles, 2022). Big data technologies will also be essential in managing the enormous amounts of data created in the metaverse (Sun et al., 2022). Metaverse technologies can offer the public sector a host of benefits, such as simplified information exchange, stronger communication with citizens, better access to public services, or benefiting from a new virtual economy. Implementations are underway in several cities around the world (Geraghty et al., 2022). In this paper, we analyze metaverse opportunities for the public sector and explore their application in the context of Germany’s Federal Employment Agency. Based on an analysis of academic literature and practical examples, we create a capability map for potential metaverse business capabilities for different areas of the public sector (broadly defined). These include education (virtual training and simulation, digital campuses that offer not just online instruction but a holistic university campus experience, etc.), tourism (virtual travel to remote locations and museums, virtual festival participation, etc.), health (employee training – as for emergency situations, virtual simulations for patient treatment – for example, for depression or anxiety, etc.), military (virtual training to experience operational scenarios without being exposed to a real-world threats, practice strategic decision-making, or gain technical knowledge for operating and repairing equipment, etc.), administrative services (document processing, virtual consultations for citizens, etc.), judiciary (AI decision-making aids, virtual proceedings, etc.), public safety (virtual training for procedural issues, special operations, or unusual situations, etc.), emergency management (training for natural disasters, etc.), and city planning (visualization of future development projects and interactive feedback, traffic management, attraction gamification, etc.), among others. We further identify several metaverse application areas for Germany's Federal Employment Agency. These applications can help it realize the goals of the German government for digital transformation that enables faster, more effective, and innovative government services. They include training of employees, training of customers, and career coaching for customers. These applications can be implemented using interactive learning games with AI agents, virtual representations of the organizational spaces, and avatars interacting with each other in these spaces. Metaverse applications will both use big data (to design the virtual environments) and generate big data (from virtual interactions). Issues related to data availability, quality, storage, processing (and related computing power requirements), interoperability, sharing, privacy and security will need to be addressed in these emerging metaverse applications (Sun et al., 2022). Special attention is needed to understand the potential for power inequities (wealth inequity, algorithmic bias, digital exclusion) due to technologies such as VR (Egliston & Carter, 2021), harmful surveillance practices (Bibri & Allam, 2022), and undesirable user behavior or negative psychological impacts (Dwivedi et al., 2022). The results of this exploratory study can inform public sector organizations of emerging metaverse opportunities and enable them to develop plans for action as more of the metaverse technologies become a reality. While the metaverse body of research is still small and research agendas are only now starting to emerge (Dwivedi et al., 2022), this study offers a building block for future development and analysis of metaverse applications.
Objective: The age of menarche is usually considered to be affected by nutritional, health-related, social, and economic factors and has significantly decreased since the mid-19th century. The present study was performed to investigate whether the timing of menarche paralleled the general acceleration of physical development, or whether this pattern differed.
Study Design: In all, 30 German studies on menarcheal age (n = > 200) since 1848 were collected. Frequency distributions were analyzed.
Results: During the second half of the 19th and the early 20th century, mean menarcheal age decreased from 18 to 12-13 years in Europe. Yet, the data fail to support the conventional hypothesis that menarcheal age mainly depends on nutritional, health, and economic factors.
Conclusions: We suggest that later than usual menarche may not necessarily be regarded as a physical illness, but in view of the apparently physiological delay of menarche in the 19th century, may be viewed as "collective social amenorrhea."
Target Audience: Obstetricians & Gynecologists and Family Physicians.
Learning Objectives: After participating in this CME activity, physicians should be better able to evaluate menarche as an indicator of developmental tempo in both historical and modern settings, compare menarche in healthy mid-19th century girls with menarche in average modern girls, and assess the marked sensitivity of full pubertal development to environmental circumstances.
Sexuelle Reifeentwicklung & Menarchealter : Bedeutung des psychosozialen Umfeldes damals und heute
(2012)
Twenty-four scientists met at Aschauhof, Altenhof, Germany, to discuss the associations between child growth and development, and nutrition, health, environment and psychology. Meta-analyses of body height, height variability and household inequality, in historic and modern growth studies published since 1794, highlighting the enormously flexible patterns of child and adolescent height and weight increments throughout history which do not only depend on genetics, prenatal development, nutrition, health, and economic circumstances, but reflect social interactions. A Quality of Life in Short Stature Youth Questionnaire was presented to cross-culturally assess health-related quality of life in children. Changes of child body proportions in recent history, the relation between height and longevity in historic Dutch samples and also measures of body height in skeletal remains belonged to the topics of this meeting. Bayesian approaches and Monte Carlo simulations offer new statistical tools for the study of human growth.