Intergenerational continuity in family behaviors partly results from socialization processes in the parental home. However, socialization is a multidimensional process. This article tests hypotheses about the relative importance of value transmission and modeling in explaining expectations of adolescence concerning the timing of leaving home, and entry into cohabitation, marriage, and parenthood. Structural equation modeling on multiactor data from over 1,000 parent–adolescent child couples in the Netherlands is used to test hypotheses. Results suggest that, in general, both value transmission and modeling are important predictors of adolescents’ expectations concerning the timing of major family events. Moreover, no differences between mothers and fathers and between boys and girls are observed in the strength of the intergenerational relationships studied.
Aircraft require significant quantities of fuel in order to generate the power required to sustain a flight. Burning this fuel causes the release of polluting particles to the atmosphere and constitutes a direct cost attributed to fuel consumption. The optimization of various aircraft operations in different flight phases such as cruise and descent, as well as terminal area movements, have been identified as a way to reduce fuel requirements, thus reducing pollution. The goal of this chapter is to briefly explain and apply different metaheuristic optimization algorithms to improve the cruise flight phase cost in terms of fuel burn. Another goal is to present an overview of the most popular commercial aircraft models. The algorithms implemented for different optimization strategies are genetic algorithms, the artificial bee colony, and the ant colony algorithm. The fuel burn aircraft model used here is in the form of a Performance Database. A methodology to create this model using a Level D aircraft research flight simulator is briefly explained. Weather plays an important role in flight optimization, and so this work explains a method for incorporating open source weather. The results obtained for the optimization algorithms show that every optimization algorithm was able to reduce the flight consumption, thereby reducing the pollution emissions and contributing to airlines’ profit margins.
This Professional Doctorate (PD) research focuses on optimizing the intermittency of CO₂-free hydrogen production using Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM) electrolysis. The project addresses challenges arising from fluctuating renewable energy inputs, which impact system efficiency, degradation, and overall cost-effectiveness. The study aims to develop innovative control strategies and system optimizations to mitigate efficiency losses and extend the electrolyzer lifespan. By integrating dynamic modeling, lab-scale testing at HAN University’s H2Lab, and real-world validation with industry partners (Fluidwell and HyET E-Trol), the project seeks to enhance electrolyzer performance under intermittent conditions. Key areas of investigation include minimizing start-up and shutdown losses, reducing degradation effects, and optimizing power allocation for improved economic viability. Beyond technological advancements, the research contributes to workforce development by integrating new knowledge into educational programs, bridging the gap between research, industry, and education. It supports the broader transition to a CO₂-free energy system by ensuring professionals are equipped with the necessary skills. Aligned with national and European sustainability goals, the project promotes decentralized hydrogen production and strengthens the link between academia and industry. Through a combination of theoretical modeling, experimental validation, and industrial collaboration, this research aims to lower the cost of green hydrogen and accelerate its large-scale adoption.
