Data-driven maintenance of military systems:Potential and challenges

The success of military missions is largely dependent on the reliability and availability of the systems that are used. In modern warfare, data is considered as an important weapon, both in offence and defence. However, collection and analysis of the proper data can also play a crucial role in reducing the number of system failures, and thus increase the system availability and military performance considerably. In this chapter, the concept of data-driven maintenance will be introduced. First, the various maturity levels, ranging from detection of failures and automated diagnostics to advanced condition monitoring and predictive maintenance are introduced. Then, the different types of data and associated decisions are discussed. And finally, six practical cases from the Dutch MoD will be used to demonstrate the benefits of this concept and discuss the challenges that are encountered in applying this in military practice

Towards a data-driven military: a multi-disciplinary perspective

Towards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Military Systems and Technology approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. War Studies take a multidisciplinary approach to the evolution of warfare, while Military Management Studies take a holistic organisational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book

From Psychology to Phylogeny: Bridging Levels of Analysis in Cultural Evolution

Cultural evolution, or change in the socially learned behavior of a population over time, is a fascinating phenomenon that is widespread in humans and present in some non-human animals. In this dissertation, I present an array of cultural evolutionary studies that bridge pattern and process in a wide range of research models including music, extremism, and birdsong. The first chapter is an introduction to the field of cultural evolution, including a bibliometric analysis of its structure. The second and third chapters are studies on the cultural dynamics of music sampling traditions in hip-hop and electronic music communities and far-right extremism in the United States, using social network analysis and epidemiological modeling, respectively. The fourth and fifth chapters are studies on how cultural transmission biases influence population-level changes in music sampling traditions and house finch song, using a combination of agent-based modeling and machine learning. The sixth chapter is a technical report on computerized birdfeeders that were used to remotely collect data on the social network structure of a wild house finch population. Lastly, the seventh chapter applies a novel phylogenetic method based on dynamic community detection to reconstruct the cultural evolution of electronic music