Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide. It catalyzes many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts, it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here, we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and the pollutant substance. We analyze a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($ NO $), nitrogen dioxide ($ {NO}_2 $), and particulate matter ($ {PM}_{10} $ and $ {PM}_{2.5} $) concentrations generically exhibit heavy tails. These are asymptotically well approximated by $ q $-exponential distributions with a given entropic index $ q $ and width parameter $ \lambda $. We observe that the power-law parameter $ q $ and the width parameter $ \lambda $ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters $ q $ and $ \lambda $ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to the properties of the geographical region. We also present results on typical time scales associated with the dynamical behavior.

This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.

The field of misinformation studies has experienced a boom of scholarship in recent years. Buoyed by the emergence of information operations surrounding the 2016 election and the rise of so-called “fake news,” researchers hailing from fields ranging from philosophy to computer science have taken up the challenge of detecting, analyzing, and theorizing false and misleading information online. In an attempt to understand the spread of misinformation online, researchers have adapted concepts from different disciplines. Concepts from epidemiology, for example, have opened doors to thinking about spread, contagion, and resistance. The life sciences offer concepts and theories to further extend what we know about how misinformation adapts; by viewing information as an organism within a complex ecosystem, we can better understand why some narratives succeed and others fail. Collaborations between misinformation researchers and life scientists to develop responsible adaptations of fitness models can bolster misinformation research.

This paper investigates the use of Large Language Models (LLMs) in engineering complex systems, demonstrating how they can support designers on detail design phases. Two aerospace cases, a system architecture definition and a CAD model generation activities are studied. The research reveals LLMs' challenges and opportunities to support designers, and future research areas to further improve their application in engineering tasks. It emphasizes the new paradigm of LLMs support compared to traditional Machine Learning techniques, as they can successfully perform tasks with just a few examples.

Designers’ roles are at a turning point of transforming design from an expert-driven design process within an assumed social and economic order to design practices that advocate design-led societal transition toward more sustainable futures. Design education should be adapted accordingly. Introducing the transition design concept into established design education promotes the sustainable society transition by involving more systems thinking from designers in various sectors. This study reports on a pilot practice and reflection on introducing the transition design concept to design students.

This paper explores the interplay of feedback principles in design and systems science. From their roots in engineering, biology, and economics, it investigates intersections between design, cybernetics and servomechanisms. The synthesis emphasizes the need for considering feedback in anticipating unintended consequences and proposes an integrative view reconciling fundamental assumptions from the different fields through simulation. This holistic approach underscores the pivotal role of feedback in understanding and addressing complex phenomena, such as rebound effects, in design science.

This paper outlines the evolution of decision-making spaces through selected instantiations, analyzing the role of design in their utility and identifying preliminary patterns in spatial layout. It builds on the combination of two research fields: A) Decision support systems and B) Creative Spaces. The paper aims to take a first step towards combining these two existing research fields by focusing on the spatial design aspects that foster the convergent aspect of creativity and to provide guidance for further research on the design of decision-making spaces.

The complexity of the products/systems requires an in-depth understanding of variability and its impact on all phases, from design to maintenance. This study explores Variability Management (VM) emphasizing its challenges. Conducting semi-structured interviews with experts at Renault Group, the research examines variability aspects, semantics, methods, challenges, and possible solutions. The findings offer practical insights into industrial-scale variability management, addressing the use case of the automotive industry.

This study proposes a Moon-based factory for space-bound products, aiming to revolutionize space exploration through In-Space Manufacturing (ISM). It defines key elements for lunar manufacturing by adapting Earth-based factory models to lunar conditions.