Harmonised standards are the cornerstone of efficient EU AI Act compliance. This paper presents one of the first systematic analyses of European technical and soon to be harmonised standardisation for organisations providing AI systems. Based on in-depth qualitative interviews with twenty-three leading European organisations developing AI applications across different sectors, such as Mistral and Helsing, and providing transparency regarding the status quo of draft standards, it examines how companies, especially start-ups and SMEs, are dealing with the contemplated standardisation under the EU AI Act and sectoral standardisation. Industry sectors covered include mobility, finance, manufacturing, healthcare, as well as defense and legal tech. Key challenges identified comprise an insufficient effective implementation period of likely less than 6 months compared to at least 12 months actually required for around thirty (partially referenced) technical standards, an imbalance of participation and influence in standardisation committees, double regulation and technical implementation hurdles as well as significant annual costs for harmonised standards compliance. Technical standards are currently reshaping global AI competition and will have a massive influence on the AI landscape as market entry barriers, particularly on start-ups. Hence, the paper offers policy recommendations based on the revealed challenges for AI providers.

Many constitutional courts use balancing in constitutional right adjudication. However, critics argue that balancing is an (self-)empowerment of the courts and a tool of judicial activism. It is claimed that constitutional courts are increasingly using this technique when ruling against the legislature, for example when striking down laws. This study empirically examines the status of balancing in the case law of the German Federal Constitutional Court. It demonstrates that text-as-data methods can be used to analyze judicial reasoning by using word embeddings to measure the use of balancing language. It is shown that the use of balancing language increased during the first fifty years of the court’s existence. Since then, there has been a decline. The court also tends not to use more balancing language in decisions overturning laws. This evidence challenges the critique’s assumption that balancing is a tool of judicial activism.

This article examines the democratic potential of global network governance. It critiques conventional legitimacy frameworks that focus on the institutional qualities of governance networks for evading deeper ethical questions about how to deal with diversity as a fundamental condition of global life. Drawing on contestation theory, English School pluralism, and radical democratic theory, I argue that democratic network governance thrives by embracing diverse norm interpretations and fostering agonistic engagement among its members. I illustrate how this idea can guide a critical analysis of global governance networks through a case study of humanitarian governance in Southeast Asia. I assess to what extent diverse humanitarian actors have been able to assert and practise their own interpretations of humanitarian norms, how they relate to the fact that others advance competing understandings, and whether the boundaries for legitimate norm enactments are drawn on legitimate grounds. By highlighting the ethical significance of these dynamics from a pluralist standpoint, the study offers a novel way of thinking about democracy in a world that is characterised by complex policy problems, diffuse authority, and vocal demands for recognition by an increasingly diverse array of actors.

The energy efficiency of emerging aircraft designs plays a key role, not only in reducing environmental impact, but also in reducing operating costs in the anticipated rise in fuel prices. The European Clean Sky 2 project HLFC-Win is investigating the feasibility of hybrid laminar flow control (HLFC) technology integrated into the outer wing leading edge for a long-haul aircraft. HLFC technology reduces aerodynamic friction drag by means of suction of the boundary layer through a micro-perforated skin to achieve laminarity and thereby improving aircraft performance. However, integrating such a system is not without its drawbacks, as the integration has an impact on the geometry, mass, aerodynamics and engine offtakes that need to be considered. Therefore, the aim of this current work is to assess the HLFC system based on a fair, objective and transparent comparison between the HLFC aircraft and an aircraft of the same technology level without HLFC. The assessment of the HLFC system is twofold, firstly estimating the mission-based performance at the overall aircraft level and secondly performing a lifecycle simulation with three scenarios to determine realistic fuel and cost savings. The mission-based performance assessment indicates a block fuel reduction of over 3 % for the design mission which averages 1.6 to 2.5 % considering a realistic route scenario and expected degradation. The economic assessment suggests a dependency on the scenario chosen, ranging from a 0.7 % increase in total cost (in an unfavourable scenario) to almost a 1 % reduction in total cost (in a favourable scenario), equivalent to $15 million saved per HLFC aircraft over its lifetime. These results support the commercial viability of HLFC technology, which offers significant aerodynamic and fuel efficiency improvements and operating cost savings to the aviation industry. Importantly, no critical barriers were identified for the integration of HLFC technology, further underscoring its potential to improve aircraft performance.

Repetitive negative thinking (RNT) is a transdiagnostic process associated with the onset, maintenance, and risk of relapse of various mental disorders. However, previous research syntheses addressing the effect of cognitive behavioral therapy (CBT) on RNT are limited to specific diagnoses, treatments, or RNT constructs (transdiagnostic RNT, worry, rumination). In the present meta-analysis, we integrate findings from randomized controlled trials (RCTs) of CBT on RNT across diagnoses, intervention types, and RNT constructs. We investigate the following questions: What is the overall transdiagnostic efficacy of CBT interventions on all post-treatment RNT outcomes? Which RNT construct is addressed most effectively? Are RNT-specific treatments superior in reducing RNT than less specific approaches? Inclusion criteria were met by 55 studies with a total of 4,970 participants. The overall post-treatment effect of CBT interventions on RNT compared to respective control groups was moderate in favor of CBT (g = −0.67). Treatment efficacy did not differ significantly by RNT construct. RNT-specific interventions (g = −0.99) were significantly more efficacious in reducing RNT than less specific approaches (g = −0.56). Treatment efficacy was not significantly enhanced by individual or in-person settings. Our results advocate a dissemination of RNT-specific treatments in research and practice and a general improvement of CBT treatments by focusing on relevant transdiagnostic processes such as RNT.

The longitudinal process that leads to university student dropout in STEM subjects can be described by referring to (a) inter-individual differences (e.g., cognitive abilities) as well as (b) intra-individual changes (e.g., affective states), (c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov et al. in Struct Equ Model 24:257–269, 2017; DSEM; Asparouhov et al. in Struct Equ Model 25:359–388, 2018; NDLC-SEM, Kelava and Brandt in Struct Equ Model 26:509–528, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student dropout in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks using a Forward Filtering Backward Sampling method. To illustrate our approach, we use an empirical example where we apply the proposed forecasting method to ILD from a large university student dropout study in math with multivariate observations collected over 50 measurement occasions from multiple students ($N=122$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$N = 122$$\end{document}). More specifically, we forecast emotions and behavior related to dropout. This allows us to predict emerging critical dynamic states (e.g., critical stress levels or pre-decisional states) 8 weeks before the actual dropout occurs.

This work investigates the spatio-temporal evolution of coherent structures in the wake of a generic high-speed train, based on a three-dimensional database from large eddy simulation. Spectral proper orthogonal decomposition (SPOD) is used to extract energy spectra and energy ranked empirical modes for both symmetric and antisymmetric components of the fluctuating flow field. The spectrum of the symmetric component shows overall higher energy and more pronounced low-rank behaviour compared with the antisymmetric one. The most dominant symmetric mode features periodic vortex shedding in the near wake, and wave-like structures with constant streamwise wavenumber in the far wake. The mode bispectrum further reveals the dominant role of self-interaction of the symmetric component, leading to first harmonic and subharmonic triads of the fundamental frequency, with remarkable deformation of the mean field. Then, the stability of the three-dimensional wake flow is analysed based on two-dimensional local linear stability analysis combined with a non-parallelism approximation approach. Temporal stability analysis is first performed for both the near-wake and the far-wake regions, showing a more unstable condition in the near-wake region. The absolute frequency of the near-wake eigenmode is determined based on spatio-temporal analysis, then tracked along the streamwise direction to find out the global mode growth rate and frequency, which indicate a marginally stable global mode oscillating at a frequency very close to the most dominant SPOD mode. The global mode wavemaker is then located, and the structural sensitivity is calculated based on the direct and adjoint modes derived from a local spatial analysis, with the maximum value localized within the recirculation region close to the train tail. Finally, the global mode shape is computed by tracking the most spatially unstable eigenmode in the far wake, and the alignment with the SPOD mode is computed as a function of streamwise location. By combining data-driven and theoretical approaches, the mechanisms of coherent structures in complex wake flows are well identified and isolated.

This study presents an approach to investigate the role of eddy viscosity in linearized mean-field analysis of broadband turbulent flows. The procedure is based on spectral proper orthogonal decomposition (SPOD), resolvent analysis and the energy budget of coherent structures and is demonstrated using the example of a turbulent jet. The focus is on the coherent component of the Reynolds stresses, the nonlinear interaction term of the fluctuating velocity component in frequency space, which appears as an unknown in the derivation of the linearized Navier–Stokes equations and which is the quantity modelled by the Boussinesq approach. For the considered jet the coherent Reynolds stresses are found to have a mostly dissipative effect on the energy budget of the dominant coherent structures. Comparison of the energy budgets of SPOD and resolvent modes demonstrates that dissipation caused by nonlinear energy transfer must be explicitly considered within the linear operator to achieve satisfactory results with resolvent analysis. Non-modelled dissipation distorts the energy balance of the resolvent modes and is not, as often assumed, compensated for by the resolvent forcing vector. A comprehensive analysis, considering different predictive and data-driven eddy viscosities, demonstrates that the Boussinesq model is highly suitable for modelling the dissipation caused by nonlinear energy transfer for the considered flow. Suitable eddy viscosities are analysed with regard to their frequency, azimuthal wavenumber and spatial dependence. In conclusion, the energetic considerations reveal that the role of eddy viscosity is to ensure that the energy the structures receive from the mean-field is dissipated.

This paper examines the replicability of studies in design research triggered by the replication crisis in psychology. It highlights the importance of replicating studies to ensure the robustness of research results and examines whether the description in a publication is sufficient to replicate. Therefore, the publication of a reference study was analysed and a replication study was conducted. The design of the replication study appears similar to the reference study, but the results differ. Possible reasons for the differences and implications for replication studies are discussed.

The understanding of prototyping has changed in recent years to an approach that accompanies the product development process. This paper examines whether classic approaches from product development are also suitable for planning prototyping sequences. The stepwise process-oriented and the problem-oriented approach are discussed. A criticality assessment is proposed as a metric for the prioritization of the functional areas and a procedure is derived from this. The procedure is illustrated using an example. The result is discussed and future steps are suggested.

The use of design methods across multiple design phases of the product development process often leads to inconsistency, the loss of transparency, and the rejection of design methods by practitioners. The authors of this work intend to develop a central modelling approach that supports consistency, based on the integrated function modelling (IFM) framework. Therefore, various design methods from the literature were examined for their techniques and content to identify indicators for supporting consistency. The results led to an enhancement of the IFM framework.

Unlocking additive manufacturing's (AM) potential requires designer expertise. Design for additive manufacturing (DfAM) addresses this need but faces barriers, such as uncertainty in scope of integration, design support selection, result validation or time investment for incorporating design support. This paper proposes a framework aligning SCRUM (agile framework) to aid designers in overcoming those barriers. The goal is to pave the way for a better exchange between academia and industry and fostering iterative development of DfAM support tailored to designer needs.

Intergovernmental collaboration is needed to address global problems. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. As AI emerges as a central catalyst in deriving effective solutions for global problems, the infrastructure that supports its data needs becomes crucial. However, data sharing between governments is often constrained due to socio-technical barriers such as concerns over data privacy, data sovereignty issues, and the risks of information misuse. Federated learning (FL) presents a promising solution as a decentralized AI methodology, enabling the use of data from multiple silos without necessitating central aggregation. Instead of sharing raw data, governments can build their own models and just share the model parameters with a central server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data-sharing challenges listed by the Organisation for Economic Co-operation and Development can be overcome by utilizing FL. Furthermore, we provide a tangible resource implementing FL linked to the Ukrainian refugee crisis that can be utilized by researchers and policymakers alike who want to implement FL in cases where data cannot be shared. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, positively impacting governments and citizens.