Open Access

The authors’ analysis of determinants of household electricity consumption is based on the 2018 Survey of Income and Expenditure (EVS), using Germany as an example. The EVS survey covered all variables identified through a scoping review, including the number and type of appliances, sociodemographic, and dwellingrelated aspects. This large representative dataset allows analyzing the effect of these determinants on electricity expenditure for German households. Expenditure on electricity is considered a reliable indicator of household electricity consumption. The determinants show weak to moderate correlations with energy expenditure in bivariate analyses. Multivariate analysis clearly shows the combined effects of householdspecific, dwelling-related, and appliance-specific factors. Models considering only one aspect overestimate the respective effect. Thus, it is essential to consider all three aspects simultaneously when explaining residential electricity consumption. The largest effects are found for electricity as the main energy source for heating, the number of household members, as well as their presence at home. Household structure is an important factor in explaining residential energy consumption while dwelling and appliance-related aspects also have an effect. Appropriate policy measures may affect the latter aspects.

The optimisation of the dynamic behavior of drive systems often involves targeted modifications of the system characteristics. Structural and parametric modifications are used to satisfy the constraints of the dynamic requirements. However, many optimisations are still achieved by intuition or parameter variations, even though more streamlined and easy-to-implement tools such as the eigenvalue perturbation method are available. In this article, the eigenvalue perturbation method in the form of an eigenvalue sensitivity analysis is used to efficiently optimise the dynamic behavior for two different use cases using different optimisation measures. This paper demonstrates how eigenvalue perturbation theory can efficiently optimise drivetrain dynamics by systematically modifying system parameters. Two case studies show how eigenvalue sensitivity analysis achieves targeted frequency shifts to avoid resonances: (1) adapting shaft stiffness and control parameters in a torsional drivetrain, and (2) adjusting structural modifications in a wind turbine bedplate. The study introduces the eigenvector tensor product as a weighting matrix, identifying key parameters for effective redesign. Compared to conventional parameter studies, this method enables precise control over system dynamics with minimal computational effort, making it highly applicable for vibration mitigation and drivetrain optimisation.

Across the global construction sector, a new generation of robotic systems is rapidly entering the market. Solutions for on-site drilling, spraying, masonry, logistics, and finishing are now being piloted at an unprecedented pace. Their deployment in emerging construction robotics hubs in Singapore, Hong Kong, Canada, Dubai, Abu Dhabi, Egypt, Denmark, Switzerland, and Germany demonstrates both the momentum of this technological shift and the considerable challenges that remain. In real-world testing environments, the integration of these robots into digital construction pipelines—particularly BIM-to-robot workflows, semantic task modeling, and robust digital twins—continues to be a bottleneck. These challenges position digitally enabled fabrication and robotics as a priority topic within academia, motivating research on methods, techniques, algorithms, and workflows that can accelerate adoption in construction. This Topical Collection brings together research spanning the emerging landscape of digitally enabled construction robotics. The contributions advance robotic fabrication, from flexible timber processes to innovative formwork, reinforcement, and earth-based additive methods, alongside computer vision, BIM integration, and sensing approaches that improve monitoring and quality assurance. The collection also includes mobile and aerial systems for inspection and mapping to support system autonomy in construction. Together, these works show how integrated perception, planning, and sociotechnical understanding of human–robot collaboration are becoming essential for reliable robotic performance in construction. While current construction robots still focus on simple, structured tasks, the advances in this topical collection point toward a more capable generation. Contributions outline principles for robot-compatible buildings through new fabrication logics and BIM-linked task data, while work on perception, BIM integration, and data fusion reduces interoperability gaps. Research on sensing and adaptive processes supports more consistent workflows, and mobile and aerial robotics provide insights for deployment and site logistics. Collectively, these developments show how digitally enabled production processes can help to overcome key systemic barriers and enable future, scalable construction robotics.

Large Language Models have advanced to a resourceful tool with many applications. One particularly interesting use case is the LLM-aided generation of ad-hoc database queries and the possibility of subsequent processing of the results in a way suiting the users intents. In this article, practical ways and experiences are described on how to effectively use LLMs to map a natural-language user query to an SQL query conforming to a specific database schema and post-processing the results of this query in order to, for example, create an appealing visualization. Best results are achieved under favorable circumstances, as, for example, a clean and meaningful named database schema.

Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios.