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This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.

European cities and communities (and beyond) require a structured overview and a set of tools as to achieve a sustainable transformation towards smarter cities/municipalities, thereby leveraging on the enormous potential of the emerging data driven economy. This paper presents the results of a recent study that was conducted with a number of German municipalities/cities. Based on the obtained and briefly presented recommendations emerging from the study, the authors propose the concept of an Urban Data Space (UDS), which facilitates an eco-system for data exchange and added value creation thereby utilizing the various types of data within a smart city/municipality. Looking at an Urban Data Space from within a German context and considering the current situation and developments in German municipalities, this paper proposes a reasonable classification of urban data that allows the relation of various data types to legal aspects, and to conduct solid considerations regarding technical implementation designs and decisions. Furthermore, the Urban Data Space is described/analyzed in detail, and relevant stakeholders are identified, as well as corresponding technical artifacts are introduced. The authors propose to setup Urban Data Spaces based on emerging standards from the area of ICT reference architectures for Smart Cities, such as DIN SPEC 91357 “Open Urban Platform” and EIP SCC. In the course of this, the paper walks the reader through the construction of a UDS based on the above-mentioned architectures and outlines all the goals, recommendations and potentials, which an Urban Data Space can reveal to a municipality/city. Finally, we aim at deriving the proposed concepts in a way that they have the potential to be part of the required set of tools towards the sustainable transformation of German and European cities in the direction of smarter urban environments, based on utilizing the hidden potential of digitalization and efficient interoperable data exchange.

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement.

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often generate or collect sensitive, real-time data used in critical application scenarios (e.g., health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victim of malicious actors. In this paper, we presented a brief overview of existing attacks and defense strategies and used this as motivation for proposing an integrated methodology for developing protection mechanisms for smart city IoT networks. The goal of this work was to lay out a theoretical plan and a corresponding pipeline of steps, i.e., a development and implementation process, for the design and application of cybersecurity solutions for urban IoT networks. The end goal of following the proposed process is the deployment and continuous improvement of appropriate IoT security measures in real-world urban IoT infrastructures. The application of the methodology was exemplified on an OMNET++-simulated scenario, which was developed in collaboration with industrial partners and a municipality.

The rise of quantum computing/technology potentially introduces significant security challenges to cloud computing, necessitating quantum-resistant encryption strategies as well as protection schemes and methods for cloud infrastructures offering quantum computing time and services (i.e. quantum clouds). This research explores various options for securing quantum clouds and ensuring privacy, especially focussing on the integration of homomorphic encryption (HE) into Eclipse Qrisp, a high-level quantum computing framework, to enhance the security of quantum cloud platforms. The study addresses the technical feasibility of integrating HE with Qrisp, evaluates performance trade-offs, and assesses the potential impact on future quantum cloud architectures. The successful implementation and Qrisp integration of three post-quantum cryptographic (PQC) algorithms demonstrates the feasibility of integrating HE with quantum computing frameworks. The findings indicate that while the Quantum One-Time Pad (QOTP) offers simplicity and low overhead, other algorithms like Chen and Gentry-Sahai-Waters (GSW) present performance trade-offs in terms of runtime and memory consumption. The study results in an overall set of recommendations for securing quantum clouds, e.g. implementing HE at data storage and processing levels, developing Quantum Key Distribution (QKD), and enforcing stringent access control and authentication mechanisms as well as participating in PQC standardization efforts.

Phases with spontaneous time-reversal ( T ) symmetry breaking are sought after for their anomalous physical properties, low-dissipation electronic and spin responses, and information-technology applications. Recently predicted altermagnetic phase features an unconventional and attractive combination of a strong T -symmetry breaking in the electronic structure and a zero or only weak-relativistic magnetization. In this work, we experimentally observe the anomalous Hall effect, a prominent representative of the T -symmetry breaking responses, in the absence of an external magnetic field in epitaxial thin-film Mn 5 Si 3 with a vanishingly small net magnetic moment. By symmetry analysis and first-principles calculations we demonstrate that the unconventional d-wave altermagnetic phase is consistent with the experimental structural and magnetic characterization of the Mn 5 Si 3 epilayers, and that the theoretical anomalous Hall conductivity generated by the phase is sizable, in agreement with experiment. An analogy with unconventional d-wave superconductivity suggests that our identification of a candidate of unconventional d-wave altermagnetism points towards a new chapter of research and applications of magnetic phases. The classification of magnets now includes altermagnets which possess opposite-spin sublattices connected by rotation and share some features with ferro- and antiferromagnets. Here the authors report the anomalous Hall effect in Mn5Si3 and interpret the results in terms of a d-wave altermagnetic phase.

The collective vibrational and magnetic response of a solid to external stimuli is encoded in the dispersion relations of phonons and magnons, respectively. Recently, the coherent drive and nonlinear manipulation of collective lattice and magnetic excitations via laser pulses has been explored, as a route to control the non-equilibrium properties of quantum materials. Device concepts that leverage coupled multiphysical dynamics must exhibit laser-induced frequency tunability controllable through external parameters. Although previous works have shown that optically driven excitations in the midinfrared can manipulate magnon frequencies, inducing deterministic red- or blue-shifts of the magnon frequency in the same material is still elusive. Here we demonstrate this concept in a nanometer-thick magnet at room temperature. Visible light pulses in combination with an external magnetic field (<200 mT) can either raise or lower the magnon frequency by up to 40% of its original value. This effect results from the interplay of the optical excitation, magnetic anisotropy and external magnetic field. Our results show how an efficient manipulation of magnons can be achieved by light and provide perspectives for the realization of logic devices optically reconfigurable on the nanosecond timescale. On-demand laser-induced frequency tuning of coherent magnons in a 20 nm thick magnet at room temperature is demonstrated. By tuning the laser fluence and magnetic field, red- or blueshift of the frequency up to 40% of its original value are achieved.

Computing the approximation quality is a crucial step in every iteration of sandwiching algorithms (also called Benson-type algorithms) used for the approximation of convex Pareto fronts, sets or functions. Two quality indicators often used in these algorithms are polyhedral gauge and epsilon indicator. In this article, we develop an algorithm to compute the polyhedral gauge and epsilon indicator approximation quality more efficiently. We derive criteria that assess whether the distance between a vertex of the outer approximation and the inner approximation needs to be recalculated. We interpret these criteria geometrically and compare them to a criterion developed by Dörfler et al. for a different quality indicator using convex optimization theory. For the bi-criteria case, we show that only two linear programs need to be solved in each iteration. We show that for more than two objectives, no constant bound on the number of linear programs to be checked can be derived. Numerical examples illustrate that incorporating the developed criteria into the sandwiching algorithm leads to a reduction in the approximation time of up to 94 % and that the approximation time increases more slowly with the number of iterations and the number of objective space dimensions.

In this article we introduce an algorithm that approximates the nondominated sets of multiobjective mixed-integer convex optimization problems. The algorithm constructs an inner and outer approximation of the front exploiting the convexity of the patches for problems with an arbitrary number of criteria. In the algorithm, the problem is decomposed into patches, which are multiobjective convex problems, by fixing the integer assignments. The patch problems are solved using (simplicial) Sandwiching. We identify parts of patches that are dominated by other patches and ensure that these patch parts are not refined further. We prove that the algorithm converges and show a bound on the reduction of the approximation error in the course of the algorithm. We illustrate the behaviour of our algorithm using some numerical examples and compare its performance to an algorithm from literature.

Patients with first-diagnosed atrial fibrillation (FDAF) exhibit major adverse cardiovascular events (MACEs) during follow-up. Preclinical models have demonstrated that thrombo-inflammation mediates adverse cardiac remodeling and atherothrombotic events. We have hypothesized that thrombin activity (FIIa) links coagulation with inflammation and cardiac fibrosis/dysfunction. Surrogate markers of the thrombo-inflammatory response in plasma have not been characterized in FDAF. In this prospective longitudinal study, patients presenting with FDAF (n = 80), and 20 matched controls, were included. FIIa generation and activity in plasma were increased in the patients with early AF compared to the patients with chronic cardiovascular disease without AF (controls; p < 0.0001). This increase was accompanied by elevated biomarkers (ELISA) of platelet and endothelial activation in plasma. Pro-inflammatory peripheral immune cells (TNF-α+ or IL-6+) that expressed FIIa-activated protease-activated receptor 1 (PAR1) (flow cytometry) circulated more frequently in patients with FDAF compared to the controls (p < 0.0001). FIIa activity correlated with cardiac fibrosis (collagen turnover) and cardiac dysfunction (NT-pro ANP/NT-pro BNP) surrogate markers. FIIa activity in plasma was higher in patients with FDAF who experienced MACE. Signaling via FIIa might be a presumed link between the coagulation system (tissue factor-FXa/FIIa-PAR1 axis), inflammation, and pro-fibrotic pathways (thrombo-inflammation) in FDAF.