Graz University of Technology

Phase singularities are a common problem in image reconstruction with auto-calibrated sensitivities due to an inherent ambiguity of the estimation problem. The purpose of this work is to develop a method for detecting and correcting phase poles in non-linear inverse (NLINV) reconstruction of MR images and coil sensitivity maps. Phase poles are detected in individual coil sensitivity maps by computing the curl in each pixel. A weighted average of the curl in each coil is computed to detect phase poles. Phase pole detection and correction is then integrated into the iteratively regularized Gauss-Newton method of the NLINV algorithm. In addition, we demonstrate the algorithm can also remove phase poles in ESPIRiT coil sensitivity maps. Phase pole correction is evaluated for reconstruction of accelerated Cartesian MPRAGE data of the brain and interactive radial real-time MRI of the human heart. For both applications, phase pole correction can be used for estimation of coil sensitivity profiles free from singularities. For NLINV, we demonstrate reliable and efficient estimation even from very small ( ) auto-calibration (AC) regions. With the proposed method, phase poles can be reliably removed in reconstructions and coil sensitivities.

Same-hardware, stack-scale evidence for PtX syngas routes is scarce. We quantify, on identical solid-oxide hardware, how operating conditions affect stack voltage and specific electricity demand to guide selection between steam electrolysis, paired downstream with RWGS, and direct co-electrolysis. We report new stack-level steam-electrolysis data and benchmark them against our previously published co-electrolysis dataset on the same five-cell electrolyte-supported stack and test rig. Measurements are non-overlapping with aligned operating windows and a single analysis pipeline. A structured design-of-experiments with regression yields sensitivities to current density, fuel-side composition/flow, and temperature. Electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT), and in-plane temperature mapping provide mechanistic context for ohmic, charge-transfer, and transport contributions. Within the tested window, co-electrolysis shows stronger voltage sensitivity to current density and the air-outlet setpoint. Differences in specific electricity demand are modest per stack yet material at plant scale. Holding hardware and analysis constant, we deliver a reproducible benchmark that supports route selection and informs thermal and current-density control in commercial PtX plants. • DoE-based quantification of parameter effects on stack voltage (steam EC). • EIS, DRT, and in-plane temperature diagnostics on a 5-cell commercial stack. • Same-hardware benchmark: co-electrolysis shows larger per-unit sensitivities. • At matched production, co-electrolysis requires 2 % higher DC power and kWh Nm − 3 .

In this Current Opinion, we revisit key terminological distinctions in sports sciences, emphasizing the implications of conflating related but distinct concepts, particularly range of motion (ROM) versus flexibility and flexibility training versus stretching. While often used interchangeably, these terms represent nuanced constructs with distinct physiological and practical implications. ROM encompasses both modifiable (e.g., soft tissue extensibility, neural control) and non-modifiable factors (e.g., bone structure), whereas flexibility is only one of several trainable components of ROM, largely referring to the extensibility of periarticular soft tissues. Misunderstanding these distinctions risks erroneous assessments and inappropriate exercise prescriptions. We also challenge the common practice of equating flexibility training with stretching. While stretching is effective to enhance flexibility, other methods, such as strength training at long muscle lengths and foam rolling, produce comparable chronic ROM gains. Furthermore, stretching has a diverse array of effects that extend beyond flexibility, including strength development and neural modulation. Mischaracterizing flexibility training as synonymous with stretching perpetuates myths and constrains innovation in training practices. By differentiating these terms, we advocate for clearer language in research and practice to avoid miscommunication and ensure effective training interventions. In summary, we propose that flexibility is only one component of ROM, and that flexibility training and stretching should not be equated. We hope this Current Opinion paper generates a healthy discussion within the sports science and medicine communities.

Abstract The homogeneous wave equation is solved by a time-domain boundary element method (BEM) using low-order shape functions for spatial, and the generalised convolution quadrature method (gCQ) by Lopez-Fernandez and Sauter for temporal discretisation. The three-dimensional array of BEM matrices according to a set of complex frequencies in Laplace domain is approximated by generalised Adaptive Cross Approximation (3D-ACA). Its rank is increased adaptively until a prescribed accuracy is reached, relying on a pure algebraic error criterion. The data slices for the selected frequency points are further processed by either the standard $$\mathcal {H}$$ H -matrices approach with ACA or by a fast multipole method (FMM). This paper compares both approaches with respect to their demands in storage and computing time. Both techniques are illustrated for calculating the sound scattered by an electric machine, for which the proposed algebraic compression techniques make time-domain BEM feasible for the first time.

A single session of complex contrast training (CCT) can induce lower-limb fatigue, making the optimization of post-CCT recovery strategies essential. Foam rolling (FR) is widely used and is believed to enhance acute recovery. Therefore, this study examined the acute effects of FR compared with passive recovery (PR) following a single bout of CCT on dynamic balance (YBT), static balance (SLST), horizontal jump performance (SLHD), vertical jump performance (CMJ), and muscle excitation measured via surface electromyography (sEMG). Twenty-one active males completed the study using a crossover design, being randomly allocated to FR and PR conditions. Assessments were performed at three time points: pre-test, immediately post-CCT (mid-test), and after the recovery intervention (post-test). Participants performed the CCT protocol prior to recovery (5 sets × 6 repetitions at 80% and 30% of 1RM). Test measures included YBT, SLST, SLHD, CMJ, and sEMG recordings from selected lower-limb muscles. FR resulted in significant acute improvements in YBT, SLST, and SLHD compared with PR (p < 0.05), whereas no meaningful differences were detected in CMJ performance (p > 0.05). Although time effects were observed in sEMG excitation for some muscles (p < 0.05), no intervention × time interactions were detected (p > 0.05). FR is an effective acute recovery strategy for enhancing dynamic and static balance as well as horizontal jump performance following CCT. However, its influence on vertical jump performance and muscle excitation appears limited. Future research should incorporate broader neuromuscular assessments, varied FR protocols, and diverse populations to better clarify underlying mechanisms and optimize recovery outcomes.

Keynote Vortrag bei der Tagung der Schulleiter*innen Bildungsregion Niederösterreich Mitte mit dem Titel "Schule.digital: Chancen durch KI & kreative Wege in die Zukunft" am 16.03.2026 in St. Pölten

Black carbon (BC) and ultrafine particles (UFPs) are two key metrics of particulate matter (PM) for climate and health. Until 2024, neither of these metrics were addressed in the EU’s ambient air quality directives, which only focused on PM10 and PM2.5, dominated by micron-sized particles. However, with the EU Directive (EU) 2024/2881 both metrics will be measured for the first time, at least at supersites. Both are also key metrics for emissions: BC is the indicator of choice for identifying combustion-related PM, and particle number (PN) of UFPs is regulated in the EU emission standards. Roadside point sampling (PS) is a measurement technique that has been used for decades, primarily for scientific studies of real-world particle emissions (Hansen and Rosen, 1990; Knoll et al., 2024). Due to the continuous roadside measurement of metrics such as BC and UFP/PN it also provides very useful information about ambient air quality at a high resolution next to roads, where large numbers of people live and travel (Farren et al., 2025).This work presents the results of emission and ambient air quality measurements taken in various European cities using roadside PS. The campaigns were performed in different seasons (spring, autumn and winter), providing interesting information in different settings. The emission results show that exceedances of defined thresholds for PS measured emission factors correspond well with the failure rates of the newly introduced PN periodic technical inspections (PTI) for checking diesel particulate filters (DPFs) in vehicles. Trends in vehicle emissions with different Euro standards provide valuable insights into real-world emission patterns which can be used to monitor the implementation of emission standards as well as for potential introductions of low emission zones. Ambient air quality measurements show that significant concentrations of BC and UFPs are observed near the road, especially during periods of high traffic and in winter. Ambient concentrations of more than 25 µg/m³ of BC were observed in winter, which is likely caused due to various emission sources (transport, industry, residential heating) and climate conditions. An outlook is shown for extending the measuring approach to additionally measure non-exhaust emissions (tire, brake, resuspension) and their influence on ambient air quality.Keywords: Black Carbon, Ultrafine Particles, Real-World Emissions, Point Sampling, Roadside Ambient Air Quality, High pollution events, Non-exhaust emissionThis work has been supported by the EU projects CARES (grant no. 814966), LENS (101056777) and Net4Cities (101138405).Farren,N.J., Knoll,M., Bergmann,A., Wagner,R.L., Shaw,M.D., Wilson,S., Bernard,Y., Carslaw,D.C., 2025. Highly disaggregated particulate and gaseous vehicle emission factors and ambient concentration apportionment using a plume regression technique. Environ.Sci.Technol. 59, 11698–11707. http://dx.doi.org/10.1021/ACS.EST.5C05015.Hansen, A. D. and Rosen, H.: Individual measurements of the emission factor of aerosol black carbon in automobile plumes, J. Air Waste Manage., 40, 1654–1657, https://doi.org/10.1080/10473289.1990.10466812, 1990.Knoll, M., Penz, M., Juchem, H., Schmidt, C., Pöhler, D., and Bergmann, A.: Large-scale automated emission measurement of individual vehicles with point sampling, Atmos. Meas. Tech., 17, 2481–2505, https://doi.org/10.5194/amt-17-2481-2024, 2024.

Model-based diagnosis is a generally applicable, principled approach to the systematic debugging of a wide range of system types such as circuits, knowledge bases, physical devices, or software. Based on a formal description of the system, it enables precise and deterministic reasoning about potential faults responsible for observed misbehavior. In software, such a formal system description can often even be extracted from the buggy program fully automatically. As logical reasoning is central to diagnosis, the performance of model-based debuggers is largely influenced by reasoning efficiency, which in turn depends on the complexity and expressivity of the system description. Since highly detailed models capturing exact semantics often exceed the capabilities of current reasoning tools, researchers have proposed more abstract representations. In this work, we thoroughly analyze system modeling techniques with a focus on fault localization in spreadsheets—one of the most widely used end-user programming paradigms. Specifically, we present three constraint model types characterizing spreadsheets at different abstraction levels, show how to extract them automatically from faulty spreadsheets, and provide theoretical and empirical investigations of the impact of abstraction on both diagnostic output and computational performance. Our main conclusions are that (i) for the model types, there is a trade-off between the conciseness of generated fault candidates and computation time, (ii) the exact model is often impractical, and (iii) a new model based on qualitative reasoning yields the same solutions as the exact one in up to more than half the cases while being orders of magnitude faster. Due to their ability to restrict the solution space in a sound way, the explored model-based techniques, rather than being used as standalone approaches, are expected to realize their full potential in combination with iterative sequential diagnosis or indeterministic but more performant statistical debugging methods.