Johannes Kepler University of Linz

• Factorial Survey Experiment isolated effects from 1700 Vignette responses. • Quantified commute duration tolerance which decreases with required office days. • Reputation and salary boost commute acceptance, but not for WFH-prioritizers. • Paid travel time significantly increases willingness to accept long commutes. • Hybrid models shift commute time budgeting from daily to weekly patterns. Understanding what drives employees’ willingness to accept longer commutes is key to designing effective hybrid working policies. This study explores how commute frequency, salary, employer reputation, and travel mode influence tolerance for commute duration, based on a factorial survey experiment involving 302 participants and over 1,700 vignette evaluations conducted across Germany, Austria, and Switzerland. The results indicate that less frequent office attendance, higher salaries, a strong organizational reputation, and more convenient travel modes (e.g., train vs. bicycle) significantly increase commute tolerance. These effects, however, vary depending on individuals’ preferences for working from home or anywhere (WFH/A). Participants with strong WFH/A preferences were notably more sensitive to frequent commuting requirements and less responsive to modest salary increases. The findings challenge traditional assumptions, such as Marchetti’s constant, by demonstrating that individuals adjust their tolerance based on hybrid working patterns and mode-specific expectations. For example, commute acceptance was substantially higher when the mode was air travel, reflecting associations with long-distance or infrequent travel. These framing effects suggest that commuting decisions are shaped as much by context and mental reference points as by time alone. The study offers practical insights for HR professionals, urban planners, and policymakers. Adjusting attendance expectations or reframing commuting time (e.g., treating it as paid work) may help expand recruitment reach, enhance retention, and align job design with the preferences of a more mobile workforce.

The purpose of this study was to characterize three recurrent, cancer-associated missense mutations in ErbB2, R143Q, R678Q, and V842I, located in the extracellular, juxtamembrane and kinase domains, respectively, to determine how single amino acid substitutions affect receptor organization and dynamics. Using confocal microscopy, Förster resonance energy transfer (FRET) and fluorescence recovery after photobleaching (FRAP), we assessed homo- and heteroassociation, lateral diffusion and tyrosine phosphorylation of ErbB2 cancer-associated variants either alone or co-expressed with EGFR. The only individual biophysical property that differentiated the mutation-activated ErbB2 variants from the wild-type was an accelerated diffusion in the absence of EGFR coexpression. Correlative analysis of the biophysical properties of ErbB2 revealed that ErbB2-activating mutations, including the cancer-associated R143Q, R678Q and V842I mutations, may promote a pre-dimerized receptor state associated with accelerated lateral mobility, but without full-scale activation implied by the lack of growth factor-independent tyrosine phosphorylation. The faster mobility of mutation-activated ErbB2 contrasted with the EGF-induced slowing down of its lateral diffusion. In summary, single amino acid substitutions across ErbB2 domains may modulate receptor dynamics, organization, and responsiveness.

Hydrogen is a key pillar in the transition to carbon neutrality, with green hydrogen production through electrolysis playing a vital role in decarbonizing industry, transport, and heating. Since electrolysis generates significant waste heat, capturing and reusing this energy provides an underused renewable source for district heating networks (DHNs), improving overall energy efficiency and economic feasibility. This study reviews projected hydrogen demand and electrolysis capacities for 2030 and up to 2050 across IEA DHC member countries to assess country-specific waste heat potentials. A stakeholder survey (n = 71) involving district heating (DH) operators, electrolyser operators, researchers, and policymakers identifies key barriers and opportunities for integration into DHNs, providing a basis for targeted policy recommendations. The analysis revealed that waste heat from alkaline electrolysis (AEL) and proton exchange membrane electrolysis (PEM-EL) at low to moderate temperatures can serve as a valuable heat source for low-temperature DHNs. Integration with conventional DHNs, which operate at higher supply temperatures, would require temperature upgrading, such as the use of supplementary heat pumps. Main implementation barriers include the need for additional infrastructure and high investment costs, proximity constraints between electrolysers and DHNs, limited awareness of the concept, and regulatory hurdles. Future strategic coordination between the hydrogen production and DH sectors, supported by targeted infrastructure planning, financial mechanisms, and regulatory frameworks, is essential to realize this potential and advance the decarbonization of the heating sector. • Country-specific projections of electrolysis capacity developments. • Quantification of waste heat potential by country. • Stakeholder survey identifying barriers and integration opportunities. • Evidence-based policy guidance to enable district heating integration.

Adhesively bonded single lap joints are common structures in lightweight design. Despite several advantages, damage initiation and propagation are critical and can be evaluated with structural health monitoring methods. This research combines analytical models and finite element analyses (FEAs) with acoustic emission (AE) measurements to predict the released mechanical energy (RME) during damage evolution. Two geometries, either with both adherends of constant thickness or with one constant and one tapered adherend, are evaluated. One adherend consists of carbon fibre reinforced polymer, the other one of additively manufactured titanium. The work input during quasi-static loading for the overlap region is calculated based on the longitudinal load and displacement, and based on the longitudinal, lateral, and bending components. For the calculation of the RME, pristine and damaged load–displacement curves are compared. The pristine trends are represented by the extrapolation of an experiment, or an analytical model, or a FEA without failure model. Whereas, the damaged trends are either given by the experimental measurements or a FEA with included failure model. Furthermore, the RME can also be gathered directly from the FEA with failure model. By linking the AE energy with the RME, the RME of a validation sample can be predicted.

Abstract Despite the increasing interest in phase change actuators for soft robotics and wearables, a systematic understanding of how their performance changes with size is still missing. Most existing designs are scaled empirically, without clear validation of whether force output, response time, and efficiency scale predictably with geometry. In this study, we investigate the scalability in size of PCAs by experimentally comparing actuator diameters ranging from 7 to 28 mm, targeting dimensions relevant for wearable and compact robotic applications.
We analyze the influence of actuator size on response time, force generation, and mechanical output using different working fluids and heating power configurations. Our approach is based on the hypothesis that power input and actuation force should scale proportionally with liquid volume and surface area to allow for an equitable comparison. Accordingly, we evaluate whether two reduced-size actuators, each operating at half the volume and half the power, can collectively reproduce the performance of a single larger actuator.
Our results reveal distinct scaling-related trade-offs between actuation speed and force efficiency, enabling a direct and quantitative comparison across sizes. Based on these findings, we introduce a compact PCA-based pumping concept and demonstrate its applicability in untethered McKibben muscles, worm-like robots, and wearable systems. This work provides a structured framework for the size-optimized design of liquid–gas phase change actuators and supports their integration into miniaturized soft robotic and assistive technologies.

Abstract Functional ability, an important health measure—especially in aging societies—is measured through limitations in activities of daily living (ADL) and instrumental activities of daily living (IADL). We use panel data from four waves of the Survey of Health, Ageing, and Retirement in Europe (SHARE) conducted between 2011 and 2022 to analyse trends in the prevalence of functional ability in Austria and ten other European countries. Our results show a stagnation or modest decline in functional limitations among the Austrian elderly, and a clear downward trend in other European countries. These findings contrast with earlier evidence from the Austrian Health Interview Survey (ATHIS), which suggested rapidly rising disability rates—possibly reflecting survey mode effects rather than worsening functional ability. Overall, our results indicate that, at a given age, Austrians and other Europeans are experiencing increasingly healthy ageing, implying that rising longevity does not necessarily extend the period lived with disability.

Large Language Models (LLMs) increasingly shape how people access, produce, and reason with information. These models do not merely generate language, they mirror the data, discourse, and cognitive patterns on which they are trained. As a result, they often reflect and amplify existing social and cognitive biases, and even stereotypical representations, influencing what information is surfaced, how perspectives are represented, and which voices are privileged or silenced. Understanding these reflections requires going beyond technical bias detection toward examining how bias emerges in LLM outputs, how users perceive and react to it, and how design choices can reinforce or mitigate biased interactions. REFLECT offers a human-centered exploration of bias in LLMs, connecting perspectives from computer science, human–computer interaction, and cognitive psychology. REFLECT examines multiple ways in which bias manifests in LLMs (from selection effects to acquiescence and stereotypical associations) and discusses what these manifestations reveal about the interaction between human data, model training, and generative processes, also exploring interaction and design strategies that can help make such reflections visible and open to critical interpretation. Designed as a half-day session, REFLECT provides a concise yet reflective introduction to understanding bias as an emergent property of LLMs. By the end, participants will be equipped with conceptual and practical tools to identify and interpret how LLMs reflect biases, fostering more transparent, accountable, and trustworthy human–AI interactions.