Research Studios Austria

The 2021 Census Bureau American Community Survey estimates that there are 55.9 million people ages 65 + residing across the fifty states. Although over 70% of people ages 55 + prefer to age-in-place and live independently, 13 million adults have difficulties living independently and need to receive assistance at home. However, only half of these homes meet the physical needs of people who choose to age-in-place. The use of technology and development of smart homes can support ageing-in-place and independent living. The current study used principles from a constructive grounded theory to analyze 67 interviews of people ages 62–92 to assess how older adults prefer to utilize technology in their homes. The results presented that nearly 85% of the participants are willing to learn and use technology in their homes when the benefits are related to their safety, health and wellbeing, daily chores, and social and emotional connection.

Abstract Linnaean taxonomy, which imposes hierarchical classifications based on morphological characteristics, has become deeply embedded in modern data architecture, from databases to metadata schemas to AI training datasets. With its hierarchical structure and rigid categorization, Linnaean taxonomy privileges one type of knowledge while marginalizing alternative taxonomies that offer more fluid, contextual, and relational understandings of the natural world. This paper examines how the legacy of Linnaean taxonomy continues to shape contemporary classification systems and artificial intelligence (AI). Indigenous knowledge systems, which include spiritual, cultural, and ecological dimensions, view entities not as isolated objects but as nodes in dynamic, interconnected networks. We draw from the French naturalist, Comte de Buffon, who, in line with Indigenous knowledge systems, viewed nature as continuous and contextual rather than discretely compartmentalized. The dominance of Linnaean-style classification in AI and data systems perpetuates colonial power dynamics and contributes to knowledge homogenization while losing Indigenous languages and classification systems crucial for addressing contemporary environmental challenges, particularly in agriculture and biodiversity conservation. In this Age of AI, we call for a holistic and ecological approach to archives. Therefore, we propose applying ‘rhizomatic hylomorphism,’ an ethnobiological, alternative classification that transcends hierarchical taxonomies to embrace multiplicity, relationality, and contextual meaning.

Abstract Intentional mistuning (IM) has turned out to be an effective measure to alleviate the maximum forced response of bladed wheels in the framework of numerous studies in the past. In particular, solutions based on two different blade designs, following, e.g., alternating or AABB patterns, have proved to be promising in this regard and moreover robust against the impact of unavoidable random mistuning. Thus, for example, a 40% reduction of the first blade bending maximum forced response has been proved experimentally for a turbine impeller of a turbocharger application. Despite this success, the technical implementation of the frequency-based mistuning pattern followed an academic solution based on locally removing material at the leading edge tip, which is not suited for the use in serial wheels since it may disturb the flow channel. In addition, the forced response of other blade modes may be affected in a negative manner. In order to overcome these problems, an alternative way of implementing intentional mistuning is suggested by applying a marginal geometric modification of the blade thickness distribution to adjust the natural frequency of the first bending mode. Finite element-based shape optimization is utilized to this end. Secondary conditions are ensuring that only the target frequency of the first bending mode is adjusted, whereas natural frequencies of other modes are kept almost unchanged.

Global climate change is a significant environmental concern, and reducing greenhouse gas emissions is crucial to mitigating this issue. Moreover, there is a need to exploit a prediction-based method to assess the future requirements of applications and (re-)schedule them with the aim of reducing completion time and energy consumption. Therefore, we consider the stochastic requirements of users and investigate an Energy-aware Prediction-based scheduling of dataflow processing on the cloud, fog, and edge method, named EPreMatch, for microservice scaling by applying a machine learning (ML) model based on gradient boosting regression (GBR) and scheduling due to ranking and matching game principles. Firstly, EPreMatch predicts the number of microservice replicas using GBR. Then, the ranking method orders the microservice replicas and devices based on completion times and energy consumption. Thereafter, the EPreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis reveals lower completion times, energy consumption, and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission compared to a related prediction-based scheduling method.

Underground hydrogen storage (UHS) has attracted increasing attention as a promising technology for the large-scale storage of renewable energy resources and the decarbonization of energy systems. This study aimed to identify critical parameters influencing UHS performance, particularly the role of hydrogen conversion via in situ methanation and hydrogen recovery during production cycles. The main focus is the Lehen field in Upper Austria, where a pilot hydrogen storage project was conducted under the leadership of RAG Austria AG. A layered reservoir model was developed on the basis of well-log data to simulate the field trials that occurred in 2016. A sensitivity analysis was performed with the one-parameter-at-a-time (OPAAT) method and the response surface methodology (RSM) to evaluate the impacts of different parameters on hydrogen methanation and hydrogen recovery. The RSM results indicate the activation energy as the most influential factor on methanation that accounts for ∼20,000 moles variation in generated methane, significantly higher than the 6000 moles variance observed in OPAAT. However, initial CO2 content contributes up to 15,000 moles of methane generation as per RSM, whereas OPAAT results in a larger impact of up to 32,000 moles. These discrepancies demonstrate the limitations of isolated parameter analyses like OPAAT, which may not accurately capture the complex interactions between factors influencing the methanation process. This research provides valuable insights for optimizing UHS performance by emphasizing the influence of reservoir parameters on storage efficiency. In addition, a robust workflow for conducting comprehensive sensitivity analyses of UHS systems is established. By understanding these key factors, the potential and predictability of large-scale UHS systems can be significantly improved. • Large-scale underground hydrogen storage assessment in a depleted gas reservoir. • Sensitivity analysis of the methanation process in underground hydrogen storage. • Analysis of the one-parameter-at-a-time (OPAAT) method and the response surface methodology (RSM). • Real-world application from the Sun Storage project in Upper Austria. • Identification of critical parameters controlling the methanation process.

ABSTRACT Salzburg Airport, as Austria's second largest, plays a crucial role in connecting international tourists to the region's prominent winter and summer tourist destinations. This study investigates the spatial and seasonal distribution of international tourists in the Province of Salzburg and its implications for public transport connectivity between the airport and tourist destinations, focusing on promoting sustainable mobility. Leveraging spatial analysis techniques and geographic information systems (GIS), the research categorises tourist flows and identifies key spatial-temporal patterns over a one-year analysis period. The study employs GIS to visualise and analyse international tourists’ overnight stay data using space-time data cubes, shedding light on spatial dynamics and mobility patterns. The findings contribute to a deeper understanding of tourist mobility patterns considering seasonal trends and their implications for sustainable transportation planning between the airport and the various tourism destinations. By integrating spatial analysis with tourism demand assessment, this research provides valuable guidance for enhancing public transport infrastructure and promoting environmentally friendly travel options in Salzburg and beyond.

Living in the city is associated with a higher risk of suffering from stress, anxiety, and depression. Due to an increase of migration to the city, the association between mental health and city life is highly relevant to society. We analyzed data of 9573 participants (Ø 55.3 years, SD = 7.4) of the Paracelsus 10,000 study (Salzburg, Austria) who were classified into having or not having depressive symptoms. Population density, green space, and noise around the home address of the participants were collected and tested for correlations with mental health defined by depressive symptoms. We additionally tested whether migration status influenced the effect of urbanization on mental health. There is a positive correlation between degree of urbanization and the probability of suffering from depressive symptoms (p = 0.011), yet this effect is independent of the migration background (p = 0.581). Participants in areas with high residential density were significantly more likely to suffer from poor mental health (p = 0.006 unadjusted). No significant association could be found between mental health and noise (p = 0.126 unadjusted) or green space neither regarding distance to closest green space (p = 0.549 unadjusted), nor size of green space (p = 0.549 unadjusted). In the Paracelsus 10,000 cohort, living in the city is associated with lower mental health, especially in participants with a high population density in the direct neighborhood. This might be due to social stress yet does not reflect minority stress in migrants. However, the influence of noise pollution and green space on mental health is limited in this cohort.

To encourage the uptake of AI in industrial use cases, tools are required to support the engineering process throughout the life cycle of the AI application that is central to the use cases. Providing guidance on using AI in industrial setups is vital for creating trustworthy, reliable, and ethically compliant AI-based solutions. The related standardization landscape and available guideline repositories are large, scattered over the web, change rapidly, and are hard to keep up with. This limits the access and ease of use of these standards and guidelines. To address these limitations, we propose developing a self-assessment tool, empowered through AI algorithms and models such as Large Language Models, to improve the process of accessing and benefiting from those standards and guidelines. This self-assessment tool will support various user groups while engineering their applications by identifying the most applicable guidelines according to the individual attributes of the specific user group. We argue that modeling specific attributes and mapping appropriate controls for self-assessments could be achieved by applying AI-based technologies. This paper outlines our ongoing efforts concerning the suggested supporting tools, offering a human-centric methodology. Additionally, we present initial results demonstrating how the needs of a particular user group can be accurately modeled. The results of this study will be used for applications that are deploying AI in an industrial setting with the objective of enabling the two most important goals of Industry 5.0, which are the well-being of workers at the center of the production process, and sustainable and resilient industries.

LongEval-Retrieval is a Web document retrieval benchmark that focuses on continuous retrieval evaluation. This test collection is intended to be used to study the temporal persistence of Information Retrieval systems and will be used as the test collection in the Longitudinal Evaluation of Model Performance Track (LongEval) at CLEF 2023. This benchmark simulates an evolving information system environment - such as the one a Web search engine operates in - where the document collection, the query distribution, and relevance all move continuously, while following the Cranfield paradigm for offline evaluation. To do that, we introduce the concept of a dynamic test collection that is composed of successive sub-collections each representing the state of an information system at a given time step. In LongEval-Retrieval, each sub-collection contains a set of queries, documents, and soft relevance assessments built from click models. The data comes from Qwant, a privacy-preserving Web search engine that primarily focuses on the French market. LongEval-Retrieval also provides a 'mirror' collection: it is initially constructed in the French language to benefit from the majority of Qwant's traffic, before being translated to English. This paper presents the creation process of LongEval-Retrieval and provides baseline runs and analysis.