Die Erstellung des Leitbildes für Corporate Social Responsibility der österreichischen Wirtschaft (CSR-Austria Leitbild). Process-review und lessons learned.

Series: Research Paper Series of the Research Focus Managing Sustainabilit

Developing Effective Measures for Reduction of the Urban Heat Island based on Urban Climate Model Simulations and Stakeholder Cooperation

The climate change projections for the Austrian cities indicate that the observed warming trend, including frequent occurrences of extreme heat events, is expected to continue in the coming decades. Due to the Urban Heat Island (UHI) effect, caused by modification of energy balance in the built-up environment, the cities are warmer than their rural surroundings and therefore more exposed to negative impacts of climate change. During prolonged heat wave events, the excess in heat combined with reduced night-time cooling, decreased ventilation and possible air pollution can cause severe health impacts on the urban population. Developing measures for reduction of the UHI effect is important in the context of sustainable urban development and climate sensitive urban planning. Number of counteracting measures such as increase in vegetation, green open spaces, green roofs, unsealing of paved surfaces, decreasing absorption of solar radiation by increasing the reflectiveness of buildings and paved surfaces, are considered in the scope of climate change adaptation strategies. Nevertheless, the effectiveness of these measures, as well as their applicability in the existing urban structure, especially in the densely-built environments is not well known. Moreover, the expected cooling effects need to be quantified and the possible application should be communicated and appropriately planned with the relevant stakeholders in order to anticipate a large-scale implementation. This study investigates the effective methods for application of climate adaptation measures to reduce the UHI effect in a densely built-up environment on an example of the residential and business district of Jakomini in the city of Graz/Styria. The current local climate conditions are simulated with the urban climate model MUKLIMO_3 of the German Weather Service (DWD) using meteorological, geomorphological and land use data from the city of Graz. The simulations with altered land use characteristics corresponding to application of different UHI counteracting measures are calculated and compared to the reference simulation. The gradual increase in green areas, existing potential for green roofs implementation, modification in reflectivity of roofs and façades as well as unsealing of paved surfaces is considered. The resulting difference in heat load is evaluated as the potential cooling effect for the area of the Jakomini district and its surroundings. Based on the model results, a set of measures with optimal climatic impact is identified in close cooperation with the city’s planning department and in accordance with already existing concepts, plans and projects. This information is communicated with the relevant stakeholder groups both from private and public sectors to get their commitment to definitely undertake measures in the test-district. Considering the respective interests and role of action of different stakeholder groups a set of target measures is selected for further technical, financial and administrative planning of implementation. The study is supported by the Austrian Research Promotion Agency (FFG) and the Climate and Energy Fund (KLIEN) within the Smart Cities project “JACKY_cool_check” (Project Nr. 855554)

Quantifying the Potential of Photonic Cooling to Improve Urban Microclimate

The observed warming trend in regional climate is expected to continue in the future, aggravating urban heat load as extreme temperatures are amplified in cities due to the urban heat island (UHI) effect. Beside causing negative health effects and reducing human comfort, this development results in an increase in urban air conditioning (AC) usage, again negatively influencing the outdoor urban microclimate due to AC waste heat emission. As cities are continiously growing (the population of e.g. Vienna increased more than 10% over the past 10 years), more and more people are affected by this additional anthropogenic heating of the urban canyon. The Viennese trend away from individual motorized traffic such as cars and towards the use of public transport, walking and cycling further leaves increased numbers of inhabitants directly exposed to excessive heat loads, highlighting the need for innovative solutions to counteract this problem. The exploratory project ‘Photonic Cooling’, funded by the Austrian Research Promotion Agency through the ‘City of the Future’ program, aims at evaluating the potential of practical and cost-effective photonic cooling techniques for the cooling of buildings. The use of the photonic cooling technology instead of conventional AC systems minimizes anthropogenic heat emissions resulting from building cooling, hence minimizing the UHI development due to AC heat release and improving the quality of life of the urban population as a result. This paper focusses on the quantification of the potential of photonic cooling to improve the urban microclimate using Vienna as a case study. To estimate the future development of the UHI, the resulting changes in cooling demand and its effect on urban temperatures, a modelling approach is used. Simulations with the MUKLIMO_3 urban climate model are performed for the city of Vienna to determine changes in urban temperature for the 2021-2050 period relative to the 1971-2000 period. These results are then used as input for an empirical model to determine future cooling demand in terms of AC electricity use in buildings. Based on existing studies for other cities a relation between AC heat release and city temperature increase is established. Combining this with the modelled future cooling demand quantifies the influence from conventional AC systems on the urban microclimate, illustrating the benefit of using passive photonic cooling techniques to cover cooling demands instead

Theoretical analysis of electronic processes occurring during ultrafast demagnetization of cobalt triggered by X-ray photons tuned to Co L3_3 resonance

Magnetization dynamics triggered with ultrashort laser pulses has been attracting significant attention, with strong focus on the dynamics excited by VIS/NIR pulses. Only recently, strong magnetic response in solid materials induced by intense X-ray pulses from free-electron lasers (FELs) has been observed. The exact mechanisms that trigger the X-ray induced demagnetization are not yet fully understood. They are subject of on-going experimental and theoretical investigations. Here, we present a theoretical analysis of electronic processes occurring during demagnetization of Co multilayer system irradiated by X-ray pulses tuned to L$_3$-absorption edge of cobalt. We show that, similarly as in the case of X-ray induced demagnetization at M-edge of Co, electronic processes play a predominant role in the demagnetization until the pulse fluence does not exceed the structural damage threshold. The impact of electronic processes can reasonably well explain the available experimental data, without a need to introduce the mechanism of stimulated elastic forward scattering.Comment: 10 pages, 4 figures (7 panels), 57 references; pdfRevTeX class; double column formatting; two appendices and 18 references added; author-created version submitted to and accepted in Physical Review B journal. arXiv admin note: text overlap with arXiv:2202.1384

Increasing efficiency of preclinical research by group sequential designs.

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain.German Federal Ministry of Education and Research (BMBF) www.bmbf.de (grant number 01EO1301)

Appointing Women to Boards: Is There a Cultural Bias?

Companies that are serious about corporate governance and business ethics are turning their attention to gender diversity at the most senior levels of business (Institute of Business Ethics, Business Ethics Briefing 21:1, 2011). Board gender diversity has been the subject of several studies carried out by international organizations such as Catalyst (Increasing gender diversity on boards: Current index of formal approaches, 2012), the World Economic Forum (Hausmann et al., The global gender gap report, 2010), and the European Board Diversity Analysis (Is it getting easier to find women on European boards? 2010). They all lead to reports confirming the overall relatively low proportion of women on boards and the slow pace at which more women are being appointed. Furthermore, the proportion of women on corporate boards varies much across countries. Based on institutional theory, this study hypothesizes and tests whether this variation can be attributed to differences in cultural settings across countries. Our analysis of the representation of women on boards for 32 countries during 2010 reveals that two cultural characteristics are indeed associated with the observed differences. We use the cultural dimensions proposed by Hofstede (Culture’s consequences: International differences in work-related values, 1980) to measure this construct. Results show that countries which have the greatest tolerance for inequalities in the distribution of power and those that tend to value the role of men generally exhibit lower representations of women on boards

Interoperability in a Heterogeneous Team of Search and Rescue Robots

Search and rescue missions are complex operations. A disaster scenario is generally unstructured, time‐varying and unpredictable. This poses several challenges for the successful deployment of unmanned technology. The variety of operational scenarios and tasks lead to the need for multiple robots of different types, domains and sizes. A priori planning of the optimal set of assets to be deployed and the definition of their mission objectives are generally not feasible as information only becomes available during mission. The ICARUS project responds to this challenge by developing a heterogeneous team composed by different and complementary robots, dynamically cooperating as an interoperable team. This chapter describes our approach to multi‐robot interoperability, understood as the ability of multiple robots to operate together, in synergy, enabling multiple teams to share data, intelligence and resources, which is the ultimate objective of ICARUS project. It also includes the analysis of the relevant standardization initiatives in multi‐robot multi‐domain systems, our implementation of an interoperability framework and several examples of multi‐robot cooperation of the ICARUS robots in realistic search and rescue missions

Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets

AbstractIn recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes.We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing samplesizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any axemined scal