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Diversity is increasingly being addressed as an innovation-promoting factor. For this reason, companies and institutions tackle the integration of a diversity management approach that enables a heterogenic perspective on innovation development. However, system-theoretical frameworks state that the implementation of diversity measures that are not tailored to the needs of the organization often leads to a rejection or reactivity with regard to the management approach. In this context, especially organizations, which are characterized by a specific hierarchical structure, a dominant habitus or specialist culture, must face the challenge of realizing a sustainable change of the corporate culture that sets the basis for implementing diversity management approaches. The presented research project focuses on analyzing the situation in a huge scientific collaborative project - so called Cluster of Excellence (CoE) - with the aim to implement a diversity - and innovation management strategy. Considering the influencing determinants, the CoE is characterized by its embeddedness in the scientific system, a complex organizational structure, and a high fluctuation rate. The paper presents a systemic approach of reflecting these factors in order to develop a diversity- and innovation management strategy. In this frame, the results of a quantitative survey of CoE employees and derived mindset-types are presented. The results show a need for taking different mindset-types into account, to be able to develop a tailored management strategy. The aim of the project is to give recommendations for developing a sustainable management concept that promotes both diversity and innovation by drawing on the persisting mindsets of organization members while reflecting top down as well as bottom up factors of implementation processes as well as the psychology of change. This paper addresses all who are concerned with the management of human resources in innovation processes and are striving for a cultural change within the framework of complex organizations.
The results of a statistical investigation of 42 fixed-wing, small to medium sized (20 kg−1000 kg) reconnaissance unmanned air vehicles (UAVs) are presented. Regression analyses are used to identify correlations of the most relevant geometry dimensions with the UAV’s maximum take-off mass. The findings allow an empirical based geometry-build up for a complete unmanned aircraft by referring to its take-off mass only. This provides a bridge between very early design stages (initial sizing) and the later determination of shapes and dimensions. The correlations might be integrated into a UAV sizing environment and allow designers to implement more sophisticated drag and weight estimation methods in this process. Additional information on correlation factors for a rough drag estimation methodology indicate how this technique can significantly enhance the accuracy of early design iterations.
Wind loads have great impact on many engineering structures. Wind storms often cause irreparable damage to the buildings which are exposed to it. Along with the earthquakes, wind represents one of the most common environmental load on structures and is relevant for limit state design. Modern wind codes indicate calculation procedures allowing engineers to deal with structural systems, which are susceptible to conduct wind-excited oscillations. In the codes approximate formulas for wind buffeting are specified which relate the dynamic problem to rather abstract parameter functions. The complete theory behind is not visible in order to simplify the applicability of the procedures. This chapter derives the underlying basic relations of the spectral method for wind buffeting and explains the main important applications of it in order to elucidate part of the theoretical background of computations after the new codes. The stochasticity of the wind processes is addressed, and the analysis of analytical as well as measurement based power spectra is outlined. Short MATLAB codes are added to the Appendix 3 which carry out the computation of a single sided auto-spectrum from a statistically stationary, discrete stochastic process. Two examples are presented.