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Spontaneous language has rarely been subjected to neuroimaging studies. This study therefore introduces a newly developed method for the analysis of linguistic phenomena observed in continuous language production during fMRI.
Most neuroimaging studies investigating language have so far focussed on single word or — to a smaller extent — sentence processing, mostly due to methodological considerations. Natural language production, however, is far more than the mere combination of words to larger units. Therefore, the present study aimed at relating brain activation to linguistic phenomena like word-finding difficulties or syntactic completeness in a continuous language fMRI paradigm. A picture description task with special constraints was used to provoke hesitation phenomena and speech errors. The transcribed speech sample was segmented into events of one second and each event was assigned to one category of a complex schema especially developed for this purpose. The main results were: conceptual planning engages bilateral activation of the precuneus. Successful lexical retrieval is accompanied – particularly in comparison to unsolved word-finding difficulties – by the left middle and superior temporal gyrus. Syntactic completeness is reflected in activation of the left inferior frontal gyrus (IFG) (area 44). In sum, the method has proven to be useful for investigating the neural correlates of lexical and syntactic phenomena in an overt picture description task. This opens up new prospects for the analysis of spontaneous language production during fMRI.
The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated
structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.