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Blood flow and ventilatory flow strongly influence the concentrations of volatile organic compounds (VOCs) in exhaled breath. The physicochemical properties of a compound (e.g., water solubility) additionally determine if the concentration of the compound in breath reflects the alveolar concentration, the concentration in the upper airways, or a mixture of both. Mathematical modeling based on mass balance equations helps to understand how measured breath concentrations are related to their corresponding blood concentrations and physiological parameters, such as metabolic rates and endogenous production rates. In addition, the influence of inhaled compounds on their exhaled concentrations can be quantified and appropriate correction formulas can be derived. Isoprene and acetone, two endogenous VOCs with very different water solubility, have been modeled to explain the essential features of their behavior in breath. This chapter introduces the theory of physiological modeling of exhaled VOCs, with examples of isoprene and acetone.
Although pilot projects are an accepted means of entry into prospects, research on the object of startups selling SaaS and use pilots to enter and to further scale within their prospect’s organization is limited. The reader can expect a collection of key practices of SaaS startups in the field of Decision Support Software. These combine the main sales-oriented elements within pilot projects that are reflected on by Customer Success Management, Change Management as well as cultural dimensions. Explorative interviews, mainly with stakeholders in Decision Support Software startups, were conducted to further gain an understanding of the research object. Results indicate that pilots are strategically used in the sales of such startups to simultaneously deal with their customer’s uncertainties and as a means for the startups to get commitment and increase their value proposition through the additional service that they offer in order to acquire an internal support basis. Customer Success Management as well as Change Management are furthermore advantageous in quickly achieving measurable results that leverage buyers and seller’s justification for further sales.
The advent of autonomous and self-driving cranes represents a significant advancement in industrial automation. One critical prerequisites for achieving this long-term goal is the accurate and reliable detection of tools guided by ropes in real-world environments. Since the tool is suspended by ropes, the tool pose cannot be controlled directly. This master’s thesis addresses the challenges of pose estimation for rope-guided tools using point cloud measurements. The proposed algorithm utilizes constraints imposed by the crane kinematics and information extracted during the segmentation process to efficiently infer the pose of the hook, therefore enabling the use of the pose for decision making in real-time critical applications. RANSAC (Random Sample and Consensus) is deployed in the segmentation process to extract geometric primitives from the point cloud which represent the ropes and distinctive parts of the tool. Since the point cloud is often to sparse for feature matching a bounding box is used to estimate the initial position of the tool. Two different methods are presented to improve the initial pose. A computationally expensive method with a high level of confidence, integrating the ICP (Iterative Closest Point) algorithm is used as a benchmark. A linear Kalman filter is used in the second method which is real-time capable. The benchmark is then used to evaluate the real-time capable approach. The core contributions of this research lie in the innovative utilization of bounding boxes for pose estimation. The findings and methodologies presented herein constitute an advancement towards the realization of autonomous and self-driving cranes.
This study deals with the energy situation in Ny-Ålesund, an Arctic research station on the archipelago Svalbard, and aims at analysing the technical feasability of a transition to renewable energies by taking into consideration both the environmental and climatic impediments.
The analysis is based on a 27 year long collection of authentic meteorological data with all its strong fluctuations, seasonal as well as yearly. Great emphasis was put on the discussion of tried-and-tested renewable technologies that were compared to a new wind-based energy device that has yet to be tested for its reliability in the harsh environment of notably the Arctic winter. Meticulous calculations lead to the result that bifacial solar modules are an efficient means even in months when the sun stands low and their combination with wind-based devices prove to generate a maximum output. Geothermal energy seems to be promising in the region, but could not be evaluated due to a crucial lack of relevant data.
The study comes to the conclusion that the research station of Ny-Ålesund could well rely on a combination of renewable energy devices to cover its energy load, but needs to keep a back-up system of diesel run generators to bridge short periods of possible dysfunctions or standstills due to meteorological circumstances. Battery storage could only contribute to solve the problem of an unfortunate interruption of the energy supply, but it cannot serve as the entire back-up system since, at present, the need would go beyond all possible dimensions.