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Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
As the potential of a next generation network (NGN) is recognised, telecommunication companies consider switching to it. Although the implementation of an NGN seems to be merely a modification of the network infrastructure, it may trigger or require changes in the whole company, because it builds upon the separation between service and transport, a flexible bundling of services to products and the streamlining of the IT infrastructure. We propose a holistic framework, structured into the layers ‘strategy’, ‘processes’ and ‘information systems’ and incorporate into each layer all concepts necessary for the implementation of an NGN, as well as the alignment of these concepts. As a first proof-of-concept for our framework we have performed a case study on the introduction of NGN in a large telecommunication company; we show that our framework captures all topics that are affected by an NGN implementation.
Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time) and describe how the result of the optimization depends on the entered remote sensing data.
A High-Throughput Functional Complementation Assay for Classification of BRCA1 Missense Variants
(2013)
A high-Q resonance-mode measurement of EIS capacitive sensor by elimination of series resistance
(2017)
An EIS capacitive sensor is a semiconductor-based potentiometric sensor, which is sensitive to the ion concentration or pH value of the solution in contact with the sensing surface. To detect a small change in the ion concentration or pH, a small capacitance change must be detected. Recently, a resonance-mode measurement was proposed, in which an inductor was connected to the EIS capacitive sensor and the resonant frequency was correlated with the pH value. In this study, the Q factor of the resonant circuit was enhanced by canceling the internal resistance of the reference electrode and the internal resistance of the inductor coil with the help of a bypass capacitor and a negative impedance converter, respectively. 1% variation of the signal in the developed system corresponded to a pH change of 3.93 mpH, which was about 1/12 of the conventional method, suggesting a better performance in detection of a small pH change.