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Energy management in distribution grids is one of the key challenges that needs to be overcome to increase the share of fluctuating renewable energies. Current control systems for energy management mainly demonstrate centralized‐ or decentralized‐hierarchical control structures. Very few systems manifest a fully decentralized multiagent‐based control structure. Multiagent‐based control systems promise to be an advantageous approach for the future distributed energy supply system because no central control entity is necessary, which eases parameterization in case of grid topology changes, and the agents are more stable against failures and changes of control topologies. Research is necessary to prove these benefits. In this study, we introduce a design of a multiagent‐based voltage control system for low‐voltage grids. In detail we introduce cooperative decision‐making processes and software solutions that allow the agents to perceive and control their environment, the agent‐discovery and localization in different types of communication networks, agent‐to‐agent communication, and the integration of the multiagent system in existing grid‐control infrastructures. Furthermore, the study proposes how different existing technologies can be combined into an applicable multiagent‐based voltage control system: the Java/OSGi‐based OpenMUC framework allows a generic field–device interaction; peer‐to‐peer discovery and session establishment functionalities are combined with the agent communication defined by the Foundation for Intelligent Physical Agents (FIPA). The ripple control‐signal technology is applied as a fallback communication between the agent and a central grid‐control center.

Mobile learning increases both flexibility and self-determined learning, often combined with a high degree of context awareness. Flexibility and context awareness includes time and location, as well as the actual individual situation. To achieve such goals, mobile learning is not just a stand-alone and independent learning environment, but is instead embedded in a broader e-learning environment. This is true for the didactic and the pedagogic concepts and the learning (content) management system, as well as the overall software architecture. XML has been proven to be adequate and a powerful technology to store content in a presentation independent manner. By defining an additional attribute inside the XML tags, it is possible to classify the content. At the same time, this will help the author generate learning material for different devices in an efficient and structured way. Also, the content can be used in different formats (XHTML, PDF, etc.) as well as with different technologies (browser, applet, MIDlet, Ajax, etc.). In order to optimise the content presentation on different mobile devices, the content has to be adapted. A necessary precondition for the adaptation process is the identification of the connected device. The classification of the identified mobile device simplifies the mapping between device capabilities and content. The ICAT (Identification, Classification, Adaptation and Tagged XML) framework addresses these issues. The proposed design patterns will help authors to generated content for such a system.

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Functional understanding of the different parts of the cardiovascular system is essential for an insight into pathomechanisms of numerous diseases. During training cardiovascular physiology, students and early-stage medical personnel should understand the role of different functional parameters for systolic and diastolic blood pressure, as well as for blood flow. The impact of isolated parameters can only be studied in models. Here physical hydraulic models are an advantage in which the students have a direct contact to the mechanical properties of the circulatory system. But these models are often difficult to handle. The aim of the present study was to develop a comprehensive model of the cardiovascular system, including a mechanical heart with valves, an elastic aorta, a more rigid peripheral artery system, a total peripheral resistance, and a venous reservoir representing the variable cardiac preload. This model allows one to vary systematically several functional parameters and to continuously record their impact on pressure and flow. This model is embedded into a computer-based teaching system (LabTutor) in which the students are guided through the handling of the model (as well as the systematic variation of parameters), and the measured data can be analyzed. This hybrid teaching system, which is routinely integrated in physiology laboratory courses of medical students, allows students to work with a complex hydraulic model of the cardiovascular system and to analyze systematically the impact of influencing variables (e.g., increased peripheral resistance or changed cardiac preload) as well as pathophysiological dysfunctions (e.g., reduced aortic compliance).