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In a smart city environment, we look at a new, upcoming generation of vehicles running on electric power supplied by on-board batteries. Best recharging options include charging at home, as well as charging at public areas. In this setting, electric vehicles will be informed about public charging stations using wireless communications. As the charging stations are shared resources, cooperating electric vehicles have the potential to avoid unbalanced use of recharging stations and lengthy waiting times.We present a model for electric vehicles and their battery depletion, vehicle mobility, charging stations, and give a solution for optimal placement of charging stations in a smart city. Our placement approach is based on genetic programming and simulation of electric vehicles which move on a real map of a European city. We show that after a few evolution steps, an optimal solution of the charging infrastructure is derived based on mean trip times of electric vehicles.

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Background. Family physicians often provide the first line of treatment for patients with depression. Many effective drugs are now available for the pharmacologic treatment of depression.Methods: We searched MEDLINE from 1991-96 under the topics of depressive disorders/treatment and antidepressant medications. Other sources were found by back-referencing from these references and from pharmacology texts.Results: Although antidepressants appear to be equally effective, selective serotonin reuptake inhibitors are frequently the drugs of choice because of their safety profile and less troublesome side effects.Conclusions: When prescribing antidepressant medications, the clinician must educate patients about potential side effects and about the amount of time that must be allowed for therapeutic efficacy. Drug interactions and concurrent medical conditions are important factors in the choice of an antidepressant.

Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not “realistic” for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.