HMD - Praxis der Wirtschaftsinformatik - Smart City

Der Begriff Smart City oder Ubiquitous City bezeichnet die Nutzung von Informations- und Kommunikationstechnologien in Städten und Agglomerationen, um den sozialen und ökologischen Lebensraum nachhaltig zu entwickeln. Dazu zählen z.B. Projekte zur Verbesserung der Mobilität, Nutzung intelligenter Systeme für Wasser- und Energieversorgung, Förderung sozialer Netzwerke, Erweiterung politischer Partizipation, Ausbau von Entrepreneurship, Schutz der Umwelt sowie Erhöhung von Sicherheit und Lebensqualität. Das Themenheft widmet sich der Vielfalt dieser webbasierten Entwicklungen und berichtet über erste Erfahrungen von Pionierprojekten aus den folgenden Anwendungsfeldern: Smart Mobility, Smart Energy, Smart Economy, Smart Environment, Smart Governance, Smart Participation und Smart Living. Das Heft soll dazu dienen, den State of the Art der intelligenten Nutzung von Webtechnologien für den urbanen Raum aufzuzeigen, um damit Chancen und Risiken aufzudecken.

Smart Hybrid Polymers for Advanced Damascene Electroplating: Combination of Superfill and Leveling Properties

A successful bottom-up fill of single Damascene test features is achieved by using a two-component additive package consisting of bis-(sodium-sulfopropyl)-disulfide (SPS) and Imep polymers (polymerizates of imidazole and epichlorohydrin). In addition, a remarkable leveling effect is observed. Clearly, the Imep additive combines bottom-up fill capabilities with leveling characteristics in one single polymer component. These unique hybrid properties of the Imep are rationalized on the basis of an extended N-NDR (N-shaped negative differential resistance) being present in the linear-sweep voltammogram of the SPS/Imep additive system during Cu electrodeposition.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Comment on “Low time resolution analysis of polar ice cores cannot detect impulsive nitrate events” by D.F. Smart et al.

Smart et al. (2014) suggested that the detection of nitrate spikes in polar ice cores from solar energetic particle (SEP) events could be achieved if an analytical system with sufficiently high resolution was used. Here we show that the spikes they associate with SEP events are not reliably recorded in cores from the same location, even when the resolution is clearly adequate. We explain the processes that limit the effective resolution of ice cores. Liquid conductivity data suggest that the observed spikes are associated with sodium or another nonacidic cation, making it likely that they result from deposition of sea salt or similar aerosol that has scavenged nitrate, rather than from a primary input of nitrate in the troposphere. We consider that there is no evidence at present to support the identification of any spikes in nitrate as representing SEP events. Although such events undoubtedly create nitrate in the atmosphere, we see no plausible route to using nitrate spikes to document the statistics of such events.