How Do Road Traffic Noise and Residential Greenness Correlate with Noise Annoyance and Long-Term Stress? Protocol and Pilot Study for a Large Field Survey with a Cross-Sectional Design

Urban areas are continuously growing, and densification is a frequent strategy to limit urban expansion. This generally entails a loss of green spaces (GSs) and an increase in noise pollution, which has negative effects on health. Within the research project RESTORE (Restorative potential of green spaces in noise-polluted environments), an extended cross-sectional field study in the city of Zurich, Switzerland, is conducted. The aim is to assess the relationship between noise annoyance and stress (self-perceived and physiological) as well as their association with road traffic noise and GSs. A representative stratified sample of participants from more than 5000 inhabitants will be contacted to complete an online survey. In addition to the self-reported stress identified by the questionnaire, hair cortisol and cortisone probes from a subsample of participants will be obtained to determine physiological stress. Participants are selected according to their dwelling location using a spatial analysis to determine exposure to different road traffic noise levels and access to GSs. Further, characteristics of individuals as well as acoustical and non-acoustical attributes of GSs are accounted for. This paper presents the study protocol and reports the first results of a pilot study to test the feasibility of the protocol

How do road traffic noise and residential greenness correlate with noise annoyance and long-term stress? Protocol and pilot study for a large field survey with a cross-sectional design

Urban areas are continuously growing, and densification is a frequent strategy to limit urban expansion. This generally entails a loss of green spaces (GSs) and an increase in noise pollution, which has negative effects on health. Within the research project RESTORE (Restorative potential of green spaces in noise-polluted environments), an extended cross-sectional field study in the city of Zurich, Switzerland, is conducted. The aim is to assess the relationship between noise annoyance and stress (self-perceived and physiological) as well as their association with road traffic noise and GSs. A representative stratified sample of participants from more than 5000 inhabitants will be contacted to complete an online survey. In addition to the self-reported stress identified by the questionnaire, hair cortisol and cortisone probes from a subsample of participants will be obtained to determine physiological stress. Participants are selected according to their dwelling location using a spatial analysis to determine exposure to different road traffic noise levels and access to GSs. Further, characteristics of individuals as well as acoustical and non-acoustical attributes of GSs are accounted for. This paper presents the study protocol and reports the first results of a pilot study to test the feasibility of the protocol

SLO-ML:A Language for Service Level Objective Modelling in Multi-cloud applications

Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow

TOSCA-MART: A Method for Adapting and Reusing Cloud Applications

To fully appreciate cloud computing powers, design and development of cloud applications should be eased and supported. The OASIS TOSCA standard enables developers to design and develop cloud applications by specifying their topologies as orchestrations of typed nodes and relationships.However, building such application topologies often results in reinventing the wheel multiple times when similar solutions are manually created for different applications by different developers having the same requirements. Thus, the reusability of existing TOSCA solutions is crucial to ease and support design and development processes. In this paper, we tackle this issue. We introduce TOSCA-MART, a method that enables deriving valid implementations for custom components from a repository of complete and validated cloud applications. The method enables developers to specify individual components in their application topologies, and illustrates how to match, adapt, and reuse existing (fragments of) applications to implement these components while fulfilling all their compliance requirements. We also characterize and validate TOSCA-MART by means of a prototypical implementation based on an open source toolchain and a case study