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The ability to produce early guaranteed performance (worst-case execution time) estimates for multicores, i.e. before software from different providers gets integrated onto the same critical system, is pivotal. This helps reducing lately-detected costly-to-handle timing violations. An existing methodology creates 'copy' (surrogate) applications from the execution in isolation of each target application. Surrogate applications can be used to upperbound multicore contention delay, and hence WCET estimates in multicores. However, this methodology has only been shown to work on a simulation environment. In this paper we show the work we have carried out to adapt this technology to a real multicore processor for the space domain. ; The research leading to this work has received funding from the European Union's H2020 programme under grant agreement No 644080 (SAFURE), by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella and Enrico Mezzetti have been partially sup-ported by MINECO under Ramon y Cajal and Juan de la Cierva-Incorporaci´on postdoctoral fellowships number RYC-2013-14717 and IJCI-2016-27396 respectively. ; Peer Reviewed ; Postprint (author's final draft)

The ability to produce early guaranteed performance (worst-case execution time) estimates for multicores, i.e. before software from different providers gets integrated onto the same critical system, is pivotal. This helps reducing lately-detected costly-to-handle timing violations. An existing methodology creates 'copy' (surrogate) applications from the execution in isolation of each target application. Surrogate applications can be used to upperbound multicore contention delay, and hence WCET estimates in multicores. However, this methodology has only been shown to work on a simulation environment. In this paper we show the work we have carried out to adapt this technology to a real multicore processor for the space domain. ; The research leading to this work has received funding from the European Union's H2020 programme under grant agreement No 644080 (SAFURE), by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella and Enrico Mezzetti have been partially sup-ported by MINECO under Ramon y Cajal and Juan de la Cierva-Incorporaci´on postdoctoral fellowships number RYC-2013-14717 and IJCI-2016-27396 respectively. ; Peer Reviewed ; Postprint (author's final draft)

An ambitious and innovative integrated land use-transport modeling system has been developed in Oregon over the past two decades. This work, completed under the Transportation and Land Use Model Integration Program (TLUMIP), included the development of two generations of models and the data required to build and use them and spawned the development of two others that have continued independently. An outreach program and collaborative development of freight data and forecasts were also included, as well as system testing and applications. A brief description of the motivation behind TLUMIP and the resulting modeling systems are presented. Perhaps more interesting is the story behind the models, describing several major model design, institutional, and methodology issues that were overcome. Using an integrated model in practice also entailed addressing a wider range of analytical requirements and stakeholder expectations about usability, accuracy, and extensibility than typically considered in academic pursuits. The key lessons learned through development and use of the models are discussed, with the hope that they will inform the development of similar large-scale modeling systems.

Urban and regional models have been developed for different policy fields at different levels of spatial and temporal resolution. But it has become apparent that policies interact across space and time and need to be modelled together. The first urban and regional models were aggregate in space and comparative-static in time. More recently, new data sources and computing techniques have stimulated ever more disaggregation in space and time culminating in agent-based, activity-based microsimulation despite its significant even larger data needs, computing requirements and theoretical problems. This paper argues for models that are instead multi-level and multi-scale in space, time and subsystems. This paper starts with a brief history of urban models and the experience of the authors with the highly integrated urban microsimulation model ILUMASS. Based on this experience, it discusses the benefits and pitfalls of microsimulation and proposes a three-level model system of spatial development, ranging from the European to the local level. The paper closes with new challenges for urban models posed by climate change, energy scarcity, new social problems and new technologies and argues that they make multi-level, multi-scale models even more important and illustrates this by ongoing work with the multi-level model for cities in the Ruhr.

Several European countries have recently introduced, or are planning to introduce, citizenship trajectories (voluntary or obligatory inclusion programmes for recent immigrants) or citizen integration tests (tests one should pass to be able to acquire permanent residence or state citizenship). Authors such as Joppke claim that this is an articulation of a more general shift towards the logic of assimilation (and away from a multicultural agenda) in integration policy paradigms of European states. Integration policies would even be converging in such a fashion that it would no longer make sense to think in terms of national models for immigrant integration. The empirical fact of diffusion of civic integration policies throughout Europe cannot be denied. This paper claims that there is, however, still sufficient distinctiveness between immigrant integration policies in order to continue and use an analytical framework that distinguishes national models.

Danish legislation aims to integrate handicapped people as much as possible. Every regional authority must offer rehabilitation; teaching; and support in the home, the school, and the workplace. The Syncentralen in Vordingborg, the Vordingborg model, offers aid to people of all ages. This center provides expert advice, it teaches low vision people, and it tests special equipment for such people. Decentralization makes recent developments in the field available to more people. Needs are registered more precisely so that research and development is focused.

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available data

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available data