Cities are complex socio-technical systems (STSs) under tremendous stress due to climate change. To incorporate resilience into urban plans and move towards evidence-based long-term decision-making, we must unravel complex land-use dynamics and the effect of climate uncertainties on cities. Currently, land-use dynamics are explored through Cellular Automata models to investigate the impacts of urban planning scenarios. What is, however, missing to support resilience decisions, is a systematic analysis of long-term climate uncertainties on land-use change. This study addresses this gap by analysing the effects of flood uncertainties on land-use patterns. While conventionally, urban planning decisions for climate uncertainty are based on a few scenarios, we use exploratory modeling to sample and combine uncertain climate variables to scenarios and understand the implications of the climate scenarios on land use via computational experiments. Specifically, we integrate flood probability maps into land-use maps to assess land suitability. Agglomerative clustering allows us to analyze the resulting land-use maps based on their similarity. Finally, we select representative maps from each cluster and compare them with the baseline map. We apply our integrated modeling approach in the Metropolitan Region of Amsterdam (MRA). Our results show spatially explicit alternatives for high-density residential development that is climate-resilient. The proposed framework can be applied to other cities to investigate the long-term impacts of climate uncertainties and adopt resilience-informed decision-making.@en

The retrogression and re-aging (RRA) processes, aimed mainly at tailoring intergranular precipitates, could significantly improve the corrosion resistance (i.e., stress corrosion cracking resistance) without considerably decreasing the strength, which signifies that an efficient control of the size, distribution and evolution of intergranular and intragranular precipitates becomes critical for the integrated properties of the (mid-)thick high-strength Al alloy plates. Compared to RRA process with retrogression at 200 °C (T77), this study investigated the impact of a modified RRA process (MT77) with lower retrogression temperatures (155-175 °C) and first-stage under-aging on the properties of a high-strength AA7050 Al alloy, in combination with detailed precipitate characterization. The study showed that the strength/microhardness of the RRA-treated alloys decreased with raising retrogression temperature and/or prolonging retrogression time, along with the increased electrical conductivity. The rapid responsiveness of microstructure/property typical of retrogression at 200 °C was obviously postponed or decreased by using MT77 process with longer retrogression time that was more suitable for treating the (mid-)thick plates. On the other hand, higher retrogression temperature facilitated more intragranular η precipitates, coarse intergranular precipitates and wide precipitate free zones, which prominently increased the electrical conductivity alongside a considerable strength loss as compared to the MT77-treated alloys. With the preferred MT77 process, the high strength approaching T6 level as well as good corrosion resistance was achieved. However, though a relatively homogeneous through-thickness strength was obtained, some small discrepancies of properties between the central and surface areas of an 86-mm thick 7050 Al alloy plate were observed, possibly related to the quenching sensitivity. The precipitate evolution and mechanistic connection to the properties were discussed and reviewed for high-strength Al alloys along with suggestions for further RRA optimization.

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This work demonstrates an optimization paradigm that generates noise abatement trajectories for an aircraft arriving at the designated airport. The fight mechanics and acoustic performance of a Boeing 747-400 aircraft are presented to calculate the noise levels at observation points on the ground. A method that estimates the probability of awakenings and the population distributionwithin the vicinities of the airport are used to calculate the total number of awakened residents as a reflection of the noise nuisance generated by the arriving aircraft. The infinite-dimensional optimal control problem is converted into a finite-dimensional parameter optimization problem using trajectory segmentation and state parameterization. Genetic algorithms are adopted to solve the converted parameter optimization problem. In the end, a numerical simulation is carried out and the results show that the conversion reduces the complexity of the optimization problem and genetic algorithms are capable of providing optimal trajectories. The results also show that it is possible that the searching does not converge at the globally optimal solution when it is terminated by pre-defined stopping criteria. @en

Dynamic safety analysis in chemical and process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Probability updating and adapting of stochastic events and dynamic processes, which evolve over time, are the key to dynamic safety analysis. Conventional risk assessment techniques, such as fault tree, event tree, and bow-tie analyses, have long been used for effective safety analysis of process plants; however, owing to their static characteristics, their application to dynamic safety analysis has been relatively limited. Bayesian methods, such as hierarchical Bayesian analysis and Bayesian network, are effective techniques with ample potential for application in dynamic safety analysis. This chapter is aimed at presenting the state-of-the-art application of Bayesian analysis and especially the Bayesian network method in dynamic safety analysis of process systems.@en

Improving the understanding and the monitoring of high elevation regions hydrology is of major relevance from both societal and environmental points of view for many Asian countries, in particular in terms of flood and drought, but also in terms of food security in a changing environment. Satellite and airborne remote sensing technologies are of utmost for such a challenge. Existing imaging spectro-radiometers, radars, microwave radiometers and backscatter LIDAR provide a very comprehensive suite of measurements over a wide rage of wavelengths, time frequencies and spatial resolutions. It is however needed to devise new algorithms to convert these radiometric measurements into useful eco-hydrological quantitative parameters for hydrological modeling and water management. The DRAGON II project entitled Key Eco-Hydrological Parameters Retrieval and Land Data Assimilation System Development in a Typical Inland River Basin of Chinas Arid Region (ID 5322) aims at improving the monitoring, understanding, and predictability of hydrological and ecological processes at catchment scale, and promote the applicability of quantitative remote sensing in watershed science. Existing Earth Observation platforms provided by the European Space Agency as well as prototype airborne systems developed in China - ENVISAT/AATSR, ALOS/PRISM and PALSAR, Airborne LIDAR - are used and combined to retrieve advanced land surface physical properties over high elevation arid regions of China. The existing synergies between this project, the CEOP-AEGIS project (FP7) and the WATER project (CAS) provide incentives for innovative studies. The investigations presented in the following report focus on the development of advanced and innovative methodologies and algorithms to monitor both the state and the trend of key eco-hydrological variables: 3D vegetation properties, land surface evaporation, glacier mass balance and drought indicators.@en