River environmental decision support system development for Suzhou Creek in Shanghai.

The Suzhou Creek Rehabilitation Project (SCRP) is one of the largest water-related environmental rehabilitation schemes ever undertaken in the vicinity of Shanghai, China. This paper details the development and application of a River Environmental Decision Support System (REDSS) for scientific planning and decision-making on the Suzhou Creek project, and illustrates the flexibility of the REDSS framework. We developed the following components: (1) a GIS-based analysis employing Component technology; (2) a "data mart" for multi-dimensional, multi-level, integrated, dynamic, and flexible data querying; and (3) a set of hydrodynamic and water quality models which can simulate complex tidal river networks. In addition, we detail how a water quality assessment model is embedded into the REDSS by employing an Identification Index Method. With the REDSS, all GIS and non-GIS components are integrated seamlessly and data from different sources can be queried simultaneously. This allows for various scenarios to be simulated and analyzed in advance to predict and assess the effects of proposed engineering and management measures. Generated information can thus support effective decisions. All operations of the REDSS can be implemented conveniently through user-friendly interfaces. The function of the REDSS framework is demonstrated through an application to Suzhou Creek. Because the REDSS characteristics are quite general, it may be applied in different geographic regions.

Metal-organic frameworks with a three-dimensional ordered macroporous structure: dynamic photonic materials.

Tuning MOFs: When a metal-organic framework (MOF) with an ordered three-dimensional macroporous structure is integrated into a film, the resulting materials have an additional optical element, which can be used as a general and effective signal transducer. This, in combination with the hierarchical pore structure, makes these films interesting dynamic photonic materials with potential applications in sensors.

Coalition-structured governance improves cooperation to provide public goods.

While the benefits of common and public goods are shared, they tend to be scarce when contributions are provided voluntarily. Failure to cooperate in the provision or preservation of these goods is fundamental to sustainability challenges, ranging from local fisheries to global climate change. In the real world, such cooperative dilemmas occur in multiple interactions with complex strategic interests and frequently without full information. We argue that voluntary cooperation enabled across overlapping coalitions (akin to polycentricity) not only facilitates a higher generation of non-excludable public goods, but it may also allow evolution toward a more cooperative, stable, and inclusive approach to governance. Contrary to any previous study, we show that these merits of multi-coalition governance are far more general than the singular examples occurring in the literature, and they are robust under diverse conditions of excludability, congestion of the non-excludable public good, and arbitrary shapes of the return-to-contribution function. We first confirm the intuition that a single coalition without enforcement and with players pursuing their self-interest without knowledge of returns to contribution is prone to cooperative failure. Next, we demonstrate that the same pessimistic model but with a multi-coalition structure of governance experiences relatively higher cooperation by enabling recognition of marginal gains of cooperation in the game at stake. In the absence of enforcement, public-goods regimes that evolve through a proliferation of voluntary cooperative forums can maintain and increase cooperation more successfully than singular, inclusive regimes.

Integration of multi-technology on oil spill emergency preparedness.

This paper focuses on the integration of technologies including Case-Based Reasoning (CBR), Genetic Algorithm (GA) and Artificial Neural Network (ANN) for establishing emergency preparedness for oil spill accidents. In CBR, the Frame method is used to define case representation, and the HEOM (Heterogeneous Euclidean-Overlap Metric) is improved to define the similarity of case properties. In GA, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation, in which technologies include the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, the Multi-Factor Integrated Fitness Function, and Niche technology for genetic operations including selection, crossover, and mutation. In ANN, a modified back-propagation algorithm is employed to train the algorithm to quickly improve system preparedness. Through the analysis of 32 fabricated oil spill cases, an oil spill emergency preparedness system based on the integration of CBR, GA and ANN is introduced. In particular, the development of ANN is presented and analyzed. The paper also discusses the efficacy of our integration approach.