Bridging the Climate Information Gap: A Framework for Engaging Knowledge Brokers and Decision Makers in State Climate Assessments

Large-scale analyses like the National Climate Assessment (NCA) contain a wealth of information critical to national and regional responses to climate change but tend to be insufficiently detailed for action at state or local levels. Many states now engage in assessment processes to meet information needs for local authorities. The goals of state climate assessments (SCAs) should be to provide relevant, actionable information to state and local authorities, and to generate primary sources, build networks and inform stakeholders. To communicate local climate impacts to decision makers, SCAs should express credibility, salience and legitimacy. They can provide information (e.g., case studies, data sets) and connect stakeholders to the NCA and its process. Based on our experience in the Vermont Climate Assessment (VCA), we present a framework to engage decision makers in SCAs using a fluid network of scientific experts and knowledge brokers to conduct subject area prioritization, data analysis and writing. The VCA addressed economic, environmental and social impacts of climate change at local scales to increase resiliency and manage risk. Knowledge brokers communicated VCA findings through their own stakeholder networks. We include a qualitative impact evaluation, and believe our framework for interaction among scientists, knowledge brokers and stakeholders to be an effective structure for SCAs and a transformative experience for students

Minimalist Self-Organization in Wireless Networks

Many fields of human endeavour, such as biology and the theory of complex systems, are now embracing the concept of self-organization based on local actions leading to a desirable global emergent behavior. While many examples, both natural and artificial, can be found of such self-organized systems, the relationship between the local rules and the global behavior remains elusive and no systematic procedure is known to engineer a specific global result. Given the increasing pervasiveness of wireless networks of all sorts, including ad hoc networks competing within narrow unlicensed bands and wireless sensor networks, self-organization could constitute the next defining paradigm in wireless communications. It can be shown that a set of heuristic principles can be leveraged to engineer a self-organized connection-oriented wireless network with minimal complexity. Such a system requires no centralization of information, yet achieves a nearly optimal global state with only a modest amount of local signaling. It will naturally and jointly balance the many parameters related to radio resource management, exhibiting great adaptability, fault tolerance and scalability

Photovoltaic research and development in Japan

The status of the Japanese photovoltaic (PV) R&D activities was surveyed through literature searches, private communications, and site visits in 1982. The results show that the Japanese photovoltaic technology is maturing rapidly, consistent with the steady government funding under the Sunshine Project. Two main thrusts of the Project are: (1) completion of the solar panel production pilot plants using cast ingot and sheet silicon materials, and (2) development of large area amorphous silicon solar cells with acceptable efficiency (10 to 12%). An experimental automated solar panel production plant rated at 500 kW/yr is currently under construction for the Sunshine Project for completion in March 1983. Efficiencies demonstrated by experimental large are amorphous silicon solar cells are approaching 8%. Small area amorphous silicon solar cells are, however, currently being mass produced and marketed by several companies at an equivalent annual rate of 2 MW/yr for consumer electronic applications. There is no evidence of an immediate move by the Japanese PV industry to enter extensively into the photovoltaic power market, domestic or otherwise. However, the photovoltaic technology itself could become ready for such an entry in the very near future, especially by making use of advanced process automation technologies

The bicycle-train travellers in the Netherlands: personal profiles and travel choices

The Netherlands seems to exhibit the unique conditions that allow cycling on the country level instead of only the city level. Moreover, the national transit system seemingly provides one crucial condition: citizens use the train and cycling systems in an integrated manner, with combined bicycle-train transport recently demonstrating strong growth. Relatively little is known about bicycle-train users, i.e. the people who combine the bicycle and the train in a single trip. In this paper, we investigate their profiles and travel choices, in terms of the modes they choose for access and egress travel, their choice of stations, and their choice of type of bicycles. Studying this specific group can add to our understanding of the role of the train system in the success of cycling in the Netherlands, in turn helping improve policy transfer to metropolitan areas in other countries. In 2017, in cooperation with the Dutch National Railways, researchers surveyed a sample of train travellers, ultimately resulting in more than 3000 completed questionnaires. Descriptive analyses revealed that, compared to train travellers who do not or rarely cycle to/from train stations, bicycle-train users are on average more likely to be young people who are engaged in full-time employment or entrepreneurs, commute to work and hold university degrees. As for their cycling behaviour, bicycle-train travellers use bicycles much more often on the home-end of train trips than on the activity-end. Furthermore, bicycle-train travellers infrequently use suburban stations on the home-end, preferring large stations in the centres of major cities instead. For those who use bicycles, shared bicycles claim a considerable share on the activity-end of a train trip

Incorporating Remotely Sensed Data into Coastal Hydrodynamic Models: Parameterization of Surface Roughness and Spatio-Temporal Validation of Inundation Area

This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness. A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation. The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM. Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient. In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model\u27s ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas. Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition

Economics for ecology ISCS'2012

Demand for electricity in Iraq has been stimulated by a growing economy and increasing number of population. In addition, electricity is subsidized in Iraq, which leads to increased demand. Nowadays the output of electricity sector in Iraq averages more than 8500 MW, while the demand is typically more than 14000 MW. Energy deficit in Iraq increased since 2003, when in the war was destroyed electricity network When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2645

Decentralized Synergetic Control of Power Systems

The objective of this dissertation is to design decentralized controllers to enhance the transient stability of power systems. Due to the nonlinearities and complexities of the system, nonlinear control design techniques are required to improve its dynamic performance. In this dissertation a synergetic control technique is being proposed to design supplementary controller that is added to the exciter of the generation unit of the system. Although this method has been previously applied to a Single Infinite Machine Bus (SMIB) system with high degree of success, it has not been employed to systems with multi machine. Also, the method has good robust characteristic like that of the popular Sliding Mode Control (SMC) technique. But the latter technique introduces steady state chattering effect which can cause wear and tear in actuating system. This gives the proposed technique a major advantage over the SMC. In this work, the method is employed for systems with multi machine. Each of the machines is considered to be a subsystem and decentralized controller is designed for each subsystem. The interconnection term of each subsystem with the rest of the system is estimated by a polynomial function of the active power generated by the subsystem. Particle Swarm Optimization (PSO) technique is employed for optimum tuning of the controller\u27s parameters. To further enhance the performance of the system by widening its range of operation, Reinforcement Learning (RL) technique is used to vary the gains of the decentralized synergetic supplementary controller in real time. The approach is illustrated with several case studies including a SMIB system with or without a Static Var Compensator (SVC), a Two Area System (TAS) with or without an SVC, a three --machines-nine-bus system and a fifty machine system. Results show that the proposed control technique provides better damping than the conventional power system stabilizers and synergetic controllers with fixed gains