State Power Plant Siting: a Sketch of the Main Features of a Possible Approach

Work on various phases of power plant technology and siting has been underway within the Environmental Quality Laboratory (EQL) at the California Institute of Technology for some time. Of particular relevance to this memorandum, a good deal of effort has been devoted to institutional aspects of the siting process. Our purpose in what follows is to draw from our past work -- and from the discussions and work of others -- a sketch of the major outlines of one possible approach to power plant siting for the state. We hope in doing so to give our present views about the issues and how they might rationally be resolved, not so much to convince as to inform, stimulate fruitful ideas, and help provide the basis for constructive debate. We ourselves are not necessarily wedded to any of the discussion that follows; we find our own minds changing from time to time as we study the problem further or confront sound suggestions from others. Part I of this memorandum briefly outlines the major features of what we see as a fruitful approach to the siting problem. Sections A through E of Part I describe some elements of the approach; Section F sketches the actual siting decision process we suggest, and in doing so shows how the elements play into the process. Section G comments briefly on a suggested role for judicial review. In Part II we attempt to reduce our ideas to a fairly precise outline for a state siting statute, and to deal with certain matters of detail not covered in Part I. Section A of Part II introduces the statutory outline by summarizing each of its provisions; Section B sets forth the outline itself. The Appendix to this memorandum depicts our suggested approach in time-line fashion; it should be helpful in reading and understanding the proposal

Interaction Between a Linearly Polarized Electromagnetic Plane and a Double Spherical Shell

An exact solution inside, outside, and within two arbitrary concentric spherical shells is presented for an impinging monochromatic linearly polarized electromagnetic wave. Specifically, the solution was found for a double-shell spherical acrylic plastic enclosure irradiated with 2450-MHz microwaves. The enclosure is used as an environmentally controlled exposure chamber for experimental animals during microwave irradiation. The analysis shows that an air foamed material, such as styrofoam, would be a better material than either Plexiglas or Teflon, provided it is sufficiently durable

Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization

An approach to support the computational aerodynamic design process is presented and demonstrated through the application of a novel multi-objective variant of the Tabu Search optimization algorithm for continuous problems to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and ultimately of the whole engine. The integrated system developed for this purpose is described. This combines the optimizer with an existing geometry parameterization scheme and a well- established CFD package. The system’s performance is illustrated through case studies – one two-dimensional, one three-dimensional – in which flow characteristics important to the overall performance of turbomachinery blades are optimized. By showing the designer the trade-off surfaces between the competing objectives, this approach provides considerable insight into the design space under consideration and presents the designer with a range of different Pareto-optimal designs for further consideration. Special emphasis is given to the dimensionality in objective function space of the optimization problem, which seeks designs that perform well for a range of flow performance metrics. The resulting compressor blades achieve their high performance by exploiting complicated physical mechanisms successfully identified through the design process. The system can readily be run on parallel computers, substantially reducing wall-clock run times – a significant benefit when tackling computationally demanding design problems. Overall optimal performance is offered by compromise designs on the Pareto trade-off surface revealed through a true multi-objective design optimization test case. Bearing in mind the continuing rapid advances in computing power and the benefits discussed, this approach brings the adoption of such techniques in real-world engineering design practice a ste

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

All-Optical Switching with Transverse Optical Patterns

We demonstrate an all-optical switch that operates at ultra-low-light levels and exhibits several features necessary for use in optical switching networks. An input switching beam, wavelength $\lambda$, with an energy density of $10^{-2}$ photons per optical cross section [$\sigma=\lambda^2/(2\pi)$] changes the orientation of a two-spot pattern generated via parametric instability in warm rubidium vapor. The instability is induced with less than 1 mW of total pump power and generates several $\mu$Ws of output light. The switch is cascadable: the device output is capable of driving multiple inputs, and exhibits transistor-like signal-level restoration with both saturated and intermediate response regimes. Additionally, the system requires an input power proportional to the inverse of the response time, which suggests thermal dissipation does not necessarily limit the practicality of optical logic devices

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Social Preferences and the Efficiency of Bilateral Exchange

Under what conditions do social preferences, such as altruism or a concern for fair outcomes, generate efficient trade? I analyze theoretically a simple bilateral exchange game: Each player sequentially takes an action that reduces his own material payoff but increases the other player’s. Each player’s preferences may depend on both his/her own material payoff and the other player’s. I identify necessary conditions and sufficient conditions on the players’ preferences for the outcome of their interaction to be Pareto efficient. The results have implications for interpreting the rotten kid theorem, gift exchange in the laboratory, and gift exchange in the field