Solar energy apparatus with apertured shield

A protective apertured shield for use about an inlet to a solar apparatus which includesd a cavity receiver for absorbing concentrated solar energy. A rigid support truss assembly is fixed to the periphery of the inlet and projects radially inwardly therefrom to define a generally central aperture area through which solar radiation can pass into the cavity receiver. A non-structural, laminated blanket is spread over the rigid support truss in such a manner as to define an outer surface area and an inner surface area diverging radially outwardly from the central aperture area toward the periphery of the inlet. The outer surface area faces away from the inlet and the inner surface area faces toward the cavity receiver. The laminated blanket includes at least one layer of material, such as ceramic fiber fabric, having high infra-red emittance and low solar absorption properties, and another layer, such as metallic foil, of low infra-red emittance properties

Owen County Transportation Vulnerability Study

In this session we discuss the Transportation Vulnerability Assessment the Indiana Department of Natural Resources and the Polis Center completed for Owen County, Indiana. The results were presented to state and community officials in a public meeting held on December 7, 2015. A focus analysis of the vulnerable assets will be performed, including developing engineering hydraulic models for the site, flood depth grids, fluvial erosion mapping, and cost estimates for suggested mitigation

Control of a Realistic Wave Energy Converter Model using Least-Squares Policy Iteration

PublishedThis is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via the DOI in this record.An algorithm has been developed for the resistive control of a non-linear model of a wave energy converter using least-squares policy iteration, which incorporates function approximation, with tabular and radial basis functions being used as features. With this method, the controller learns the optimal PTO damping coefficient in each sea state for the maximization of the mean generated power. The performance of the algorithm is assessed against two on-line reinforcement learning schemes: Q-learning and SARSA. In both regular and irregular waves, least-squares policy iteration outperforms the other strategies, especially when starting from unfavourable conditions for learning. Similar performance is observed for both basis functions, with a smaller number of radial basis functions underfitting the Q-function. The shorter learning time is fundamental for a practical application on a real wave energy converter. Furthermore, this work shows that least-squares policy iteration is able to maximize the energy absorption of a wave energy converter despite strongly non-linear effects due to its model-free nature, which removes the influence of modelling errors. Additionally, the floater geometry has been changed during a simulation to show that reinforcement learning control is able to adapt to variations in the system dynamics.This work was supported partly by the Energy Technologies Institute and the Research Councils Energy Programme (grant EP/J500847/), partly by the Engineering and Physical Sciences Research Council (grant EP/J500847/1), and partly by Wave Energy Scotland

Authors' Response

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97166/1/jfo12080.pd

Intermediate filament–membrane attachments function synergistically with actin-dependent contacts to regulate intercellular adhesive strength

By tethering intermediate filaments (IFs) to sites of intercellular adhesion, desmosomes facilitate formation of a supercellular scaffold that imparts mechanical strength to a tissue. However, the role IF–membrane attachments play in strengthening adhesion has not been directly examined. To address this question, we generated Tet-On A431 cells inducibly expressing a desmoplakin (DP) mutant lacking the rod and IF-binding domains (DPNTP). DPNTP localized to the plasma membrane and led to dissociation of IFs from the junctional plaque, without altering total or cell surface distribution of adherens junction or desmosomal proteins. However, a specific decrease in the detergent-insoluble pool of desmoglein suggested a reduced association with the IF cytoskeleton. DPNTP-expressing cell aggregates in suspension or substrate-released cell sheets readily dissociated when subjected to mechanical stress whereas controls remained largely intact. Dissociation occurred without lactate dehydrogenase release, suggesting that loss of tissue integrity was due to reduced adhesion rather than increased cytolysis. JD-1 cells from a patient with a DP COOH-terminal truncation were also more weakly adherent compared with normal keratinocytes. When used in combination with DPNTP, latrunculin A, which disassembles actin filaments and disrupts adherens junctions, led to dissociation up to an order of magnitude greater than either treatment alone. These data provide direct in vitro evidence that IF–membrane attachments regulate adhesive strength and suggest furthermore that actin- and IF-based junctions act synergistically to strengthen adhesion

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

A comparative study of arbitration algorithms for the Alpha 21364 pipelined router

Interconnection networks usually consist of a fabric of interconnected routers, which receive packets arriving at their input ports and forward them to appropriate output ports. Unfortunately, network packets moving through these routers are often delayed due to conflicting demand for resources, such as output ports or buffer space. Hence, routers typically employ arbiters that resolve conflicting resource demands to maximize the number of matches between packets waiting at input ports and free output ports. Efficient design and implementation of the algorithm running on these arbiters is critical to maximize network performance.This paper proposes a new arbitration algorithm called SPAA (Simple Pipelined Arbitration Algorithm), which is implemented in the Alpha 21364 processor's on-chip router pipeline. Simulation results show that SPAA significantly outperforms two earlier well-known arbitration algorithms: PIM (Parallel Iterative Matching) and WFA (Wave-Front Arbiter) implemented in the SGI Spider switch. SPAA outperforms PIM and WFA because SPAA exhibits matching capabilities similar to PIM and WFA under realistic conditions when many output ports are busy, incurs fewer clock cycles to perform the arbitration, and can be pipelined effectively. Additionally, we propose a new prioritization policy called the Rotary Rule, which prevents the network's adverse performance degradation from saturation at high network loads by prioritizing packets already in the network over new packets generated by caches or memory.Mukherjee, S.; Silla Jiménez, F.; Bannon, P.; Emer, J.; Lang, S.; Webb, D. (2002). A comparative study of arbitration algorithms for the Alpha 21364 pipelined router. ACM SIGPLAN Notices. 37(10):223-234. doi:10.1145/605432.605421S223234371

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10⁶ 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org