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Sea level rise and changing storm frequency and intensity resulting from climate change create tremendous amounts of uncertainty for coastal species. Intertidal species may be especially affected since they are dependent on daily inundation and exposure. The eastern oyster Crassostrea virginica is an economically and biologically important sessile intertidal species ranging from Canada to the Gulf of Mexico. Declines and changes in distribution of oyster populations has forced commercial harvesting to spread from subtidal to intertidal reefs. We investigated the potential responses of intertidal C. virginica to sea level rise, and the response of larval settlement to sedimentation which is likely to increase with higher water levels and storm frequency. Inundation was used as a proxy for sea level rise. We hypothesized four possible outcomes for intertidal oyster reefs as a result of changes in inundation due to sea level rise: (a) intertidal reefs become subtidal and remain in place, (b) intertidal reefs will be lost, (c) intertidal reefs migrate shoreward upslope and remain intertidal, and (d) intertidal reefs will grow in elevation and remain intertidal. To test the plausibility of these four outcomes, oyster ladders were placed at two sites within Apalachicola Bay, Florida, USA. Ladders supported oyster recruitment mats at five heights within the range of intertidal elevations. The bottom-most mat was placed near mean low tide, and the top mat near mean high tide to investigate the effect of tidal inundation time on C. virginica. Sediment traps were attached to ladders with openings at equal elevation to the oyster mats. Ladders were deployed for one year starting in June 2012, and again in June 2013, during peak oyster recruitment season. Monthly for six months during year one, sediment was collected from traps, dried to constant weight and weighed to obtain a monthly average for total sediment at each elevation. At the end of one year, oyster mats were collected from the field and examined for the ...

The uniformity of different nuclear regions as a function of the number of valence protons and neutrons (counted from the nearest closed shell) has been exploited for the parameterization of calculations for nuclei far from stability within the IBA model. Predictions are given for low lying levels, E2 transition rates, and binding energies for nuclei in the r-process path in the A = 150 and A = 190 mass regions. 6 refs., 6 figs.

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

I derive and test predictions from the classic Mussa and Rosen (1978) second-degree price discrimination model using data from the United States clothes washer market. I find evidence consistent with price discrimination in the market response to energy efficiency policy changes. Concurrent with the effective dates of both the new 2004 and 2007 federal minimum efficiency and ENERGY STAR standards, within-model clothes washer prices dropped on average. The heterogeneous pattern of price reduction across market segments, and adjustments in the menu of products, were consistent with predictions from the price-discrimination model, and not with a perfectly competitive market.

Issued as part of Scientific Papers of the Bureau of Standards, Volume 15, 1919-20. ; Running title: Computation of radiation data. ; Includes bibliographical references. ; Mode of access: Internet.

Thesis. 1977. M.Arch.A.S.--Massachusetts Institute of Technology. Dept. of Architecture. ; MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. ; Bibliography : leaves 174-178. ; by Conrad Heeschen. ; M.Arch.A.S.

The Brookhaven National Laboratory pulp and paper industry process model is used to project market penetration of energy conserving technologies and purchased energy for the paper industry. The risk adjusted discount rate concept is incorporated to penalize the capital costs of unproven technologies. For this analysis, the production-cost minimization objective is relaxed to explore near-minimum cost industry alternatives. Allowing paper production to include all possibilities within 1% of the absolute minimum costs results in very significant variations in both projected market penetration levels and energy use. Industry model results relying solely on the cost minimization objective for market penetration and energy demand forecasts may be highly suspect.

Africa’s development challenges may be summed up as follows: investment in human development including nutrition, health, education, water, and sanitation; increasing agricultural productivity by smallholder farmers; attaining an adequate threshold of infrastructure: roads, railroads, energy, ports, and communications; and finally gaining access to global markets and fair trade.

Linac Energy Management (LEM) is a control system program which calculates, and optionally implements, magnet setpoint settings (BDESs) following a change in Energy (such as a change in the number, phase, and amplitude of active klystrons). The change is made relative to those magnets’ existing BDES setpoints by a factor encoding the change in energy. LEM is necessary because changes in the number, phase, and amplitude of the active klystrons (the so-called "Klystron complement") change the beam’s rigidity, and therefore, to maintain constant optics, one has to change focusing gradients and bend fields. This paper describes the basic process and some of the implementation lessons learned for LEM at the LCLS. Proceedings of the 1st International Particle Accelerator Conference, IPAC2010, Kyoto, Japan