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As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But historical records are unreliable and constant re-assessment of active residential PV locations is resource intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning set up based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available dataset, but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids.

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks.

Computational fluid dynamics (CFD)-discrete element method (DEM) simulations are designed to model a pseudo-two-dimensional (2D) fluidized bed, in which bed thickness is minimal compared to height and length. Predicted bed behavior varies as the simulations are conducted on increasingly refined computational grids. Pseudo-2D simulation results, in which a single computational cell spans the bed thickness, are compared against fully-three-dimensional (3D) simulations results. Both pseudo-2D and fully-3D simulations exhibit high accuracy when sufficiently refined. Indicators of bed behavior, such as bed height, bed height fluctuation, bubble generation frequency, and segregation, do not appear to converge as the cell size is reduced. The Koch-Hill and Gidaspow drag laws are alternately employed in the simulations, resulting in different trends of results with computational grid refinement. Grid refinement studies are used to quantify the change in results with grid refinement for both three-dimensional, uniform refinement, and for two-dimensional refinement on pseudo-2D computational grids. Grid refinement study results indicate the total drag converges as the computational grid is refined, for both 3D and pseudo-2D approaches. The grid refinement study results are also used to distinguish the relatively grid-independent results using the Koch-Hill drag law from the highly grid-dependent Gidaspow drag law results. Computational cell size has a significant impact on CFD-DEM results for fluidized beds, but the grid refinement study method can be used to quantify the resulting numerical error.

Urban vacant lots are often a contentious feature in cities, seen as overgrown, messy eyesores that plague neighborhoods. We propose a shift in this perception to locations of urban potential, because vacant lots may serve as informal greenspaces that maximize urban biodiversity while satisfying residents’ preferences for their design and use. Our goal was to assess what kind of vacant lots are ecologically valuable by assessing their biotic contents and residents’ preferences within a variety of settings. We surveyed 150 vacant lots throughout Baltimore, Maryland for their plant and bird communities, classified the lot’s setting within the urban matrix, and surveyed residents. Remnant vacant lots had greater vegetative structure and bird species richness as compared to other lot origins, while vacant lot settings had limited effects on their contents. Residents preferred well-maintained lots with more trees and less artificial cover, support of which may increase local biodiversity in vacant lots. Collectively, we propose that vacant lots with a mixture of remnant and planted vegetation can act as sustainable urban greenspaces with the potential for some locations to enhance urban tree cover and bird habitat, while balancing the needs and preferences of city residents.

Raman spectroscopy provides valuable information on the physicochemical properties of hard tissues. While the technique can analyze tissues in their native state, analysis of fixed, embedded, and sectioned specimens may be necessary on certain occasions. The information on the effects of fixatives and embedding media on Raman spectral properties is limited. We examined the effect of ethanol and glycerol as fixatives and a variety of embedding media (Araldite, Eponate, Technovit, glycol methacrylate, polymethyl methacrylate, and LR white) on Raman spectral properties (mineralization, crystallinity, and carbonation) measured from the cortical bone of mouse humeri. Humeri were fixed in ethanol or glycerol, followed by embedding in one of the media. Nonfixed, freeze-dried, and fixed but not embedded sections were also examined. Periosteal, endosteal, and midosteal regions of the intracortical envelope were analyzed. Raman spectra of fixative solutions and embedding media were also recorded separately in order to examine the specifics of overlap between spectra. We found significant effects of fixation, embedding, and anatomical location on Raman spectral properties. The interference of ethanol with tissue seemed to be relatively less pronounced than that of glycerol. However, there was no single combination of fixation and embedding that left Raman spectral parameters unaltered. We conclude that careful selection of a fixation and embedding combination should be made based on the parameter of interest and the type of tissue. It may be necessary to process multiple samples from the tissue, each using a combination appropriate for the Raman parameter in question.

The detected variety in chemistry and circumstellar shell morphology of the limited sample of Galactic post-AGB stars is so large that there is no consensus yet on how the different objects are linked by evolutionary channels. The evaluation is complicated by the fact that their distances and hence luminosities remain largely unknown. Via cross-correlation of the Spitzer SAGE catalogue with optical catalogues we selected a sample of LMC post-AGB candidates based on their [8]-[24] colour index and estimated luminosity. We determined the fundamental properties of the central stars of 105 of these objects using low-resolution, optical spectra that we obtained at Siding Spring Observatory and SAAO, and constructed a catalogue of 70 high probability and 1337 candidate post-AGB stars that is available at the CDS. The sample forms an ideal testbed for stellar evolution theory predictions of the final phase of low- and intermediate-mass stars, because the distance and hence luminosity and also the current and initial mass of these objects is well constrained. About half of the objects in our sample of post-AGB candidates show a spectral energy distribution (SED) that is indicative of a disc rather than an expanding and cooling AGB remnant. Like in the Galaxy, the disc sources are likely associated with binary evolution. Important side products of this research are catalogues of candidate young stellar objects, candidate supergiants with circumstellar dust, and discarded objects for which a spectrum was obtained. These too are available at the CDS.

Abstract Building energy consumption is vulnerable to climate change due to the direct relationship between outside temperature and space cooling/heating. This work quantifies how the relationship between climate change and building energy consumption varies across a range of building types at different spatiotemporal scales based on estimates in 925 U.S. locations. Large increases in building energy consumption are found in the summer (e.g., 39% increase in August for the secondary school building), especially during the daytime (e.g., >100% increase for the warehouse building, 5–6 p.m.), while decreases are found in the winter. At the spatial scale of climate-zones, annual energy consumption changes range from −17% to +21%, while at the local scale, changes range from −20% to +24%. Buildings in the warm-humid (Southeast) climate zones show larger changes than those in other regions. The variation of impact within climate zones can be larger than the variation between climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. The large variations found in the relationship between climate change and building energy consumption highlight the importance of assessing climate change impacts at local scales, and the need for adaptation/mitigation strategies tailored to different building types.

Climate change has been linked to distribution shifts and population declines of numerous animal and plant species, particularly in montane ecosystems. The majority of studies suggest both that low-elevation avian and small mammal species are shifting up in elevation and that high-elevation avian communities are either shifting further upslope or relocating completely with an increase in average local temperatures. However, recent research suggests numerous high elevation montane species are either not shifting or are shifting down in elevation despite the local increasing temperature trends, perhaps as a result of the increased precipitation at high elevations. In this study, we examine common vertebrate species distributions across the Hubbard Brook valley in the White Mountain National Forest, including resident and migratory songbirds and small mammals, in relation to historic spring temperature and precipitation. We found no directional change in distributions through time for any of the species. However, we show that the majority of low-elevation bird species in our study area respond to warm spring temperatures by shifting upslope. All bird species that shifted were long-distance migrants. Each low-elevation migrant species responded differently to warm spring temperatures, through upslope distribution expansion, downslope distribution contraction, or total distribution shift upslope. In contrast, we found a majority of high-elevation bird species and both high- and low-elevation mammal species did not shift in response to spring temperature or precipitation and may be subject to more complex climate trends. The heterogeneous response to climate change highlights the need for more comprehensive studies on the subject and careful consideration for appropriate species and habitat management plans in northeastern montane regions.