Enabling Solar Power with Residential Water Heater Load-Shifting

As governments and international organizations aim to increase renewable energy production in response to climate change, there is a growing demand for energy storage. A temporal mismatch in the daily profiles of solar energy production and residential energy use causes an imbalance between generation and demand. Load shifting presents a key grid control strategy that minimizes this mismatch and the resulting rapid ramp rates in fossil fuel-based electricity generation. Water heaters are a principal contributor to residential electricity consumption from the grid, and they offer a good potential for thermal energy storage due to low ambient energy loss and water’s high specific heat capacity. Load shifting of water heating loads maximizes self-consumption of solar power in residential buildings thereby improving homeowner utility costs and the ability of the electric grid to accept greater levels of intermittent solar generation. Water heating load data was generated using the U.S. Department of Energy Residential Prototype Building Models for a cycling 50-gallon (189 L) electric water heater in a single-family house in Los Angeles with a roof-mounted solar photovoltaic system. The effects of load shifting on the fraction of the water-heating loads to the total house electric loads, the fraction of the water heating load met by solar power, and end-user electricity bills using time-of-use (TOU) electricity rates were analyzed. This study shows that shifting the load of water heaters to coincide with the solar profile lowers electricity costs significantly more than a solar photovoltaic system without load shifting which sends power to the grid using a net metering agreement. The results suggest that widespread use of smart water heaters that optimize load shifting by predicting hot water consumption and the solar profile could present an important value for the integration of renewable energy onto the grid

Estimating Population Abundance Using Sightability Models: R SightabilityModel Package

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys (Steinhorst and Samuel 1989). Estimation proceeds in 2 stages: (1) Sightability trials are conducted with marked individuals, and logistic regression is used to estimate the probability of detection as a function of available covariates (e.g., visual obstruction, group size). (2) The fitted model is used to adjust counts (from future surveys) for animals that were not observed. A modified Horvitz-Thompson estimator is used to estimate abundance: counts of observed animal groups are divided by their inclusion probabilites (determined by plot-level sampling probabilities and the detection probabilities estimated from stage 1). We provide a brief historical account of the approach, clarifying and documenting suggested modifications to the variance estimators originally proposed by Steinhorst and Samuel (1989). We then introduce a new R package, SightabilityModel, for estimating abundance using this technique. Lastly, we illustrate the software with a series of examples using data collected from moose (Alces alces) in northeastern Minnesota and mountain goats (Oreamnos americanus) in Washington State

Used-habitat calibration plots: a new procedure for validating species distribution, resource selection, and step-selection models

“Species distribution modeling” was recently ranked as one of the top five “research fronts” in ecology and the environmental sciences by ISI's Essential Science Indicators (Renner and Warton 2013), reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity. Identifying habitat characteristics that are not well-predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations

Generalized Functional Responses for Species Distributions

Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches

A ‘How to’ Guide for Interpreting Parameters in Habitat-Selection Analyses

Habitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modelling of both movement and habitat-selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat-selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step-selection analyses using the AMT package By providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users

An Historical Overview and Update of Wolf-Moose Interactions in Northeastern Minnesota

Wolf (Canis lupus) and moose (Alces americanus) populations in northeastern Minnesota, USA, have fluctuated for decades and, based on helicopter counts, moose numbers declined to a new low from 2006 to about 2012. Other steep declines were found in 1991 and 1998 during periods when moose counts were done with fixed-wing aircraft; these declines also appeared to be real. Winter wolf numbers, monitored in part of the moose range, had been increasing since about 2002 to the highest population in decades in 2009. However, from 2009 to 2016, wolves decreased precipitously, and the moose- population decline leveled off from 2012 to 2017. Calf:population ratios from 1985 to 1997 and from 2005 to 2016 were inversely related to wolf numbers in the wolf-study area the previous winter both as wolves increased and decreased in abundance. Similarly, log annual growth rates of moose numbers were negatively correlated with counts of wolves in the prior year. Other factors such as nutrition and parasites, and possibly climate change, likely have been involved in the recent moose decline. However, wolves, as in other areas, appear to have contributed to the decline in the northeastern Minnesota moose population at least in part through predation on calves, supporting earlier reports

Проект установки отмывки электролитического порошка титана от фторидных солей электролита производительностью 30 кг/час

Выпускная квалификационная работа в форме дипломного проекта 118 с., 24 рис., 23 табл., 46 источников, 1 приложение. Ключевые слова: электролиз, катодный осадок, отмывка. Цель работы – спроектировать цех для проведения процесса отмывки титанового порошка. Основные конструктивные, технологические и технико-эксплуатационные характеристики: Объем и габаритные размеры основного аппарата составляют: V = 0,25 м3, D = 0,8 м, H = 1,8 м. Степень внедрения: проект находится на стадии разработки. Область применения: Химическая технология редких металлов. Экономическая эффективность/значимость работы: экономический эффект – снижение себестоимости титанового порошка и увеличение прибыли.Die Abschlußqualifikationsarbeit in Form vom Diplomprojekt 118 mit., 24 Abb., 23 Tabellen, 46 Quellen, 1 Anlage. Die Stichwörter: die Elektrolyse, die Kathodenablagerung, das Abwaschen. Das Ziel der Arbeit – die Abteilung für die Verwirklichung des Prozesses des Abwaschens des Titanpulvers zu entwerfen. Die Haupt- konstruktiven, technologischen und techniko-Betriebscharakteristiken: der Umfang und die Ladeumfänge des Hauptapparates bilden: V = 0,25 м3, D = 0,8 m, H = 1,8 m Die Stufe der Einführung: das Projekt befindet sich auf dem Stadium der Entwicklung. Das Gebiet der Anwendung: die Chemische Technologie der seltenen Metalle. Die Wirtschaftseffektivität/Bedeutsamkeit der Arbeit: der Nutzeffekt – die Selbstkostenverringerung des Titanpulvers und die Vergrößerung des Gewinnes

Establishing the link between habitat selection and animal population dynamics

Although classical ecological theory (e.g., on ideal free consumers) recognizes the potential effect of population density on the spatial distribution of animals, empirical species distribution models assume that species–habitat relationships remain unchanged across a range of population sizes. Conversely, even though ecological models and experiments have demonstrated the importance of spatial heterogeneity for the rate of population change, we still have no practical method for making the connection between the makeup of real environments, the expected distribution and fitness of their occupants, and the long-term implications of fitness for population growth. Here, we synthesize several conceptual advances into a mathematical framework using a measure of fitness to link habitat availability/selection to (density-dependent) population growth in mobile animal species. A key feature of this approach is that it distinguishes between apparent habitat suitability and the true, underlying contribution of a habitat to fitness, allowing the statistical coefficients of both to be estimated from multiple observation instances of the species in different environments and stages of numerical growth. Hence, it leverages data from both historical population time series and snapshots of species distribution to predict population performance under environmental change. We propose this framework as a foundation for building more realistic connections between a population's use of space and its subsequent dynamics (and hence a contribution to the ongoing efforts to estimate a species' critical habitat and fundamental niche). We therefore detail its associated definitions and simplifying assumptions, because they point to the framework's future extensions. We show how the model can be fit to data on species distributions and population dynamics, using standard statistical methods, and we illustrate its application with an individual-based simulation. When contrasted with nonspatial population models, our approach is better at fitting and predicting population growth rates and carrying capacities. Our approach can be generalized to include a diverse range of biological considerations. We discuss these possible extensions and applications to real data

A SARS-CoV-2 spike binding DNA aptamer that inhibits pseudovirus infection by an RBD-independent mechanism

The receptor binding domain (RBD) of the spike glycoprotein of the coronavirus SARS‐CoV‐2 (CoV2‐S) binds to the human angiotensin converting enzyme 2 (ACE2) representing the initial contact point for leveraging the infection cascade. We used an automated selection process and identified an aptamer that specifically interacts with CoV2‐S. The aptamer does not bind to the RBD of CoV2‐S and does not block the interaction of CoV2‐S with ACE2. Notwithstanding, infection studies revealed potent and specific inhibition of pseudoviral infection by the aptamer. The present study opens up new vistas in developing SARS‐CoV2 infection inhibitors, independent of blocking the ACE2 interaction of the virus and harnesses aptamers as potential drug candidates and tools to disentangle hitherto inaccessible infection modalities, which is of particular interest in light of the increasing number of escape mutants that are currently being reported