Interplay of porous media and fracture stimulation in sedimentary enhanced geothermal systems : Red River Formation, Williston Basin, North Dakota

Fracture stimulated enhanced geothermal systems (EGS) can be installed in both crystalline rocks and sedimentary basins. The Red River Formation (Ordovician), which lies between 3.6 and 4.2 km depth in the Williston Basin, is a viable site for installation of sedimentary EGS (SEGS). SEGS is possible there because temperatures in the formation surpass 140° Celsius and the permeability is 0.1-38 mD; fracture stimulation can be utilized to improve performance. The main objectives of this project were 1) to determine the spatial variation of the intrinsic properties of the Red River Formation across the study area, and 2) to understand the natural fracture orientation/location in the subsurface of the study area. Maps of the intrinsic properties of the Red River Formation-- including depth to the top of the formation, depth to the bottom of the formation, porosity, heat flow, geothermal gradient, and temperature-- were produced by the Kriging interpolation method in ArcGIS. A GIS and geostatistical analysis was completed to show that there is a satisfactory correlative relationship between the surface lineaments and the basement faults in the study area. Consequently, the orientations and locations of the surface lineaments and basement faults were combined in a shapefile to represent the area’s discrete fracture network. In the future, the results of these two analyses can be utilized to create a reservoir simulation model of an SEGS in the Red River Formation; the purpose of this model would be to ascertain the thermal response of the reservoir to fracture stimulation

Impacting Adherence to Infant Safe Sleep Practice

This paper provides a complete microsystem assessment of a pediatric unit. The assessment includes identification of a process problem with infant safe sleep practice in the hospital setting. The paper explores six different published articles regarding clinical practice guidelines, best practices, and evidence-based interventions. The literature review indicates agreement amongst various authors following infant safe sleep guidelines in the hospital setting. The literature review also indicates that caregiver role modeling while in the hospital setting is the most influential component that affects parents’ practices upon return to the home setting. The clinical project will include baseline needs assessments of both bedside caregivers in the hospital setting and parents, then coaching sessions with bedside caregivers will occur. Baseline safe sleep audit data will also be collected. Concurrently, a product analysis will take place to address the identified barrier of storage space. A cost benefit analysis will show that the crib pockets would save the health system money, and possibly save an infant’s life. A post assessment will occur to measure effectiveness of coaching sessions. Safe sleep audit data will be graphed to indicate effectiveness of the interventions

The Gremlin Graph Traversal Machine and Language

Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph $G$, a traversal $\Psi$, and a set of traversers $T$. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the $\Psi$ of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.Comment: To appear in the Proceedings of the 2015 ACM Database Programming Languages Conferenc

Taxing the Poor: State Income Tax Policies Make a Big Difference to Working Families

An NCCP analysis of state tax policy finds that a significant number of states continue to push the working poor deeper into poverty by imposing income tax liabilities on poverty-level earnings–liabilities that in some states reach hundreds of dollars. In contrast to states that tax the poor, a growing number of states offer supports to poor families through refundable tax credits, providing income supplements that that can reach up to almost two thousand dollars per family

Non-geometric tilt-to-length coupling in precision interferometry: mechanisms and analytical descriptions

This paper is the second in a set of two investigating tilt-to-length (TTL) coupling. TTL describes the cross-coupling of angular or lateral jitter into an interferometric phase signal and is an important noise source in precision interferometers, including space gravitational wave detectors like LISA. We discussed in 10.1088/2040-8986/ac675e the TTL coupling effects originating from optical path length changes, i.e. geometric TTL coupling. Within this work, we focus on the wavefront and detector geometry dependent TTL coupling, called non-geometric TTL coupling, in the case of two interfering fundamental Gaussian beams. We characterise the coupling originating from the properties of the interfering beams, i.e. their absolute and relative angle at the detector, their relative offset and the individual beam parameters. Furthermore, we discuss the dependency of the TTL coupling on the geometry of the detecting photodiode. Wherever possible, we provide analytical expressions for the expected TTL coupling effects. We investigate the non-geometric coupling effects originating from beam walk due to the angular or lateral jitter of a mirror or a receiving system. These effects are directly compared with the corresponding detected optical path length changes in 10.1088/2040-8986/ac675e. Both together provide the total interferometric readout. We discuss in which cases the geometric and non-geometric TTL effects cancel one-another. Additionally, we list linear TTL contributions that can be used to counteract other TTL effects. Altogether, our results provide key knowledge to minimise the total TTL coupling noise in experiments by design or realignment

Wide-Field Multi-Parameter FLIM: Long-Term Minimal Invasive Observation of Proteins in Living Cells.

Time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) is a remarkable tool to monitor the dynamics of fluorophore-tagged protein domains inside living cells. We propose a Wide-Field Multi-Parameter FLIM method (WFMP-FLIM) aimed to monitor continuously living cells under minimum light intensity at a given illumination energy dose. A powerful data analysis technique applied to the WFMP-FLIM data sets allows to optimize the estimation accuracy of physical parameters at very low fluorescence signal levels approaching the lower bound theoretical limit. We demonstrate the efficiency of WFMP-FLIM by presenting two independent and relevant long-term experiments in cell biology: 1) FRET analysis of simultaneously recorded donor and acceptor fluorescence in living HeLa cells and 2) tracking of mitochondrial transport combined with fluorescence lifetime analysis in neuronal processes

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.Peer reviewe