Predictors of Insect Diversity and Abundance in a Fragmented Tallgrass Prairie Ecosystem

Despite providing many services, the tallgrass prairie and its ecological community is one of the most endangered ecosystems in North America. Remaining habitat exists as remnants in a highly-fragmented landscape. To make informed conservation decisions we need to better understand the effects of this fragmentation. Using the ecologically important insect groups, ants and ground beetles, this study provides baseline data on the biological diversity of southeast Nebraska prairies and investigates what management, landscape, and habitat characteristics affect them. Pitfall trap sampling was conducted in 23 tallgrass remnants scattered throughout the Southeast Prairies Biologically Unique Landscape in 2010 and 2011. Multi-model inference was used for analysis of the data. Twenty-eight species of ants were collected with the majority being grassland-obligates. With a positive correlation, model selection results indicate that Shannon diversity of grassland ants is best predicted by the average number of grass species per m2 while their abundance is positively associated with the amount of nearby haymeadow. Most ants belonged to the Opportunist and Cold Climate Specialist functional groups. A comparison with prior studies indicates this functional group composition to be most similar to cool-temperate forests. Though different habitats, their cooler climates likely produce this similar composition. Nineteen species of ground beetles were collected, with two species comprising nearly 95% of the collection. These two species are incapable of flight, a physiological factor that may contribute to their high abundances by leaving them hidden from predators. As with grassland ants, the strongest predictor of Shannon diversity for ground beetles was the average number of grass species per m2. Results suggest that ants and ground beetles are non-randomly distributed in relation to landscape, habitat, and management factors. High abundances of grassland-obligate ants are associated with high amounts of haymeadow suggesting these areas may be a priority for ant conservation. Results also suggest that sites with more grass species sustain more diverse communities of ants and ground beetles, information that can be incorporated into relevant conservation decisions

Determining White Noise Forcing From Eulerian Observations in the Navier Stokes Equation

The Bayesian approach to inverse problems is of paramount importance in quantifying uncertainty about the input to and the state of a system of interest given noisy observations. Herein we consider the forward problem of the forced 2D Navier Stokes equation. The inverse problem is inference of the forcing, and possibly the initial condition, given noisy observations of the velocity field. We place a prior on the forcing which is in the form of a spatially correlated temporally white Gaussian process, and formulate the inverse problem for the posterior distribution. Given appropriate spatial regularity conditions, we show that the solution is a continuous function of the forcing. Hence, for appropriately chosen spatial regularity in the prior, the posterior distribution on the forcing is absolutely continuous with respect to the prior and is hence well-defined. Furthermore, the posterior distribution is a continuous function of the data. We complement this theoretical result with numerical simulation of the posterior distribution

Cluster, Classify, Regress: A General Method For Learning Discountinous Functions

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising from simulation of plasma fusion in a tokamak.Comment: 12 files,6 figure

A Bayesian analysis of classical shadows

The method of classical shadows heralds unprecedented opportunities for quantum estimation with limited measurements [H.-Y. Huang, R. Kueng, and J. Preskill, Nat. Phys. 16, 1050 (2020)]. Yet its relationship to established quantum tomographic approaches, particularly those based on likelihood models, remains unclear. In this article, we investigate classical shadows through the lens of Bayesian mean estimation (BME). In direct tests on numerical data, BME is found to attain significantly lower error on average, but classical shadows prove remarkably more accurate in specific situations -- such as high-fidelity ground truth states -- which are improbable in a fully uniform Hilbert space. We then introduce an observable-oriented pseudo-likelihood that successfully emulates the dimension-independence and state-specific optimality of classical shadows, but within a Bayesian framework that ensures only physical states. Our research reveals how classical shadows effect important departures from conventional thinking in quantum state estimation, as well as the utility of Bayesian methods for uncovering and formalizing statistical assumptions.Comment: 8 pages, 5 figure

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant

Proteomic profiling of halloysite clay nanotube exposure in intestinal cell co-culture

Halloysite is aluminosilicate clay with a hollow tubular structure with nanoscale internal and external diameters. Assessment of halloysite biocompatibility has gained importance in view of its potential application in oral drug delivery. To investigate the effect of halloysite nanotubes on an in vitro model of the large intestine, Caco-2/HT29-MTX cells in monolayer co-culture were exposed to nanotubes for toxicity tests and proteomic analysis. Results indicate that halloysite exhibits a high degree of biocompatibility characterized by an absence of cytotoxicity, in spite of elevated pro-inflammatory cytokine release. Exposure-specific changes in expression were observed among 4081 proteins analyzed. Bioinformatic analysis of differentially expressed protein profiles suggest that halloysite stimulates processes related to cell growth and proliferation, subtle responses to cell infection, irritation and injury, enhanced antioxidant capability, and an overall adaptive response to exposure. These potentially relevant functional effects warrant further investigation in in vivo models and suggest that chronic or bolus occupational exposure to halloysite nanotubes may have unintended outcomes

Control of a Glove-Based Grasp Assist Device

A grasp assist system includes a glove and sleeve. The glove includes a digit, i.e., a finger or thumb, and a force sensor. The sensor measures a grasping force applied to an object by an operator wearing the glove. The glove contains a tendon connected at a first end to the digit. The sleeve has an actuator assembly connected to a second end of the tendon and a controller in communication with the sensor. The controller includes a configuration module having selectable operating modes and a processor that calculates a tensile force to apply to the tendon for each of the selectable operating modes to assist the grasping force in a manner that differs for each of the operating modes. A method includes measuring the grasping force, selecting the mode, calculating the tensile force, and applying the tensile force to the tendon using the actuator assembly

Growth, microstructure, and failure of crazes in glassy polymers

We report on an extensive study of craze formation in glassy polymers. Molecular dynamics simulations of a coarse-grained bead-spring model were employed to investigate the molecular level processes during craze nucleation, widening, and breakdown for a wide range of temperature, polymer chain length $N$, entanglement length $N_e$ and strength of adhesive interactions between polymer chains. Craze widening proceeds via a fibril-drawing process at constant drawing stress. The extension ratio is determined by the entanglement length, and the characteristic length of stretched chain segments in the polymer craze is $N_e/3$. In the craze, tension is mostly carried by the covalent backbone bonds, and the force distribution develops an exponential tail at large tensile forces. The failure mode of crazes changes from disentanglement to scission for $N/N_e\sim 10$, and breakdown through scission is governed by large stress fluctuations. The simulations also reveal inconsistencies with previous theoretical models of craze widening that were based on continuum level hydrodynamics