Forecasting Responses of Seagrass Distributions to Changing Water Quality Using Monitoring Data

Extensive data sets on water quality and seagrass distributions in Florida Bay have been assembled under complementary, but independent, monitoring programs. This paper presents the landscape-scale results from these monitoring programs and outlines a method for exploring the relationships between two such data sets. Seagrass species occurrence and abundance data were used to define eight benthic habitat classes from 677 sampling locations in Florida Bay. Water quality data from 28 monitoring stations spread across the Bay were used to construct a discriminant function model that assigned a probability of a given benthic habitat class occurring for a given combination of water quality variables. Mean salinity, salinity variability, the amount of light reaching the benthos, sediment depth, and mean nutrient concentrations were important predictor variables in the discriminant function model. Using a cross-validated classification scheme, this discriminant function identified the most likely benthic habitat type as the actual habitat type in most cases. The model predicted that the distribution of benthic habitat types in Florida Bay would likely change if water quality and water delivery were changed by human engineering of freshwater discharge from the Everglades. Specifically, an increase in the seasonal delivery of freshwater to Florida Bay should cause an expansion of seagrass beds dominated by Ruppia maritima and Halodule wrightii at the expense of the Thalassia testudinum-dominated community that now occurs in northeast Florida Bay. These statistical techniques should prove useful for predicting landscape-scale changes in community composition in diverse systems where communities are in quasi-equilibrium with environmental drivers.

Modelling the size distribution of cosmic voids

The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Intercity Bus Route Evaluation for Statewide Planning, 1981

The goal of this research was to evaluate the needs of the intercity common carrier bus service in Iowa. Within the framework of the overall goal, the objectives were to: (1) Examine the detailed operating cost and revenue data of the intercity carriers in Iowa; (2) Develop a model or models to estimate demand in cities and corridors served by the bus industry; (3) Develop a cost function model for estimating a carrier's operating costs; (4) Establish the criteria to be used in assessing the need for changes in bus service; (5) Outline the procedures for estimating route operating costs and revenues and develop a matrix of community and social factors to be considered in evaluation; and (6) Present a case study to demonstrate the methodology. The results of the research are presented in the following chapters: (1) Introduction; (2) Intercity Bus Research and Development; (3) Operating Characteristics of Intercity Carriers in Iowa; (4) Commuter Carriers; (5) Passenger and Revenue Forecasting Models; (6) Operating Cost Relationships; (7) Social and General Welfare Aspects of Intercity Bus Service; (8) Case Study Analysis; and (9) Additional Service Considerations and Recommendations.

An R package for simulating metapopulation dynamics and range expansion under environmental change

The metapopulation paradigm is central in ecology and conservation biology to understand the dynamics of spatially-structured populations in fragmented landscapes. Metapopulations are often studied using simulation modelling, and there is an increasing demand of user-friendly software tools to simulate metapopulation responses to environmental change. Here we describe the MetaLandSim R package, mwhich integrates ideas from metapopulation and graph theories to simulate the dynamics of real and virtual metapopulations. The package offers tools to (i) estimate metapopulation parameters from empirical data, (ii) to predict variation in patch occupancy over time in static and dynamic landscapes, either real or virtual, and (iii) to quantify the patterns and speed of metapopulation expansion into empty landscapes. MetaLandSim thus provides detailed information on metapopulation processes, which can be easily combined with land use and climate change scenarios to predict metapopulation dynamics and range expansion for a variety of taxa and ecological systems.

Modelling the Halo mass number counts in modified gravity cosmological scenarios

In this Thesis work we investigate some of different cosmological background scenarios using one of the main probes used in cosmology: the halo mass function. The observed abundance of galaxy clusters (or similarly DM haloes) can indeed be compared to its theoretical predictions to derive fundamental constrains on the cosmological scenario assumed. Given the importance of exploring and constraining models degenerate with the ΛCDM one, we test the applicability of some notable halo mass function models to these scenarios. To this purpose, we made use of the DUSTGRAIN-pathfinder N-body simulations, which assume cosmological scenarios that include modified gravity in the form of f(R) models and massive neutrinos. We carried on the analysis of 3 simulation snapshots at different redshifts, z = 0, 0.5, 1, building multiple samples of dark matter haloes by applying different overdensity thresholds during the procedure of halo identification. We started our analysis by considering the halo mass function model introduced by Despali et al. (2016), who proposed a parametrization that encapsulates the effect of the different halo mass definitions and the relative evolution with the redshift. We calibrated the main parameters of this relation by using the ΛCDM halo catalogues extracted from the DUSTGRAIN-pathfinder simulations, fitting the measured halo abundances at all redshifts and density thresholds. Afterwards we tested the same model parametrization with halo catalogues extracted from the simulations implementing both modified gravity and massive neutrinos. We repeated therefore the calibration procedure on these data to search for discrepancies with respect to the ΛCDM model. Finally we focused the analysis on the cosmological models implementing modified gravity only. We took our ΛCDM calibrated halo mass function and we modified it with the additional f (R) gravity form proposed by Gupta et al. (2022).

Intracellular mechanisms of specific beta-adrenoceptor antagonists involved in improved cardiac function and survival in a genetic model of heart failure

beta-blockers, as class, improve cardiac function and survival in heart failure (HF). However, the molecular mechanisms underlying these beneficial effects remain elusive. In the present study, metoprolol and carvedilol were used in doses that display comparable heart rate reduction to assess their beneficial effects in a genetic model of sympathetic hyperactivity-induced HF (alpha(2A)/alpha(2C)-ARKO mice). Five month-old HF mice were randomly assigned to receive either saline, metoprolol or carvedilol for 8 weeks and age-matched wild-type mice (WT) were used as controls. HF mice displayed baseline tachycardia, systolic dysfunction evaluated by echocardiography, 50% mortality rate, increased cardiac myocyte width (50%) and ventricular fibrosis (3-fold) compared with WT. All these responses were significantly improved by both treatments. Cardiomyocytes from HF mice showed reduced peak [Ca(2+)](i) transient (13%) using confocal microscopy imaging. Interestingly, while metoprolol improved [Ca(2+)](i) transient, carvedilol had no effect on peak [Ca(2+)](i) transient but also increased [Ca(2+)] transient decay dynamics. We then examined the influence of carvedilol in cardiac oxidative stress as an alternative target to explain its beneficial effects. Indeed, HF mice showed 10-fold decrease in cardiac reduced/oxidized glutathione ratio compared with WT, which was significantly improved only by carvedilol treatment. Taken together, we provide direct evidence that the beneficial effects of metoprolol were mainly associated with improved cardiac Ca(2+) transients and the net balance of cardiac Ca(2+) handling proteins while carvedilol preferentially improved cardiac redox state. (C) 2008 Elsevier Inc. All rights reserved.

POLYETHYLENE GLYCOL ADDITION DOES NOT IMPROVE EXOGENOUS SURFACTANT FUNCTION IN AN EXPERIMENTAL MODEL OF MECONIUM ASPIRATION SYNDROME

Meconium (MEC) is a potent inactivator of pulmonary surfactant. The authors studied the effects of polyethylene glycol addition to the exogenous surfactant over the lung mechanics and volumes. Human meconium was administrated to newborn rabbits. Animals were ventilated for 20 minutes and dynamic compliance, ventilatory pressure, and tidal volume were recorded. Animals were randomized into 3 study groups: MEC group (without surfactant therapy); S100 group (100 mg/kg surfactant); and PEG group (100 mg/kg porcine surfactant plus 5% PEG). After ventilation, a pulmonary pressure-volume curve was built. Histological analysis was carried out to calculate the mean alveolar size (Lm) and the distortion index (DI). Both groups treated with surfactant showed higher values of dynamic pulmonary compliance and lower ventilatory pressure, compared with the MEC group (P .05). S100 group had a larger maximum lung volume, V30, compared with the MEC group (P .05). Lm and DI values were smaller in the groups treated with surfactant than in the MEC group (P .05). No differences were observed between the S100 and PEG groups. Animals treated with surfactant showed significant improvement in pulmonary function as compared to nontreated animals. PEG added to exogenous surfactant did not improve lung mechanics or volumes.

Impulse-response function of splanchnic circulation with model-independent constraints: theory and experimental validation

Modeling physiological processes using tracer kinetic methods requires knowledge of the time course of the tracer concentration in blood supplying the organ. For liver studies, however, inaccessibility of the portal vein makes direct measurement of the hepatic dual-input function impossible in humans. We want to develop a method to predict the portal venous time-activity curve from measurements of an arterial time-activity curve. An impulse-response function based on a continuous distribution of washout constants is developed and validated for the gut. Experiments with simultaneous blood sampling in aorta and portal vein were made in 13 anesthetized pigs following inhalation of intravascular [O-15] CO or injections of diffusible 3-O[ C-11] methylglucose (MG). The parameters of the impulse-response function have a physiological interpretation in terms of the distribution of washout constants and are mathematically equivalent to the mean transit time ( T) and standard deviation of transit times. The results include estimates of mean transit times from the aorta to the portal vein in pigs: (T) over bar = 0.35 +/- 0.05 min for CO and 1.7 +/- 0.1 min for MG. The prediction of the portal venous time-activity curve benefits from constraining the regression fits by parameters estimated independently. This is strong evidence for the physiological relevance of the impulse-response function, which includes asymptotically, and thereby justifies kinetically, a useful and simple power law. Similarity between our parameter estimates in pigs and parameter estimates in normal humans suggests that the proposed model can be adapted for use in humans.

Biometal muscle to restore atrial transport function in a permanent atrial fibrillation animal model: a potential tool in the treatment of end-stage heart failure.

BACKGROUND: Half of the patients with end-stage heart failure suffer from persistent atrial fibrillation (AF). Atrial kick (AK) accounts for 10-15% of the ejection fraction. A device restoring AK should significantly improve cardiac output (CO) and possibly delay ventricular assist device (VAD) implantation. This study has been designed to assess the mechanical effects of a motorless pump on the right chambers of the heart in an animal model. METHODS: Atripump is a dome-shaped biometal actuator electrically driven by a pacemaker-like control unit. In eight sheep, the device was sutured onto the right atrium (RA). AF was simulated with rapid atrial pacing. RA ejection fraction (EF) was assessed with intracardiac ultrasound (ICUS) in baseline, AF and assisted-AF status. In two animals, the pump was left in place for 4 weeks and then explanted. Histology examination was carried out. The mean values for single measurement per animal with +/-SD were analysed. RESULTS: The contraction rate of the device was 60 per min. RA EF was 41% in baseline, 7% in AF and 21% in assisted-AF conditions. CO was 7+/-0.5 l min(-1) in baseline, 6.2+/-0.5 l min(-1) in AF and 6.7+/-0.5 l min(-1) in assisted-AF status (p<0.01). Histology of the atrium in the chronic group showed chronic tissue inflammation and no sign of tissue necrosis. CONCLUSIONS: The artificial muscle restores the AK and improves CO. In patients with end-stage cardiac failure and permanent AF, if implanted on both sides, it would improve CO and possibly delay or even avoid complex surgical treatment such as VAD implantation.

Kinematically complete analysis of the CLAS data on the proton structure Function F2 in a Regge-Dual model

The recently measured inclusive electron-proton cross section in the nucleon resonance region, performed with the CLAS detector at the Thomas Jefferson Laboratory, has provided new data for the nucleon structure function F2 with previously unavailable precision. In this paper we propose a description of these experimental data based on a Regge-dual model for F2. The basic inputs in the model are nonlinear complex Regge trajectories producing both isobar resonances and a smooth background. The model is tested against the experimental data, and the Q2 dependence of the moments is calculated. The fitted model for the structure function (inclusive cross section) is a limiting case of the more general scattering amplitude equally applicable to deeply virtual Compton scattering. The connection between the two is discussed.

Simulating maize phenology as a function of air temperature with a linear and a nonlinear model

The objective of this study was to adapt a nonlinear model (Wang and Engel - WE) for simulating the phenology of maize (Zea mays L.), and to evaluate this model and a linear one (thermal time), in order to predict developmental stages of a field-grown maize variety. A field experiment, during 2005/2006 and 2006/2007 was conducted in Santa Maria, RS, Brazil, in two growing seasons, with seven sowing dates each. Dates of emergence, silking, and physiological maturity of the maize variety BRS Missões were recorded in six replications in each sowing date. Data collected in 2005/2006 growing season were used to estimate the coefficients of the two models, and data collected in the 2006/2007 growing season were used as independent data set for model evaluations. The nonlinear WE model accurately predicted the date of silking and physiological maturity, and had a lower root mean square error (RMSE) than the linear (thermal time) model. The overall RMSE for silking and physiological maturity was 2.7 and 4.8 days with WE model, and 5.6 and 8.3 days with thermal time model, respectively.

An experimental model for the measurement of renal function after temporary ureteral obstruction in the rat

We present the results obtained with a ureterovesical implant after ipsilateral ureteral obstruction in the rat, suitable for the study of renal function after deobstruction in these animals. Thirty-seven male Wistar rats weighing 260 to 300 g were submitted to distal right ureteral ligation and divided into 3 groups, A (N = 13, 1 week of obstruction), B (N = 14, 2 weeks of obstruction) and C (N = 10, 3 weeks of obstruction). The animals were then submitted to ureterovesical implantation on the right side and nephrectomy on the left side. During the 4-week follow-up period serum levels of urea and creatinine were measured on the 2nd, 7th, 14th, 21st and 28th day and compared with preoperative levels. The ureterovesical implantation included a psoas hitch procedure and the ureter was pulled into the bladder using a transvesical suture. During the first week of the postoperative period 8 animals died, 4/13 in group A (1 week of obstruction) and 4/14 in group B (2 weeks of obstruction). When compared to preoperative serum levels, urea and creatinine showed a significant increase (P<0.05) on the 2nd postoperative day in groups A and B, with a gradual return to lower levels. However, the values in group B animals were higher than those in group A at the end of the follow-up. In group C, 2/10 animals (after 3 weeks of obstruction) were sacrificed at the time of ureterovesical implantation due to infection of the obstructed kidneys. The remaining animals in this group were operated upon but all of them died during the first week of follow-up due to renal failure. This technique of ureterovesical implantation in the rat provides effective drainage of the upper urinary tract, permitting the development of an experimental model for the study of long-term renal function after a period of ureteral obstruction