Fee Shifting: A Proposal to Solve Maine’s Intractable Access to Justice Problem

The Maine Legislature should enact a new statute to award attorney’s fees in civil cases to poor litigants against their opponents. Under the proposed statute the opponent must be a corporation or other legal entity and the poor litigant must be the prevailing party in the case. The statute proposed is needed because multiple studies show that there has been an unrelenting decline during the last four decades of the poor’s access to justice. Their numbers increase and the support of the federal government declines. For those who find themselves in legal positions opposing the poor, there is little deterrent to a decision to violate the legal rights of the poor. While some people who are without attorneys seek the help of the courts pro se, the barriers of pro se procedure are insurmountable. Pro se representation is helpful only to the extent that a person is well educated and can devote significant resources to study, writing, and oral advocacy of legal questions. A state must provide some effective means of assuring equal access to its courts and tribunals

Studies of hot B subdwarfs. Part 2: Energy distributions of three bright sdB/sdOB stars in the 950-5500 angstrom range

Voyager ultraviolet spectrometer observations of the subdwarf B or OB stars HD 205805, UV 1758+36 and Feige 66 are presented. All three objects display the H I Layman series in absorption. These observations are combined with low dispersion IUE spectrophotometry and with Stroemgren photometry to construct virtually complete energy distributions, which extend over the range 950-5500 angstroms. Effective temperatures based on model atmosphere calculations for high gravity, hydrogen rich stars are determined. Our analyses yield T Sub e 28,200 + or - 1300 K for HD 205805, T sub e 31, 800 + or - 1100 K for UV 1758+36, and T sub e 35,700 + or - 1500 K for Feige 66. The importance of far ultraviolet observations below L sub alpha in reducing the uncertainties associated with the interstellar extinction and the degradation of the IUE sensitivity is emphasized

Spectral analysis and abundances of the post-HB star HD 76431

HD76431 is a slow rotating post-HB star that shows an underabundance of helium by 0.5 dex relative to the solar value. These observational facts suggest that atomic diffusion could be active in its atmosphere. We have used the MMT and Bok spectra to estimate the atmospheric parameters of the target star using the model atmospheres and synthetic spectra calculated with TLUSTY and SYNSPEC. The derived values of the effective temperature, surface gravity, helium abundance are consistent with those obtained by Ramspeck et al. (2001b). It appears that NLTE effect are not important for HD76431. We have used Stokes I spectra from ESPaDOnS at CFHT to perform an abundance analysis and a search for observational evidence of vertical stratification of the abundance of certain elements. The results of our abundance analysis are in good agreement with previously published data with respect to average abundances. Our numerical simulations show that carbon and nitrogen reveal signatures of vertical abundance stratification in the atmosphere of HD76431. It appears that the carbon abundance increases toward the deeper atmospheric layers. Nitrogen also shows a similar behaviour, but in deeper atmospheric layers we obtain a significant dispersion for the estimates of its abundance. To our knowledge, this is the first demonstration of vertical abundance stratification of metals in a post-HB star and up to now it is the hottest star to show such stratification features. We also report the detection of two SiIII and one TiIII emission lines in the spectra of HD76431 that were not detected in previous studies.Comment: 7 pages, 5 figures, accepted for publication in MNRA

In operando XAS investigation of reduction and oxidation processes in cobalt and iron mixed spinels during the chemical loop reforming of ethanol

FeCo2O4 and CoFe2O4 nanoparticles have been studied as oxygen carriers for the Chemical Loop Reforming (CLR) of ethanol. By using in operando X-ray absorption spectroscopy we have followed in real time the chemical and structural changes that take place on the materials as a function of temperature and reactive atmosphere (i.e. ethanol/water streams). During the first step of CLR for both oxides the most active chemical species are the cations in the tetrahedral sites, irrespective of their chemical nature. Quite rapidly the spinel structure is transformed into a mix of wustite-type oxide and metal alloys, but the formation of a metal phase is easier in the case of cobalt, while iron shows a marked preference to form wustite type oxide. Despite the good reducibility of FeCo2O4 imparted by the high amount of cobalt, its performance in the production of hydrogen is quite poor due to an inefficient oxidation by water steam, which is able to oxidize only the outer shell of the nanoparticles. In contrast, CoFe2O4 due to the residual presence of a reducible wustite phase shows a higher hydrogen yield. Moreover, by combining the structural information provided by X-ray absorption spectroscopy with the analysis of the byproducts of ethanol decomposition we could infer that FeCo2O4 is more selective than CoFe2O4 for the selective dehydrogenation of ethanol to acetaldehyde because of the higher amount of Fe(III) ions in tetrahedral sites

Ecological neighborhoods as a framework for umbrella species selection

Umbrella species are typically chosen because they are expected to confer protection for other species assumed to have similar ecological requirements. Despite its popularity and substantial history, the value of the umbrella species concept has come into question because umbrella species chosen using heuristic methods, such as body or home range size, are not acting as adequate proxies for the metrics of interest: species richness or population abundance in a multi-species community for which protection is sought. How species associate with habitat across ecological scales has important implications for understanding population size and species richness, and therefore may be a better proxy for choosing an umbrella species. We determined the spatial scales of ecological neighborhoods important for predicting abundance of 8 potential umbrella species breeding in Nebraska using Bayesian latent indicator scale selection in N-mixture models accounting for imperfect detection. We compare the conservation value measured as collective avian abundance under different umbrella species selected following commonly used criteria and selected based on identifying spatial land cover characteristics within ecological neighborhoods that maximize collective abundance. Using traditional criteria to select an umbrella species resulted in sub-maximal expected collective abundance in 86% of cases compared to selecting an umbrella species based on land cover characteristics that maximized collective abundance directly. We conclude that directly assessing the expected quantitative outcomes, rather than ecological proxies, is likely the most efficient method to maximize the potential for conservation success under the umbrella species concept

Hybrid expansions for local structural relaxations

A model is constructed in which pair potentials are combined with the cluster expansion method in order to better describe the energetics of structurally relaxed substitutional alloys. The effect of structural relaxations away from the ideal crystal positions, and the effect of ordering is described by interatomic-distance dependent pair potentials, while more subtle configurational aspects associated with correlations of three- and more sites are described purely within the cluster expansion formalism. Implementation of such a hybrid expansion in the context of the cluster variation method or Monte Carlo method gives improved ability to model phase stability in alloys from first-principles.Comment: 8 pages, 1 figur

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

Estimating the Use of Public Lands: Integrated Modeling of Open Populations with Convolution Likelihood Ecological Abundance Regression

We present an integrated open population model where the population dynamics are defined by a differential equation, and the related statistical model utilizes a Poisson binomial convolution likelihood. Key advantages of the proposed approach over existing open population models include the flexibility to predict related, but unobserved quantities such as total immigration or emigration over a specified time period, and more computationally efficient posterior simulation by elimination of the need to explicitly simulate latent immigration and emigration. The viability of the proposed method is shown in an in-depth analysis of outdoor recreation participation on public lands, where the surveyed populations changed rapidly and demographic population closure cannot be assumed even within a single day