On the statistical modeling of persistence in total ozone anomalies

Geophysical time series sometimes exhibit serial correlations that are stronger than can be captured by the commonly used first‐order autoregressive model. In this study we demonstrate that a power law statistical model serves as a useful upper bound for the persistence of total ozone anomalies on monthly to interannual timescales. Such a model is usually characterized by the Hurst exponent. We show that the estimation of the Hurst exponent in time series of total ozone is sensitive to various choices made in the statistical analysis, especially whether and how the deterministic (including periodic) signals are filtered from the time series, and the frequency range over which the estimation is made. In particular, care must be taken to ensure that the estimate of the Hurst exponent accurately represents the low‐frequency limit of the spectrum, which is the part that is relevant to long‐term correlations and the uncertainty of estimated trends. Otherwise, spurious results can be obtained. Based on this analysis, and using an updated equivalent effective stratospheric chlorine (EESC) function, we predict that an increase in total ozone attributable to EESC should be detectable at the 95% confidence level by 2015 at the latest in southern midlatitudes, and by 2020–2025 at the latest over 30°–45°N, with the time to detection increasing rapidly with latitude north of this range

Management of Swallowing Disorders: A Program for Professionals Working in Rural Areas

Research indicates that 74% of all nursing home patients experience eating difficulties sometime during their stay (Trupe, Siebens, & Siebens, 1984). Additionally, 59% of patients suffering from stroke experience some degree of dysphagia and aspiration difficulties (Echelard, Thoppil, & Melvin 1984). A significant number of the high risk dysphagia patients described above suffer from life threatening aspiration pneumonia. Consequently the management of swallowing disorders (Dysphagia) is of critical concern to hospital and nursing home personnel. Patients specficially at risk for dysphagia, according to recent studies, include those with head injury, stroke (CVA), and cerebral palsy. Also, patients experiencing cancer of the swallowing structures, diseases or disorders of the cranial nerves, and other neurological dysfuctions have been identified to be at increased risk. In rural areas, however, sophisticated diagnostic equipment that would facilitate dysphagia diagnosis is often unavailable. In addition, rural hospital and nursing home personnel (occupational therapists, physical therapists, speech pathologists and registered nurses) are often in a position to identify early signs of dysphagia but may not be trained in dysphagia identification or treatment. Consequently, it is imperative for these care providers to learn the screening skills necessary to identify dysphagia. When patients are identified as having, or being at an increased risk for, swallowing disorders are are treated accordingly, additional problems resulting from undiagnosed dysphagia may be prevented

PC program extending the two-stage polynomial growth curve model to allow missing data

A stand-alone, menu-driven PC program, written in GAUSS386i, extending the analysis of one-sample longitudinal data sets satisfying the two-stage polynomial growth curve model (Ten Have et al., Am J Hum Biol, 3 (1991) 269-279) to allow missing data is described, illustrated and made available to interested readers. The method and the program are illustrated using data previously analyzed by the authors (Schneiderman and Kowalski, Am J Phys Anthropol, 67 (1985) 323-333) but with several randomly chosen data points discarded and treated as missing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30475/1/0000103.pd

Evaluation of the inter-annual variability of stratospheric chemical composition in chemistry-climate models using ground-based multi species time series

The variability of stratospheric chemical composition occurs on a broad spectrum of timescales, ranging from day to decades. A large part of the variability appears to be driven by external forcings such as volcanic aerosols, solar activity, halogen loading, levels of greenhouse gases (GHG), and modes of climate variability (quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO)). We estimate the contributions of different external forcings to the interannual variability of stratospheric chemical composition and evaluate how well 3-D chemistry-climate models (CCMs) can reproduce the observed response-forcing relationships. We carry out multivariate regression analyses on long time series of observed and simulated time series of several traces gases in order to estimate the contributions of individual forcings and unforced variability to their internannual variability. The observations are typically decadal time series of ground-based data from the international Network for the Detection of Atmospheric Composition Change (NDACC) and the CCM simulations are taken from the CCMVal-2 REF-B1 simulations database. The chemical species considered are column O3, HCl, NO2, and N2O. We check the consistency between observations and model simulations in terms of the forced and internal components of the total interannual variability (externally forced variability and internal variability) and identify the driving factors in the interannual variations of stratospheric chemical composition over NDACC measurement sites. Overall, there is a reasonably good agreement between regression results from models and observations regarding the externally forced interannual variability. A much larger fraction of the observed and modelled interannual variability is explained by external forcings in the tropics than in the extratropics, notably in polar regions. CCMs are able to reproduce the amplitudes of responses in chemical composition to specific external forcings. However, CCMs tend to underestimate very substantially the internal variability and hence the total interannual variability for almost all species considered. This lack of internal variability in CCMs might partly originate from the surface forcing of these CCMs by analysed SSTs. The results illustrate the potential of NDACC ground-based observations for evaluating CCMs

Complex-valued wavelet lifting and applications

Signals with irregular sampling structures arise naturally in many fields. In applications such as spectral decomposition and nonparametric regression, classical methods often assume a regular sampling pattern, thus cannot be applied without prior data processing. This work proposes new complex-valued analysis techniques based on the wavelet lifting scheme that removes ‘one coefficient at a time’. Our proposed lifting transform can be applied directly to irregularly sampled data and is able to adapt to the signal(s)’ characteristics. As our new lifting scheme produces complex-valued wavelet coefficients, it provides an alternative to the Fourier transform for irregular designs, allowing phase or directional information to be represented. We discuss applications in bivariate time series analysis, where the complex-valued lifting construction allows for coherence and phase quantification. We also demonstrate the potential of this flexible methodology over real-valued analysis in the nonparametric regression context

Green Crab (Carcinus maenas) Foraging Efficiency Reduced by Fast Flows

Predators can strongly influence prey populations and the structure and function of ecosystems, but these effects can be modified by environmental stress. For example, fluid velocity and turbulence can alter the impact of predators by limiting their environmental range and altering their foraging ability. We investigated how hydrodynamics affected the foraging behavior of the green crab (Carcinus maenas), which is invading marine habitats throughout the world. High flow velocities are known to reduce green crab predation rates and our study sought to identify the mechanisms by which flow affects green crabs. We performed a series of experiments with green crabs to determine: 1) if their ability to find prey was altered by flow in the field, 2) how flow velocity influenced their foraging efficiency, and 3) how flow velocity affected their handling time of prey. In a field study, we caught significantly fewer crabs in baited traps at sites with fast versus slow flows even though crabs were more abundant in high flow areas. This finding suggests that higher velocity flows impair the ability of green crabs to locate prey. In laboratory flume assays, green crabs foraged less efficiently when flow velocity was increased. Moreover, green crabs required significantly more time to consume prey in high velocity flows. Our data indicate that flow can impose significant chemosensory and physical constraints on green crabs. Hence, hydrodynamics may strongly influence the role that green crabs and other predators play in rocky intertidal communities

Energy Consumption and Economic Growth - New Insights into the Cointegration Relationship

This paper examines the long-run relationship between energy consumption and real GDP, including energy prices, for 25 OECD countries from 1981 to 2007. The distinction between common factors and idiosyncratic components using principal component analysis allows to distinguish between developments on an international and a national level as drivers of the long-run relationship. Indeed, cointegration between the common components of the underlying variables indicates that international developments dominate the long-run relationship between energy consumption and real GDP. Furthermore, the results suggest that energy consumption is price-inelastic. Causality tests indicate the presence of a bi-directional causal relationship between energy consumption and economic growth.Dieses Papier untersucht unter Einbeziehung von Energiepreisen die langfristige Beziehung zwischen Energieverbrauch und realen BIP für 25 OECD Länder von 1981 bis 2007. Durch die Unterscheidung von gemeinsamen und idiosynkratischen Komponenten mit Hilfe einer Faktoranalyse kann zwischen Entwicklungen auf internationaler und nationaler Ebene als Treiber der Langfristbeziehung differenziert werden. In der Tat deutet Kointegration zwischen den gemeinsamen Faktoren der zugrundeliegenden Variablen darauf hin, dass internationale Entwicklungen die langfristige Beziehung zwischen Energieverbrauch und realem BIP dominieren. Des Weiteren suggerieren die ökonometrischen Ergebnisse, dass der Energieverbrauch relativ preisunelastisch ist. Kausalitätstests ergeben eine bidirektionale kausale Beziehung zwischen Energieverbrauch und Wirtschaftswachstum

The Role of Macroeconomic Fundamentals in Malaysian Post Recession Growth

This study aims to find out the role of macroeconomic fundamentals in Malaysian post recession growth. The selected macroeconomic variables are exports, imports, price level, money supply, interest rate, exchange rate and government expenditure. The technique of cointegration was employed to assess the long run equilibrium relationships among the variables. Then, this study performs the Granger causality tests based on VECM to establish the short run causality among the variables. The long-run cointegrating relationship shown that an increase in exports, government expenditure or depreciation of exchange rate will promote long-term economic growth while increase in inflation, interest rate and imports will tamper the Malaysian economic growth. The results of short-run Granger-causality indicated that price level and government spending Granger-caused economic growth in the short-run. In conclusion, based on the results of long-run and short run analysis, the fiscal policy is probably the most appropriate tool in promoting economic growth in Malaysia during the post recession period