Effect of the September 11, 2001 terrorist attack on a state highway patrol trooper's heart rate variability

BACKGROUND: On September 11, 2001, terrorists attacked the United States. By coincidence, a North Carolina highway patrol trooper was wearing an ambulatory ECG Holter monitor at this time as part of an air pollution study. METHODS: Heart rate variability parameters were analyzed: standard deviation of normal to normal beat intervals (SDNN) and percentage of interval differences >50 ms (PNN50). RESULTS: The trooper's heart rate variability changed immediately after learning about the terrorist attacks. Heart rate increased and PNN50 decreased, while SDNN increased strongly. CONCLUSIONS: These changes suggest strong emotional sympathetic stress associated with parasympathetic withdrawal in response to the news about the terrorist attack. [Authors]

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

Sources of comparative advantage in polluting industries

We study the determinants of comparative advantage in polluting industries. We combine data on environmental policy at the country level with data on pollution intensity at the industry level to show that countries with laxer environmental regulation have a comparative advantage in polluting industries. Further, we address the potential problem of reverse causality. We propose an instrument for environmental regulation based on meteorological determinants of pollution dispersion identified by the atmospheric pollution literature. We find that the effect of environmental regulation on the pattern of trade is causal and comparable in magnitude to the effect of physical and human capital.

Sources of comparative advantage in polluting industries (with appendix)

We study the determinants of comparative advantage in polluting industries. We combine dataon environmental policy at the country level with data on pollution intensity at the industry levelto show that countries with laxer environmental regulation have a comparative advantage in polluting industries. Further, we address the potential problem of reverse causality. We propose aninstrument for environmental regulation based on meteorological determinants of pollution dispersion identified by the atmospheric pollution literature. We find that the effect of environmentalregulation on the pattern of trade is causal and comparable in magnitude to the effect of physicaland human capital.

Making Predictions with Textual Contents

Forecasting real-world quantities with basis on information from textual descriptions has recently attracted significant interest as a research problem, although previous studies have focused on applications involving only the English language. This document presents an experimental study on the subject of making predictions with textual contents written in Portuguese, using documents from three distinct domains. I specifically report on experiments using different types of regression models, using state-of-the-art feature weighting schemes, and using features derived from cluster-based word representations. Through controlled experiments, I have shown that prediction models using the textual information achieve better results than simple baselines such as taking the average value over the training data, and that richer document representations (i.e., using Brown clusters and the Delta- TF-IDF feature weighting scheme) result in slight performance improvements.

2007 Greenhouse Gas Emissions from Selected Iowa Source Categories

The Department’s 2007 Greenhouse Gas Inventory is a refinement of previous statewide inventories. It is a bottom-up inventory of two sectors – fossil fuel combustion at federally-recognized major sources of air pollution and fossil fuel combustion and ethanol fermentation at dry mill ethanol plants. This is the first bottomup greenhouse gas inventory conducted for Iowa and the first bottom-up greenhouse gas inventory of ethanol plants in the nation that the Department is aware of. In a bottom-up inventory, facility-specific activity data is used to calculate emissions. In a top-down inventory, aggregate activity data is used to calculate emissions. For example, this bottom-up inventory calculates greenhouse gas emissions from the fossil fuel combustion at each individual facility instead of using the total amount of fossil fuel combusted state-wide, which would be a top-down inventory method. The advantage to a bottom-up inventory is that the calculations are more accurate than a top-down inventory. However, because the two methods differ, the results from a bottom-up inventory are not directly comparable to a top-down inventory.

Importancia de la información contable para el análisis y predicción de la viabilidad de las explotaciones agrícolas

Spanish and Western agriculture show a continuous decrease in the numberof farms. One of the main factors for this trend is the economicnon-viability of many of the existing farms. In addition, interrelationshipof agriculture with other industries is growing. Thus, policymakers, banks,creditors and other stakeholders are interested in predicting farm viability.The aim of this paper is to provide empirical evidence that the use ofaccounting-based information could significantly improve understandingand prediction of various degrees of farm viability. Two multinomial logitmodels were applied to a sample of farms of Catalonia, Spain. One modelincluded non-accounting-based variables, while the other also consideredaccounting-based variables. It was found that accounting added significantinformation to predict various degrees of farm viability. This findingreveals, both the need of encouraging the little existing use of accountingby farms and to develop appropriate accounting standards for agriculture.

The policy relevance of wear emissions from road transport, now and in the future : an international workshop report and consensus statement

Road transport emissions are a major contributor to ambient particulate matter concentrations and have been associated with adverse health effects. Therefore, these emissions are targeted through increasingly stringent European emission standards. These policies succeed in reducing exhaust emissions, but do not address "nonexhaust" emissions from brake wear, tire wear, road wear, and suspension in air of road dust. Is this a problem? To what extent do nonexhaust emissions contribute to ambient concentrations of PM10 or PM2.5? In the near future, wear emissions may dominate the remaining traffic-related PM10 emissions in Europe, mostly due to the steep decrease in PM exhaust emissions. This underlines the need to determine the relevance of the wear emissions as a contribution to the existing ambient PM concentrations, and the need to assess the health risks related to wear particles, which has not yet received much attention. During a workshop in 2011, available knowledge was reported and evaluated so as to draw conclusions on the relevance of traffic-related wear emissions for air quality policy development. On the basis of available evidence, which is briefly presented in this paper, it was concluded that nonexhaust emissions and in particular suspension in air of road dust are major contributors to exceedances at street locations of the PM10 air quality standards in various European cities. Furthermore, wear-related PM emissions that contain high concentrations of metals may (despite their limited contribution to the mass of nonexhaust emissions) cause significant health risks for the population, especially those living near intensely trafficked locations. To quantify the existing health risks, targeted research is required on wear emissions, their dispersion in urban areas, population exposure, and its effects on health. Such information will be crucial for environmental policymakers as an input for discussions on the need to develop control strategies.

Why did Swiss citizens refuse a comprehensive second-hand smoke ban?

The ill effects of second-hand smoke are now well documented. To protect the population from exposure to tobacco smoke, comprehensive smoking bans are necessary as expressed in the WHO Framework Convention on Tobacco Control and its guidelines. Switzerland has only a partial smoking ban full of exceptions which has been in effect since 2010, which reproduces the so-called Spanish model. In September 2012, the Swiss citizens refused a proposal for a more comprehensive ban. This case study examines the reasons behind this rejection and draws some lessons that can be learnt from it.

Comparison of clinical and angiographic prognostic risk scores in elderly patients presenting with acute coronary syndrome and referred for percutaneous coronary intervention.

BACKGROUND: Multiple risk prediction models have been validated in all-age patients presenting with acute coronary syndrome (ACS) and treated with percutaneous coronary intervention (PCI); however, they have not been validated specifically in the elderly. METHODS: We calculated the GRACE (Global Registry of Acute Coronary Events) score, the logistic EuroSCORE, the AMIS (Acute Myocardial Infarction Swiss registry) score, and the SYNTAX (Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery) score in a consecutive series of 114 patients ≥75 years presenting with ACS and treated with PCI within 24 hours of hospital admission. Patients were stratified according to score tertiles and analysed retrospectively by comparing the lower/mid tertiles as an aggregate group with the higher tertile group. The primary endpoint was 30-day mortality. Secondary endpoints were the composite of death and major adverse cardiovascular events (MACE) at 30 days, and 1-year MACE-free survival. Model discrimination ability was assessed using the area under receiver operating characteristic curve (AUC). RESULTS: Thirty-day mortality was higher in the upper tertile compared with the aggregate lower/mid tertiles according to the logistic EuroSCORE (42% vs 5%; odds ratio [OR] = 14, 95% confidence interval [CI] = 4-48; p <0.001; AUC = 0.79), the GRACE score (40% vs 4%; OR = 17, 95% CI = 4-64; p <0.001; AUC = 0.80), the AMIS score (40% vs 4%; OR = 16, 95% CI = 4-63; p <0.001; AUC = 0.80), and the SYNTAX score (37% vs 5%; OR = 11, 95% CI = 3-37; p <0.001; AUC = 0.77). CONCLUSIONS: In elderly patients presenting with ACS and referred to PCI within 24 hours of admission, the GRACE score, the EuroSCORE, the AMIS score, and the SYNTAX score predicted 30 day mortality. The predictive value of clinical scores was improved by using them in combination.

Predictors of onset of cannabis and other drug use in male young adults: results from a longitudinal study.

BACKGROUND: The use of cannabis and other illegal drugs is particularly prevalent in male young adults and is associated with severe health problems. This longitudinal study explored variables associated with the onset of cannabis use and the onset of illegal drug use other than cannabis separately in male young adults, including demographics, religion and religiosity, health, social context, substance use, and personality. Furthermore, we explored how far the gateway hypothesis and the common liability to addiction model are in line with the resulting prediction models. METHODS: The data were gathered within the Cohort Study on Substance Use Risk Factors (C-SURF). Young men aged around 20 years provided demographic, social, health, substance use, and personality-related data at baseline. Onset of cannabis and other drug use were assessed at 15-months follow-up. Samples of 2,774 and 4,254 individuals who indicated at baseline that they have not used cannabis and other drugs, respectively, in their life and who provided follow-up data were used for the prediction models. Hierarchical logistic stepwise regressions were conducted, in order to identify predictors of the late onset of cannabis and other drug use separately. RESULTS: Not providing for oneself, having siblings, depressiveness, parental divorce, lower parental knowledge of peers and the whereabouts, peer pressure, very low nicotine dependence, and sensation seeking were positively associated with the onset of cannabis use. Practising religion was negatively associated with the onset of cannabis use. Onset of drug use other than cannabis showed a positive association with depressiveness, antisocial personality disorder, lower parental knowledge of peers and the whereabouts, psychiatric problems of peers, problematic cannabis use, and sensation seeking. CONCLUSIONS: Consideration of the predictor variables identified within this study may help to identify young male adults for whom preventive measures for cannabis or other drug use are most appropriate. The results provide evidence for both the gateway hypothesis and the common liability to addiction model and point to further variables like depressiveness or practising of religion that might influence the onset of drug use.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.