Product market deregulation and the US employment miracle

We consider the dynamic relationship between product market entry regulation and equilibrium unemployment. The main theoretical contribution is combining a job matching model with monopolistic competition in the goods market and individual bargaining. We calibrate the model to US data and perform a policy experiment to assess whether the decrease in trend unemployment during the 1980s and 1990s could be directly attributed to product market deregulation. Under our baseline calibration, our results suggest that a decrease of less than two-tenths of a percentage point of unemployment rates can be attributed to product market deregulation, a surprisingly small amount. © 2008 Elsevier Inc. All rights reserved.Haefke acknowledges financial support from EU grant HPMF-CT-2001-01252 and CICYT grant SEC2001-0792.Peer Reviewe

Elemente und Ephemeride des Kometen 1904 a.

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Twenty-five years of cloud base height measurements by ceilometer in Ny-Ålesund, Svalbard

Clouds are a key factor for the Arctic amplification of global warming, but their actual appearance and distribution are still afflicted by large uncertainty. On the Arctic-wide scale, large discrepancies are found between the various reanalyses and satellite products, respectively. Although ground-based observations by remote sensing are limited to point measurements, they have the advantage of obtaining extended time series of vertically resolved cloud properties. Here, we present a 25-year data record of cloud base height measured by ceilometer at the Ny-Ålesund, Svalbard, Arctic site. We explain the composition of the three sub-periods with different instrumentation contributing to the data set, and show examples of potential application areas. Linked to cyclonic activity, the cloud base height provides essential information for the interpretation of the surface radiation balance and contributes to the understanding of meteorological processes. Furthermore, it is a useful auxiliary component for the analysis of advanced technologies that provide insight into cloud microphysical properties, like the cloud radar. The long-term time series also allows derivation of an annual cycle of the cloud occurrence frequency, revealing the more frequent cloud cover in summer and the lowest cloud cover amount in April. However, as the use of different ceilometer instruments over the years potentially imposed inhomogeneities onto the data record, any long-term trend analysis should be avoided.The Ny-Ålesund cloud base height data from August 1992 to July 2017 are provided in a high temporal resolution of 5&thinsp;min (1&thinsp;min) before (after) July 1998, respectively, at the PANGAEA repository (https://doi.org/10.1594/PANGAEA.880300).</p

Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund

The French–German Arctic research base AWIPEV (the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research – AWI – and the French Polar Institute Paul Emile Victor – PEV) at Ny-Ålesund, Svalbard, is a unique station for monitoring cloud-related processes in the Arctic. For the first time, data from a set of ground-based instruments at the AWIPEV observatory are analyzed to characterize the vertical structure of clouds. For this study, a 14-month dataset from Cloudnet combining observations from a ceilometer, a 94 GHz cloud radar, and a microwave radiometer is used. A total cloud occurrence of ∼81 %, with 44.8 % multilayer and 36 % single-layer clouds, was found. Among single-layer clouds the occurrence of liquid, ice, and mixed-phase clouds was 6.4 %, 9 %, and 20.6 %, respectively. It was found that more than 90 % of single-layer liquid and mixed-phase clouds have liquid water path (LWP) values lower than 100 and 200 g m−2, respectively. Mean values of ice water path (IWP) for ice and mixed-phase clouds were found to be 273 and 164 g m−2, respectively. The different types of single-layer clouds are also related to in-cloud temperature and the relative humidity under which they occur. Statistics based on observations are compared to ICOsahedral Non-hydrostatic (ICON) model output. Distinct differences in liquid-phase occurrence in observations and the model at different environmental temperatures lead to higher occurrence of pure ice clouds. A lower occurrence of mixed-phase clouds in the model at temperatures between −20 and −5 ∘C becomes evident. The analyzed dataset is useful for satellite validation and model evaluation

The use of classification and regression trees to predict the likelihood of seasonal influenza

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therap

Steel corrosion in reinforced alkali-activated materials

The development of alkali-activated materials (AAMs) as an alternative to Portland cement (PC) has seen significant progress in the past decades. However, there still remains significant uncertainty regarding their long term performance when used in steel-reinforced structures. The durability of AAMs in such applications depends strongly on the corrosion behaviour of the embedded steel reinforcement, and the experimental data in the literature are limited and in some cases inconsistent. This letter elucidates the role of the chemistry of AAMs on the mechanisms governing passivation and chloride-induced corrosion of the steel reinforcement, to bring a better understanding of the durability of AAM structures exposed to chloride. The corrosion of the steel reinforcement in AAMs differs significantly from observations in PC; the onset of pitting (or the chloride ‘threshold’ value) depends strongly on the alkalinity, and the redox environment, of these binders. Classifications or standards used to assess the severity of steel corrosion in PC appear not to be directly applicable to AAMs due to important differences in pore solution chemistry and phase assemblage

The importance of sex as a risk factor for hospital readmissions due to pulmonary diseases

BACKGROUND: Pulmonary diseases are a common and costly cause of 30-day readmissions. Few studies have focused on the difference in risk for rehospitalization between men and women in older patients. In this study we analyzed the association between sex and the risk of readmission in a cohort of patients admitted to the hospital for chronic obstructive pulmonary disease (COPD) exacerbation and other major pulmonary diseases. METHODS: This was a retrospective cohort study based on administrative data collected in the Veneto Region in 2016. We included 14,869 hospital admissions among residents aged 6565\u2009years for diagnosis related groups (DRGs) of the most common disorders of the respiratory system: bronchitis and asthma, pneumonia, pulmonary edema, respiratory failure, and COPD. Multilevel logistic regressions were performed to test the association between 30-day hospital readmission and sex, adjusting for confounding factors. RESULTS: For bronchitis and asthma, male patients had significantly higher odds of 30-day readmission than female patients (adjusted odds ratio (aOR), 2.07; 95% confidence interval (CI), 1.11-3.87). The odds of readmission for men were also significantly higher for pneumonia (aOR, 1.40; 95% CI, 1.13-1.72), for pulmonary edema and respiratory failure (aOR, 1.28; 95% CI, 1.05-1.55), and for COPD (aOR, 1.34; 95% CI, 1.00-1.81). CONCLUSIONS: This study found that male sex is a major risk factors for readmission in patients aged more than 65\u2009years with a primary pulmonary diagnosis. More studies are needed to understand the underlying determinants of this phenomena and to provide targets for future interventions

The power of data mining in diagnosis of childhood pneumonia

Childhood pneumonia is the leading cause of death of children under the age of five globally. Diagnostic information on presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia, and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (Creactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (Lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians