The global financial crisis and its aftermath: Economic and political recalibration in the non-sovereign Caribbean

© 2017, © 2017 CALACS. The small non-sovereign island jurisdictions (SNIJs) of the Caribbean have a privileged position in the global political economy, with significant political and economic autonomy on the one hand, and useful protections and support structures provided by their metropolitan powers on the other. However, the global financial and economic crisis of 2007–2008 highlighted starkly some of the fragilities of this privileged status; in particular their economic vulnerability and the unequal and often fractious relationship with their metropolitan powers. This article considers the British, Dutch, French, and US jurisdictions and the short- and longer-term impacts of the crisis. The article’s key concern is to assess the extent to which the instability in the global economy over the last decade has affected both the economic and political dynamic of these jurisdictions, and to what extent their unique position in the global political economy has been compromised

Determining the chemical composition of cloud condensation nuclei

This second progress report describes the status of the project one and one-half years after the start. The goal of the project is to develop the instrumentation to collect cloud condensation nuclei (CCN) in sufficient amounts to determine their chemical composition, and to survey the CCN composition in different climates through a series of field measurements. Our approach to CCN collection is to first form droplets on the nuclei under simulated cloud humidity conditions, which is the only known method of identifying CCN from the background aerosol. Under cloud chamber conditions, the droplets formed become larger than the surrounding aerosol, and can then be removed by inertial impaction. The residue of the evaporated droplets represents the sample to be chemically analyzed. Two size functions of CCN particles are collected by first forming droplets on the large particles are collected by first forming droplets on the large CCN in a haze chamber at 100% relative humidity, and then activating the remaining CCN at 1% supersaturation in a cloud chamber. The experimental apparatus is a serious flow arrangement consisting of an impactor to remove the large aerosol particles, a haze chamber to form droplets on the remaining larger CCN, another impactor to remove the haze droplets containing the larger CCN particles for chemical analysis, a continuous flow diffusion (CFD) cloud chamber to form droplets on the remaining smaller CCN, and a third impactor to remove the droplets for the small CCN sample. Progress is documented here on the development of each of the major components of the flow system. Chemical results are reported on tests to determine suitable wicking material for the different plates. Results of computer modeling of various impactor flows are discussed

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy. Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome

Effects of climate and land-use changes on fish catches across lakes at a global scale

Globally, our knowledge on lake fisheries is still limited despite their importance to food security and livelihoods. Here we show that fish catches can respond either positively or negatively to climate and land-use changes, by analyzing time-series data (1970–2014) for 31 lakes across five continents. We find that effects of a climate or land-use driver (e.g., air temperature) on lake environment could be relatively consistent in directions, but consequential changes in a lake-environmental factor (e.g., water temperature) could result in either increases or decreases in fish catch in a given lake. A subsequent correlation analysis indicates that reductions in fish catch was less likely to occur in response to potential climate and land-use changes if a lake is located in a region with greater access to clean water. This finding suggests that adequate investments for water-quality protection and water-use efficiency can provide additional benefits to lake fisheries and food security

Performance Evaluation of a Fast Mobility-Based Particle Spectrometer for Aircraft Exhaust

The Cambustion DMS500, a novel aerosol sizing instrument with fast time resolution, was first employed to sample jet engine particulate matter emissions during Project APEX. This paper compares the performance of the DMS500 to that of traditional aerosol instruments for sampling jet engine exhaust aerosol under field conditions during this campaign. The observed geometric mean diameter with respect to the particle number (D g) ranged from 15 to 45 nm, and with respect to the mass (third moment) distribution (D gM) from 21 to 112 nm, the geometric standard deviation (σ g) ranged from 1.22 to 1.90 and the total number concentration (N) ranged from 6 x 10 3 to 3.3 x 10 5/cm 3 (after dilution). On average, the D g, D gM, σ g, and N of the DMS500 size distributions differed by -9, -7, +1, and +30% from the reference values of the traditional instruments. Compared with the reference values, both D g and σ g of the DMS500 showed a small but statistically significant decrease with increasing particle size. Effects due to particle shape appeared to be the most likely explanation for the observed size-related trends. The 30% disagreement in concentration measurements is reasonable when the sensitivity of the 3022 condensation particle counter to pressure fluctuations encountered during measurements at the engine exhaust nozzle is taken into account

Physical Characterization of Aerosol Emissions from a Commercial Gas Turbine Engine

This paper discusses the results of the Aircraft Particle Emissions Experiment Project for the physical characterization of total (nonvolatile plus volatile) aerosol emissions (emission factors, hydration properties, and distribution shape parameters) by extractive sampling from an on-wing CFM56-2C1 engine. Samples were extracted at the engine exit plane (1 m) as well as locations 10 and 30 m downstream. Three different fuels were used in this study: base fuel, high-sulfur fuel, and high-aromatic fuel. For the 1 and 10-m probe locations, strong and sometimes nonlinear dependencies were observed on fuel flow rate and no statistically significant dependencies were observed for fuel composition. At 30 m, the onset of gas-to-particle conversion was apparent for low- to medium-fuel flow rates. The soluble mass fraction was found to increase with distance from the engine exit plane and with increasing fuel aromatic and sulfur content. An intercomparison of gas and particle sampling trains showed that gas-to-particle conversion is a serious sample train artifact for gas sampling trains in which dilution cannot be achieved at the probe tip