Improved cover for cadmium sulfide solar cells

Solar cell performance and radiation resistance is improved by application of 1-mil thickness of Teflon FEP protective material. Cells produce 30 percent more power than similar cells with conventional Kapton covers

Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling

One of the most challenging aspects in the analysis and modelling of financial markets, including Credit Default Swap (CDS) markets, is the presence of an emergent, intermediate level of structure standing in between the microscopic dynamics of individual financial entities and the macroscopic dynamics of the market as a whole. This elusive, mesoscopic level of organisation is often sought for via factor models that ultimately decompose the market according to geographic regions and economic industries. However, at a more general level the presence of mesoscopic structure might be revealed in an entirely data-driven approach, looking for a modular and possibly hierarchical organisation of the empirical correlation matrix between financial time series. The crucial ingredient in such an approach is the definition of an appropriate null model for the correlation matrix. Recent research showed that community detection techniques developed for networks become intrinsically biased when applied to correlation matrices. For this reason, a method based on Random Matrix Theory has been developed, which identifies the optimal hierarchical decomposition of the system into internally correlated and mutually anti-correlated communities. Building upon this technique, here we resolve the mesoscopic structure of the CDS market and identify groups of issuers that cannot be traced back to standard industry/region taxonomies, thereby being inaccessible to standard factor models. We use this decomposition to introduce a novel default risk model that is shown to outperform more traditional alternatives.Comment: Quantitative Finance (2021

The effect of sub-grid rainfall variability on the water balance and flux exchange processes resolved at climate scale: the European region contrasted to Central Africa and Amazon rainforests

International audienceThis paper investigates the effect of sub-grid rainfall variability on the simulation of land surface hydrologic processes of three regions (Europe, Africa and Amazon) with contrasting precipitation and vegetation characteristics. The sub-grid rainfall variability is defined in terms of the rainfall coverage fraction at the model's grid cells, and the statistical distribution of rain rates within the rain-covered areas. A statistical-dynamic approach is devised to incorporate the above variability properties into the canopy interception process of a land surface model. Our results reveal that incorporation of sub-grid rainfall variability significantly impacts the land-atmosphere water vapor exchanges. Specifically, it alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (canopy interception loss, ground evaporation and plant transpiration). This further influences the soil water, and to a lesser effect surface/vegetation temperatures and surface heat fluxes. It is shown that, overall, rainfall variability exerts less of an impact on the land-atmosphere flux exchanges over Europe compared to Africa and Amazon

Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area

Correspondence: No substantial long-term bias in the Cenozoic benthic foraminifera oxygen-isotope record

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The Impact of Antimicrobial Resistance and Aging in VAP Outcomes: Experience from a Large Tertiary Care Center

Background: Ventilator associated pneumonia (VAP) is a serious infection among patients in the intensive care unit (ICU). Methods: We reviewed the medical charts of all patients admitted to the adult intensive care units of the Massachusetts General Hospital that went on to develop VAP during a five year period. Results: 200 patients were included in the study of which 50 (25%) were infected with a multidrug resistant pathogen. Increased age, dialysis and late onset (≥5 days from admission) VAP were associated with increased incidence of resistance. Multidrug resistant bacteria (MDRB) isolation was associated with a significant increase in median length of ICU stay (19 vs. 16 days, p = 0.02) and prolonged duration of mechanical ventilation (18 vs. 14 days, p = 0.03), but did not impact overall mortality (HR 1.12, 95% CI 0.51–2.46, p = 0.77). However, age (HR 1.04 95% CI 1.01–1.07, p = 0.003) was an independent risk factor for mortality and age ≥65 years was associated with increased incidence of methicillin-resistant Staphylococcus aureus (MRSA) infections (OR 2.83, 95% CI 1.27–6.32, p = 0.01). Conclusions: MDRB-related VAP is associated with prolonged ICU stay and mechanical ventilation. Interestingly, age ≥ 65 years is associated with MRSA VAP