Search

Driven by the data requirements envisioned for the fifth generation (5G) of wireless services, the mobile community is focusing on breaking the spectrum gridlock that characterizes cellular technology. In this context, researchers and industries have identified millimeter-wave (mmWave) communications as a key enabler for providing unprecedented radio access capacity. Nevertheless, due to the specific differences between this technology and microwave systems, there are multiple research challenges spanning from the hardware to the overall system architecture. The goal of this column is to provide an overview of these challenges and discuss the most promising solutions to make 5G mmWave communications a reality.

The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS‐5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a 1 month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root‐mean‐square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 h forecast, increased observation error only yields a slight decline in forecast skill in the extratropics and no discernible degradation of forecast skill in the tropics. Analysis increments are strongly impacted by observation error.Analysis quality is modestly impacted by observation error.Forecast skill is minimally impacted by observation error.

AbstractBackground: Atherosclerosis is a major problem in end-stage renal disease (ESRD) patients treated by hemodialysis and the prevalence of carotid artery disease is much higher in this group than in the general population. Repeated exposure to cytokine-inducing material, derived from dialysate, may induce a chronic inflammatory state, that could contribute to the atherosclerotic process. Endotoxin is mainly cleared from plasma by the sCD14, the soluble form of the endotoxin receptor CD14. The levels of sCD14 are associated with a polymorphism, –159 C/T, of the CD14 gene. Methods and Results: We determined the genotype for the –159 C/T polymorphism in 158 haemodialysis patients and 168 healthy controls. In patients we investigated the association between the CD14 polymorphism and carotid artery disease. With a prospective follow-up study we assessed whether the CD14 polymorphism shows any relationship with cardiovascular mortality. The polymorphic frequency was comparable between patients and controls. In patients, we found a significant difference in the prevalence of carotid artery disease between groups divided by genotype: CC 87.0%, CT 71.7%, TT 48.9% (p = 0.0093). In dialysis patients with hypertension the CC polymorphism was associated with an increased cardiovascular mortality. Conclusions: These results demonstrate an association between the –159 C/T polymorphism of the CD14 gene and carotid artery disease in dialysis patients. We hypothesize that the low plasma clearance of endotoxin associated with the CC genotype facilitates the atherogenic action of endotoxin-derived cytokines in haemodialysis patients.Copyright © 2005 S. Karger AG, Basel

The moisture flux associated with a return‐flow event in the Gulf of Mexico is studied with the aid of a regional forecast model and its adjoint. The adjoint model allows us to determine the derivatives of some aspect of the model output (a scalar, J) with respect to elements of the control vector (initial and boundary conditions). We choose Jto be the northward moisture flux through a vertical cross section in the northwestern corner of the Gulf at the time when the moisture surge in the US is a maximum. The sensitivities (∇J) are used in conjunction with a set of optimal perturbations to estimate the impact of each of the perturbations on the moisture flux. The perturbations are chosen to have the structure of the sensitivities (making them optimal as described in the text), with magnitudes based on the (assumed) uncertainties in the model's initial and boundary conditions. The linear impact estimate is simply the projection of the perturbation onto the sensitivity fields. In complementary fashion, we introduce perturbations into the nonlinear forecast model and decompose the actual impact on Jinto thermodynamic and dynamic components, in order to better understand the nature of the sensitivity. From this numerical experiment we conclude the following. Jis more sensitive to the temperature field than to the other prognostic variables, roughly by a factor of 3 or 4, based on the optimal perturbations used here. The sensitivity to each of the 3‐D prognostic variables peaks in the lower atmosphere near 85 kPa. The sensitivity of Jto the sea surface temperature (Ts, a fixed boundary condition) increases with forecast length. More than 30 h before the verification time, the impact of an optimal perturbation to Tsis comparable to the impact of an optimal perturbation to the whole 3‐D air temperature field. The zones of sensitivity to the Tsare intimately tied to the trajectories of low‐level air which terminate at the cross section. The impact of an optimal perturbation to the topography along the Sierra Madre Oriental mountains is comparable to the impact of optimal perturbations in each of the 3‐D prognostic variables (except T).

Abstract