Collocating satellite-based radar and radiometer measurements — methodology and usage examples

Collocations between two satellite sensors are occasions where both sensors observe the same place at roughly the same time. We study collocations between the Microwave Humidity Sounder (MHS) on-board NOAA-18 and the Cloud Profiling Radar (CPR) on-board CloudSat. First, a simple method is presented to obtain those collocations and this method is compared with a more complicated approach found in literature. We present the statistical properties of the collocations, with particular attention to the effects of the differences in footprint size. For 2007, we find approximately two and a half million MHS measurements with CPR pixels close to their centrepoints. Most of those collocations contain at least ten CloudSat pixels and image relatively homogeneous scenes. In the second part, we present three possible applications for the collocations. Firstly, we use the collocations to validate an operational Ice Water Path (IWP) product from MHS measurements, produced by the National Environment Satellite, Data and Information System (NESDIS) in the Microwave Surface and Precipitation Products System (MSPPS). IWP values from the CloudSat CPR are found to be significantly larger than those from the MSPPS. Secondly, we compare the relation between IWP and MHS channel 5 (190.311 GHz) brightness temperature for two datasets: the collocated dataset, and an artificial dataset. We find a larger variability in the collocated dataset. Finally, we use the collocations to train an Artificial Neural Network and describe how we can use it to develop a new MHS-based IWP product. We also study the effect of adding measurements from the High Resolution Infrared Radiation Sounder (HIRS), channels 8 (11.11 μm) and 11 (8.33 μm). This shows a small improvement in the retrieval quality. The collocations described in the article are available for public use.

Prebiotic potential of a maize-based soluble fibre and impact of dose on the human gut microbiota

Dietary management of the human gut microbiota towards a more beneficial composition is one approach that may improve host health. To date, a large number of human intervention studies have demonstrated that dietary consumption of certain food products can result in significant changes in the composition of the gut microbiota i.e. the prebiotic concept. Thus the prebiotic effect is now established as a dietary approach to increase beneficial gut bacteria and it has been associated with modulation of health biomarkers and modulation of the immune system. Promitor™ Soluble Corn Fibre (SCF) is a well-known maize-derived source of dietary fibre with potential selective fermentation properties. Our aim was to determine the optimum prebiotic dose of tolerance, desired changes to microbiota and fermentation of SCF in healthy adult subjects. A double-blind, randomised, parallel study was completed where volunteers (n = 8/treatment group) consumed 8, 14 or 21 g from SCF (6, 12 and 18 g/fibre delivered respectively) over 14-d. Over the range of doses studied, SCF was well tolerated Numbers of bifidobacteria were significantly higher for the 6 g/fibre/day compared to 12g and 18g/fibre delivered/day (mean 9.25 and 9.73 Log10 cells/g fresh faeces in the pre-treatment and treatment periods respectively). Such a numerical change of 0.5 Log10 bifidobacteria/g fresh faeces is consistent with those changes observed for inulin-type fructans, which are recognised prebiotics. A possible prebiotic effect of SCF was therefore demonstrated by its stimulation of bifidobacteria numbers in the overall gut microbiota during a short-term intervention.

Closed-loop feedback computation model of dynamical reputation based on the local trust evaluation in business-to-consumer e-commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.