A Rare Case of Sinonasal Cavernous Hemangioma

Abstract He mangio mas are co mmon benign lesions of the head and neck which predominantly orig inate fro m lips, tongue and buccal mucos

Rice Production in Sri Lanka

Crop Production/Industries,

Statistical downscaling of general circulation model outputs to precipitation accounting for non-stationarities in predictor-predictand relationships

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data arcHIVe and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP)

Anchor centric virtual coordinate systems in wireless sensor networks: from self-organization to network awareness

2012 Fall.Includes bibliographical references.Future Wireless Sensor Networks (WSNs) will be collections of thousands to millions of sensor nodes, automated to self-organize, adapt, and collaborate to facilitate distributed monitoring and actuation. They may even be deployed over harsh geographical terrains and 3D structures. Low-cost sensor nodes that facilitate such massive scale networks have stringent resource constraints (e.g., in memory and energy) and limited capabilities (e.g., in communication range and computational power). Economic constraints exclude the use of expensive hardware such as Global Positioning Systems (GPSs) for network organization and structuring in many WSN applications. Alternatives that depend on signal strength measurements are highly sensitive to noise and fading, and thus often are not pragmatic for network organization. Robust, scalable, and efficient algorithms for network organization and reliable information exchange that overcome the above limitations without degrading the network's lifespan are vital for facilitating future large-scale WSN networks. This research develops fundamental algorithms and techniques targeting self-organization, data dissemination, and discovery of physical properties such as boundaries of large-scale WSNs without the need for costly physical position information. Our approach is based on Anchor Centric Virtual Coordinate Systems, commonly called Virtual Coordinate Systems (VCSs), in which each node is characterized by a coordinate vector of shortest path hop distances to a set of anchor nodes. We develop and evaluate algorithms and techniques for the following tasks associated with use of VCSs in WSNs: (a) novelty analysis of each anchor coordinate and compressed representation of VCSs; (b) regaining lost directionality and identifying a 'good' set of anchors; (c) generating topology preserving maps (TPMs); (d) efficient and reliable data dissemination, and boundary identification without physical information; and (f) achieving network awareness at individual nodes. After investigating properties and issues related to VCS, a Directional VCS (DVCS) is proposed based on a novel transformation that restores the lost directionality information in VCS. Extreme Node Search (ENS), a novel and efficient anchor placement scheme, starts with two randomly placed anchors and then uses this directional transformation to identify the number and placement of anchors in a completely distributed manner. Furthermore, a novelty-filtering-based approach for identifying a set of 'good' anchors that reduces the overhead and power consumption in routing is discussed. Physical layout information such as physical voids and even relative physical positions of sensor nodes with respect to X-Y directions are absent in a VCS description. Obtaining such information independent of physical information or signal strength measurements has not been possible until now. Two novel techniques to extract Topology Preserving Maps (TPMs) from VCS, based on Singular Value Decomposition (SVD) and DVCS are presented. A TPM is a distorted version of the layout of the network, but one that preserves the neighborhood information of the network. The generalized SVD-based TPM scheme for 3D networks provides TPMs even in situations where obtaining accurate physical information is not possible. The ability to restore directionality and topology-based Cartesian coordinates makes VCS competitive and, in many cases, a better alternative to geographic coordinates. This is demonstrated using two novel routing schemes in VC domain that outperform the well-known physical information-based routing schemes. The first scheme, DVC Routing (DVCR) uses the directionality recovered by DVCS. Geo-Logical Routing (GLR) is a technique that combines the advantages of geographic and logical routing to achieve higher routability at a lower cost by alternating between topology and virtual coordinate spaces to overcome local minima in the two domains. GLR uses topology domain coordinates derived solely from VCS as a better alternative for physical location information. A boundary detection scheme that is capable of identifying physical boundaries even for 3D surfaces is also proposed. "Network awareness" is a node's cognition of its neighborhood, its position in the network, and the network-wide status of the sensed phenomena. A novel technique is presented whereby a node achieves network awareness by passive listening to routine messages associated with applications in large-scale WSNs. With the knowledge of the network topology and phenomena distribution, every node is capable of making solo decisions that are more sensible and intelligent, thereby improving overall network performance, efficiency, and lifespan. In essence, this research has laid a firm foundation for use of Anchor Centric Virtual Coordinate Systems in WSN applications, without the need for physical coordinates. Topology coordinates, derived from virtual coordinates, provide a novel, economical, and in many cases, a better alternative to physical coordinates. A novel concept of network awareness at nodes is demonstrated

Statistical Downscaling of General Circulation Model Outputs to Precipitation - part 1: calibration and validation

This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article

Statistical Downscaling of General Circulation Model Outputs to Precipitation - part 2: bias-correction and future projections

This article is the second of a series of two articles. In the first article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia. In that study, it was seen that the downscaling model developed with NCEP/NCAR reanalysis outputs performs much better than the model developed with HadCM3 outputs. Furthermore, it was found that there is large bias in HadCM3 outputs which needs to be corrected. In this article, the downscaling model developed with NCEP/NCAR reanalysis outputs was used to downscale HadCM3 20th century climate experiment outputs to monthly precipitation over the period 1950-1999. In all four seasons, the precipitation downscaled with HadCM3 20th century outputs, displayed a large scatter and the majority of precipitation was overestimated. The precipitation downscaled with HadCM3 outputs was bias-corrected against the observed precipitation pertaining to the period 1950-1999, using three techniques: (1) equidistant quantile mapping (EDQM), (2) monthly bias-correction (MBC) and (3) nested bias-correction (NBC). Although all these bias-correction techniques were able to adequately correct the statistics of downscaled precipitation, the magnitude of the scatter of precipitation remained almost the same. Considering the performances and its ability to correct the cumulative distribution of precipitation, EDQM was selected for the bias-correction of future precipitation projections. HadCM3 outputs for the A2 and B1 greenhouse gas scenarios were introduced to the downscaling model and the downscaled precipitation for the period 2000-2099 was bias-corrected with the EDQM technique. Both A2 and B1 scenarios indicated a rise in the average of future precipitation in winter and a drop in it in summer and spring. These scenarios showed an increase in the maximum monthly precipitation in all seasons and an increase in percentage of months with zero precipitation in summer, autumn and spring. © 2014 Royal Meteorological Societ