FEMA’s Capability Development After Katrina

After Action Reports for Hurricane Isaac & Sandy concluded that WebEOC was correct choice for FEMA’s Crisis Management System: real time data easily shared between FEMA Headquarters, Regions and Incident Management Assistance Teams; cloud capability allowed use on any web connected device, laptop, tablet, iPad, smart phone; intuitive System - Offgoing personnel able to train incoming reliefs on new features or changes within minutes; widespread use of WebEOC through out country in 19 other Federal Departments and Agencies, 40 States, hundreds of cities/counties and industry provided a number of users that had prior experience using WebEOC and reduced learning curve experienced when new systems are introduced; focusing on a single shared web database reduced creation of new single purpose databases, spreadsheets and share point sites allowing best practices to be captured, refined, shared and continued

Supply management capability

The objective of this thesis is to define supply management capability. The thesis study what factors consist of supply management capability, and which of those factors are critical to achieving competitive advantage. One objective is also to study how firms can measure their supply management capability. This study is a qualitative research. The thesis examines the literature regarding to supply management and the context of capability and there are used Delphi panel to examine the current and future insights of supply management professionals concerning of supply management skills and capability. The empirical data of the thesis was collected by interviews. The Delphi panel was used in data collection and analysis and for prioritization of the factors of supply management capability. The thesis includes lists of factors of supply management capability. Main findings of the study were that there is no one clear, generally suitable set of supply management skills which bring competitive advantage for all firms and the most important factors of supply management capability, according to the experts, are total cost analysis, customer focus, general business view, market knowledge and supplier relationships. In this study the supply management capability is defined as organization’s overall capacity and ability to achieve a holistic understanding of purchasing needs, manage its suppliers and collaborative partners, and conduct its internal tasks, routines and responsibilities in a way that achieves desired results. The results of this thesis show also that Finnish firms need more right kind of supply management knowledge.

Predictive Capability of HPV and Pap Tests in Screening for Cervical Cancer over a Three-Year Follow-up

Purpose To compare the predictive capability of HPV and Pap smear tests for screening pre-cancerous lesions of the cervix over a three-year follow-up, in a population of users of the Brazilian National Health System (SUS). Methods This is a retrospective cohort study of 2,032 women with satisfactory results for Pap smear and HPV tests using second-generation hybrid capture,made in a previous study. We followed them for 36 months with data obtained from medical records, the Cervix Cancer Information System (SISCOLO), and the Mortality Information System (SIM). The outcome was a histological diagnosis of cervical intraepithelial neoplasia grade 2 or more advanced lesions (CIN2ş). We constructed progression curves of the baseline test results for the period, using the Kaplan-Meier method, and estimated sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios for each test. Results A total of 1,440 women had at least one test during follow-up. Progression curves of the baseline test results indicated differences in capability to detect CIN2ş (p < 0.001) with significantly greater capability when both tests were abnormal, followed by only a positive HPV test. The HPV test was more sensitive than the Pap smear (88.7% and 73.6%, respectively; p < 0.05) and had a better negative likelihood ratio (0.13 and 0.30, respectively). Specificity and positive likelihood ratio of the tests were similar. Conclusions These findings corroborate the importance of HPV test as a primary cervical cancer screening.

Performance management through innovation capability in SMEs

Small and medium-sized enterprises (SMEs) are assuredly important to maintain strong economic growth. How to manage and maintain SMEs’ performance is a sizable challenge, and requires an understanding of the drivers of performance. Innovation capability has been suggested to be one of these key drivers. In order to manage innovation capability– performance relationship, it has to be measured. SMEs may have distinct characteristics that separate them being just smaller versions of large firms. Performance measurement and management of innovation capability is challenging, because SMEs usually have some drawbacks compared to large firms. Thus, it is unclear whether theories developed to understand large firms apply to SMEs. This research contributes to the existing discussion on performance management through innovation capability in the SME context. First, it aims at increasing understanding of the role of innovation capability in performance management. Second, it aims at clarifying the role of performance measurement in developing innovation capability. Thus, the main objective of the research is to study how to manage performance through measuring and managing innovation capability. The thesis is based on five research articles that follow a positivist approach. From a methodological point of view, quantitative and complementing conceptual methods of data collection are utilized. This research indicates that the performance management and measurement play a significant role in innovation capability in SMEs. This research makes three main contributions. First, it gives empirical evidence on the connection between innovation capability and SME performance. Second, it illustrates the connection between performance measurement and innovation capability. Thirdly, it clarifies how to measure the relationship between innovation capability and performance.

Design and Development of Compact Chipless RFID Tags with High Data Encoding Capacity

The main objective of this thesis is to develop a compact chipless RFID tag with high data encoding capacity. The design and development of chipless RFID tag based on multiresonator and multiscatterer methods are presented first. An RFID tag using using SIR capable of 79bits is proposed. The thesis also deals with some of the properties of SIR like harmonic separation, independent control on resonant modes and the capability to change the electrical length. A chipless RFID reader working in a frequency band of 2.36GHz to 2.54GHz has been designed to show the feasibility of the RFID system. For a practical system, a new approach based on UWB Impulse Radar (UWB IR) technology is employed and the decoding methods from noisy backscattered signal are successfully demonstrated. The thesis also proposes a simple calibration procedure, which is able to decode the backscattered signal up to a distance of 80cm with 1mW output power.

An observing system simulation experiment for climate monitoring with GNSS radio occulation data: setup and testbed study

The long-term stability, high accuracy, all-weather capability, high vertical resolution, and global coverage of Global Navigation Satellite System (GNSS) radio occultation (RO) suggests it as a promising tool for global monitoring of atmospheric temperature change. With the aim to investigate and quantify how well a GNSS RO observing system is able to detect climate trends, we are currently performing an (climate) observing system simulation experiment over the 25-year period 2001 to 2025, which involves quasi-realistic modeling of the neutral atmosphere and the ionosphere. We carried out two climate simulations with the general circulation model MAECHAM5 (Middle Atmosphere European Centre/Hamburg Model Version 5) of the MPI-M Hamburg, covering the period 2001–2025: One control run with natural variability only and one run also including anthropogenic forcings due to greenhouse gases, sulfate aerosols, and tropospheric ozone. On the basis of this, we perform quasi-realistic simulations of RO observables for a small GNSS receiver constellation (six satellites), state-of-the-art data processing for atmospheric profiles retrieval, and a statistical analysis of temperature trends in both the “observed” climatology and the “true” climatology. Here we describe the setup of the experiment and results from a test bed study conducted to obtain a basic set of realistic estimates of observational errors (instrument- and retrieval processing-related errors) and sampling errors (due to spatial-temporal undersampling). The test bed results, obtained for a typical summer season and compared to the climatic 2001–2025 trends from the MAECHAM5 simulation including anthropogenic forcing, were found encouraging for performing the full 25-year experiment. They indicated that observational and sampling errors (both contributing about 0.2 K) are consistent with recent estimates of these errors from real RO data and that they should be sufficiently small for monitoring expected temperature trends in the global atmosphere over the next 10 to 20 years in most regions of the upper troposphere and lower stratosphere (UTLS). Inspection of the MAECHAM5 trends in different RO-accessible atmospheric parameters (microwave refractivity and pressure/geopotential height in addition to temperature) indicates complementary climate change sensitivity in different regions of the UTLS so that optimized climate monitoring shall combine information from all climatic key variables retrievable from GNSS RO data.

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.

Serving GODAE Data and Products to the Ocean Community

The Global Ocean Data Assimilation Experiment (GODAE [http:// www.godae.org]) has spanned a decade of rapid technological development. The ever-increasing volume and diversity of oceanographic data produced by in situ instruments, remote-sensing platforms, and computer simulations have driven the development of a number of innovative technologies that are essential for connecting scientists with the data that they need. This paper gives an overview of the technologies that have been developed and applied in the course of GODAE, which now provide users of oceanographic data with the capability to discover, evaluate, visualize, download, and analyze data from all over the world. The key to this capability is the ability to reduce the inherent complexity of oceanographic data by providing a consistent, harmonized view of the various data products. The challenges of data serving have been addressed over the last 10 years through the cooperative skills and energies of many individuals.

The relationship between absolute and proportion scores of serial order memory: simulation predictions and empirical data

The capability of a feature model of immediate memory (Nairne, 1990; Neath, 2000) to predict and account for a relationship between absolute and proportion scoring of immediate serial recall when memory load is varied (the list-length effect, LLE) is examined. The model correctly predicts the novel finding of an LLE in immediate serial order memory similar to that observed with free recall and previously assumed to be attributable to the long-term memory component of that procedure (Glanzer, 1972). The usefulness of formal models as predictive tools and the continuity between short-term serial order and longer term item memory are considered.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Blind equalisation of multilevel PAM data for nonminimum phase channels via second- and fourth-order cumulants

This paper introduces a new blind equalisation algorithm for the pulse amplitude modulation (PAM) data transmitted through nonminimum phase (NMP) channels. The algorithm itself is based on a noncausal AR model of communication channels and the second- and fourth-order cumulants of the received data series, where only the diagonal slices of cumulants are used. The AR parameters are adjusted at each sample by using a successive over-relaxation (SOR) scheme, a variety of the ordinary LMS scheme, but with a faster convergence rate and a greater robustness to the selection of the ‘step-size’ in iterations. Computer simulations are implemented for both linear time-invariant (LTI) and linear time-variant (LTV) NMP channels, and the results show that the algorithm proposed in this paper has a fast convergence rate and a potential capability to track the LTV NMP channels.

A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS

The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.

Data assimilation for state and parameter estimation: application to morphodynamic modelling

Data assimilation is predominantly used for state estimation; combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts we need good estimates of both the current system state and the model parameters. This paper presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three dimensional variational(3D-Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from SAR satellite imagery and a ship-borne survey is shown to significantly improve the predictive capability of the model over a 2 year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this paper considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D-Var data assimilation algorithm for joint state-parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation-based state-parameter estimation in coastal morphodynamic modelling.

Towards global drought early warning capability: expanding international cooperation for the development of a framework for global drought monitoring and forecasting

Drought is a global problem that has far-reaching impacts and especially 47 on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF) and the recent progress made towards its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global real-time drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental to global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in-situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress towards meeting these challenges and developing a global system.