Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Dynamic data retrieval on the World Wide Web

Methods for accessing data on the Web have been the focus of active research over the past few years. In this thesis we propose a method for representing Web sites as data sources. We designed a Data Extractor data retrieval solution that allows us to define queries to Web sites and process resulting data sets. Data Extractor is being integrated into the MSemODB heterogeneous database management system. With its help database queries can be distributed over both local and Web data sources within MSemODB framework. ^ Data Extractor treats Web sites as data sources, controlling query execution and data retrieval. It works as an intermediary between the applications and the sites. Data Extractor utilizes a twofold “custom wrapper” approach for information retrieval. Wrappers for the majority of sites are easily built using a powerful and expressive scripting language, while complex cases are processed using Java-based wrappers that utilize specially designed library of data retrieval, parsing and Web access routines. In addition to wrapper development we thoroughly investigate issues associated with Web site selection, analysis and processing. ^ Data Extractor is designed to act as a data retrieval server, as well as an embedded data retrieval solution. We also use it to create mobile agents that are shipped over the Internet to the client's computer to perform data retrieval on behalf of the user. This approach allows Data Extractor to distribute and scale well. ^ This study confirms feasibility of building custom wrappers for Web sites. This approach provides accuracy of data retrieval, and power and flexibility in handling of complex cases. ^

Dynamic data retrieval on the world wide web

Methods for accessing data on the Web have been the focus of active research over the past few years. In this thesis we propose a method for representing Web sites as data sources. We designed a Data Extractor data retrieval solution that allows us to define queries to Web sites and process resulting data sets. Data Extractor is being integrated into the MSemODB heterogeneous database management system. With its help database queries can be distributed over both local and Web data sources within MSemODB framework. Data Extractor treats Web sites as data sources, controlling query execution and data retrieval. It works as an intermediary between the applications and the sites. Data Extractor utilizes a two-fold "custom wrapper" approach for information retrieval. Wrappers for the majority of sites are easily built using a powerful and expressive scripting language, while complex cases are processed using Java-based wrappers that utilize specially designed library of data retrieval, parsing and Web access routines. In addition to wrapper development we thoroughly investigate issues associated with Web site selection, analysis and processing. Data Extractor is designed to act as a data retrieval server, as well as an embedded data retrieval solution. We also use it to create mobile agents that are shipped over the Internet to the client's computer to perform data retrieval on behalf of the user. This approach allows Data Extractor to distribute and scale well. This study confirms feasibility of building custom wrappers for Web sites. This approach provides accuracy of data retrieval, and power and flexibility in handling of complex cases.

Structural damage estimation using dynamic data and multi-objective optimization

Peer reviewed

(Fig. 4) Multiproxy data set of box core MSM5/5-712-1

A multiproxy data set of an AMS radiocarbon dated 46 cm long sediment core from the continental margin off western Svalbard reveals multidecadal climatic variability during the past two millennia. Investigation of planktic and benthic stable isotopes, planktic foraminiferal fauna, and lithogenic parameters aims to unveil the Atlantic Water advection to the eastern Fram Strait by intensity, temperatures, and salinities. Atlantic Water has been continuously present at the site over the last 2,000 years. Superimposed on the increase in sea ice/icebergs, a strengthened intensity of Atlantic Water inflow and seasonal ice-free conditions were detected at ~ 1000 to 1200 AD, during the well-known Medieval Climate Anomaly (MCA). However, temperatures of the MCA never exceeded those of the 20th century. Since ~ 1400 AD significantly higher portions of ice rafted debris and high planktic foraminifer fluxes suggest that the site was located in the region of a seasonal highly fluctuating sea ice margin. A sharp reduction in planktic foraminifer fluxes around 800 AD and after 1730 AD indicates cool summer conditions with major influence of sea ice/icebergs. High amounts of the subpolar planktic foraminifer species Turborotalia quinqueloba in size fraction 150-250 µm indicate strengthened Atlantic Water inflow to the eastern Fram Strait already after ~ 1860 AD. Nevertheless surface conditions stayed cold well into the 20th century indicated by low planktic foraminiferal fluxes. Most likely at the beginning of the 20th century, cold conditions of the terminating Little Ice Age period persisted at the surface whereas warm and saline Atlantic Water already strengthened, hereby subsiding below the cold upper mixed layer. Surface sediments with high abundances of subpolar planktic foraminifers indicate a strong inflow of Atlantic Water providing seasonal ice-free conditions in the eastern Fram Strait during the last few decades.

Minimum Data Set (MDS) Section Q Education: Helping People Return to the Community, February 12, 2014

Explanation of Minimum Data Set (MDS), implementation of Section Q, overview of the program, local contacts and functions, Referral Agency information, role and assistance provided by Long-Term care Ombudsman

Minimum Data Set (MDS) Section Q Education, February 21, 2014

Information regarding possible questions on Section Q within the Minimum Data Set.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Effects of Exposure to the Sound from Seismic Airguns on Pallid Sturgeon and Paddlefish - Data Set

Dataset for publication in PLOS One

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.

Using SeaDataNet Management System to preserve the XBT Data-Set of the Mediterranean Sea

significant amount of Expendable Bathythermograph (XBT) data has been collected in the Mediterranean Sea since 1999 in the framework of operational oceanography activities. The management and storage of such a volume of data poses significant challenges and opportunities. The SeaDataNet project, a pan-European infrastructure for marine data diffusion, provides a convenient way to avoid dispersion of these temperature vertical profiles and to facilitate access to a wider public. The XBT data flow, along with the recent improvements in the quality check procedures and the consistence of the available historical data set are described. The main features of SeaDataNet services and the advantage of using this system for long-term data archiving are presented. Finally, focus on the Ligurian Sea is included in order to provide an example of the kind of information and final products devoted to different users can be easily derived from the SeaDataNet web portal.

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.