Successes and failures of digital libraries /

"Papers presented at the 35th annual Clinic on Library Applications of Data Processing, March 22-24, 1998."

Guide to reporting highway statistics: procedures for the compilation, analysis and reporting of: state motor-fuel, motor-vehicle, driver-license and motor-carrier data; highway finance data of state and local governments.

Mode of access: Internet.

Waveform generator signal processing software. Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

Development of a data link from PDP 11/34 computer to HP 9830 calculator. Final report.

National Highway Traffic Safety Administration, East Liberty, Ohio

An on-line electro-optical video processing system.

Vita.

System and circuit design of the transformatrix coefficient processor and output data channel,

Vita.

Procedures for analyzing data obtained from LANDSAT multispectral imagery /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Redemptioners and indentured servants in the colony and commonwealth of Pennsylvania, [electronic resource]

Bibliography: p. 120-125.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.

Data quality initiatives - striving for continuous improvements

Although managers consider accurate, timely, and relevant information as critical to the quality of their decisions, evidence of large variations in data quality abounds. Over a period of twelve months, the action research project reported herein attempted to investigate and track data quality initiatives undertaken by the participating organisation. The investigation focused on two types of errors: transaction input errors and processing errors. Whenever the action research initiative identified non-trivial errors, the participating organisation introduced actions to correct the errors and prevent similar errors in the future. Data quality metrics were taken quarterly to measure improvements resulting from the activities undertaken during the action research project. The action research project results indicated that for a mission-critical database to ensure and maintain data quality, commitment to continuous data quality improvement is necessary. Also, communication among all stakeholders is required to ensure common understanding of data quality improvement goals. The action research project found that to further substantially improve data quality, structural changes within the organisation and to the information systems are sometimes necessary. The major goal of the action research study is to increase the level of data quality awareness within all organisations and to motivate them to examine the importance of achieving and maintaining high-quality data.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.

OSTEOTOMIA SEGMENTAR DE MAXILA CASO CLÍNICO

A Osteotomia Segmentar de Maxila ou Osteotomia Le Fort I Segmentada é um procedimento que tem se tornado, cada vez mais comum, nas cirugias para as correções das deformidades dentofaciais, conhecidas como Cirurgias Ortognáticas. Este procedimento é muito bem indicado para a correção das discrepâncias maxilares, nos diferentes planos e num único tempo cirúrgico, otimizando assim, o tempo de tratamento a que o paciente é submetido. A estabilidade esquelética transversal e a oclusal dos pacientes, que são submetidos a este tipo de osteotomia, tem sido objeto de estudo na literatura , assim como também, os potenciais riscos e complicações inerentes a este procedimento como, a desvitalização dentária, fístula oro-nasal, perda dentária, necrose de algum segmento da maxila ou até mesmo, de toda a maxila. O objetivo deste trabalho é apresentar o caso clínico de um paciente submetido à osteotomia segmentar de maxila, e fazer uma revisão da literatura abrangendo os últimos 10 anos, com artigos que abordam a estabilidade deste tipo de procedimento, assim como também os potenciais riscos e complicações aos pacientes submetidos a este procedimento. Utilizando algumas palavras chave na base de dados eletrônica PUBMED, 12 artigos foram selecionados para este trabalho, no período de 2002 a 2012. A Osteotomia Segmentar de Maxila é um procedimento estável e seguro, com baixo índice de complicação, quando indicado corretamente e com os devidos cuidados no pré, trans e pós operatórios.

Neural networks for time-varying data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central difficulty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.