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.

Switching and generation of ultrashort pulses using all-fibre devices

Serial and parallel interconnection of photonic devices is integral to the construction of any all-optical data processing system. This thesis presents results from a series of experiments centering on the use of the nonlinear-optical loop mirror (NOLM) switch in architectures for the manipulation and generation of ultrashort pulses. Detailed analysis of soliton switching in a single NOLM and cascade of two NOLM's is performed, centering on primary limitations to device operation, effect of cascading on amplitude response, and impact of switching on the characteristics of incident pulses. By using relatively long input pulses, device failure due to stimulated Raman generation is postponed to demonstrate multiple-peaked switching for the first time. It is found that while cascading leads to a sharpening of the overall switching characteristic, pulse spectral and temporal integrity is not significantly degraded, and emerging pulses retain their essential soliton character. In addition, by including an asymmetrically placed in-fibre Bragg reflector as a wavelength selective loss element in the basic NOLM configuration, both soliton self-switching and dual-wavelength control-pulse switching are spectrally quantised. Results are presented from a novel dual-wavelength laser configuration generating pulse trains with an ultra-low rms inter-pulse-stream timing jitter level of 630fs enabling application in ultrafast switching environments at data rates as high as 130GBits/s. In addition, the fibre NOLM is included in architectures for all-optical memory, demonstrating storage and logical inversion of a 0.5kByte random data sequence; and ultrafast phase-locking of a gain-switched distributed feedback laser at 1.062GHz, the fourteenth harmonic of the system baseband frequency. The stringent requirements for environmental robustness of these architectures highlight the primary weaknesses of the NOLM in its fibre form and recommendations to overcome its inherent drawbacks are presented.

Exploratory database visualisation: the application and assessment of data and dimensionality reduction

This thesis describes the development of a complete data visualisation system for large tabular databases, such as those commonly found in a business environment. A state-of-the-art 'cyberspace cell' data visualisation technique was investigated and a powerful visualisation system using it was implemented. Although allowing databases to be explored and conclusions drawn, it had several drawbacks, the majority of which were due to the three-dimensional nature of the visualisation. A novel two-dimensional generic visualisation system, known as MADEN, was then developed and implemented, based upon a 2-D matrix of 'density plots'. MADEN allows an entire high-dimensional database to be visualised in one window, while permitting close analysis in 'enlargement' windows. Selections of records can be made and examined, and dependencies between fields can be investigated in detail. MADEN was used as a tool for investigating and assessing many data processing algorithms, firstly data-reducing (clustering) methods, then dimensionality-reducing techniques. These included a new 'directed' form of principal components analysis, several novel applications of artificial neural networks, and discriminant analysis techniques which illustrated how groups within a database can be separated. To illustrate the power of the system, MADEN was used to explore customer databases from two financial institutions, resulting in a number of discoveries which would be of interest to a marketing manager. Finally, the database of results from the 1992 UK Research Assessment Exercise was analysed. Using MADEN allowed both universities and disciplines to be graphically compared, and supplied some startling revelations, including empirical evidence of the 'Oxbridge factor'.

Approximation of Experimental Data by Bezier Curves

Very often the experimental data are the realization of the process, fully determined by some unknown function, being distorted by hindrances. Treatment and experimental data analysis are substantially facilitated, if these data to represent as analytical expression. The experimental data processing algorithm and the example of using this algorithm for spectrographic analysis of oncologic preparations of blood is represented in this article.

An improved memory management scheme for large scale graph computing engine GraphChi

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of 'pin ' from TurboGraph and 'ghost' from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

An Algorithmic Solution for Management of Related Text Objects with Application in Phytopharmacy

This paper presents an algorithmic solution for management of related text objects, in which are integrated algorithms for their extraction from paper or electronic format, for their storage and processing in a relational database. The developed algorithms for data extraction and data analysis enable one to find specific features and relations between the text objects from the database. The algorithmic solution is applied to data from the field of phytopharmacy in Bulgaria. It can be used as a tool and methodology for other subject areas where there are complex relationships between text objects.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

Pulsed parabolic equation model of acoustic transmission (PPEMAT)

Underwater sound is very important in the field of oceanography where it is used for remote sensing in much the same way that radar is used in atmospheric studies. One way to mathematically model sound propagation in the ocean is by using the parabolic-equation method, a technique that allows range dependent environmental parameters. More importantly, this method can model sound transmission where the source emits either a pure tone or a short pulse of sound. Based on the parabolic approximation method and using the split-step Fourier algorithm, a computer model for underwater sound propagation was designed and implemented. This computer model differs from previous models in its use of the interactive mode, structured programming, modular design, and state-of-the-art graphics displays. In addition, the model maximizes the efficiency of computer time through synchronization of loosely coupled dual processors and the design of a restart capability. Since the model is designed for adaptability and for users with limited computer skills, it is anticipated that it will have many applications in the scientific community.

Intelligent retrieval system for conditions of contract documents in construction

The outcome of this research is an Intelligent Retrieval System for Conditions of Contract Documents. The objective of the research is to improve the method of retrieving data from a computer version of a construction Conditions of Contract document. SmartDoc, a prototype computer system has been developed for this purpose. The system provides recommendations to aid the user in the process of retrieving clauses from the construction Conditions of Contract document. The prototype system integrates two computer technologies: hypermedia and expert systems. Hypermedia is utilized to provide a dynamic way for retrieving data from the document. Expert systems technology is utilized to build a set of rules that activate the recommendations to aid the user during the process of retrieval of clauses. The rules are based on experts knowledge. The prototype system helps the user retrieve related clauses that are not explicitly cross-referenced but, according to expert experience, are relevant to the topic that the user is interested in.

In Vivo Characterization of Myocardial Tissue Post Myocardial Infarction Using Combined Fluorescence and Diffuse Reflectance Spectroscopy

Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on - (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region - one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees / severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.