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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
471

Spatio-temporal Traffic Flow Prediction

Gebresilassie, Mesele Atsbeha January 2017 (has links)
The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.
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472

Clustering Methods as a Recruitment Tool for Smaller Companies / Klustermetoder som ett verktyg i rekrytering för mindre företag

Thorstensson, Linnea January 2020 (has links)
With the help of new technology it has become much easier to apply for a job. Reaching out to a larger audience also results in a lot of more applications to consider when hiring for a new position. This has resulted in that many big companies uses statistical learning methods as a tool in the first step of the recruiting process. Smaller companies that do not have access to the same amount of historical and big data sets do not have the same opportunities to digitalise their recruitment process. Using topological data analysis, this thesis explore how clustering methods can be used on smaller data sets in the early stages of the recruitment process. It also studies how the level of abstraction in data representation affects the results. The methods seem to perform well on higher level job announcements but struggles on basic level positions. It also shows that the representation of candidates and jobs has a huge impact on the results. / Ny teknologi har förenklat processen för att söka arbete. Detta har resulterat i att företag får tusentals ansökningar som de måste ta hänsyn till. För att förenkla och påskynda rekryteringsprocessen har många stora företag börjat använda sig av maskininlärningsmetoder. Mindre företag, till exempel start-ups, har inte samma möjligheter för att digitalisera deras rekrytering. De har oftast inte tillgång till stora mängder historisk ansökningsdata. Den här uppsatsen undersöker därför med hjälp av topologisk dataanalys hur klustermetoder kan användas i rekrytering på mindre datauppsättningar. Den analyserar också hur abstraktionsnivån på datan påverkar resultaten. Metoderna visar sig fungera bra för jobbpositioner av högre nivå men har problem med jobb på en lägre nivå. Det visar sig också att valet av representation av kandidater och jobb har en stor inverkan på resultaten.
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473

Transcription factor analysis of longitudinal mRNA expression data / Transkriptionsfaktorsanalys av longitudinellmRNA-uttrycksdata

Jangerstad, August January 2020 (has links)
Transcription factors (TFs) are key regulatory proteins that regulate transcriptionthrough precise, but highly variable binding events to cis-regulatory elements.The complexity of their regulatory patterns makes it difficult to determinethe roles of different TFs, a task which the field is still struggling with.Experimental procedures for this purpose, such as knock out experiments, arehowever costly and time consuming, and with the ever-increasing availabilityof sequencing data, computational methods for inferring the activity of TFsfrom such data have become of great interest. Current methods are howeverlacking in several regards, which necessitates further exploration of alternatives. A novel tool for estimating the activity of individual TFs over time fromlongitudinal mRNA expression data was in this project therefore put togetherand tested on data from Mus musculus liver and brain. The tool is based onprincipal component analysis, which is applied to data subsets containing theexpression data of genes likely regulated by a specific TF to acquire an estimationof its activity. Though initial tests on 17 selected TFs showed issues withunspecific trends in the estimations, further testing is required for a statementon the potential of the estimator. / Transcriptionsfaktorer (TFer) är viktiga regulatoriska protein som reglerar transkriptiongenom att binda till cis-regulatoriska element på precisa, menmycketvarierande vis. Komplexiteten i deras regulatoriska mönster gör det svårt attavgöra vilka roller olika TFer har, vilket är en uppgift som fältet fortfarandebrottas med. Experimentella procedurer i detta syfte, till exempel "knockout"experiment, är dock kostsamma och tidskrävande, och med den evigt ökandetillgången på sekvenseringsdata har metoder för att beräkna TFers aktivitetfrån sådan data fått stort intresse. De beräkningsmetoder som finns idag bristerdock på flera punker, vilket erfordrar ett fortsatt sökande efter alternativ. Ett nytt vektyg för att upskatta aktiviteten hos individuella TFer över tidmed hjälp av longitunell mRNA-uttrycksdata utvecklades därför i det här projektetoch testades på data från Mus musculus lever och hjärna. Verktyget ärbaserat på principalkomponentsanalys, som applicerades på set med uttrycksdatafrån gener sannolikt reglerade av en specifik TF för att erhålla en uppskattningav dess aktivitet. Trots att de första testerna för 17 utvalda TFer påvisadeproblem med ospecifika trender i upskattningarna krävs forsatta tester för attkunna ge ett tydligt svar på vilken potential estimatorn har.
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474

The preferences of homebuyers with a negative outlook on the real estate market : Investigating the preferences of homebuyers in Stockholm / Preferenser för bostadsköpare med en negative syn på fastighetsmarknaden

Johansson, Mattias, Rosendahl, Caroline January 2018 (has links)
The real estate market is constantly fluctuating and when the market slows down, it becomes more difficult to sell real estate. Because of this, it is of importance to construction companies to increase their understanding of homebuyer preferences in order for them to build condominiums that there is a demand for in a declining market, and thereby create a more liquid real estate market.  The aim of this thesis is to examine what the differences in preferences are between homebuyers who are positive and negative to the market development. To achieve this objective, three hypotheses were formulated regarding preferences of homebuyers with a negative outlook on the real estate market concerning location, size and functionality. Using a survey and questionnaire, data from potential homebuyers was collected at real estate viewings and via the internet. Once the respondents’ answers were collected, a principal component analysis was performed in order to find out how the different statements correlated with each other. After removing the statements that did not load correctly, three dimensions clearly corresponded to each of the three hypotheses. Testing the internal reliability for the three dimensions resulted in low values for dimensions 2 and 3, referring to size and functionality. However, internal reliability was good for dimension 1 referring to location. Performing an independent t-test on homebuyers with a positive and respective negative outlook on the market development showed no significant results for our hypotheses. The null-hypothesis is thus not rejected, meaning that for this sample, there are no significant differences in preferences of homebuyers with a positive and negative market outlook regarding location, size or functionality of their housing. / Fastighetsmarknaden befinner sig i konstant rörelse och när marknaden övergår i en negativ trend blir det svårare att sälja fastigheter. På grund av detta är det viktigt för byggföretag att öka sin förståelse av bostadsköpares preferenser så att de kan bygga efterfrågade bostäder på en nedåtgående marknad, och därigenom skapa en mer likvid fastighetsmarknad. Syftet med denna uppsats är att undersöka vad skillnaderna i preferenser är mellan bostadsköpare som är positiva och negativa till marknadsutvecklingen. För att uppnå detta mål formulerades tre hypoteser avseende preferenser hos bostadsköpare med negativ syn på marknadsutvecklingen gällande område, storlek och funktionalitet. Med hjälp av en enkät och frågeformulär samlades data från potentiella bostadsköpare vid bostadsvisningar och via internet. När respondenternas svar samlats in genomfördes en principalkomponentanalys för att ta reda på hur de olika påståendena korrelerade med varandra. Efter att ha tagit bort de påståenden som inte laddades korrekt var det tydligt att tre dimensioner korresponderade mot var och en av de tre hypoteserna. Test av den interna konsistensen för de tre dimensionerna resulterade i låga värden för dimensionerna 2 och 3, med hänvisning till storlek och funktionalitet. Den interna konsistensen var dock bra för dimension 1 med hänvisning till område. Ett oberoende t-test avseende bostadsköpare med positiv respektive negativ inställning till marknadsutvecklingen genomfördes men visade inte på några signifikanta resultat för våra hypoteser. Nollhypotesen kan således inte förkastas, vilket innebär att det för vårt indata inte finns några signifikanta skillnader i preferenser hos bostadsköpare med positiv respektive negativ syn på marknaden avseende område, storlek eller funktionalitet gällande deras bostäder.
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475

Visual Analytics of Big Data from Molecular Dynamics Simulation

Rajendran, Catherine Jenifer Rajam 12 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations. The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein. Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions. Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs. Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins. Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.
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476

Ant and spider dynamics in complex riverine landscapes of the Scioto River basin, Ohio: implications for riparian ecosystem structure and function

Tagwireyi, Paradzayi 29 September 2014 (has links)
No description available.
477

Stochastic Modeling of Geometric Mistuning and Application to Fleet Response Prediction

Henry, Emily Brooke January 2014 (has links)
No description available.
478

Critical Analysis of Dimensionality Reduction Techniques and Statistical Microstructural Descriptors for Mesoscale Variability Quantification

Galbincea, Nicholas D. January 2017 (has links)
No description available.
479

ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING

Ergin, Leanna N. 07 August 2017 (has links)
No description available.
480

Analyzing Recycling Habits in Mahoning County, Ohio

Yengwia , Lawrenzo N. January 2017 (has links)
No description available.

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