Using ArchivesSpace to Support Research Data Curation

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Building Dynamic Data Centers for Fast Delivery of New Data and Data Updates

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

HiberActive: Pro-Active Archiving of Web References from Scholarly Articles

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Research Data Management in the Cloud

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Preparing for CRIS: Challenges and Opportunities for Systems Integration at Finnish Universities

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Purdue University Research Repository (PURR): An institutional data management service with a virtual research environment, data publication, and archiving

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Data Flows and Engaging Visualisations for Your Repositories

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

GIS in Digital Repositories

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Designing a Bit Preservation System

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Very High Energy Gamma-Rays emitting BL LAC's Population Study

Context: BL Lacs are the most numerous extragalactic objects which are detected in Very High Energy (VHE) gamma-rays band. They are a subclass of blazars. Large flux variability amplitude, sometimes happens in very short time scale, is a common characteristic of them. Significant optical polarization is another main characteristics of BL Lacs. BL Lacs' spectra have a continuous and featureless Spectral Energy Distribution (SED) which have two peaks. Among 1442 BL Lacs in the Roma-BZB catalogue, only 51 are detected in VHE gamma-rays band. BL Lacs are most numerous (more than 50% of 514 objects) objects among the sources that are detected above 10 GeV by FERMI-LAT. Therefore, many BL Lacs are expected to be discovered in VHE gamma-rays band. However, due to the limitation on current and near future technology of Imaging Air Cherenkov Telescope, astronomers are forced to predict whether an object emits VHE gamma-rays or not. Some VHE gamma-ray prediction methods are already introduced but still are not confirmed. Cross band correlations are the building blocks of introducing VHE gamma-rays prediction method. Aims: We will attempt to investigate cross band correlations between flux energy density, luminosity and spectral index of the sample. Also, we will check whether recently discovered MAGIC J2001+435 is a typical BL Lac. Methods: We select a sample of 42 TeV BL Lacs and collect 20 of their properties within five energy bands from literature and Tuorla blazar monitoring program database. All of the data are synchronized to be comparable to each other. Finally, we choose 55 pair of datasets for cross band correlations finding and investigating whether there is any correlation between each pair. For MAGIC J2001+435 we analyze the publicly available SWIFT-XRT data, and use the still unpublished VHE gamma-rays data from MAGIC collaboration. The results are compared to the other sources of the sample. Results: Low state luminosity of multiple detected VHE gamma-rays is strongly correlated luminosities in all other bands. However, the high state does not show such strong correlations. VHE gamma-rays single detected sources have similar behaviour to the low state of multiple detected ones. Finally, MAGIC J2001+435 is a typical TeV BL Lac. However, for some of the properties this source is located at the edge of the whole sample (e.g. in terms of X-rays flux). Keywords: BL Lac(s), Population study, Correlations finding, Multi wavelengths analysis, VHE gamma-rays, gamma-rays, X-rays, Optical, Radio

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Schools as Oral Health Promoters – Evaluation of National Sweet Selling Recommendation and Oral Health Education Material

The aims were to find out 1) if schools’ oral health practices were associated with pupils’ oral health behaviour and whether 2) the national sweet-selling recommendation and 3) distributing oral health material (OHEM) affected schools as oral health promoters. Three independently collected datasets from Finnish upper comprehensive schools (N=988) were used: longitudinal oral health practices data (n=258) with three-year follow up (2007 n=480, 2008 n=508, 2009 n=593) from principals’ online questionnaires, oral health behaviour data from pupils participating in the national School Health Promotion Study (n=970 schools) and oral health education data from health education teachers’ online questionnaires (2008 n=563, 2009 n=477 teachers). Oral health practices data and oral health behaviour data were combined (n=414) to answer aim 1. For aims 2 and 3, oral health practices data and oral health education data were used independently. School sweet selling and an open campus policy were associated with pupils’ use of sweet products and tobacco products during school time. The National Recommendation was quite an effective way to reduce the number of sweet-selling schools, but there were large regional differences and a lack of a clear oral health policy in the schools. OHEM did not increase the proportion of teachers teaching oral health, but teachers started to cover oral health topics more frequently. Women started to use OHEM more often than men did. Schools’ oral health policy should include prohibiting the selling of sweet products in school by legislative actions, enabling healthy alternatives instead, and setting a closed campus policy to protect pupils from school-time sweet consuming and smoking.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Digital content and making it viral in social media

Tutkimuksen tavoite on selvittää digitaalisen sisällön ominaisuuksia, jotka vaikuttavat ryhtyvätkö kuluttajat jakamaan, tykkäämään ja kommentoimaan sitä sosiaalisessa mediassa. Tällä pyritään auttamaan yrityksiä ymmärtämään paremmin viraalisuutta, jotta he pystyisivät tuottamaan ja julkaisemaan nettisivuillaan ja sosiaalisessa mediassa parempaa sisältöä, jota kuluttajat jakaisivat enemmän. Tutkimus toteutetaan muodostamalla hypoteeseja mahdollisista ominaisuuksista kirjallisuuden perusteella ja testaamalla niitä regressioanalyyseillä empiirisessä osiossa. Tulokset paljastavat yhdeksän piirrettä, jotka lisäävät viraalisuutta: kiinnostavuus, neutraalisuus, yllättävyys, viihdyttävyys, epäkäytännöllisyys, artikkelin ja Facebook julkaisun pituus, eri sisältö taktiikoiden käyttö (erityisesti blogit ja kuvat lisäävät viraalisuutta) sekä kun mielipidevaikuttaja tai kuuluisuus jakaa sisällön.