Geo-enabling repositories - techniques, tools, and challenges

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

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Minimizing Conducted RF-Emissions in Switch Mode Power Supplies Using Spread-Spectrum Techniques

Switching power supplies are usually implemented with a control circuitry that uses constant clock frequency turning the power semiconductor switches on and off. A drawback of this customary operating principle is that the switching frequency and harmonic frequencies are present in both the conducted and radiated EMI spectrum of the power converter. Various variable-frequency techniques have been introduced during the last decade to overcome the EMC problem. The main objective of this study was to compare the EMI and steady-state performance of a switch mode power supply with different spread-spectrum/variable-frequency methods. Another goal was to find out suitable tools for the variable-frequency EMI analysis. This thesis can be divided into three main parts: Firstly, some aspects of spectral estimation and measurement are presented. Secondly, selected spread spectrum generation techniques are presented with simulations and background information. Finally, simulations and prototype measurements from the EMC and the steady-state performance are carried out in the last part of this work. Combination of the autocorrelation function, the Welch spectrum estimate and the spectrogram were used as a substitute for ordinary Fourier methods in the EMC analysis. It was also shown that the switching function can be used in preliminary EMC analysis of a SMPS and the spectrum and autocorrelation sequence of a switching function correlates with the final EMI spectrum. This work is based on numerous simulations and measurements made with the prototype. All these simulations and measurements are made with the boost DC/DC converter. Four different variable-frequency modulation techniques in six different configurations were analyzed and the EMI performance was compared to the constant frequency operation. Output voltage and input current waveforms were also analyzed in time domain to see the effect of the spread spectrum operation on these quantities. According to the results presented in this work, spread spectrum modulation can be utilized in power converter for EMI mitigation. The results from steady-state voltage measurements show, that the variable-frequency operation of the SMPS has effect on the voltage ripple, but the ripple measured from the prototype is still acceptable in some applications. Both current and voltage ripple can be controlled with proper main circuit and controller design.

Graphene from Graphite by Chemical and Physical Techniques.

Graphene is a material with extraordinary properties. Its mechanical and electrical properties are unparalleled but the difficulties in its production are hindering its breakthrough in on applications. Graphene is a two-dimensional material made entirely of carbon atoms and it is only a single atom thick. In this work, properties of graphene and graphene based materials are described, together with their common preparation techniques and related challenges. This Thesis concentrates on the topdown techniques, in which natural graphite is used as a precursor for the graphene production. Graphite consists of graphene sheets, which are stacked together tightly. In the top-down techniques various physical or chemical routes are used to overcome the forces keeping the graphene sheets together, and many of them are described in the Thesis. The most common chemical method is the oxidisation of graphite with strong oxidants, which creates a water-soluble graphene oxide. The properties of graphene oxide differ significantly from pristine graphene and, therefore, graphene oxide is often reduced to form materials collectively known as reduced graphene oxide. In the experimental part, the main focus is on the chemical and electrochemical reduction of graphene oxide. A novel chemical route using vanadium is introduced and compared to other common chemical graphene oxide reduction methods. A strong emphasis is placed on electrochemical reduction of graphene oxide in various solvents. Raman and infrared spectroscopy are both used in in situ spectroelectrochemistry to closely monitor the spectral changes during the reduction process. These in situ techniques allow the precise control over the reduction process and even small changes in the material can be detected. Graphene and few layer graphene were also prepared using a physical force to separate these materials from graphite. Special adsorbate molecules in aqueous solutions, together with sonic treatment, produce stable dispersions of graphene and few layer graphene sheets in water. This mechanical exfoliation method damages the graphene sheets considerable less than the chemical methods, although it suffers from a lower yield.

Surface studies of limestones and dolostones : characterisation using various techniques and batch dissolution experiments with hydrochloric acid solutions

In this thesis, stepwise titration with hydrochloric acid was used to obtain chemical reactivities and dissolution rates of ground limestones and dolostones of varying geological backgrounds (sedimentary, metamorphic or magmatic). Two different ways of conducting the calculations were used: 1) a first order mathematical model was used to calculate extrapolated initial reactivities (and dissolution rates) at pH 4, and 2) a second order mathematical model was used to acquire integrated mean specific chemical reaction constants (and dissolution rates) at pH 5. The calculations of the reactivities and dissolution rates were based on rate of change of pH and particle size distributions of the sample powders obtained by laser diffraction. The initial dissolution rates at pH 4 were repeatedly higher than previously reported literature values, whereas the dissolution rates at pH 5 were consistent with former observations. Reactivities and dissolution rates varied substantially for dolostones, whereas for limestones and calcareous rocks, the variation can be primarily explained by relatively large sample standard deviations. A list of the dolostone samples in a decreasing order of initial reactivity at pH 4 is: 1) metamorphic dolostones with calcite/dolomite ratio higher than about 6% 2) sedimentary dolostones without calcite 3) metamorphic dolostones with calcite/dolomite ratio lower than about 6% The reactivities and dissolution rates were accompanied by a wide range of experimental techniques to characterise the samples, to reveal how different rocks changed during the dissolution process, and to find out which factors had an influence on their chemical reactivities. An emphasis was put on chemical and morphological changes taking place at the surfaces of the particles via X-ray Photoelectron Spectroscopy (XPS) and Scanning Electron Microscopy (SEM). Supporting chemical information was obtained with X-Ray Fluorescence (XRF) measurements of the samples, and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) measurements of the solutions used in the reactivity experiments. Information on mineral (modal) compositions and their occurrence was provided by X-Ray Diffraction (XRD), Energy Dispersive X-ray analysis (EDX) and studying thin sections with a petrographic microscope. BET (Brunauer, Emmet, Teller) surface areas were determined from nitrogen physisorption data. Factors increasing chemical reactivity of dolostones and calcareous rocks were found to be sedimentary origin, higher calcite concentration and smaller quartz concentration. Also, it is assumed that finer grain size and larger BET surface areas increase the reactivity although no certain correlation was found in this thesis. Atomic concentrations did not correlate with the reactivities. Sedimentary dolostones, unlike metamorphic ones, were found to have porous surface structures after dissolution. In addition, conventional (XPS) and synchrotron based (HRXPS) X-ray Photoelectron Spectroscopy were used to study bonding environments on calcite and dolomite surfaces. Both samples are insulators, which is why neutralisation measures such as electron flood gun and a conductive mask were used. Surface core level shifts of 0.7 ± 0.1 eV for Ca 2p spectrum of calcite and 0.75 ± 0.05 eV for Mg 2p and Ca 3s spectra of dolomite were obtained. Some satellite features of Ca 2p, C 1s and O 1s spectra have been suggested to be bulk plasmons. The origin of carbide bonds was suggested to be beam assisted interaction with hydrocarbons found on the surface. The results presented in this thesis are of particular importance for choosing raw materials for wet Flue Gas Desulphurisation (FGD) and construction industry. Wet FGD benefits from high reactivity, whereas construction industry can take advantage of slow reactivity of carbonate rocks often used in the facades of fine buildings. Information on chemical bonding environments may help to create more accurate models for water-rock interactions of carbonates.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

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.

Novel virtual environment and real-time simulation based methods for improving life-cycle efficiency of non-road mobile machinery

Virtual environments and real-time simulators (VERS) are becoming more and more important tools in research and development (R&D) process of non-road mobile machinery (NRMM). The virtual prototyping techniques enable faster and more cost-efficient development of machines compared to use of real life prototypes. High energy efficiency has become an important topic in the world of NRMM because of environmental and economic demands. The objective of this thesis is to develop VERS based methods for research and development of NRMM. A process using VERS for assessing effects of human operators on the life-cycle efficiency of NRMM was developed. Human in the loop simulations are ran using an underground mining loader to study the developed process. The simulations were ran in the virtual environment of the Laboratory of Intelligent Machines of Lappeenranta University of Technology. A physically adequate real-time simulation model of NRMM was shown to be reliable and cost effective in testing of hardware components by the means of hardware-in-the-loop (HIL) simulations. A control interface connecting integrated electro-hydraulic energy converter (IEHEC) with virtual simulation model of log crane was developed. IEHEC consists of a hydraulic pump-motor and an integrated electrical permanent magnet synchronous motorgenerator. The results show that state of the art real-time NRMM simulators are capable to solve factors related to energy consumption and productivity of the NRMM. A significant variation between the test drivers is found. The results show that VERS can be used for assessing human effects on the life-cycle efficiency of NRMM. HIL simulation responses compared to that achieved with conventional simulation method demonstrate the advances and drawbacks of various possible interfaces between the simulator and hardware part of the system under study. Novel ideas for arranging the interface are successfully tested and compared with the more traditional one. The proposed process for assessing the effects of operators on the life-cycle efficiency will be applied for wider group of operators in the future. Driving styles of the operators can be analysed statistically from sufficient large result data. The statistical analysis can find the most life-cycle efficient driving style for the specific environment and machinery. The proposed control interface for HIL simulation need to be further studied. The robustness and the adaptation of the interface in different situations must be verified. The future work will also include studying the suitability of the IEHEC for different working machines using the proposed HIL simulation method.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Artificial reflecting structures based on metamaterials

This thesis studies metamaterial-inspired mirrors which provide the most general control over the amplitude and phase of the reflected wavefront. The goal is to explore practical possibilities in designing fully reflective electromagnetic structures with full control over reflection phase. The first part of the thesis describes a planar focusing metamirror with the focal distance less than the operating wavelength. Its practical applicability from the viewpoint of aberrations when the incident angle deviates from the normal one is verified numerically and experimentally. The results indicate that the proposed focusing metamirror can be efficiently employed in many different applications due to its advantages over other conventional mirrors. In the second part of the thesis a new theoretical concept of reflecting metasurface operation is introduced based on Huygens’ principle. This concept in contrast to known approaches takes into account all the requirements of perfect metamirror operation. The theory shows a route to improve the previously proposed metamirrors through tilting the individual inclusions of the structure at a chosen angle from normal. It is numerically tested and the results demonstrate improvements over the previous design.

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.

Accelerating food safety and quality control: The use of antibody engineering techniques in the detec-tion of low-molecular weight food contaminants

The increased awareness and evolved consumer habits have set more demanding standards for the quality and safety control of food products. The production of foodstuffs which fulfill these standards can be hampered by different low-molecular weight contaminants. Such compounds can consist of, for example residues of antibiotics in animal use or mycotoxins. The extremely small size of the compounds has hindered the development of analytical methods suitable for routine use, and the methods currently in use require expensive instrumentation and qualified personnel to operate them. There is a need for new, cost-efficient and simple assay concepts which can be used for field testing and are capable of processing large sample quantities rapidly. Immunoassays have been considered as the golden standard for such rapid on-site screening methods. The introduction of directed antibody engineering and in vitro display technologies has facilitated the development of novel antibody based methods for the detection of low-molecular weight food contaminants. The primary aim of this study was to generate and engineer antibodies against low-molecular weight compounds found in various foodstuffs. The three antigen groups selected as targets of antibody development cause food safety and quality defects in wide range of products: 1) fluoroquinolones: a family of synthetic broad-spectrum antibacterial drugs used to treat wide range of human and animal infections, 2) deoxynivalenol: type B trichothecene mycotoxin, a widely recognized problem for crops and animal feeds globally, and 3) skatole, or 3-methyindole is one of the two compounds responsible for boar taint, found in the meat of monogastric animals. This study describes the generation and engineering of antibodies with versatile binding properties against low-molecular weight food contaminants, and the consecutive development of immunoassays for the detection of the respective compounds.

Production Quality Analysis of Special Elevator Cars Based on Six Sigma Approach

Project scope is to utilize Six Sigma DMAIC approach and lean principles to improve production quality of the case company. Six Sigma tools and techniques are explored through a literature review and later used in the quality control phase. The focus is set on the Pareto analysis to demonstrate the most evident development areas in the production. Materials that are not delivered to the customer or materials that damaged during transportation comprise the biggest share of all feedbacks. The goal is set to reduce these feedbacks by 50 %. Production observation pointed out that not only material shortages but also over-production is a daily situation. As a result, an initial picking list where the purchased and own production components can be seen, is created, reduction of over- and underproduction and material marking improvement are seen the most competitive options so that the goal can be reached. The picking list development should still continue to make sure that the list can be used not only in the case study but also in the industrial scale. The reduction of material missing category can be evaluated reliably not sooner than in few years because it takes time to gather the needed statistical information.

Spectral Retinal Images Reconstruction

While red-green-blue (RGB) image of retina has quite limited information, retinal multispectral images provide both spatial and spectral information which could enhance the capability of exploring the eye-related problems in their early stages. In this thesis, two learning-based algorithms for reconstructing of spectral retinal images from the RGB images are developed by a two-step manner. First, related previous techniques are reviewed and studied. Then, the most suitable methods are enhanced and combined to have new algorithms for the reconstruction of spectral retinal images. The proposed approaches are based on radial basis function network to learn a mapping from tristimulus colour space to multi-spectral space. The resemblance level of reproduced spectral images and original images is estimated using spectral distance metrics spectral angle mapper, spectral correlation mapper, and spectral information divergence, which show a promising result from the suggested algorithms.