Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Testing a large-scale forestry scenario model by means of successive inventories on a forest property

Abstract

Development of Collaborative Product Structure Management

The purpose of this thesis is to develop an environment or network that enables effective collaborative product structure management among stakeholders in each unit, throughout the entire product lifecycle and product data management. This thesis uses framework models as an approach to the problem. Framework model methods for development of collaborative product structure management are proposed in this study, there are three unique models depicted to support collaborative product structure management: organization model, process model and product model. In the organization model, the formation of product data management system (eDSTAT) key user network is specified. In the process model, development is based on the case company’s product development matrix. In the product model framework, product model management, product knowledge management and design knowledge management are defined as development tools and collaboration is based on web-based product structure management. Collaborative management is executed using all these approaches. A case study from an actual project at the case company is presented as an implementation; this is to verify the models’ applicability. A computer assisted design tool and the web-based product structure manager, have been used as tools of this collaboration with the support of the key user. The current PDM system, eDSTAT, is used as a piloting case for key user role. The result of this development is that the role of key user as a collaboration channel is defined and established. The key user is able to provide one on one support for the elevator projects. Also the management activities are improved through the application of process workflow by following criteria for each project milestone. The development shows effectiveness of product structure management in product lifecycle, improved production process by eliminating barriers (e.g. improvement of two-way communication) during design phase and production phase. The key user role is applicable on a global scale in the company.

Small and Large Scale Dynamics in the Milky Way

This thesis contains dynamical analysis on four different scales: the Solar system, the Sun itself, the Solar neighbourhood, and the central region of the Milky Way galaxy. All of these topics have been handled through methods of potential theory and statistics. The central topic of the thesis is the orbits of stars in the Milky Way. An introduction into the general structure of the Milky Way is presented, with an emphasis on the evolution of the observed value for the scale-length of the Milky Way disc and the observations of two separate bars in the Milky Way. The basics of potential theory are also presented, as well as a developed potential model for the Milky Way. An implementation of the backwards restricted integration method is shown, rounding off the basic principles used in the dynamical studies of this thesis. The thesis looks at the orbit of the Sun, and its impact on the Oort cloud comets (Paper IV), showing that there is a clear link between these two dynamical systems. The statistical atypicalness of the orbit of the Sun is questioned (Paper I), concluding that there is some statistical typicalness to the orbit of the Sun, although it is not very significant. This does depend slightly on whether one includes a bar, or not, as a bar has a clear effect on the dynamical features seen in the Solar neighbourhood (Paper III). This method can be used to find the possible properties of a bar. Finally, we look at the effect of a bar on a statistical system in the Milky Way, seeing that there are not only interesting effects depending on the mass and size of the bar, but also how bars can capture disc stars (Paper II).

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

Environments of Acitive Galaxies

This dissertation presents studies on the environments of active galaxies. Paper I is a case study of a cluster of galaxies containing BL Lac object RGB 1745+398. We measured the velocity dispersion, mass, and richness of the cluster. This was one of the most thorough studies of the environments of a BL Lac object. Methods used in the paper could be used in the future for studying other clusters as well. In Paper II we studied the environments of nearby quasars in the Sloan Digital Sky Survey (SDSS). We found that quasars have less neighboring galaxies than luminous inactive galaxies. In the large-scale structure, quasars are usually located at the edges of superclusters or even in void regions. We concluded that these low-redshift quasars may have become active only recently because the galaxies in low-density environments evolve later to the phase where quasar activity can be triggered. In Paper III we extended the analysis of Paper II to other types of AGN besides quasars. We found that different types of AGN have different large-scale environments. Radio galaxies are more concentrated in superclusters, while quasars and Seyfert galaxies prefer low-density environments. Different environments indicate that AGN have different roles in galaxy evolution. Our results suggest that activity of galaxies may depend on their environment on the large scale. Our results in Paper III raised questions of the cause of the environment-dependency in the evolution of galaxies. Because high-density large-scale environments contain richer groups and clusters than the underdense environments, our results could reflect smaller-scale effects. In Paper IV we addressed this problem by studying the group and supercluster scale environments of galaxies together. We compared the galaxy populations in groups of different richnesses in different large-scale environments. We found that the large-scale environment affects the galaxies independently of the group richness. Galaxies in low-density environments on the large scale are more likely to be star-forming than those in superclusters even if they are in groups with the same richness. Based on these studies, the conclusion of this dissertation is that the large-scale environment affects the evolution of galaxies. This may be caused by different “speed” of galaxy evolution in low and high-density environments: galaxies in dense environments reach certain phases of evolution earlier than galaxies in underdense environments. As a result, the low-density regions at low redshifts are populated by galaxies in earlier phases of evolution than galaxies in high-density regions.

Testing Platform Development for Large Scale Solar Integration

Currently, a high penetration level of Distributed Generations (DGs) has been observed in the Danish distribution systems, and even more DGs are foreseen to be present in the upcoming years. How to utilize them for maintaining the security of the power supply under the emergency situations, has been of great interest for study. This master project is intended to develop a control architecture for studying purposes of distribution systems with large scale integration of solar power. As part of the EcoGrid EU Smart Grid project, it focuses on the system modelling and simulation of a Danish representative LV network located in Bornholm island. Regarding the control architecture, two types of reactive control techniques are implemented and compare. In addition, a network voltage control based on a tap changer transformer is tested. The optimized results after applying a genetic algorithm to five typical Danish domestic loads are lower power losses and voltage deviation using Q(U) control, specially with large consumptions. Finally, a communication and information exchange system is developed with the objective of regulating the reactive power and thereby, the network voltage remotely and real-time. Validation test of the simulated parameters are performed as well.

Challenges in managing large-scale agile software development transformation

Adapting and scaling up agile concepts, which are characterized by iterative, self-directed, customer value focused methods, may not be a simple endeavor. This thesis concentrates on studying challenges in a large-scale agile software development transformation in order to enhance understanding and bring insight into the underlying factors for such emerging challenges. This topic is approached through understanding the concepts of agility and different methods compared to traditional plan-driven processes, complex adaptive theory and the impact of organizational culture on agile transformational efforts. The empirical part was conducted by a qualitative case study approach. The internationally operating software development case organization had a year of experience of an agile transformation effort during it had also undergone organizational realignment efforts. The primary data collection was conducted through semi-structured interviews supported by participatory observation. As a result the identified challenges were categorized under four broad themes: organizational, management, team dynamics and process related. The identified challenges indicate that agility is a multifaceted concept. Agile practices may bring visibility in issues of which many are embedded in the organizational culture or in the management style. Viewing software development as a complex adaptive system could facilitate understanding of the underpinning philosophy and eventually solving the issues: interactions are more important than processes and solving a complex problem, such a novel software development, requires constant feedback and adaptation to changing requirements. Furthermore, an agile implementation seems to be unique in nature, and agents engaged in the interaction are the pivotal part of the success of achieving agility. In case agility is not a strategic choice for whole organization, it seems additional issues may arise due to different ways of working in different parts of an organization. Lastly, detailed suggestions to mitigate the challenges of the case organization are provided.