Project owner’s and outside engineering consultant’s role in power plant implementation projects

The purpose of this study was to investigate the nature of co-operation between a project owner and an outside engineering consultant in combined heat and power plant implementation projects. Moreover, as another focal subject of the study was to familiarize the purchasing behavior of the energy producer and how an outside engineering consultant participated into different stages of the purchasing process. The study was carried out as a multiple case study including altogether six Finnish power plant implementation projects that had been taken into commercial use during 1995 – 2015. By adjusting the findings of empirical interview data and comparing those to the theoretical framework concerning, among others, Finnish energy production, engineering consulting businesses, delivery methods of construction project and finally the purchasing process, it can be concluded that especially in the power plant implementation projects in the past have a great influence to decisions made during the project. The role of the main engineering consultant is to act as an assistant, who helps to achieve the project goals successfully rather than an advisor who only knows how the project should be conducted. At least in these five project cases this was the case, meaning that the final decision power always remaining with project owner.

Engineering of Business Critical Web Applications

Research focus of this thesis is to explore options for building systems for business critical web applications. Business criticality here includes requirements for data protection and system availability. The focus is on open source software. Goals are to identify robust technologies and engineering practices to implement such systems. Research methods include experiments made with sample systems built around chosen software packages that represent certain technologies. The main research focused on finding a good method for database data replication, a key functionality for high-availability, database-driven web applications. Research included also finding engineering best practices from books written by administrators of high traffic web applications. Experiment with database replication showed, that block level synchronous replication offered by DRBD replication software offered considerably more robust data protection and high-availability functionality compared to leading open source database product MySQL, and its built-in asynchronous replication. For master-master database setups, block level replication is more recommended way to build high-availability into the system. Based on thesis research, building high-availability web applications is possible using a combination of open source software and engineering best practices for data protection, availability planning and scaling.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

The impact of a new partnership outsourcing arrangement: a routine-based case study in detail engineering

This thesis investigated the contemporary phenomenon of detail engineering outsourcing. The case organization had pursued a new outsourcing approach with a trusted partner. The goal of this empirical study was to examine the impact of the consequential partnership outsourcing arrangement. Particularly, the beneficence of the arrangement was evaluated based on the underlying organizational routine and the long-term economic implications of its performance outcome. The case study was needed, as the unit will likely have to rely on such distance outsourcing arrangements more and more in the future, and understanding on the impact of such operations is needed. The main findings revealed that the new outsourcing arrangement is not currently a very attractive strategic option for organizing production. The benefits which stem from the emerged, unique engineering project routine are not significant enough to make the arrangement an advantageous one, especially since increasing partnering costs are being met. This conclusion was drawn via the extended transaction cost view. Benchmarking was done in reliance to an old arrangement from which the new pursuit was a departure from. The case study then enlightened the engineering unit on the impact of its strategic maneuver by combining the routines-theory framework with contemporary methods of governance structure evaluation. Through this, it was shown that greater efforts are needed to make the new outsourcing approach a more beneficial one. However, the studied arrangement was seen to inhold potential for better results. The findings can be used to capitalize on this.

Adaptation of TRIZ Method for Problem Solving in Process Engineering

TRIZ is one of the well-known tools, based on analytical methods for creative problem solving. This thesis suggests adapted version of contradiction matrix, a powerful tool of TRIZ and few principles based on concept of original TRIZ. It is believed that the proposed version would aid in problem solving, especially those encountered in chemical process industries with unit operations. In addition, this thesis would help fresh process engineers to recognize importance of various available methods for creative problem solving and learn TRIZ method of creative problem solving. This thesis work mainly provides idea on how to modify TRIZ based method according to ones requirements to fit in particular niche area and solve problems efficiently in creative way. Here in this case, the contradiction matrix developed is based on review of common problems encountered in chemical process industry, particularly in unit operations and resolutions are based on approaches used in past to handle those issues.

Methods of genetic diversity creation and functional display for directed evolution experiments

Protein engineering aims to improve the properties of enzymes and affinity reagents by genetic changes. Typical engineered properties are affinity, specificity, stability, expression, and solubility. Because proteins are complex biomolecules, the effects of specific genetic changes are seldom predictable. Consequently, a popular strategy in protein engineering is to create a library of genetic variants of the target molecule, and render the population in a selection process to sort the variants by the desired property. This technique, called directed evolution, is a central tool for trimming protein-based products used in a wide range of applications from laundry detergents to anti-cancer drugs. New methods are continuously needed to generate larger gene repertoires and compatible selection platforms to shorten the development timeline for new biochemicals. In the first study of this thesis, primer extension mutagenesis was revisited to establish higher quality gene variant libraries in Escherichia coli cells. In the second study, recombination was explored as a method to expand the number of screenable enzyme variants. A selection platform was developed to improve antigen binding fragment (Fab) display on filamentous phages in the third article and, in the fourth study, novel design concepts were tested by two differentially randomized recombinant antibody libraries. Finally, in the last study, the performance of the same antibody repertoire was compared in phage display selections as a genetic fusion to different phage capsid proteins and in different antibody formats, Fab vs. single chain variable fragment (ScFv), in order to find out the most suitable display platform for the library at hand. As a result of the studies, a novel gene library construction method, termed selective rolling circle amplification (sRCA), was developed. The method increases mutagenesis frequency close to 100% in the final library and the number of transformants over 100-fold compared to traditional primer extension mutagenesis. In the second study, Cre/loxP recombination was found to be an appropriate tool to resolve the DNA concatemer resulting from error-prone RCA (epRCA) mutagenesis into monomeric circular DNA units for higher efficiency transformation into E. coli. Library selections against antigens of various size in the fourth study demonstrated that diversity placed closer to the antigen binding site of antibodies supports generation of antibodies against haptens and peptides, whereas diversity at more peripheral locations is better suited for targeting proteins. The conclusion from a comparison of the display formats was that truncated capsid protein three (p3Δ) of filamentous phage was superior to the full-length p3 and protein nine (p9) in obtaining a high number of uniquely specific clones. Especially for digoxigenin, a difficult hapten target, the antibody repertoire as ScFv-p3Δ provided the clones with the highest affinity for binding. This thesis on the construction, design, and selection of gene variant libraries contributes to the practical know-how in directed evolution and contains useful information for scientists in the field to support their undertakings.

Combining Good Practices : Method to study Introductory Physics in Engineering Education

Julkaisumaa: 056 BE BEL Belgia

Comparison and problems between Traditional and Agile software development methods

In today’s world because of the rapid advancement in the field of technology and business, the requirements are not clear, and they are changing continuously in the development process. Due to those changes in the requirements the software development becomes very difficult. Use of traditional software development methods such as waterfall method is not a good option, as the traditional software development methods are not flexible to requirements and the software can be late and over budget. For developing high quality software that satisfies the customer, the organizations can use software development methods, such as agile methods which are flexible to change requirements at any stage in the development process. The agile methods are iterative and incremental methods that can accelerate the delivery of the initial business values through the continuous planning and feedback, and there is close communication between the customer and developers. The main purpose of the current thesis is to find out the problems in traditional software development and to show how agile methods reduced those problems in software development. The study also focuses the different success factors of agile methods, the success rate of agile projects and comparison between traditional and agile software development.

Thick Section Laser Beam Welding of Structural Steels: Methods for Improving Welding Efficiency

Laser beam welding (LBW) is applicable for a wide range of industrial sectors and has a history of fifty years. However, it is considered an unusual method with applications typically limited to welding of thin sheet metal. With a new generation of high power lasers there has been a renewed interest in thick section LBW (also known as keyhole laser welding). There was a growing body of publications during 2001-2011 that indicates an increasing interest in laser welding for many industrial applications, and in last ten years, an increasing number of studies have examined the ways to increase the efficiency of the process. Expanding the thickness range and efficiency of LBW makes the process a possibility for industrial applications dealing with thick metal welding: shipbuilding, offshore structures, pipelines, power plants and other industries. The advantages provided by LBW, such as high process speed, high productivity, and low heat input, may revolutionize these industries and significantly reduce the process costs. The research to date has focused on either increasing the efficiency via optimizing process parameters, or on the process fundamentals, rather than on process and workpiece modifications. The argument of this thesis is that the efficiency of the laser beam process can be increased in a straightforward way in the workshop conditions. Throughout this dissertation, the term “efficiency” is used to refer to welding process efficiency, specifically, an increase in efficiency refers an increase in weld’s penetration depth without increasing laser power level or decreasing welding speed. These methods are: modifications of the workpiece – edge surface roughness and air gap between the joining plates; modification of the ambient conditions – local reduction of the pressure in the welding zone; modification of the welding process – preheating of the welding zone. Approaches to improve the efficiency are analyzed and compared both separately and combined. These experimentally proven methods confirm previous findings and contribute additional evidence which expand the opportunities for laser beam welding applications. The focus of this research was primarily on the effects of edge surface roughness preparation and pre-set air gap between the plates on weld quality and penetration depth. To date, there has been no reliable evidence that such modifications of the workpiece give a positive effect on the welding efficiency. Other methods were tested in combination with the two methods mentioned above. The most promising - combining with reduced pressure method - resulted in at least 100% increase in efficiency. The results of this thesis support the idea that joining those methods in one modified process will provide the modern engineering with a sufficient tool for many novel applications with potential benefits to a range of industries.

A software engineering approach to distributed systems for composite modeling

The goal of this thesis is to define and validate a software engineering approach for the development of a distributed system for the modeling of composite materials, based on the analysis of various existing software development methods. We reviewed the main features of: (1) software engineering methodologies; (2) distributed system characteristics and their effect on software development; (3) composite materials modeling activities and the requirements for the software development. Using the design science as a research methodology, the distributed system for creating models of composite materials is created and evaluated. Empirical experiments which we conducted showed good convergence of modeled and real processes. During the study, we paid attention to the matter of complexity and importance of distributed system and a deep understanding of modern software engineering methods and tools.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

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.