Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto

ADAPTIVE SEARCH AND THE PRELIMINARY DESIGN OF GAS TURBINE BLADE COOLING SYSTEMS

This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.Rolls Royce plc., Bristol (UK

The ecology of technology : the co-evolution of technology and organization

In this day and age, arguing that technology is a powerful force that drives many economic processes is like preaching to the choir. Nevertheless, despite the widespread realization of the important role of technology in our modern day society, an intimate understanding of the process of technological change is still lacking. This study seeks to provide more insight into the concept of technological change by characterizing it as a socio-cultural evolutionary process of variation, selection and retention. According to this logic, variety (or novelty) is created by (random or non-random) mutations (i.e., organizations and individuals that (re-) combine existing components in novel ways). This variety is subsequently selected out by the stakeholders in the environment, such as individuals, organizations, and institutions. In other words, the variety is then retained in the structural characteristics of the environment, commonly referred to as organizational routines and technological paradigms. Finally, these structural characteristics subsequently provide the context in/from which new mutations (or variations) are created. From there, the cycle can be repeated. Because, nowadays, technology is mostly developed in an organizational context, the appropriate place to study technology and technological change is in the context of organization science, which is an academic discipline that studies all facets of organization. Even though technology deserves a central role in any organization theory, technology has not yet penetrated fully the domain of organization science. The only domain in which technology has a central role is within evolutionary economics, a school of economic thought that was influenced by evolutionary biology. Even though evolutionary economics has surely added much to our understanding of the process of technological change, in our view, this school of thought mainly concentrates its attention on idiosyncratic accounts of variety creation and their subsequent selection by the environment. Much less attention has been attributed to how the selection environment (or the structural characteristics thereof) determines the variety creation. Consequently, insights from organizational ecology, which has its center of gravity at the selection environment, can add value over and above the ones originating from evolutionary economics. The key source of inspiration of organizational ecology is bioecology, which makes it evolutionary economics’ counterpart in sociology. In this study, we therefore seek to close the evolutionary circle by developing a structural or ecological perspective of technological change. After all, holding both links between variety and selection in focus at the same time (i.e., how variety is selected by the environment, and how the selection environment facilitates and constrains the creation of variety) provides for a truly evolutionary model of technological change. Accordingly, we define our research objective as follows: Research objective: To develop an ecology of technology in organization science. Because this objective is rather vague and abstract, we formulate several research questions to provide more direction in our quest to fulfill our objective. We formulate our first research question as follows. Research question 1: What is the importance of biotechnology? Providing an answer to this research question is the subject of Chapter 2. As a means of introducing biotechnology, we first describe biotechnology’s central dogma (i.e., DNA as the building block of life). Moreover, we provide a timeline to get a certain feel of the history and evolution of biotechnology, and list numerous socio-economic trends to get an idea of the importance of biotechnology in society. These trends clearly illustrate that biotechnology drives important social and economic events. Next, we evaluate biotechnology’s position in the overall technological landscape. Our main finding is that, despite its sharply increasing societal and economic importance, biotechnology still has not yet conquered a place in the technological core of our society. Reviewing the developments within synthetic biology (in this domain, complex systems are designed by (re-)combining DNA into biological parts that represent biological functions and, as such, is the domain where all aspects of biotechnology come together), it becomes clear that biotechnology as a whole is not yet in the growth stage of technological convergence that is characterized by a stable configuration of component technologies (i.e., a dominant design). Moreover, on the basis of the future expectations of experts, we conclude that biotechnology is a strategic technology that is nowhere near its peak influence, and that we can expect the importance to increase even further over the coming years. Obviously, whether biotechnology can deliver on its promise and materialize the expectations of insiders is not certain. Even when biotechnology delivers on only a small part of the promise, though, its impact will already be gigantic. For example, consider the fact that, in a 2007 interview, Craig Venter – who is one of the most well-renowned biotechnologists today – said that, in 20 years time, synthetic genomics is going to become the standard for making anything (Aldhous, 2007). So, in conclusion, biotechnology is a technology that is still emerging and does yet not display a stable and predictable pattern of growth that characterizes mature (i.e., non-emerging) technologies. Our next research question thus is as follows. Research question 2: How to study the growth of an emerging technology? In Chapter 3, on the basis of ecological insights and principles, we develop a structural or systemic view towards technology, and hereby take into explicit account the embedded nature of technology. That is, we propose that it adds value to view technology as a system composed of a set of interdependent components (or subsystems). More specifically, by relying on density dependence theory from organizational ecology, we effectively develop a multilevel framework that can be used to empirically study emerging technologies. Moreover, we employ the concept of the technological niche from organizational ecology, with its associated dimensions of crowding (associated with processes of competition) and status (associated with processes of legitimation), and add diversity as a key dimension. Through sophisticated multivariate analysis of biotechnology patents from the United States Patent and Trademark Office (USPTO), we validate this model, which we label the ‘ecology of technology’. However, we also discover some anomalies, which point to the limitations of our model, the most important being its rather static nature. Because emerging technologies are characterized by fluid patterns of growth, a static model is a severe misrepresentation of the evolution of emerging technologies. Our next research question naturally follows from this. Research question 3: How to study the evolution of an emerging technology? On the basis of insights from evolutionary economics, Chapter 4 distinguishes between two stages of technological development, namely the stages of divergence and convergence (that connect nicely with the seed and growth stage of life cycle theory). The focal element is what is generally referred to as the deep structure (in the context of technology also commonly referred to as a dominant design) that facilitates cumulative changes by reducing uncertainty and enabling specialization and integration through standardization. The stage of divergence is characterized by the absence of a deep structure, while the stage of convergence is characterized by its presence. So, in the latter stage, there is a relatively stable configuration of the system’s component technologies that results in relatively stable and predictable patterns of growth. On the basis of these insights, we adapt our multi-level model to identify these different stages of development at the component level. More specifically, if there is a mutualistic relationship between a component and the system (i.e., if system density contributes positively to component entry), the component is argued to have a dominant design. As we are dealing with an emerging technology, our main interest lies in the transition from the initial seed stage of technological divergence (i.e., the absence of a deep structure) to a growth stage of technological convergence (i.e., the existence of a deep structure), or the creation of a deep structure. This means that we do not take into account the revolutionary transition from a stage of convergence into divergence (i.e., the maturity and decline stage in life cycle theory). Not only do we refine our predictions regarding the effects of our existing dimensions (i.e., multilevel density dependence, crowding, status, and focal diversity), but, by further taking into account the lineage of technology, we refine our dimension of diversity by adding antecedent and descendant diversity as additional dimensions to the technological niche. This results in an intricate model that can be used to study the growth and evolution of an emerging technology. We demonstrate this by an empirical investigation of biotechnology patents from the USPTO and hereby provide further support for our ‘ecology of technology’. In the light of our research objective, before we answer the question of what the precise consequences are for organizations, we ask ourselves how we can effectively integrate our findings at the organizational level of analysis. We thus formulate our next research question accordingly. Research question 4: How can we integrate technology into the theory of the organization specifictechnological niche? In Chapter 5, we use a process of logical formalization to represent the theory of the organization-specific technological niche in a formal logical language. The reason for doing so is threefold. First, this forces us to explicate all underlying assumptions and to remove any inconsistencies to make the argument logically sound. Second, this requires us to supplement the theory so that it is complete, without missing elements. Third and finally, it results in a logically sound and complete theory fragment ready for extension by integrating the insights from the study of the evolution of technology. We choose nonmonotonic logic as the language in which we represent our arguments because nonmonotonic logic is better suited for theory building, and this connects better to the current wave of formalization in non-monotonic logic in organizational ecology. On the basis of this analysis, we already make two important theoretical extensions. First, by distinguishing between crowding in technological and market space, we tie technological crowding to both competition and legitimation. To be precise, technological crowding results in competition mainly if the crowding organization is a competitor of the focal organization. Second, uncertainty mediates the relationship between the perceived and actual technological quality of the organization. More specifically, under uncertainty, the actual quality of an organization’s technology cannot be readily observed so that resource controllers have to rely on status (i.e., historic technological quality) instead. With this formalized, logically sound and complete theory fragment in hand, we can turn to the question of the organizational consequences. We thus pose our next research question as follows. Research question 5: What are the consequences of integrating several technological insights into thetheory of the organization-specific technological niche? In Chapter 6, we integrate four technological insights from Chapters 3 and 4 into our formalized theory fragment from the previous chapter. These insights are: (1) multiple technological domains exist that have (2) different stages of development, (3) different levels of uncertainty, and (4) different growth rates. On the basis of these four insights, we extend the theory of the organization-specific technological niche considerably. For crowding, we demonstrate that the effect of crowding is not only conditional upon the identity of the other organization, but also on the stage of technological development. We also add non-crowding to the mix. Regarding the effect of (non-)crowding, in the stage of divergence, multiple competing design configurations exist, and crowding (non-crowding) increases (decreases) the competitiveness of the supported design configuration, having a legitimating (competition) effect. In contrast, in the stage of convergence, crowding (non-crowding) loses its legitimating (competition) function and results in competitive (legitimation) pressure. For status, the most important consequences are that: (1) status is domain dependent, and (2) its effect is dependent upon the stage of technological development (i.e., the effect of status is higher in the stage of divergence). We also add two additional dimensions, which are (1) technological opportunities (that can be represented by the growth rate of the domain), and (2) technological diversity (measured by the distribution of activities over alternative domains). By operationalizing performance as a two-dimensional vector, we suggest that the dimensions of the technological niche are related to different performance measures in distinct temporal relationships. However, even though this theoretical extension is certainly valuable, the subsequent question is whether these extensions hold when subjected to advanced empirical tests. We therefore formulate our next research question as follows. Research question 6: Can we find proof for our extended theory of the organization-specific technological niche? In Chapter 7, we empirically test several of our theoretical extensions of the organization-specific technological niche. Our dependent variable is biotechnology innovation (i.e., the number of biotechnology patents). Through a sophisticated empirical analysis, we find strong support for our extended theory. However, we also encounter some inconsistencies and anomalies. This seems to connect to the fact that processes of competition and legitimation are more appropriately defined at lower levels of analysis (i.e., at the component instead of at the system level). Moreover, due to the dual role of a direct technological tie (i.e., it can have both a competing and a legitimating function) that forms the basis for our measure of status, status is better defined at the component level of analysis. In contrast, biotechnological quality can be aggregated to the system level without losing significance. We thus find strong support for this dimension. Furthermore, we also clearly demonstrate the importance of taking into account the different dimensions of technological diversity (i.e., antecedent, focal, and descendant), with a vital role for antecedent diversity, which logically connects with the notion of absorptive capacity. The subsequent question is what this means for the broader academic debate regarding the (co-)evolution of technology and organization. We formulate our next research question accordingly. Research question 7: What are the implications for the study of the (co-)evolution of technology and organization? In the final chapter of this dissertation, we start by stating the main contribution of this dissertation, which is that we develop a dynamic multilevel model that can be used to empirically study the evolution of an emerging technology. As this model is based on the assumption that technology can effectively be studied as a system composed of an interacting set of components, we pay explicit attention to the embedded nature of technology. Hence, when studying the evolution of technology, it is inappropriate to focus on a single level of analysis and using a multilevel perspective adds value over and above any single level study. That is, technology (e.g., biotechnology) is composed of a set of technological components (e.g., biotechnology’s component technologies) while, at the same time, being embedded in a larger technological system (i.e., technological landscape). It is precisely this multilevel nature of technology that gives it the potential to close part of the chasm in the debate between organizational adaptation (i.e., the dominant perspective in evolutionary economics) and environmental selection (i.e., the dominant perspective in organizational ecology). More specifically, by defining technology at different levels of analysis (e.g., invention, component, system, and landscape), it is possible to tie the evolution of technology to the evolution of organization at different levels of analysis (i.e., individual organization, population of organizations, community, and society). This enables studying the evolution of technology and organization in unison, and thus provides the basis for a co-evolutionary model of technology and organization. Employing a multilevel perspective to both technology and organization at the same time, and defining technology and organization as nested hierarchies tied together at multiple levels of analysis, effectively allows an analyzes of how stable configurations travels upwards in this hierarchy. After all, "it is the information about stable configurations […] that guides the process of evolution" (Simon, 1952: 473)

Derating NichePSO

The search for multiple solutions is applicable to many fields (Engineering [54][67], Science [75][80][79][84][86], Economics [13][59], and others [51]). Multiple solutions allow for human judgement to select the best solution from a group of solutions that best match the search criteria. Finding multiple solutions to an optimisation problem has shown to be difficult to solve. Evolutionary computation (EC) and more recently Particle Swarm Optimisation (PSO) algorithms have been used in this field to locate and maintain multiple solutions with fair success. This thesis develops and empirically analyses a new method to find multiple solutions within a convoluted search space. The method is a hybrid of the NichePSO [14] and the sequential niche technique (SNT)[8]. The original SNT was developed using a Genetic Algorithm (GA). It included restrictions such as knowing or approximating the number of solutions that exist. A further pitfall of the SNT is that it introduces false optima after modifying the search space, thereby reducing the accuracy of the solutions. However, this can be resolved with a local search in the unmodified search space. Other sequential niching algorithms require that the search be repeated sequentially until all solutions are found without considering what was learned in previous iterations, resulting in a blind and wasteful search. The NichePSO has shown to be more accurate than GA based algorithms [14][15]. It does not require knowledge of the number of solutions in the search space prior to the search process. However, the NichePSO does not scale well for problems with many optima [16]. The method developed in this thesis, referred to as the derating NichePSO, combines SNT with the NichePSO. The main objective of the derating NichePSO is to eliminate the inaccuracy of SNT and to improve the scalability of the NichePSO. The derating NichePSO is compared to the NichePSO, deterministic crowding [23] and the original SNT using various multimodal functions. The performance of the derating NichePSO is analysed and it is shown that the derating NichePSO is more accurate than SNT and more scalable than the NichePSO.Dissertation (MSc)--University of Pretoria, 2007.Computer ScienceMScUnrestricte

ACARORUM CATALOGUS IX. Acariformes, Acaridida, Schizoglyphoidea (Schizoglyphidae), Histiostomatoidea (Histiostomatidae, Guanolichidae), Canestrinioidea (Canestriniidae, Chetochelacaridae, Lophonotacaridae, Heterocoptidae), Hemisarcoptoidea (Chaetodactylidae, Hyadesiidae, Algophagidae, Hemisarcoptidae, Carpoglyphidae, Winterschmidtiidae)

The 9th volume of the series Acarorum Catalogus contains lists of mites of 13 families, 225 genera and 1268 species of the superfamilies Schizoglyphoidea, Histiostomatoidea, Canestrinioidea and Hemisarcoptoidea. Most of these mites live on insects or other animals (as parasites, phoretic or commensals), some inhabit rotten plant material, dung or fungi. Mites of the families Chetochelacaridae and Lophonotacaridae are specialised to live with Myriapods (Diplopoda). The peculiar aquatic or intertidal mites of the families Hyadesidae and Algophagidae are also included.Publishe

Improving & applying single-cell RNA sequencing

The cell is the fundamental building block of life. With the advent of single-cell RNA sequencing (scRNA-seq), we can for the first time assess the transcriptome of many individual cells. This has profound implications for biological and medical questions and is especially important to characterize heterogeneous cell populations and rare cells. However, the technology is technically and computationally challenging as complementary DNA (cDNA) needs to be generated and amplified from minute amounts of mRNA and sequenceable libraries need to be efficiently generated from many cells. This requires to establish different protocols, identify important caveats, benchmark various methods and improve them if possible. To this end, we analysed amplification bias and its effect on detecting differentially expressed genes in several bulk and a single-cell RNA sequencing methods. We found that correcting for amplification bias is not possible computationally but improves the power of scRNA-seq considerably, though neglectable for bulk-RNA-seq. In the second study we compared six prominent scRNA-seq protocols as more and more single-cell RNA-sequencing are becoming available, but an independent benchmark of methods is lacking. By using the same mouse embryonic stem cells (mESCs) and exogenous mRNA spike-ins as common reference, we compared six important scRNA-seq protocols in their sensitivity, accuracy and precision to quantify mRNA levels. In agreement with our previous study, we find that the precision, i.e. the technical variance, of scRNA-seq methods is driven by amplification bias and drastically reduced when using unique molecular identifiers to remove amplification duplicates. To assess the combined effects of sensitivity and precision and to compare the cost-efficiency of methods we compared the power to detect differentially expressed genes among the tested scRNA-seq protocols using a novel simulation framework. We find that some methods are prohibitively inefficient and others show trade-offs depending on the number of cells per sample that need to be analysed. Our study also provides a framework for benchmarking further improvements of scRNA-seq protocol and we published an improved version of our simulation framework powsimR. It uniquely recapitulates the specific characteristics of scRNA-seq data to enable streamlined simulations for benchmarking both wet lab protocols and analysis algorithms. Furthermore, we compile our experience in processing different types of scRNA-seq data, in particular with barcoded libraries and UMIs, and developed zUMIs, a fast and flexible scRNA-seq data processing software overcoming shortcomings of existing pipelines. In addition, we used the in-depth characterization of scRNA-seq technology to optimize an already powerful scRNA-seq protocol even further. According to data generated from exogenous mRNA spike-ins, this new mcSCRB-seq protocol is currently the most sensitive scRNA-seq protocol available. Single-cell resolution makes scRNA-seq uniquely suited for the understanding of complex diseases, such as leukemia. In acute lymphoblastic leukemia (ALL), rare chemotherapy-resistant cells persist as minimal residual disease (MRD) and may cause relapse. However, biological mechanisms of these relapse-inducing cells remain largely unclear because characterisation of this rare population was lacking so far. In order to contribute to the understanding of MRD, we leveraged scRNA-seq to study minimal residual disease cells from ALL. We obtained and characterised rare, chemotherapy-resistant cell populations from primary patients and patient cells grown in xenograft mouse models. We found that MRD cells are dormant and feature high expression of adhesion molecules in order to persist in the hematopoietic niche. Furthermore, we could show that there is plasticity between resting, resistant MRD cells and cycling, therapy-sensitive cells, indicating that patients could benefit from strategies that release MRD cells from the niche. Importantly, we show that our data derived from xenograft models closely resemble rare primary patient samples. In conclusion, my work of the last years contributes towards the development of experimental and computational single-cell RNA sequencing methods enabling their widespread application to biomedical problems such as leukemia

Genetic characterization of Rhodococcus rhodochrous ATCC BAA-870 with emphasis on nitrile hydrolysing enzymes

Includes abstract.Includes bibliographical references.Rhodococcus rhodochrous ATCC BAA-870 (BAA-870) had previously been isolated on selective media for enrichment of nitrile hydrolysing bacteria. The organism was found to have a wide substrate range, with activity against aliphatics, aromatics, and aryl aliphatics, and enantioselectivity towards beta substituted nitriles and beta amino nitriles, compounds that have potential applications in the pharmaceutical industry. This makes R. rhodochrous ATCC BAA-870 potentially a versatile biocatalyst for the synthesis of a broad range of compounds with amide and carboxylic acid groups that can be derived from structurally related nitrile precursors. The selectivity of biocatalysts allows for high product yields and better atom economy than nonselective chemical methods of performing this reaction, such as acid or base hydrolysis. In order to apply BAA-870 as a nitrile biocatalyst and to mine the organism for biotechnological uses, the genome was sequenced using Solexa technology and an Illumina Genome Analyzer. The Solexa sequencing output data was analysed using the Solexa Data Analysis Pipeline and a total of 5,643,967 reads, 36-bp in length, were obtained providing 4,273,289 unique sequences. The genome sequence data was assembled using the software Edena, Velvet, and Staden. The best assembly data set was then annotated automatically using dCAS and BASys. Further matepaired sequencing, contracted to the company BaseClear® BV in Leiden, the Netherlands, was performed in order to improve the completeness of the data. The scaffolded Illumina and mate-paired sequences were further assembled and annotated using BASys. BAA-870 has a GC content of 65% and contains 6997 predicted protein-coding sequences (CDS). Of this, 54% encodes previously identified proteins of unknown function. The completed 5.83 Mb genome (with a sequencing coverage of 135 X) was submitted to the NCBI Genome data bank with accession number PRJNA78009. The genome sequence of R. rhodochrous ATCC BAA-870 is the seventh rhodococcal genome to be submitted to the NCBI and the first R. rhodochrous subtype to be sequenced. An analysis of the genome for nitril