Internationalization is one of the most complicated elements in Small- and Medium-sized Enterprise (SME) expansion. Researchers seem to agree more and more that none of the theories in this field can solely explain the dynamics of the internationalization of Small and Medium-sized Enterprises particularly small knowledge- and service-intensive firms. There are different theories and approaches toward the SMEs‘ internationalization; however there is one predictor in common among them: Firm‟s knowledge resources (Yli-Renko, Autio, & Tontti, 2002).Since the value-adding processes of firms are increasingly based on the creation and exploitation of knowledge, the natural focus of attention shifts from the control of static, firm-specific resources to the acquisition, assimilation, and exploitation of firm-specific knowledge (Bettis & Hitt, 1995; Grant, 1996; J.Nahapiet & Ghoshal, 1998). In today‘s global competitive landscape, firms succeed not because they have control over scarce resources, but because they have the ability to gain the knowledge, learn and use this learning more efficiently than others. In comparison with big companies SMEs have relatively less resources, which make knowledge very vital for their survival and growth. (Mejri & Umemoto, 2010)There has not been much empirical research on knowledge resources and capabilities although the importance of knowledge-related process is widely acknowledged. There is a notable limitation in SME literature on influence of knowledge that can only offer limited insight into firm‘s foreign market operations. In other words, there is a gap in the literature about the different types of knowledge and their role in the internationalization process and therefor this research has set it goal to answer the aforementioned issues.This research has used qualitative approach and case study research design, and six semistructured interviews were conducted with small Swedish firms that involved in international activities. Since this is an exploratory study, the data from the six cases was quite managable. Analysis was conducted by coding the interviews and categorization of the codes. The codes were interpreted and three types of knowledge were extracted based on both the data and theories; Technological Knowledge, Business Knowledge and Market-specific Knowledge. The main characteristics of each company were put together with regard to the three types of knowledge. The next step in analysis was to find out if there were any differences or similarities between the companies when it came to internationalization process. By using the aforementioned results a farmework was developed. The framework presents the role of each Knowledge in the internationalization process and is the key finding of this research.The results from this study indicate the significant role of different types of knowledge as the main source of competitive advantage for SMEs to go to international markets. However the result of this study also designates that the role of knowledge in the internationalization process must be understood in the context of the industry, the company and the people involved.

The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

Business Process (BP) design reflects managerial needs and may directly influence business performance. A good design could substantially increase managerial performance, while a bad one would be inefficient, lack of flexibility, mess cost effective and eventually miss the business strategy. The widespread of information technology has raised the need to redesign or modify business processes in order to fit the trend of automation and computerization. As a result, business process reengineering (BPR) has gained much attention in 1990s. In recent years, a new paradigm, called Service Science, Management and Engineering (SSME), becomes a new management innovation. Service process design becomes a new science that can be applied to support service innovation and management. Previous research on BPR includes two major directions: one focuses on managerial aspects of business processes, including the planning, implementation, and critical factors of BPR; the other focuses on the design aspects pf business processes with a target of making processes more efficient. For research on process design, most deal with the syntactic structure of the process. They analyze the syntax structure of a process. This can help find design errors such as deadlocks, livelocks, and even infinite loops in a process. Not many studies have investigated whether a process design meets its managerial goals. This research presents a knowledge-based approach to dealing with the managerial issue of whether a process design matches specific managerial goals. This thesis contains a new business process modeling method that allows a business process to be diagnosed by knowledge-based rules. We have defined three managerial goals in process design: effectiveness, efficiency, and flexibility. Each activity in a business process has its goal. Through the analysis of activities and their associated goals, we can determine whether a business process is properly designed. In order to show the feasibility of the proposed approach, we have implemented a JAVA-based prototype expert system and used it to check two sample business processes. The contributions of the study are two-fold. Academically, it proposed a new approach for business process diagnosis, which can help determine whether a process meets its managerial goal. In practice, businesses can use the concepts developed in the thesis to make their business processes more effective by matching activities with intended managerial goals.

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Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. However, identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. We research and investigate the benefits of using knowledge-based algorithms to solve clinical prediction problems. Because we are interested in identifying biomarkers that are also feasible in clinical prediction models, we focus on two analytical components: feature selection and predictive model selection. In addition to data variance, we must also consider the variance of analytical methods. There are many existing feature selection algorithms, each of which may produce different results. Moreover, it is not trivial to identify model parameters that maximize the sensitivity and specificity of clinical prediction. Thus, we introduce a method that uses independently validated biological knowledge to reduce the space of relevant feature selection algorithms and to improve the reliability of clinical predictors. Finally, we implement several functions of this knowledge-based method as a web-based, user-friendly, and standards-compatible software application.

In open office settings, auditory distractions coming from surrounding work environment are shown to be a considerable source of indirect costs to an organization, such as performance costs, behavioral costs, and healthcare costs, to name a few. These costs are substantial to affect the net productivity of an organization, where productivity is equal to revenue minus the costs. This research argues that the costs of auditory distractions should be estimated when evaluating the value of a workspace for an organization. However, since organizational decisions are generally guided by cost-benefit analysis and a precise dollar figure cannot be attached to the stated indirect costs because these are subjective in nature; therefore, these are generally ignored. Costs that are critical to sustainability and development of a business and the fact that cost-benefit approach is no longer appropriate for these decisions, a more robust decision-based approach to workspace selection is proposed. Decision-based approach is seen as an organized approach to select between workspace options under uncertainty and risk wherein the selected workspace is maximized in terms of some expected utility. Here utility is defined as the measurement of strength or intensity of a person's preferences. Decision-based approach include consideration of a multitude of environmental decision variables, objective or subjective, in a single equation and processing of the same in a limited amount of time with rationality and consistency. A multi-attribute workspace choice utility decision model is developed with the intent to facilitate systematic understanding and analysis of workspace alternatives for an organization. This research shows how the decision-making approach to workspace selection simplifies the problem by providing a structure that is easily comprehensible, and allows simultaneous processing of both, qualitative and quantitative conflicting objectives, through a single decision-making model. In doing so, this research firmly establishes the importance of workspace's adaptability to auditory distractions for office workers, particularly knowledge workers, who are constantly undertaking a range of complex tasks. The study holistically and systematically addresses the fundamental issue prevalent in state-of-the-art North American open plan office settings of substantiality of two extremely contrasting requirements, concentration and collaboration, in the same workspace and work environment at a given time.

This thesis explores new ways to integrate manufacturing processes information in to design phases. Through the use of design for manufacturing (DfM) concept, and looking at relations between its potential application in architectural production and its implementation using digital manufacturing technologies, the author implemented a DfM model that varies from previous models by incorporated learning in the process. This process was based on the incremental development and refinement of design heuristics and metrics. The DfM model developed in this research is a process model to be implemented as a framework within educational settings. The proposed model is based in two basic strategies; first a process description in the form of alternative design strategies; and second, the implementation of design heuristics and design metrics. Subsequently, the author tested and refined the model using a sequence of case studies with students. In the final stage, the research evaluated and further developed the DfM model in a component design case study. The general purpose in performing this case studies sequence was to test the proposed DfM model. The second objective was to refine the DfM model by capturing knowledge from the case studies. As a summary, this research conceptualizes from this top-down development approach to create a design for manufacturing model that integrates design and construction in architecture, based on three possible applications fields; DfM teaching approaches development, design processes improvement; and DfM methods development. The final purpose is to provide better foundational constructs for architectural education and to improve teaching approaches that integrate design and manufacturing.

Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.

<p>Intensive competition on the global market puts great pressure on manufacturing companies to develop and produce products that meet requirements from customers and investors. One key factor in meeting these requirements is the efficiency of the product development and the production preparation process. Design automation is a powerful tool to increase efficiency in these two processes.</p><p>The benefits of automating the production preparation process are shortened led-time, improved product performance, and ultimately decreased cost. Further, automation is beneficial as it increases the ability to adapt products to new product specifications with production preparations done in few or in a single step. During the automation process, knowledge about the production preparation process is collected and stored in central systems, thus allowing full control over the design of production equipments.</p><p>Three main topics are addressed in this thesis: the flexibility of design automation systems, knowledge bases containing conflicting rules, and the automation of the finite element analysis process. These three topics are discussed in connection with the production preparation process of rotary draw bending.</p><p>One conclusion drawn from the research is that it is possible to apply the concept of design automation to the production preparation process at different levels of automation depending on characteristics of the implemented knowledge. In order to make design automation systems as flexible as possible, the concept of object orientation should be adapted when building the knowledge base and when building the products geometrical representations. It is possible to automate the process of setting up, running, and interpreting finite element analyses to a great extent and making the automated finite element analysis process a part of the global design automation system.</p>

Predicting the structure of complexes formed by two interacting proteins is an important problem in computation structural biology. Proteins perform many of their functions by binding to other proteins. The structure of protein-protein complexes provides atomic details about protein function and biochemical pathways, and can help in designing drugs that inhibit binding. Docking computationally models the structure of protein-protein complexes, given three-dimensional structures of the individual chains. Protein docking methods have two phases. In the first phase, a comprehensive, coarse search is performed for optimally docked models. In the second refinement and reranking phase, the models from the first phase are refined and reranked, with the expectation of extracting a small set of accurate models from the pool of thousands of models obtained from the first phase. In this thesis, new algorithms are developed for the refinement and reranking phase of docking. New scoring functions, or potentials, that rank models are developed. These potentials are learnt using large-scale machine learning methods based on mathematical programming. The procedure for learning these potentials involves examining hundreds of thousands of correct and incorrect models. In this thesis, hierarchical constraints were introduced into the learning algorithm. First, an atomic potential was developed using this learning procedure. A refinement procedure involving side-chain remodeling and conjugate gradient-based minimization was introduced. The refinement procedure combined with the atomic potential was shown to improve docking accuracy significantly. Second, a hydrogen bond potential, was developed. Molecular dynamics-based sampling combined with the hydrogen bond potential improved docking predictions. Third, mathematical programming compared favorably to SVMs and neural networks in terms of accuracy, training and test time for the task of designing potentials to rank docking models. The methods described in this thesis are implemented in the docking package DOCK/PIERR. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer's disease.R. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer’s disease. / text