Regional evolution and waves of growth:a knowledge-based perspective

The evolution of a regional economy and its competitiveness capacity may involve multiple independent trajectories, through which different sets of resources and capabilities evolve together. However, there is a dearth of evidence concerning how these trends are occurring across the globe. This paper seeks to present evidence in relation to the recent development of the globe’s most productive regions from the viewpoint of their growth trajectories, and the particular form of growth they are experiencing. The aim is to uncover the underlying structure of the changes in knowledge-based resources, capabilities and outputs across regions, and offer an analysis of these regions according to an uncovered set of key trends. The analysis identifies three key trends by which the economic evolution and growth patterns of these regions are differentiated—namely the Fifth Wave Growth, the Third & Fourth Wave Growth, and Government-led Third Wave Growth. Overall, spectacular knowledge-based growth of leading Chinese regions is evident, highlighting a continued shift of knowledge-based resources to Asia. In addition, a superstructure is observed at the global scale, consisting of two separate continuums that explicitly distinguish Chinese regions from the rest in terms of regional growth trajectories.

Knowledge-based system for casting process selection

Design of casting entails the knowledge of various interacting factors that are unique to casting process, and, quite often, product designers do not have the required foundry-specific knowledge. Casting designers normally have to liaise with casting experts in order to ensure the product designed is castable and the optimum casting method is selected. This two-way communication results in long design lead times, and lack of it can easily lead to incorrect casting design. A computer-based system at the discretion of a design engineer can, however, alleviate this problem and enhance the prospect of casting design for manufacture. This paper proposes a knowledge-based expert system approach to assist casting product designers in selecting the most suitable casting process for specified casting design requirements, during the design phase of product manufacture. A prototype expert system has been developed, based on production rules knowledge representation technique. The proposed system consists of a number of autonomous but interconnected levels, each dealing with a specific group of factors, namely, casting alloy, shape and complexity parameters, accuracy requirements and comparative costs, based on production quantity. The user interface has been so designed to allow the user to have a clear view of how casting design parameters affect the selection of various casting processes at each level; if necessary, the appropriate design changes can be made to facilitate the castability of the product being designed, or to suit the design to a preferred casting method.

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Inventory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large numbers of examples. The results of three representative cases are summarized. Finally a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.

Regional evolution and waves of growth:a knowledge-based perspective

The evolution of a regional economy and its competitiveness capacity may involve multiple independent trajectories through which different sets of resources and capabilities evolve together. However, there is a dearth of evidence concerning how these trends are occurring across the globe. Based on the underlying tenets of the streams of research relating to regional competitiveness, knowledge cities/regions, and knowledge-based urban development, this paper seeks to present an empirical approach to establishing such evidence in relation to the recent development of the globe’s most productive regions from the viewpoint of their growth trajectories and the particular form of growth they are experiencing. The aim is to uncover the underlying structure of the changes in knowledge-based resources, capabilities and outputs across regions, and offer an analysis of these regions according to an uncovered set of key trends. The analysis identifies three key trends by which the economic evolution and growth patterns of these regions are differentiated – namely the Fifth Wave Growth, the Third & Fourth Wave Growth, and Government-led Third Wave Growth. Overall, spectacular knowledge-based growth of leading Chinese regions is evident, highlighting a continued shift of knowledge-based resources to Asia. In addition, a superstructure is observed at the global scale, consisting of two separate continuums that explicitly distinguish Chinese regions from the rest in terms of regional growth trajectories. © 2014 Elsevier Ltd. All rights reserved. © 2014 Elsevier Ltd. All rights reserved.

Knowledge-based Robot Control

The paper is related with the problem of developing autonomous intelligent robots for complex environments. In details it outlines a knowledge-based robot control architecture that combines several techniques in order to supply an ability to adapt and act autonomously in complex environments. The described architecture has been implemented as a robotic system that demonstrates its operation in dynamic environment. Although the robotic system demonstrates a certain level of autonomy, the experiments show that there are situation, in which the developed base architecture should be complemented with additional modules. The last few chapters of the paper describe the experimentation results and the current state of further research towards the developed architecture.

Knowledge-Based Approach to Document Analysis

The paper presents an approach to extraction of facts from texts of documents. This approach is based on using knowledge about the subject domain, specialized dictionary and the schemes of facts that describe fact structures taking into consideration both semantic and syntactic compatibility of elements of facts. Actually extracted facts combine into one structure the dictionary lexical objects found in the text and match them against concepts of subject domain ontology.

Using ORG-Master for Knowledge Based Organizational Change

* Under Knowledge Infrastructure we imply all the means that enable effective knowledge management within organization ~ knowledge process support.

A Proposed Structure of Knowledge-Based Hybrid Intelligent Systems for Sophisticated Environments

The paper deals with a problem of intelligent system’s design for complex environments. There is discussed a possibility to integrate several technologies into one basic structure. One possible structure is proposed in order to form a basis for intelligent system that would be able to operate in complex environments. The basic elements of the proposed structure have found their implemented in software system. This software system is shortly presented in the paper. The most important results of experiments are outlined and discussed at the end of the paper. Some possible directions of further research are sketched.

Knowledge-based Virtual Reconstruction of Museum Artifacts

Within the framework of heritage preservation, 3D scanning and modeling for heritage documentation has increased significantly in recent years, mainly due to the evolution of laser and image-based techniques, modeling software, powerful computers and virtual reality. 3D laser acquisition constitutes a real development opportunity for 3D modeling based previously on theoretical data. The representation of the object information rely on the knowledge of its historic and theoretical frame to reconstitute a posteriori its previous states. This project proposes an approach dealing with data extraction based on architectural knowledge and Laser statement informing measurements, the whole leading to 3D reconstruction. The experimented Khmer objects are exposed at Guimet museum in Paris. The purpose of this digital modeling meets the need of exploitable models for simulation projects, prototyping, exhibitions, promoting cultural tourism and particularly for archiving against any likely disaster and as an aided tool for the formulation of virtual museum concept.

A knowledge based system of principled negotiation for complex business contract

Automated negotiation systems can do better than human being in many aspects, and thus are applied into many domains ranging from business to computer science. However, little work about automating negotiation of complex business contract has been done so far although it is a kind of the most important negotiation in business. In order to address this issue, in this paper we developed an automated system for this kind of negotiation. This system is based on the principled negotiation theory, which is the most effective method of negotiation in the domain of business. The system is developed as a knowledge-based one because a negotiating agent in business has to be economically intelligent and capable of making effective decisions based on business experiences and knowledge. Finally, the validity of the developed system is shown in a real negotiation scenario where on behalf of human users, the system successfully performed a negotiation of a complex business contract between a wholesaler and a retailer. © 2013 Springer-Verlag Berlin Heidelberg.

NLSE-based modelling of a random distributed feedback fiber laser

Random distributed feedback (DFB) fiber lasers have attracted a great attention since first demonstration [1]. Despite big advance in practical laser systems, random DFB fiber laser spectral properties are far away to be understood or even numerically modelled. Up to date, only generation power could be calculated and optimized numerically [1,2] or analytically [3] within the power balance model. However, spectral and statistical properties of random DFB fiber laser can not be found in this way. Here we present first numerical modelling of the random DFB fiber laser, including its spectral and statistical properties, using NLSE-based model. © 2013 IEEE.

Comprehensive secondary pyrolysis in fluidized-bed fast pyrolysis of biomass, a fluid dynamics based modelling effort

Homogenous secondary pyrolysis is category of reactions following the primary pyrolysis and presumed important for fast pyrolysis. For the comprehensive chemistry and fluid dynamics, a probability density functional (PDF) approach is used; with a kinetic scheme comprising 134 species and 4169 reactions being implemented. With aid of acceleration techniques, most importantly Dimension Reduction, Chemistry Agglomeration and In-situ Tabulation (ISAT), a solution within reasonable time was obtained. More work is required; however, a solution for levoglucosan (C6H10O5) being fed through the inlet with fluidizing gas at 500 °C, has been obtained. 88.6% of the levoglucosan remained non-decomposed, and 19 different decomposition product species were found above 0.01% by weight. A homogenous secondary pyrolysis scheme proposed can thus be implemented in a CFD environment and acceleration techniques can speed-up the calculation for application in engineering settings.

Towards the knowledge-based design of universal influenza epitope ensemble vaccines

Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly-conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highly-conserved and experimentally-verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96% and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97% and 88% coverage of observed subtypes.

Adaptation in knowledge-based teams: An examination of team composition, leader sensegiving, and cognitive, behavioral, and motivational mechanisms

Organizations are increasingly relying on teams to do the work that has traditionally been done by individuals. At the same time, the environments in which these organizations and teams operate have been becoming progressively more complex and uncertain. These trends raise important questions about the factors that enable teams to adapt. In response to these questions, the current study sought to identify the cognitive, behavioral, and motivational processes and emergent states that promote a team's adaptation to unforeseen changes and novel events, and the team compositional characteristics and leadership processes that enabled these processes and emergent states. Two hundred twenty two undergraduate students from a large Southeastern University composed 74 3-person teams, and participated in a computerized decision-making simulation where each team formed the governing body (i.e., Mayor's cabinet) for two separate simulated cities, and made strategic decisions about city operations. Participants were randomly assigned to one of three roles, distributing expertise and creating mutual interdependence. External team leader sensegiving was manipulated through video recorded communications from an external team leader. Results indicate that team cognitive ability, achievement striving, and psychological collectivism, as well as external team leader sensegiving, were all related to the similarity and quality of team members' strategy-focused mental models (cognitive emergent states), and to the amount of information sharing among members (behavioral process). In turn, teams with more similar and higher quality mental models, and who shared greater levels of information, were found to have a greater ability to react and adapt to environmental changes, and to have greater levels of decision-making effectiveness. Results indicate a pattern of relationships consistent with hypotheses, and have important implications for organizations and knowledge-based teams charged with management responsibilities. Organizations should staff teams with the compositional characteristics that enable the development of similar and high quality mental models, and that promote information sharing among teammates. Similarly, organizations which train and develop leaders to engage in sensegiving behaviors enable team adaptability and promote enhanced decision-making effectiveness when faced with unforeseen changes and novel situations.

Managing supply chain knowledge-based linkages for improving operational performance

Purpose: This paper aims to explore the role of internal and external knowledgebased linkages across the supply chain in achieving better operational performance. It investigates how knowledge is accumulated, shared, and applied to create organization-specific knowledge resources that increase and sustain the organization's competitive advantage. Design/methodology/approach: This paper uses a single case study with multiple, embedded units of analysis, and the social network analysis (SNA) to demonstrate the impact of internal and external knowledge-based linkages across multiple tiers in the supply chain on the organizational operational performance. The focal company of the case study is an Italian manufacturer supplying rubber components to European automotive enterprises. Findings: With the aid of the SNA, the internal knowledge-based linkages can be mapped and visualized. We found that the most central nodes having the most connections with other nodes in the linkages are the most crucial members in terms of knowledge exploration and exploitation within the organization. We also revealed that the effective management of external knowledge-based linkages, such as buyer company, competitors, university, suppliers, and subcontractors, can help improve the operational performance. Research limitations/implications: First, our hypothesis was tested on a single case. The analysis of multiple case studies using SNA would provide a deeper understanding of the relationship between the knowledge-based linkages at all levels of the supply chain and the integration of knowledge. Second, the static nature of knowledge flows was studied in this research. Future research could also consider ongoing monitoring of dynamic linkages and the dynamic characteristic of knowledge flows. Originality/value: To the best of our knowledge, the phrase 'knowledge-based linkages' has not been used in the literature and there is lack of investigation on the relationship between the management of internal and external knowledge-based linkages and the operational performance. To bridge the knowledge gap, this paper will show the importance of understanding the composition and characteristics of knowledge-based linkages and their knowledge nodes. In addition, this paper will show that effective management of knowledge-based linkages leads to the creation of new knowledge and improves organizations' operational performance.