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Zola, Nazo. "Organisational learning through scarce skills transfer : a case study in the Eastern Cape Province." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86560.
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Thesis (MPhil)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Knowledge Transfer is one of the key knowledge management practices that organisations employ to ensure cross-pollination of knowledge across their various divisions. It should be a cornerstone of a learning organisation and should pervade the entire organisation in all its manifestations. In general it is a question whether public sector organisations in South African are employing such practices in their quest to render services effectively, efficiently and economically. This thesis focuses on an attempt at knowledge transfer in a department in an underdeveloped province, i.e. the Department of Roads and Public Works in the Eastern Cape. It centres on a case study of Cuban engineers who were contracted by the South African government to design and build infrastructure. The thesis is divided into the following chapters: Chapter 1: deals with the problem of knowledge transfer in a developing context. The chapter focuses on the objectives of the research and sketches a contextual backdrop to the study. Chapter 2: discusses the key concepts of Learning, Organisational Learning, Knowledge, Knowledge Transfer, and Knowledge Transfer Strategies. It also identifies barriers to knowledge transfer and highlights a few suggestions on how to deal with those barriers. Chapter 3: deals with the case study of six Cuban engineers and presents the results of the case study. Chapter 4: describes some of the local initiatives taken by the Department to cater for the needed skills in their sector. Chapter 5: evaluates the topic by bringing the literature discussed in chapter two to bear on the findings of the case study.
AFRIKAANSE OPSOMMING: Kennisoordrag is een van die kern kennisbestuurspraktyke waardeur organisasies kruisbestuiwing van kennis oor ‘n verskeidenheid onderafdelings moontlik maak. Dit behoort die basis van ‘n ‘learning organisation’ te wees en die hele organisasie te deursuur. In die algemeen is dit ‘n vraag of publieke sektor organisasies in Suid-Afrika sodanige praktyke aanwend in hulle pogings om dienste te lewer. Hierdie tesis fokus op ‘n poging tot kennisoordrag in ‘n departement wat in ‘n onderontwikkelde provinsie in Suid-Afrika geleë is, naamlik die departement Paaie en Openbare Werke in die Oos-Kaap. Die tesis draai om ‘n gevallestudie van Kubaanse ingenieurs wat deur die Suid-Afrikaanse regering gekontrakteer was om infrastruktuur te ontwerp en te bou. Die tesis is verdeel in die volgende hoofstukke: HOOFSTUK 1 handel oor die probleem van kennisoordrag binne ‘n ontwikkelingskonteks. Dit sit die doel van die studie uiteen en beskryf die sosiale konteks daarvan. HOOFSTUK 2 bespreek die kernkonsepte, naamlik Leer, Organisatorise Leer, Kennis, Kennisoordrag en Kennisoordragstrategieë. Dit identifiseer ook faktore wat kennisoordrag teenwerk en bespreek moontlike oplossings vir laasgenoemde probleem. HOOFSTUK 3 behels ‘n gevallestudie van 6 Kubaanse ingenieurs en bied die resultate daarvan aan. HOOFSTUK 4 beskryf sommige lokale inisiatiewe deur die Department om kennisoordrag te bevorder. HOOFSTUK 5 evalueer die onderwerp deur die literatuur in hoofstuk 2 in verband te bring met die gevallestudie.
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Deng, Weikun. "Amélioration du diagnostic et du pronostic dans des conditions de données rares et de connaissances limitées par l'apprentissage automatique informé par la physique et auto-supervisé." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP107.
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Cette thèse aborde le défi des « données éparses et des connaissances rares » dans le développement d’un modèle générique de pronostic et de gestion de la santé (PHM). Elle met en lumière l'efficacité des modèles hybrides combinant la modélisation basée sur la physique (PBM) et l'apprentissage automatique (ML), notamment l'apprentissage automatique informé par la physique (PIML) et l'apprentissage auto-supervisé (SSL) pour apprendre à partir de données non étiquetées. La thèse apporte ainsi des contributions significatives aux théories PIML et SSL et à leurs applications pratiques dans le PHM.La première contribution est une solution générique d'architecture et de stratégie d'apprentissage pour le PIML. Diverses approches sont analysées et la théorie mimétique est proposée pour concevoir des neurones et connexions flexibles et physiquement cohérents, aboutissant au Réseau Neuronal Mimétique des Éléments Finis du Rotor (RFEMNN). Le RFEMNN reconnaît efficacement les défauts à travers diverses structures de rotor. Pour améliorer la capacité de diagnostic du RFEMNN avec peu de données, une stratégie d'apprentissage par renforcement alignée avec la physique est proposée. Une architecture générique PIML avec des branches PI et basées sur les données est développée, impliquant un processus en trois étapes : pré-formation de la branche basée sur les données, formation de la branche PI, et formation conjointe. Cette méthode assure des performances supérieures aux modèles basés sur les données dans un contexte de données éparses. De plus, le modèle CNN dilaté utilisant cette approche prédit efficacement la RUL des batteries lithium-ion avec des données de petits cycles. La deuxième contribution est une stratégie SSL pour l'apprentissage à partir de données non étiquetées, introduisant un modèle Siamese CNN-LSTM avec une fonction de perte contrastive personnalisée. Ce modèle extrait des représentations robustes en maximisant les différences dans les mêmes échantillons présentés dans des ordres séquentiels variés. Des tâches en aval sont proposées comme objectifs intermédiaires pour aligner les représentations avec les exigences en aval. Le modèle Siamese CNN-LSTM excelle à prédire la RUL sur le dataset PRONOSTIA et reste stable même avec une augmentation de la rareté des données d'apprentissage.La contribution finale étend les concepts de PIML pour la découverte active des connaissances sur des données non étiquetées et intègre le SSL dans la formation PIML en trois étapes. Une nouvelle structure PI liquide et un modèle PI-CNN-Selective state space model (CNN-SSM) sont développés. Liquid PI introduit des neurones à portes et des connexions liquides qui s'adaptent dynamiquement, acquérant des connaissances physiques grâce à une recherche optimisée. Appliquée dans le suivi du couple des manipulateurs robotisés, cette approche découvre des connaissances en utilisant des opérateurs physiques de base et des poids dynamiques. Le Liquid PI CNN-SSM traite des séquences d'entrée de longueur variable sans prétraitement du signal, optimisant les ressources en nécessitant seulement 600 KB pour gérer 23,9 GB de données. Il atteint des performances de pointe dans des tâches de pronostic mixtes, y compris la dégradation des roulements, l'usure des outils de coupe, le vieillissement des batteries et la fatigue des tubes CFRP. Les travaux futurs appliqueront des lois d'échelle spécifiques au PHM et utiliseront de vastes ensembles de données synthétiques et industrielles pour construire un macro-modèle. Ce modèle pourrait intégrer des capacités de diagnostic et de pronostic avec un traitement de séquence infinie, transformant les méthodologies et les solutions de PHMThis thesis addresses the critical challenge of “sparse data and scarce knowledge” in developing a generic Prognostics and Health Management (PHM) model. A comprehensive literature review highlights the efficacy of hybrid models combining physics-based modeling with machine learning, focusing on Physics-Informed Machine Learning (PIML) and Self-Supervised Learning (SSL) for enhanced learning from unlabeled data. Thereby, this thesis contributes to advancing both PIML and SSL theories and their practical applications in PHM.The first contribution is developing a generic architectural and learning strategy solution for PIML. Various informed approaches are analyzed, and the mimetic theory is proposed to design flexible, physically consistent neurons and interlayer connections. This novel approach leads to the development of the Rotor Finite Elements Mimetic Neural Network (RFEMNN), which mimics rotor finite element-based dynamics to adjust weight distribution and data flow within the neural network. RFEMNN effectively localizes and recognizes compound faults across multiple rotor structures and conditions. To enhance RFEMNN's few-shot diagnostic capability, constraint projection theory and a reinforcement learning strategy are proposed, aligning the learning process with physics. A generic PIML architecture with parallel, independent PI and data-driven branches is proposed, involving a three-stage training process: pre-training the data-driven branch, freezing it to train the PI branch, and joint training of both branches. This method combines optimized local branches into a comprehensive global model, ensuring the PIML model's performance exceeds original data-driven models under spare data context. Moreover, the solid electrolyte interphase growth-informed Dilated CNN model using this approach showcases its superiority, surpassing leading models in predicting lithium-ion battery RUL with small-cycle data.The second contribution is developing an innovative SSL strategy for unlabeled data learning, introducing a Siamese CNN-LSTM model with a custom contrastive loss function. This model extracts robust feature representations by maximizing differences in the same samples presented in varied sequential orders. Variants of downstream tasks are proposed as intermediate objectives in SSL pretext learning, integrating downstream structures into the pre-training model to align representations with downstream requirements. Under this strategy, the proposed Siamese CNN-LSTM excels at predicting RUL on PRONOSTIA-bearing dataset and remains stable even as training data sparsity increases.The final contribution extends PIML concepts for active knowledge discovery on unlabeled data and integrates SSL into the second phase of PIML's three-step training, utilizing both labeled and unlabeled data. A novel Liquid PI structure and an end-to-end Liquid PI-CNN-Selective state space model (CNN-SSM) are developed. The Liquid PI design introduces gated neurons and liquid interlayer connections that adapt dynamically, acquiring physics knowledge through an optimized search within a predefined operator pool. Demonstrated in torque monitoring of robot manipulators, this approach efficiently discovers knowledge using basic physical operators and dynamic weights from unlabeled data. The Liquid PI CNN-SSM processes variable-length input sequences without signal preprocessing, optimizing resources by requiring only 600 KB to handle 23.9 GB of data. It achieves state-of-the-art performance in mixed prognostic tasks, including bearing degradation, tool wear, battery aging, and CFRP tube fatigue, showcasing the originality and versatility of the proposed approach.Future work will apply PHM-specific scaling laws and train on extensive synthetic and industry datasets to build a cross-modal macro-model. It could integrate diagnostic-prognostic capabilities with infinite sequence length processing, continuing to transform PHM methodologies and solutions
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Gallie, Karen Ann. "Development of a knowledge about aging scale." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25395.
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The purpose of this study was to develop a reliable and valid knowledge about aging scale. Two hundred and ninety-eight subjects (128 males, 170 females) from the University of British Columbia, Simon Fraser University, and members of the general population, ranging from 17 to 65 years of age, and having 0 to 12 years of post secondary education, participated in this study. Subjects were chosen on the basis of having gerontological, versus no gerontological training.
Subjects responded to computer randomized Likert scale questionnaires consisting of the initial 60 item Proto Knowledge About Aging Scale, Palmore's Facts on Aging Quiz (FAQ), and Kogan's Old People Scale (OP).
Responses to the initial Proto scale were used to construct a psychometrically appropriate 40 item scale that consisted of three factor dimensions interpreted as Psychological, Biological Change, and Social Lifestyle/Histological Change. This 40 item scale had a Chronbach's alpha of 0.839 and a construct validity value of 0.701. Analysis of Covariance results indicated that the independent variables of age, gender, and years of post secondary education, had no significant extraneous confounding influence (p≤ 0.05) on Proto scale results. However, type of training did influence Proto scale results, with those subjects having gerontological training scoring significantly higher (Duncan's Multiple Range Test p≤ 0.05) than those with no gerontological training.
Investigation into Proto's scale characteristics were further analyzed in relation to the subjects in this investigation, Palmore's FAQ, and Kogan's OP scale, with discussion focussing on Proto's psychometric rigor as compared to Palmore's FAQ.Education, Faculty of
Educational Studies (EDST), Department of
Graduate
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Noori, Sheak Rashed Haider. "A Large Scale Distributed Knowledge Organization System." Doctoral thesis, Università degli studi di Trento, 2011. https://hdl.handle.net/11572/368691.
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The revolution of Internet and the Web takes the computer and information technology into a new age. The information on the web is growing very fast. The progress of information and communication technologies has made accessible a large amount of information, which have provided each of us with access to far more information than we can comprehend or manage. This emphasizes the difficulty with the resulting semantic heterogeneity of the diverse sources. Human knowledge is a living organism and as such evolves in time where different people having different viewpoints and using different terminology among people of different cultures and languages, intensify the heterogeneity of the sources even more. These introduce some concrete problems like natural language disambiguation, information retrieval and information integration. Nevertheless, the problem is quite well known in almost every branch of knowledge and has been independently approached by several communities for several decades. To make this huge amount of existing information accessible and manageable while also solving the semantic heterogeneity problem, namely the problem of diversity in knowledge, and therefore support interoperability, it is essential to have a large scale high quality collaborative knowledge base along with a suitable structure as a common ground on which interoperability among people and different systems should be possible. It will play the role of a reference point for communication, assigning clear meaning by accurate disambiguation to exchanged information, communication and automating complex tasks. However, successfully building large scale knowledge bases with maximum coverage is not possible by a single person or a small group of people without collaborative support. It extremely depends on expert community based support. Therefore, it is necessary for experts to work together on knowledge base building. Furthermore, it is very natural that these expert users will be geographically distributed. Web 2.0 has the potential to support information sharing, interoperability and collaboration on the Web. Simplicity, flexibility and easy to use services make it an interactive and collaborative platform which allows them to create or edit their content. The exponential expansion of the Web users and the potentials of Web 2.0 make it the natural platform of choice for developing knowledge bases collaboratively. We propose a highly flexible knowledge base system, which takes into account diversity of knowledge and its evolution in time. The work presented in this thesis is part of a larger project. More specifically the goal of this thesis is to create a powerful and easy to use knowledge base management system to help people in building, organizing a high quality knowledge base and making accessible their knowledge and to support interoperability in real world scenarios.
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Noori, Sheak Rashed Haider. "A Large Scale Distributed Knowledge Organization System." Doctoral thesis, University of Trento, 2011. http://eprints-phd.biblio.unitn.it/569/1/PhD_Thesis_Noori.pdf.
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The revolution of Internet and the Web takes the computer and information technology into a new age. The information on the web is growing very fast. The progress of information and communication technologies has made accessible a large amount of information, which have provided each of us with access to far more information than we can comprehend or manage. This emphasizes the difficulty with the resulting semantic heterogeneity of the diverse sources. Human knowledge is a living organism and as such evolves in time where different people having different viewpoints and using different terminology among people of different cultures and languages, intensify the heterogeneity of the sources even more. These introduce some concrete problems like natural language disambiguation, information retrieval and information integration. Nevertheless, the problem is quite well known in almost every branch of knowledge and has been independently approached by several communities for several decades. To make this huge amount of existing information accessible and manageable while also solving the semantic heterogeneity problem, namely the problem of diversity in knowledge, and therefore support interoperability, it is essential to have a large scale high quality collaborative knowledge base along with a suitable structure as a common ground on which interoperability among people and different systems should be possible. It will play the role of a reference point for communication, assigning clear meaning by accurate disambiguation to exchanged information, communication and automating complex tasks. However, successfully building large scale knowledge bases with maximum coverage is not possible by a single person or a small group of people without collaborative support. It extremely depends on expert community based support. Therefore, it is necessary for experts to work together on knowledge base building. Furthermore, it is very natural that these expert users will be geographically distributed. Web 2.0 has the potential to support information sharing, interoperability and collaboration on the Web. Simplicity, flexibility and easy to use services make it an interactive and collaborative platform which allows them to create or edit their content. The exponential expansion of the Web users and the potentials of Web 2.0 make it the natural platform of choice for developing knowledge bases collaboratively. We propose a highly flexible knowledge base system, which takes into account diversity of knowledge and its evolution in time. The work presented in this thesis is part of a larger project. More specifically the goal of this thesis is to create a powerful and easy to use knowledge base management system to help people in building, organizing a high quality knowledge base and making accessible their knowledge and to support interoperability in real world scenarios.
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Shoop, Jessica A. "SENIOR INFORMATION TECHNOLOGY (IT) LEADER CREDIBILITY: KNOWLEDGE SCALE, MEDIATING KNOWLEDGE MECHANISMS, AND EFFECTIVENESS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491489274525242.
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Andersson, Martin. "Studies of Knowledge, Location and Growth." Licentiate thesis, Jönköping University, JIBS, Economics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-986.
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Johnson, Michelle E., and Amy Malkus. "Design and Validation of a Nutrition Knowledge Scale for Preschoolers." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/4584.
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Yi, Jialin. "A measure of knowledge sharing behavior scale development and validation /." [Bloomington, Ind.] : Indiana University, 2005. http://wwwlib.umi.com/dissertations/fullcit/3204302.
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Thesis (Ph.D.)--Indiana University, Dept. of Instructional Systems Technology, School of Education, 2005.Source: Dissertation Abstracts International, Volume: 67-01, Section: A, page: 0067. Adviser: Thomas Schwen. "Title from dissertation home page (viewed Jan. 22, 2007)."
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Zhang, Xi. "Knowledge discovery from large-scale biological networks and their relationships." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/23353.
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The ultimate aim of postgenomic biomedical research is to understand mechanisms of cellular systems in a systematical way. It is therefore necessary to examine various biomolecular networks and to investigate how the interactions between biomolecules determine biological functions within cellular systems. Rapid advancement in high-throughput techniques provides us with increasing amounts of large-scale datasets that could be transformed into biomolecular networks. Analyzing and integrating these biomolecular networks have become major challenges. I approached these challenges by developing novel methods to extract new knowledge from various types of biomolecular networks.
Protein-protein interactions and domain-domain interactions are extremely important in a wide range of biological functions. However, the interaction data are incomplete and inaccurate due to experimental limitations. Therefore, I developed a novel algorithm to predict interactions between membrane proteins in yeast based on the protein interaction network and the domain interaction network. In addition, I also developed a novel algorithm, a gram-based interaction analysis tool (GAIA), to identify interacting domains by integrating the protein primary sequences, the domain annotations and interactions and the structural annotations of proteins. Biological assessment against several metrics indicated that both algorithms were capable of satisfactory performance, facilitating the elucidation of cell interactome. Predicting biological pathways is one of major challenges in systems biology. I proposed a novel integrated approach, called Pandora, which used network topology to predict biological pathways by integrating four types of biological evidence (protein-protein interactions, genetic interactions, domain-domain interactions, and semantic similarity of GO terms). I demonstrated that Pandora achieved better performance compared to other predictive approaches, allowing the reconstruction of biological pathways and the delineation of cellular machinery in a systematic view. Finally, I focused on investigating biological network perturbations in diseases. I developed a novel algorithm to capture highly disturbed sub-networks in the human interactome as the signatures linked to cancer outcomes. This method was applied to breast cancer and yielded improved predictive performance, providing the possibility to predict the outcome of cancers based on “network-based gene signatures”. These methods and tools contributed to the analysis and understanding of a wide variety of biological networks and the relationships between them.
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Andersson, Henrik. "Knowledge management and throughput optimization in large-scale software development." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119607.
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Large-scale software development companies delivering market-driven products have introduced agile methodologies as the way of working to a big extent. Even though there are many benefits with an agile way of working, problems occur when scaling agile because of the increased complexity. One explicit problem area is to evolve deep product knowledge, which is a domain specific knowledge that cannot be developed anywhere else but at the specific workplace. This research aims to identify impediments for developing domain specific knowledge and provide solutions to overcome these challenges in order to optimize knowledge growth and throughput. The result of the research shows that impediments occur in four different categories, based on a framework for knowledge sharing drivers. These are people-related, task-related, structure-related and technology-related. The challenging element with knowledge growth is to integrate the training into the feature development process, without affecting the feature throughput negatively. The research also shows that by increasing the knowledge sharing, the competence level of the whole organization can be increased, and thereby be beneficial from many perspectives, such as feature-throughput and code quality.
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Johnson, Michelle E. "Initial Stages of Creating a Scale for Preschoolers: Measuring Nutrition Knowledge, Beliefs, Behaviors- Preliminary Results." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/8544.
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Pace, Laura. "Development of the Knowledge and Attitudes Toward Near-death Experiences Scale." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc500204/.
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The purpose of this study was to develop an instrument to measure healthcare professionals’ knowledge and attitudes about near-death experiences (NDEs) that would demonstrate acceptable psychometric properties. In consultation with a focus group of six NDE experts, I developed the 50-item Knowledge and Attitudes toward Near-Death Experiences Scale (KANDES), including the 24-item KANDES–Attitude subscale (KANDES-A) and the 26-item KANDES–Knowledge subscale (KANDES-K). Including a pilot administration in which feedback indicated no need for revision, a total of 256 professional and student counselors completed the KANDES. Separate reliability and validity analyses were conducted for each subscale. For the KANDES–A, Cronbach’s alpha was .909, and Pearson’s r for test-retest was .748, both indicating acceptable reliability. An exploratory factor analysis indicated four factors to retain and yielded a factor solution that explained 54.87% of the variance, an acceptable amount of variance to substantiate construct validity. For the KANDES–K, Cronbach’s alpha was .816, indicating acceptable reliability. For each of the scale’s three domains, Cronbach’s alpha was .816 for Domain 1: NDE Content, .817 for Domain 2: NDE Aftereffects, and .631 for Domain 3: Experiencer Characteristics, indicating acceptable reliability. Pearson’s r for test-retest on the total KANDES–K was .812, further demonstrating acceptable reliability.
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Tachmazidis, Ilias. "Large-scale reasoning with nonmonotonic and imperfect knowledge through mass parallelization." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/27005/.
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Due to the recent explosion of available data coming from the Web, sensor readings, social media, government authorities and scientific databases, both academia and industry have increased their interest in utilizing this knowledge. Processing huge amounts of data introduces several scientific and technological challenges, and creates new opportunities. Existing works on large-scale reasoning through mass parallelization (namely parallelization based on utilizing a large number of processing units) concentrated on monotonic reasoning, which can process only consistent datasets. The question arises whether and how mass parallelization can be applied to reasoning with huge amounts of imperfect (e.g. inconsistent, incomplete) information. Potential scenarios involving such imperfect data and knowledge include ontology evolution, ontology repair and smart city applications combining a variety of heterogeneous data sources. In this thesis, we overcome the limitations of monotonic reasoning, by studying several nonmonotonic logics that have the ability to handle imperfect knowledge, and it is shown that large-scale reasoning is indeed achievable for such complex knowledge structures. This work is mainly focused on adapting existing methods, thus ensuring that the proposed solutions are parallel and scalable. Initially, preliminaries and literature review are presented in order to introduce the reader to basic background and the state-of-the-art considering large-scale reasoning. Subsequently, each chapter presents an approach for large-scale reasoning over a given logic. Large-scale reasoning over defeasible logic is supported allowing conflict resolution by prioritizing the superiority among rules in the rule set. A solution for stratified semantics is presented where rules may contain both positive and negative subgoals, thus allowing reasoning over missing information in a given dataset. The approach for stratified semantics is generalized in order to fully support the well-founded semantics, where recursion through negation is allowed. Finally, conclusion includes observations from a preliminary investigation on a restricted form of answer set programming, a generic evaluation framework for large-scale reasoning, a discussion of the main findings of this work, and opportunities for future work.
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Barlow, Steven Todd. "Spatial Knowledge Acquired Through Navigation in a Large-Scale Virtual Environment." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19990323-145423.
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The effects of changes in elevation, route distance, route complexity, and non-spatial information on memory for a virtual environment were investigated. Thirty college students learned the layout of a two-story virtual environment through a series of navigation tasks. Participants were required to learn the route from a starting point to each of 14 rooms in the environment. After completing six blocks of learning trials, the participants estimated route distances and directions, completed a priming task in which they identified the floor that each room was on, and navigated novel routes that were not traversed in the learning trials. The results indicated that the elevation, route distance, and route complexity, affected both learning and memory for the environment. In contrast, non-spatial information had no effect on memory for room location or the layout of the virtual environment.
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Thorpe, Manjusha Natasha. "Enabling scale up in the process industry : a knowledge management perspective." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708287.
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Arrascue, Ayala Victor Anthony [Verfasser], and Georg [Akademischer Betreuer] Lausen. "Towards an effective consumption of large-scale knowledge graphs for recommendations." Freiburg : Universität, 2020. http://d-nb.info/1223366189/34.
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Karlsson, Jenny. "Generalizability Theory and a Scale Measuring Emotion Knowledge in Preschool Children." Thesis, Stockholms universitet, Psykologiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-138153.
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Schwartz, Yannick. "Large-scale functional MRI analysis to accumulate knowledge on brain functions." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112056/document.
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Comment peut-on accumuler de la connaissance sur les fonctions cérébrales ? Comment peut-on bénéficier d'années de recherche en IRM fonctionnelle (IRMf) pour analyser des processus cognitifs plus fins et construire un modèle exhaustif du cerveau ? Les chercheurs se basent habituellement sur des études individuelles pour identifier des régions cérébrales recrutées par les processus cognitifs. La comparaison avec l'historique du domaine se fait généralement manuellement pas le biais de la littérature, qui permet de définir des régions d'intérêt dans le cerveau. Les méta-analyses permettent de définir des méthodes plus formelles et automatisables pour analyser la littérature. Cette thèse examine trois manières d'accumuler et d'organiser les connaissances sur le fonctionnement du cerveau en utilisant des cartes d'activation cérébrales d'un grand nombre d'études. Premièrement, nous présentons une approche qui utilise conjointement deux expériences d'IRMf similaires pour mieux conditionner une analyse statistique. Nous montrons que cette méthode est une alternative intéressante par rapport aux analyses qui utilisent des régions d'intérêts, mais demande cependant un travail manuel dans la sélection des études qui l'empêche de monter à l'échelle. A cause de la difficulté à sélectionner automatiquement les études, notre deuxième contribution se focalise sur l'analyse d'une unique étude présentant un grand nombre de conditions expérimentales. Cette méthode estime des réseaux fonctionnels (ensemble de régions cérébrales) et les associe à des profils fonctionnels (ensemble pondéré de descripteurs cognitifs). Les limitations de cette approche viennent du fait que nous n'utilisons qu'une seule étude, et qu'elle se base sur un modèle non supervisé qui est par conséquent plus difficile à valider. Ce travail nous a cependant apporté la notion de labels cognitifs, qui est centrale pour notre dernière contribution. Cette dernière contribution présente une méthode qui a pour objectif d'apprendre des atlas fonctionnels en combinant plusieurs jeux de données. [Henson2006] montre qu'une inférence directe, c.a.d. la probabilité d'une activation étant donné un processus cognitif, n'est souvent pas suffisante pour conclure sur l'engagement de régions cérébrales pour le processus cognitif en question. Réciproquement, [Poldrack 2006] présente l'inférence inverse qui est la probabilité qu'un processus cognitif soit impliqué étant donné qu'une région cérébrale est activée, et décrit le risque de raisonnements fallacieux qui peuvent en découler. Pour éviter ces problèmes, il ne faut utiliser l'inférence inverse que dans un contexte où l'on suffisamment bien échantillonné l'espace cognitif pour pouvoir faire une inférence pertinente. Nous présentons une méthode qui utilise un « meta-design » pour décrire des tâches cognitives avec un vocabulaire commun, et qui combine les inférences directe et inverse pour mettre en évidence des réseaux fonctionnels qui sont cohérents à travers les études. Nous utilisons un modèle prédictif pour l'inférence inverse, et effectuons les prédictions sur de nouvelles études pour s'assurer que la méthode n'apprend pas certaines idiosyncrasies des données d'entrées. Cette dernière contribution nous a permis d'apprendre des réseaux fonctionnels, et de les associer avec des concepts cognitifs. Nous avons exploré différentes approches pour analyser conjointement des études d'IRMf. L'une des difficultés principales était de trouver un cadre commun qui permette d'analyser ensemble ces études malgré leur diversité. Ce cadre s'est instancié sous la forme d'un vocabulaire commun pour décrire les tâches d'IRMf. et a permis d'établir un modèle statistique du cerveau à grande échelle et d'accumuler des connaissances à travers des études d'IRM fonctionnelleHow can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process. Conversely, [Poldrack 2006] describes reverse inference as the probability of a cognitive process given an activation, but warns of a logical fallacy in concluding on such inference from evoked activity. Avoiding this issue requires to perform reverse inference with a large coverage of the cognitive space. We present a framework that uses a "meta-design" to describe many different tasks with a common vocabulary, and use forward and reverse inference in conjunction to outline functional networks that are consistently represented across the studies. We use a predictive model for reverse inference, and perform prediction on unseen studies to guarantee that we do not learn studies' idiosyncrasies. This final contribution permits to learn functional atlases, i.e. functional networks associated with a cognitive concept. We explored different possibilities to jointly analyse multiple fMRI experiments. We have found that one of the main challenges is to be able to relate the experiments with one another. As a solution, we propose a common vocabulary to describe the tasks. [Henson 2006] advocates the use of forward and reverse inference in conjunction to associate cognitive functions to brain regions, which is only possible in the context of a large scale analysis to overcome the limitations of reverse inference. This framing of the problem therefore makes it possible to establish a large statistical model of the brain, and accumulate knowledge across functional neuroimaging studies
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Hopkinson, Terry. "The Middle Palaeolithic leaf points of Europe : ecology, knowledge and scale /." Oxford : J. and E. Hedges, 2007. http://catalogue.bnf.fr/ark:/12148/cb41161635k.
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Johnson, Michelle E., and Amy J. Malkus. "Creating a Scale for Preschoolers: Measuring Nutrition Knowledge, Beliefs, and Behaviors." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/4304.
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PEDE, ELENA CAMILLA. "Building resilience towards natural hazards: cross-‐scale knowledge and institutional linkages." Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2611359.
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Our environment is becoming increasingly complex: the rapid urbanisation, often accompanied by uncontrolled use of land, occupation of unsafe environments as well as the increased rate of occurrence of climate events are introducing elements of uncertainty (Pinna, 2002). The idea of certainty or security that was fundamental to risk management in the past, collapses. In this context, the notion of ‘risk society’, introduced by Ulrich Beck in 1992, is considered as a shifting paradigm in world security, where our modern society becomes ever more interdependent and more complex, and consequently more vulnerable to threats and risks. Traditionally, planning plays a central role in the scientific management of risks mostly based on the control of calculated risks. But, the increasing uncertainty and the emerging of new types of risks require an alternative path of planning practice that acknowledges and interacts with society’s risk implications. Despite its lack of clarity, resilience offers opportunities to the uncertainty and insecurity of contemporary context (Davoudi et al., 2012).
The overall aim of this research is the exploration of a new path of planning for resilience that responds to the increasing uncertainty in the context of global ‘risk society’. It would contribute to the new dynamic of safety and security by drawing on risk dimensions modifications. Several areas of knowledge are developed within this research aimed to improve ways of governing society resilience that covers the continuous system for mitigation, readiness, and resistance in the context of risks. Out of that, new areas of collaboration are identified among urban and emergency planners, decision makers, and the citizens to strengthen societal resilience.
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Cross, Emily L. "Investigating The Discrepancy Between Theory And Empirical Research In Knowledge Transfer And Innovation." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15925/1/Emily_Cross_Thesis.pdf.
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The aim of this study is to explore the relationship between knowledge transfer and innovation. The literature documents that increasing knowledge transfer can both increase and decrease an organisation's capability to innovate. This study proposes a model that integrates these two theoretical standpoints and in doing so hypothesizes a non-linear (parabolic) relationship between knowledge transfer and innovation. Empirical research to date, including the work of Storey and Kelly (2002), document a linear relationship between knowledge transfer and innovation. In order to investigate the possibility of a non-linear relationship this study proposes and tests refinements to Storey and Kelly's methodology. A refined survey questionnaire was sent to all the local government councils within two Australian states and both linear and quadratic statistical analyses were performed. The results of this pilot study revealed linear relationships between four of the six knowledge transfer questions and the innovation measurement question. A further refinement to the methodology is recommended before a non-linear relationship between knowledge transfer and innovation is rejected.
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Cross, Emily L. "Investigating The Discrepancy Between Theory And Empirical Research In Knowledge Transfer And Innovation." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15925/.
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The aim of this study is to explore the relationship between knowledge transfer and innovation. The literature documents that increasing knowledge transfer can both increase and decrease an organisation's capability to innovate. This study proposes a model that integrates these two theoretical standpoints and in doing so hypothesizes a non-linear (parabolic) relationship between knowledge transfer and innovation. Empirical research to date, including the work of Storey and Kelly (2002), document a linear relationship between knowledge transfer and innovation. In order to investigate the possibility of a non-linear relationship this study proposes and tests refinements to Storey and Kelly's methodology. A refined survey questionnaire was sent to all the local government councils within two Australian states and both linear and quadratic statistical analyses were performed. The results of this pilot study revealed linear relationships between four of the six knowledge transfer questions and the innovation measurement question. A further refinement to the methodology is recommended before a non-linear relationship between knowledge transfer and innovation is rejected.
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Idrobo, Carlos Julián. "Ponta Negra Ethnoecology of Practice: Intergenerational Knowledge Continuity in the Atlantic Forest Coast of Brazil." Springer Open, 2012. http://hdl.handle.net/1993/23528.
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The intergenerational continuity of knowledge has become a concern as small-scale societies worldwide balance the challenges of adapting to environmental change associated with globalization while retaining continuity in their ways of life. This dissertation examines the intergenerational continuity of environmental knowledge through the conceptual lens of an Ethnoecology of Practice framework (EofP) developed to guide this research. Integrating insights from political ecology, social wellbeing and adaptive learning, the EofP provides theoretical and methodological tools based on practice theory to examine the knowledge of small-scale societies. Based on fieldwork in the community of Ponta Negra (Atlantic Forest Coast, Brazil), this dissertation uses a qualitative case study strategy of inquiry guided by a phenomenological worldview. Methods included participant observation, semi-structured interviews covering livelihoods, life histories and marine and terrestrial knowledge themes, document review and a census. Chapters 4 and 5 examine the perception of marine and terrestrial natural resources by tracing their social life from harvesting grounds to exchange and consumption sites. Chapters 6 and 9 analyse historical and contemporary adaptation to environmental change. While Chapter 6 describes the adoption of the pound net fishery, Chapter 9 illustrates contemporary modes of learning associated with natural resource harvesting and presents the processes associated with production of new knowledge through the example of local participation in the tourism economy. Chapter 7 examines local perspectives on livelihood transition from a social wellbeing perspective and highlights factors underlying the continuity of natural resource harvesting practice in Ponta Negra. Chapter 8 discusses how the term Caiçara, as used in biodiversity conservation and tourism development discourses, circumscribes the relation between coastal people and their local environments to a subsistence economy, denying their current economic engagements as well as their desires and aspirations. This dissertation contributes to ethnobiological understandings of the intergenerational continuity of knowledge by providing a framework and grounding evidence that demonstrates how knowledge is generated through context-specific practice attuned to dynamic environments that leads to individual innovation. It provides a theoretical contribution to our understanding of framing and creating processes inherent to human-in environment relations that lead to fluidity in ways of life over time.
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Barnickel, Thorsten. "Large scale knowledge extraction from biomedical literature based on semantic role labeling." kostenfrei, 2009. https://mediatum2.ub.tum.de/node?id=802669.
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Tödtling, Franz, Tanja Sinozic, and Alexander Auer. "Knowledge bases, multi-scale interaction and transformation of the Vienna medical cluster." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/5199/1/Knowledge_bases_Vienna_Medtech_sre%2Ddisc%2D2016_03.pdf.
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The health sector and medical technologies are of an increasing importance in society and for regional and national economies. Much like other life sciences industries, the medical devices sector relies upon specific factors and knowledge processes that shape and support its innovation capabilities and competitiveness. Previous studies have shown that growth and innovation in this sector depend on specific local factors and conditions as well as on markets and knowledge-interdependencies at higher spatial scales. There is still a research gap on the detailed nature of these driving factors and relationships, however. In this research, we have investigated these issues for the Vienna medical devices cluster that is part of the wider life sciences sector in this region. The main aims of the study were to generate insights into how different economic, knowledge- and policy conditions, and their spatial scales, interact to support and hinder development of the medical devices industry in Vienna, and to draw policy conclusions based on these findings. (authors' abstract)Series: SRE - Discussion Papers
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Garavito-Bermúdez, Diana. "Learning ecosystem complexity : A study on small-scale fishers’ ecological knowledge generation." Doctoral thesis, Stockholms universitet, Institutionen för pedagogik och didaktik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-133601.
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Small-scale fisheries are learning contexts of importance for generating, transferring, and updating ecological knowledge of natural environments through everyday work practices. The rich knowledge fishers have of local ecosystems is the result of the intimate relationship fishing communities have had with their natural environments across generations (see e.g. Urquhart and Acott 2013). This relationship develops strong emotional bonds to the physical and social place. For fishing communities and fishers – who depend directly on local ecosystems to maintain their livelihoods – fishing environments are natural places for living, working and defining themselves. Previous research on fishers’ ecological knowledge has mainly been descriptive, i.e., has focused on aspects such as reproduction, nutrition and spatial-temporal distribution and population dynamics, from a traditional view of knowledge that only recognises scientific knowledge as the true knowledge. By doing this, fishers’ ecological knowledge has been investigated separately from the learning contexts in which it is generated, ignoring the influence of social, cultural and historical aspects that characterise fishing communities, and the complex relationships between fishers and the natural environments they live and work in. This thesis investigates ecological knowledge among small-scale fishers living and working in the ecosystems of Lake Vättern and the Blekinge Archipelago (Baltic Sea) in Sweden and explores how ecological knowledge is generated with particular regard to the influences of work and nature on fishers’ knowledge of ecosystems. The aim of this thesis is to contribute to the knowledge and understanding of informal learning processes of ecosystem complexity among small-scale fishers. This knowledge further contributes to the research field of ecological knowledge and sustainable use and management of natural resources. It addresses the particular research questions of what ecological knowledge fishers generate, and how its generation is influenced by their fishing work practices and relationships to nature. The thesis consists of three articles. Article I focuses on the need to address the significant lack of theoretical and methodological frameworks for the investigation of the cognitive aspects involved in the generation of ecological knowledge. Article II deals with the need to develop theoretical, methodological and empirical frameworks that avoid romanticising and idealising users’ ecological knowledge in local (LEK), indigenous (IEK) and traditional (TEK) ecological knowledge research, by rethinking it as being generated through work practices. Article III addresses the lack of studies that explicitly explore theories linking complex relations and knowledge that humans form within and of ecosystems. It also addressed the lack of attention from environmental education researchers to theory and empirical studies of ‘sense of place’ research, with a particular focus on environmental learning. Research into the question of what ecological knowledge fishers generate shows differences in their ways of knowing ecosystem complexity. These differences are explained in terms of the influences of the species being fished, and the sociocultural contexts distinguishing fishers’ connection to the fishing profession (i.e., familial tradition or entrepreneurship) (Article I), but also by the fishing strategies used (Article II). Results answering the research question of how work practices influence fishers’ knowledge of ecosystem complexity show a way of rethinking their ecological knowledge as generated in a continuous process of work (Article II), thus, far from romantic views of knowledge. Results answering the research question of how fishers’ relationships to nature influence their knowledge of ecosystem complexity demonstrate the complex interconnections between psychological processes such as identity construction, proximity maintenance and attachment to natural environments (Article III). Finally, more similarities than differences between fishers’ knowledge were found, despite the variation in cases chosen, with regards to landscape, target species, regulations systems and management strategies, fishing environments scales, as well as cultural and social contexts.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.
Ecological knowledge and sustainable resource management: The role of knowledge acquisition in enhancing the adaptive capacity of co-management arrangements
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Nkantah, Dianabasi Edet. "Large-scale connectionist natural language parsing using lexical semantic and syntactic knowledge." Thesis, Nottingham Trent University, 2007. http://irep.ntu.ac.uk/id/eprint/317/.
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Syntactic parsing plays a pivotal role in most automatic natural language processing systems. The research project presented in this dissertation has focused on two main characteristics of connectionist models for natural language processing: their adaptability to different tagging conventions, and their ability to use multiple linguistic constraints in parallel during sentence processing. In focusing on these key characteristics, an existing hybrid connectionist, shift-reduce corpus-based parsing model has been modified. This parser, which had earlier been trained to acquire linguistic knowledge from the Lancaster Parsed Corpus, has been adapted to learn linguistic knowledge from the Wall Street Journal Corpus. This adaptation is a novel demonstration that this connectionist parser, and by extension, other similar connectionist models, is able to adapt to more than one syntactic tagging convention; this implies their ability to adapt to the underlying linguistic theories used to annotate these corpora. The parser has also been adapted to integrate shallow lexical semantic information with syntactic information for full syntactic parsing. This approach was used to investigate the effect of shallow lexical semantic information on full syntactic parsing. In pursuing the aims of this project, a novel algorithm for semantic tagging of nouns in the Wall Street Journal Corpus has been developed. The lexical semantic information used in this semantic annotation algorithm was extracted from WordNet, an online lexical resource. Using only syntactic information in making parsing decisions, this parsing model was tested on test sets of sentences that were not used during training. The parser generalised to parse these test sentences with an F-measure of 72.5% and 59.5% on sentences from the Lancaster Parsed Corpus and Wall Street Journal Corpus, respectively. On the integration of shallow lexical semantic information with syntactic information in its input representation, the parser generalised to parse test sentences from the Wall Street Journal Corpus with an F-measure of 56.75%. Although this integration did not seem to improve the parser's overall training/generalisation performance, given its present configuration, it did appear to improve the parser's decision making concerning preposition phrase attachment.
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Cook, Brian Robert. "Knowledges, controversies and floods : national-scale flood management in Bangladesh." Thesis, Durham University, 2010. http://etheses.dur.ac.uk/371/.
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This research explores the views, beliefs and knowledges of experts responsible for flood management in Bangladesh. As one of the most disaster-prone nations on Earth, and in response to the neglect of power-holding experts within the existing literature, this project analyses the differences between academic accounts of flooding, labelled the ‘prevailing understanding’, and the local expertise of those responsible for management. Relative to the entrenched narrative, local experts are surveyed and an alternate interpretation is constructed using their knowledge. This combination of textual and perception-based analyses accounts for the complex interrelations between competing forms of knowing. It is on this juxtaposition that the research contributes to new knowledge. The thesis is based on research conducted in Bangladesh between November 2007 and March 2008. To accomplish its objectives, using prominent debates as entry points, academic and government sources are used to account for the lineage of the prevailing understanding. On the basis of this narrative, qualitative interviews with 54 experts explore the construction of flood management knowledge and its relationship with decision making. The experts describe and justify understandings of flood management that are contextual, adaptive and indefinite, challenging many of the assumptions associated with the prevailing understanding. The findings inform several findings: that individuals close to the poverty line are uniquely vulnerable; that disasters merge with management to produce second-generation events; and that a hybrid socio-physical context is both a product and a producer of flood management knowledge. Overall, despite the already complex issue of flooding, managers in Bangladesh consider increasingly issues as diverse as poverty, environmental sustainability and economic and human development. Given the scope of the controversy surrounding flood management, the findings show how analyses of competing knowledges, assumptions and framings can aid the interrogation of prevailing knowledge to generate original findings
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Jacob, Yanick. "Nursing Knowledge on Pressure Injury Prevention in the Intensive Care Unit." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7810.
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Over 60,000 hospital patients die each year from complications associated with hospital-acquired pressure injuries (HAPIs). Pressure-injury rates have increased by 2% within the past decade as life expectancy has also increased due to high cost in Medicare. Evidence shows that the incidence of pressure injuries (PIs) in healthcare facilities is increasing, with high rates of occurrence in intensive care units (ICUs). At the clinical site for which this project was developed, multiple in-services had been provided to staff regarding PIs, but uncertainty persisted about how knowledgeable the nurses were. This project, using the Academic Center for Evidence Star Model of Knowledge Transformation improved the nurses' knowledge and their practice related to PI prevention in the ICU, as well as to translate evidence into nursing practice. A literature review was conducted on PI prevention to inform the project. The project provided an educational program for intensive care nurses on PI prevention and determined, based on participants' pre- and posttest responses, that nurses' knowledge improved as a result of participation. This project, involving 55 nurses, includes information on the Pieper-Zulkowski Pressure Ulcer Knowledge Test (PZ-PUKT) measuring pressure knowledge which resulted in an 85% improvement on injury prevention, 76% in wound description, as well as, 62% in the Braden Scale. Improvements in knowledge and practice resulting from nurses' participation in an evidence-based education session on PI prevention may bring positive social change to the organization at which this project was conducted.
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Skadiang, Barbara. "Dimensions of organisational knowledge management (OKM). A study on malaysian managers using the multidimensional USQ KM scale." University of Southern Queensland, Faculty of Business, 2009. http://eprints.usq.edu.au/archive/00006252/.
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[Abstract]The case for knowledge management (KM) is firmly established in the wealth of extant literature available and is well corroborated in business best practices.However, the broad scope of KM and its multiple approaches in varied contexts,while credited for their diversity and flexibility, also call for greater universality and coherence in several areas, one of which is in the assessment of organisational knowledge management (OKM) practice. While literature attests to the dynamics of the different dimensions of KM, very limited research hasbeen conducted using a standardised multidimensional scale. This dissertation pertains to the application of an in-house developed instrument, called the USQ KM scale, to empirically study the three key dimensions of OKM identified through literature review, namely OKM strategy, OKM culture and OKM process/technology. The instrument was employed to assess the extent of OKM practice in organisations in Malaysia. Results of the survey on 153 managers provided insights into the state of OKM practice in Malaysia while confirming the mutually dependent relationship between the three dimensions of OKM andgiving rise to the researcher’s proposed conceptual tripartite OKM model. In addition, demographic factors such as the number of years of service in the organisation and years of service in a specific organisational role were found to have an impact on OKM, supporting previous empirically tested truths about OKM besides revealing a few unique traits in the Malaysian sample. The implications of the study for organisations in Malaysia as well as in other developing countries within similar contexts are especially meaningful for smaller local businesses without the benefit of multinational affiliations usually associated with superior KM capability afforded by greater economies of scale and more sizeable budgets for infrastructure support.
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Martínez-Mejía, Pablo Rolando Molnar Joseph J. "Understanding small and medium scale tilapia culture in Nicaragua." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Fall/Dissertations/MARTINEZ-MEJIA_PABLO_53.pdf.
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Geist, Kamile. "Sources of Knowledge in Music Therapy Clinical Practice." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1478173980933032.
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Dunn, Rochelle L. "The knowledge and competencies of effective school counselor supervision." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086109491.
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Thesis (Ph. D.)--Ohio State University, 2004.Title from first page of PDF file. Document formatted into pages; contains xiii, 159 p. : ill. Advisor: Susan J. Sears, College of Education. Includes bibliographical references (p. 102-109).
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Kislov, Roman. "Multiprofessional communities of practice in a large-scale healthcare knowledge mobilisation initiative : a qualitative case study of boundary, identity and knowledge sharing." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/multiprofessional-communities-of-practice-in-a-largescale-healthcareknowledge-mobilisation-initiative-a-qualitative-case-study-of-boundary-identity-andknowledge-sharing(5fb0248f-f12c-46e0-b13e-a5a4c0a216e9).html.
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This thesis explores the development of multiprofessional communities of practice within the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for Greater Manchester - a large-scale UK-based healthcare knowledge mobilisation partnership between the University of Manchester and local NHS organisations. In particular, it examines the role of pre-existing boundaries and identities in the process of community formation and develops our understanding of knowledge sharing across multiple interconnected communities of practice. The project deploys a qualitative single embedded case study as its research methodology, embracing 45 interviews and 69 hours of direct observation supplemented by documentary analysis, all of which were undertaken in 2010-2011. Three overlapping implementation contexts within the CLAHRC for Greater Manchester are explored, each of them reported in a different empirical paper: (1) a multi-professional community of practice emerging from a specialised project team driving an implementation project; (2) multiprofessional communities of practice operating within and across primary healthcare settings; and (3) the knowledge mobilisation initiative as a constellation of multiple communities of practice. The key theoretical contribution of this thesis is threefold. First, it demonstrates that a multiprofessional team can develop characteristics typical for a community of practice, identifies the mechanisms and consequences of this conversion and argues that teams and communities of practice do not need to be seen as mutually exclusive entities. Second, it introduces a notion of selective permeability of boundaries, whereby boundaries developing around a community of practice enable knowledge exchange between such a community and certain out-groups while impeding knowledge sharing with others. Finally, it enhances our understanding of large-scale knowledge mobilisation initiatives as emerging constellations of interconnected practices, describes a boundary between the fields of applied health research and research implementation and questions the role of implementation as a boundary practice bridging the real-time gap between the producers and users of research. The main practical contribution of this work is the formulation of a developmental approach to communities of practice, which lies midway between the analytical and instrumental perspectives previously described in the literature and can be beneficial in those cases where strong pre-existing boundaries make the emergence of a new community of practice problematic. This approach calls for the maximal utilisation of existing organic communities and for improving communication within and between them rather than attempting to foster a heterogeneous community centred on a time-limited project.
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Harrington, Brian. "ASKNet : automatically creating semantic knowledge networks from natural language text." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:1c7154d3-f7d1-493e-b521-4e5ceb540038.
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This thesis details the creation of ASKNet (Automated Semantic Knowledge Network), a system for creating large scale semantic networks from natural language texts. Using ASKNet as an example, we will show that by using existing natural language processing (NLP) tools, combined with a novel use of spreading activation theory, it is possible to efficiently create high quality semantic networks on a scale never before achievable. The ASKNet system takes naturally occurring English text (e.g., newspaper articles), and processes them using existing NLP tools. It then uses the output of those tools to create semantic network fragments representing the meaning of each sentence in the text. Those fragments are then combined by a spreading activation based algorithm that attempts to decide which portions of the networks refer to the same real-world entity. This allows ASKNet to combine the small fragments together into a single cohesive resource, which has more expressive power than the sum of its parts. Systems aiming to build semantic resources have typically either overlooked information integration completely, or else dismissed it as being AI-complete, and thus unachievable. In this thesis we will show that information integration is both an integral component of any semantic resource, and achievable through a combination of NLP technologies and novel applications of spreading activation theory. While extraction and integration of all knowledge within a text may be AI-complete, we will show that by processing large quantities of text efficiently, we can compensate for minor processing errors and missed relations with volume and creation speed. If relations are too difficult to extract, or we are unsure which nodes should integrate at any given stage, we can simply leave them to be picked up later when we have more information or come across a document which explains the concept more clearly. ASKNet is primarily designed as a proof of concept system. However, this thesis will show that it is capable of creating semantic networks larger than any existing similar resource in a matter of days, and furthermore that the networks it creates of are sufficient quality to be used for real world tasks. We will demonstrate that ASKNet can be used to judge semantic relatedness of words, achieving results comparable to the best state-of-the-art systems.
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Brandt, Nicola. "Market power, economies of scale and the role of knowledge for economic growth." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=96536660X.
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Nunez, Jose Luis. "DEVELOPMENT OF A KNOWLEDGE MANAGEMENT MODEL IN LARGE-SCALE INTERNATIONAL SPACE SCIENCE PROJECTS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2873.
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Large-scale international science projects (LISPs) are those projects where two or more countries formally agree to cooperate toward the achievement of a scientific, research and development, or engineering goal. In general, only projects exceeding $1 billion U.S. are considered LISPs, so sheer size commands attention, and because they are so costly and visible, failure can lead to significant scientific, financial and political consequences. This researched focused on how 7 different critical success factors impacted the level of technical interface knowledge shared among international partners involved in a large-scale international space science project (LISSP) – the International Space Station (ISS), which is currently under assembly and testing at the Kennedy Space Center, Florida. The research methodology employed documentation review, individual interviews and surveys of experienced engineers and managers from three different countries associated with the ISS. The research methodology was applied to three different cases (retrospectively) involving the processing of flight hardware from the three different international partners. The analysis showed that only 5 out of the 7-factor model played a significant role in the level of knowledge sharing between partners. The developed model provides future international partnerships with critical success factors that they can apply to their specific project / mission teams in order to improve the level of knowledge shared between them.Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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McCarter, Renee Joy. "The development and validation of a scale to measure adolescents' knowledge of epilepsy." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427884.
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Claman, Erica E. "Construction and Validation of a "Counselor Knowledge of Attitudes Toward Transgender Issues" Scale." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1382966132.
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Ritze, Dominique [Verfasser], and Christian [Akademischer Betreuer] Bizer. "Web-Scale Web Table to Knowledge Base Matching / Dominique Ritze ; Betreuer: Christian Bizer." Mannheim : Universitätsbibliothek Mannheim, 2017. http://d-nb.info/1144857732/34.
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Dunham, Annette Heather. "Knowledge Management in the Context of an Ageing Workforce: Organizational Memory and Mentoring." Thesis, University of Canterbury. Psychology, 2010. http://hdl.handle.net/10092/4716.
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Organizational memory has significant potential for companies’ competitive advantage, with mentoring considered a particularly effective method of transferring this knowledge. Older workers are often considered ideal mentors because of their experience and alleged willingness to pass on their knowledge. There is an associated assumption that these workers anticipate and experience positive outcomes from mentoring others. This thesis tested if these assumptions hold up in 21st century workplaces - some discriminatory practices towards older workers and a career contract that no longer guarantees employment, may discourage knowledge sharing.
An organizational memory scale was constructed to help test the assumptions and an exploratory factor analysis involving 143 employees from eight companies resulted in 21 items and five correlated factors including socio-political knowledge, job knowledge, external network, history, and industry knowledge. Two confirmatory factor analyses, the first involving 287 employees and the second 115 retirees, found support for five correlated first-order factors and a second order factor, organizational memory.
In a third study involving 134 employees, support was found for a model of organizational memory and empowerment. Age was found to relate to organizational memory but this relationship was mediated by organizational tenure. In turn, organizational memory was found to relate to psychological empowerment and the frequency with which participants were requested to share knowledge at work. Organizational memory, empowerment and request to train and mentor others also positively related to organization-based self-esteem.
In the fourth study, an organizational case study involving 78 employees, support was found for a model of organizational memory and the intention to mentor within the context of an aging workforce. Generativity and the expected cost of the time and effort involved in mentoring mediated the relationship between organizational memory (specifically, socio-
political knowledge) and the intention to mentor. Furthermore those participants with high scores on both organizational memory and occupational self-efficacy anticipated more cost in time and effort, and indicated less intention to mentor, than those with high organizational memory but low occupational self-efficacy. These findings challenge the assumption that experienced workers are, as a matter of course, willing to mentor others.
In a final study involving 96 retired individuals, there were no significant differences found between retirees with and those without experience as a mentor, in career satisfaction and unwelcome work ruminations. However notably, the study showed that participants did experience unwelcome work ruminations even (as in the case of some) well into retirement. The thesis concludes with a summary of findings as they relate to the assumptions under examination, an outline of the overall implications of the findings for future research and for organizational practice, and closing remarks about the overall research contribution of the thesis.
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Bullard, Daniel Peterson. "The impact of context manipulation on knowledge development in a balancing task." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258741368.
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Ryman-Tubb, Nick. "Understanding payment card fraud through knowledge extraction from neural networks using large-scale datasets." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/812310/.
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A novel approach to knowledge extraction from neural network classifiers when applied to payment card fraud detection is proposed. Existing Fraud Management Systems (FMS) use neural network classifiers but do not have the ability to explain their learnt patterns of fraud. Rule extraction from such classifiers with a high level of abstraction and linguistic simplicity is proposed. Decompositional knowledge extraction methods are found to be too reliant on the architecture of the fraud classifer and current pedagogical rule extraction methods produce rules that are not sufficiently comprehensible. In this thesis the Sparse Oracle-based Adaptive Rule (SOAR) pedagogical extraction algorithm is proposed to extract generalising rules that explain patterns of fraud. SOAR uses sensitivity analysis to avoid the exhaustive searches of other pedagogical methods. By projecting into discretised space, polytopes are formed by SOAR covering the class convex hull of the classifier surface. A methodological and verifiable empirical evaluation on publicly available datasets in various domains is undertaken. These results show that SOAR extracts comprehensible rules that are sound from a deep learning neural network. When SOAR is applied to large datasets provided by payment card issuers it discovered new fraud types that were of key interest to payment risk/fraud analysts. SOAR provides an improved understanding of fraud vectors that will lead to a more secure payment process through informed payment fraud prevention steps and this work could therefore alter how fraud management is undertaken in the future.
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Mellema, Angelika. "The relationship between self-knowledge accessibility and the predictive utility of personality scale responses." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ27699.pdf.
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Woo, Matthew. "Validation of a scale to measure friendship knowledge and skills of intellectually disabled adults." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09SB/09sbw873.pdf.
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Melo, André [Verfasser], and Heiko [Akademischer Betreuer] Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://d-nb.info/1167160584/34.
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Erwin, Nicholas Daniel. "Investigating Sustainability Literacy at SIU Carbondale using the Assessment of Sustainability Knowledge (ASK) Scale." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2875.
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This research investigates how the ASK (Assessment of Sustainability Knowledge) Scale, developed by Zwickle and Jones (2018), can be used to assess the current state of sustainability knowledge at Southern Illinois University Carbondale (SIUC). The goal of this research is to identify an assessment process that is efficient and meaningful for SIUC in future planning and programming. To answer the research questions, students were surveyed online in Spring 2020 using the ASK Scale survey. The survey consisted of all 12 unaltered questions from the ASK Scale, in addition to questions about participant’s year in school and major. The questions from the survey were analyzed using Microsoft Excel to find the results. This study found that the average ASK score among students at SIUC is 8.61 (out of 12.00), which shows that students who took this survey do possess a relatively high sustainability literacy. The results show that students at SIUC have the highest understanding of the social domain of sustainability (average score 84.63%), followed by the environmental domain (average score 74.11%), while the economic domain was the lowest (average score 59.17%). This shows that SIUC has ample opportunity to integrate the concepts of economic sustainability into their curriculum and programming. The research also found that students at SIUC are experiencing an increase in sustainability knowledge as they move through their academic career. This research shows that the ASK Scale can be used as an effective tool for assessing students’ current sustainability literacy.
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Melo, André Verfasser], and Heiko [Akademischer Betreuer] [Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-459801.
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