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Kwon, Hongwoo. "Self-identification and self-knowledge." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62418.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2010.<br>"September 2010." Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 119-122).<br>The traditional view has it that self-locating beliefs are distinctive in that they have distinctive contents. Against this, I claim that the distinctive element of self-locating beliefs should be placed outside contents. If someone believes that he himself is hungry, he not only has a propositional belief of a certain particular person that he is hungry, but also identifies himself as that particular person. The latter is not a matter of propositional belief, but a matter of taking a first personal perspective on that person's actions, beliefs and experiences. A subject takes his actions and beliefs to be "up to" himself, and regards his experiences as giving information about where he is located in the world. All these phenomena are shown to be related to the peculiar ways in which we come to know certain facts about ourselves. So self-identification is conceptually connected to self-knowledge. The three chapters discuss some parts or aspects of this reasoning. Chapter 1, "Perry's Problem and Moore's Paradox," claims that Perry's problem of the essential indexical and Moore's paradox are essentially a single problem applied to two different aspects of our rational activities, actions and beliefs, respectively. Chapter 2, "On What the Two Gods Might Not Know," defends what may be called an ability hypothesis about self-locating knowledge, drawing on David Lewis's ability hypothesis about phenomenal knowledge. What the gods might lack is best viewed as the abilities of self-knowledge. Chapter 3, "What Is the First Person Perspective?" asks what it is to take a first person perspective and view oneself as the author of one's own actions. It is a matter of taking a deliberative stance toward one's own actions, which in turn can be best understood as the special ways in which we know them.<br>by Hongwoo Kwon.<br>Ph.D.
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Eyecioglu, Ozmutlu Asli. "Paraphrase identification using knowledge-lean techniques." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/65497/.
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This research addresses the problem of identification of sentential paraphrases; that is, the ability of an estimator to predict well whether two sentential text fragments are paraphrases. The paraphrase identification task has practical importance in the Natural Language Processing (NLP) community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods for identifying paraphrases should help to improve the performance of NLP systems that require language understanding. This includes key applications such as machine translation, information retrieval and question answering amongst others. Over the course of the last decade, a growing body of research has been conducted on paraphrase identification and it has become an individual working area of NLP. Our objective is to investigate whether techniques concentrating on automated understanding of text requiring less resource may achieve results comparable to methods employing more sophisticated NLP processing tools and other resources. These techniques, which we call “knowledge-lean”, range from simple, shallow overlap methods based on lexical items or n-grams through to more sophisticated methods that employ automatically generated distributional thesauri. The work begins by focusing on techniques that exploit lexical overlap and text-based statistical techniques that are much less in need of NLP tools. We investigate the question “To what extent can these methods be used for the purpose of a paraphrase identification task?” For the two gold standard data, we obtained competitive results on the Microsoft Research Paraphrase Corpus (MSRPC) and reached the state-of-the-art results on the Twitter Paraphrase Corpus, using only n-gram overlap features in conjunction with support vector machines (SVMs). These techniques do not require any language specific tools or external resources and appear to perform well without the need to normalise colloquial language such as that found on Twitter. It was natural to extend the scope of the research and to consider experimenting on another language, which is poor in resources. The scarcity of available paraphrase data led us to construct our own corpus; we have constructed a paraphrasecorpus in Turkish. This corpus is relatively small but provides a representative collection, including a variety of texts. While there is still debate as to whether a binary or fine-grained judgement satisfies a paraphrase corpus, we chose to provide data for a sentential textual similarity task by agreeing on fine-grained scoring, knowing that this could be converted to binary scoring, but not the other way around. The correlation between the results from different corpora is promising. Therefore, it can be surmised that languages poor in resources can benefit from knowledge-lean techniques. Discovering the strengths of knowledge-lean techniques extended with a new perspective to techniques that use distributional statistical features of text by representing each word as a vector (word2vec). While recent research focuses on larger fragments of text with word2vec, such as phrases, sentences and even paragraphs, a new approach is presented by introducing vectors of character n-grams that carry the same attributes as word vectors. The proposed method has the ability to capture syntactic relations as well as semantic relations without semantic knowledge. This is proven to be competitive on Twitter compared to more sophisticated methods.
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Esmaili, Ali. "Control relevant model identification with prior knowledge /." *McMaster only, 2001.
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Panait, Andreea Mihaela. "Security aspects of zero knowledge identification schemes." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112340.
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In this thesis we follow two directions: Zero Knowledge Protocols and the Discrete Logarithm Problem. In each direction we present the necessary background and we give a new approach for some parts of the existing protocols.<br>The new parts are dedicated to the soundness property of the Schnorr Identification Scheme and to the security of the sum+-Protocol. Since both directions are very well-known and studied in the field of cryptography, they are presented with many details so that the new results are easy to follow.<br>In writing this thesis we have tried to present the material in a specific order and in a manner easy to read even by beginners in cryptography.
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Newk-Fon, Hey Tow William Foong Kim. "A Study on the Contribution of Knowledge Identification to Knowledge Management Effectiveness." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/56424.
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Many organisations have blind spots; they often do not know what they know already. This research investigates how organisations establish what knowledge they have and to what extent this process of identifying relevant and needed knowledge that exists within organisational boundaries (a process referred to as Knowledge Identification or KI) contributes to effective Knowledge Management (KM). Following a three-phased, mixed-method research design, results confirmed the critical nature of the relationship between KI and KM.
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Tedmori, Sara. "Exploiting email : extracting knowledge to support knowledge sharing." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/3580.
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Effective management of knowledge assets is key to surviving in today's competitive business environment. This is particularly true for large organisations, where employees have difficulties identifying where or with whom the knowledge lies. Expertise is one of the most important knowledge assets and largely resides in the heads of employees. Many attempts have been made to help locate employees with the right expertise; however, the existing systems (often referred to as expertise finding systems) carry several flaws. In organisations, there are several potential sources where expertise evidence might be found. These sources have been used by the existing approaches to profile employees' expertise. Unfortunately, there has been limited research showing whether these sources contain useful evidence of expertise. Moreover, the majority of existing approaches have not been designed to integrate with the organisations' work practices; nor have they investigated the socio-ethical challenges associated with the adoption of such systems. Therefore, there is a need for expert finding systems that utilise useful sources of expertise and integrate into existing work practices. Through industry involvement, this research has explored and validated email content as a source for expertise profiling. This thesis provides an overview of the traditional and current approaches to expertise finding. The development and implementation of the EKE (Email Knowledge Extraction) system which tries to overcome the aforementioned challenges is presented. EKE has been evaluated by end-users from both industry and academia. The evaluation results suggest that EKE is a useful system that encourages participation, and that in many cases may assist in the management of knowledge within organisations.
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Palacio, Adriana Maria. "On identification, zero-knowledge, and plaintext-aware-encryption." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3213078.
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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed June 27, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 132-139).
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Tomczak, Jakub. "Algorithms for knowledge discovery using relation identification methods." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2563.
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In this work a coherent survey of problems connected with relational knowledge representation and methods for achieving relational knowledge representation were presented. Proposed approach was shown on three applications: economic case, biomedical case and benchmark dataset. All crucial definitions were formulated and three main methods for relation identification problem were shown. Moreover, for specific relational models and observations’ types different identification methods were presented.<br>Double Diploma Programme, polish supervisor: prof. Jerzy Świątek, Wrocław University of Technology
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Behrooz, A. "Meta description of experimental identification of medical knowledge." Thesis, Brunel University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373085.
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Heino, Perttu M. "Fluid property reasoning in knowledge-based hazard identification." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/32041.
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The study of serious accidents, which have occurred in the chemical process industry in recent times, highlights the need to understand fluid property related phenomena and the interactions between chemicals under abnormal process conditions or with abnormal fluid compositions. Consideration of these issues should be common practice in professional safety analysis work, and computer programs designed to support this work have to be able to deal with them.
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Sands, Shannon, and Joel Nielsen. "Consumer Knowledge of Acetaminophen Safety, Dosing, and Identification." The University of Arizona, 2012. http://hdl.handle.net/10150/623666.
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Class of 2012 Abstract<br>Specific Aims: The objective of this study is to evaluate consumers’ knowledge about over the counter (OTC) products containing acetaminophen (APAP).
Methods: Doctor of pharmacy student researchers set up a booth at consenting community pharmacies and invited consumers to participate in a 10-15 minute knowledge assessment. The booth contained a table displaying several OTC medication bottles/packages. Adult participants: a) answered baseline questions verbally about their APAP knowledge and associated risks; b) identified OTC products at the booth that contain APAP; and c) calculated and demonstrated dosing of APAP. The researchers asked follow-up questions and assessed the accuracy of the dosing. Participants received APAP educational brochures upon completion.
Main Results: Eighty percent of subjects reported not knowing what the abbreviation “APAP” means, and almost half of those who said that they knew what it means were incorrect. Very few participants were able to correctly identify the products containing APAP even with the product packaging information, with the percentage of incorrect responses as to whether a product contains APAP or not varying from 4.9% to 31.6%. More than 40% of the pediatric doses were incorrectly dosed for both of the pediatric formulations, even with the majority of subjects being parents.
Conclusions: Consumers are not able to identify which over-the-counter products contain APAP even with the product packaging before them, and they do not know what the abbreviation “APAP” means. Better packaging and product ingredient information should be developed, and the abbreviation “APAP” should be avoided. Pediatric APAP products should be re-evaluated regarding safety and dosing.
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Sands, Shannon, Joel Nielsen, and Terri Warholak. "Consumer Knowledge of Acetaminophen Safety, Dosing, and Identification." The University of Arizona, 2012. http://hdl.handle.net/10150/614521.
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Class of 2012 Abstract<br>Specific Aims: The objective of this study is to evaluate consumers’ knowledge about over the counter (OTC) products containing acetaminophen (APAP).
Methods: Doctor of pharmacy student researchers set up a booth at consenting community pharmacies and invited consumers to participate in a 10-15 minute knowledge assessment. The booth contained a table displaying several OTC medication bottles/packages. Adult participants: a) answered baseline questions verbally about their APAP knowledge and associated risks; b) identified OTC products at the booth that contain APAP; and c) calculated and demonstrated dosing of APAP. The researchers asked follow-up questions and assessed the accuracy of the dosing. Participants received APAP educational brochures upon completion.
Main Results: Eighty percent of subjects reported not knowing what the abbreviation “APAP” means, and almost half of those who said that they knew what it means were incorrect. Very few participants were able to correctly identify the products containing APAP even with the product packaging information, with the percentage of incorrect responses as to whether a product contains APAP or not varying from 4.9% to 31.6%. More than 40% of the pediatric doses were incorrectly dosed for both of the pediatric formulations, even with the majority of subjects being parents.
Conclusions: Consumers are not able to identify which over-the-counter products contain APAP even with the product packaging before them, and they do not know what the abbreviation “APAP” means. Better packaging and product ingredient information should be developed, and the abbreviation “APAP” should be avoided. Pediatric APAP products should be re-evaluated regarding safety and dosing.
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Heino, Perttu. "Fluid property reasoning in knowledge-based hazard identification /." Espoo [Finland] : Technical Research Centre of Finland, 1999. http://www.vtt.fi/inf/pdf/publications/1999/P393.pdf.
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Ponzoni, Luca. "Knowledge-based identification of functional domains in proteins." Doctoral thesis, SISSA, 2016. http://hdl.handle.net/20.500.11767/4920.
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The characterization of proteins and enzymes is traditionally organised according to the sequence-structure-function paradigm.
The investigation of the inter-relationships between these three properties has motivated the development of several experimental and computational techniques, that have made available an unprecedented amount of sequence and structural data. The interest in developing comparative methods for rationalizing such copious information has, of course, grown in parallel.
Regarding the structure-function relationship, for instance, the availability of experimentally resolved protein structures and of computer simulations have improved our understanding of the role of proteins' internal dynamics in assisting their functional rearrangements and activity.
Several approaches are currently available for elucidating and comparing proteins' internal dynamics. These can capture the relevant collective degrees of freedom that recapitulate the main conformational changes. These collective coordinates have the potential to unveil remote evolutionary relationships between proteins, that are otherwise not easily accessible from purely sequence- or structure-based investigations.
Starting from this premise, in the first chapter of this thesis I will present a novel and general computational method that can detect large-scale dynamical correlations in proteins by comparing different representative conformers. This is accomplished by applying dimensionality-reduction techniques to inter-amino acid distance fluctuation matrices. As a result, an optimal quasi-rigid domain decomposition of the protein or macromolecular assembly of interest is identified, and this facilitates the functionally-oriented interpretation of their internal dynamics.
Building on this approach, in the second chapter I will discuss its systematic application to a class of membrane proteins of paramount biochemical interest, namely the class A G protein-coupled receptors. The comparative analysis of their internal dynamics, as encoded by the quasi-rigid domains, allowed us to identify recurrent patterns in the large-scale dynamics of these receptors. This, in turn, allowed us to single out a number of key functional sites. These were, for the most part, previously known -- a fact that at the same time validates the method, and gives confidence for the viability of the other, novel sites.
Finally, for the last part of the thesis, I focussed on the sequence-structure relationship. In particular, I considered the problem of inferring structural properties of proteins from the analysis of large multiple sequence alignments of homologous sequences. For this purpose, I recasted the strategies developed for the dynamical features extraction in order to identify compact groups of coevolving residues, based only on the knowledge of amino acid variability in aligned primary sequences.
Throughout the thesis, many methodological techniques have been taken into considerations,
mainly based on concepts from graph theory and statistical data analysis (clustering). All these topics are explained in the methodological sections of each chapter.
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Schmidt, Daniel P. "Identifying Knowledge Gaps Using a Graph-based Knowledge Representation." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1588866076446257.
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Buck, Arlene J. "Automated knowledge acquisition tool for identification of generic tasks /." Online version of thesis, 1990. http://hdl.handle.net/1850/10577.
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Hiatt, Sara Renee. "Middle School Teachers' Knowledge and Training Regarding Anxiety Identification." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1531384416690094.
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Côté, Jean. "Identification and conceptualization of expert high performance gymnastic coaches' knowledge." Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6810.
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An expert system approach (Buchanan et al., 1983) was used to identify and conceptualize the knowledge of 17 Canadian expert high performance gymnastic coaches. The selection of expert high performance coaches was based on multiple criteria. First, a minimum of 10 years of coaching experience was required. Second, each of the expert coaches required a performance outcome measure, and thus needed to have developed at least one international and two national level gymnasts. Finally, each expert coach had to be recognized by Canada's national coach as one of the best in Canada for developing elite gymnasts. By using a qualitative research method based on the traditions of cognitive anthropology (Spradley, 1979) and symbolic interactionism (Blumer, 1969; Glaser & Strauss, 1967), this study focused on the first two stages of the knowledge aquisition process for building an expert system: identification and conceptualization. The results of the identification stage indicated that the interview transcripts of coaches of males and females were divided into 595 and 560 meaningful episodes of information or "meaning units" (Tesch, 1990), respectively. The inductive analysis process allowed these meaning units to be regrouped into 134 properties, 28 categories, and 6 components. The components emerging from the analysis were the same for coaches of males and females and consisted of: (1) competition, (2) training, (3) organization, (4) coach's personal characteristics, (5) gymnast's personal characteristics and level of development, and (6) contextual factors. The categories and properties of coaches' knowledge varied slightly in number and by their nature for coaches of males and coaches of females. Attempts to explain differences in the categories of knowledge elicited by coaches of males and coaches of females were made in light of the evident age-related and gender specific task differences in men's and women's gymnastics. The results of the conceptualization stage indicated that the coaches' mental model of various situations was built through the assessment of three "peripheral components," consisting of their own personal characteristics, the gymnasts' personal characteristics and level of development, and some contextual factors. The mental model resulting from this assessment guided the coaches for their intervention in the "competition," "training," and "organization" components, defined as the "coaching process." The large arsenal of coaches' organized hierarchically through the difference properties, categories, and components allows expert coaches to rapidly assess situations that do not fit their mental model and, consequently make the appropriate changes. (Abstract shortened by UMI.)
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Zhong, Bijuan. "Inter-party Cooperation and Knowledge Creation in IJVs:An organizational identification Perspective." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1369997513.
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Guthrie, Samuel Ashley. "A knowledge-based assignment methodology for remains identification following a mass disaster." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/24563.
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Tian, Ye. "Knowledge-fused Identification of Condition-specific Rewiring of Dependencies in Biological Networks." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/52557.
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Gene network modeling is one of the major goals of systems biology research. Gene network modeling targets the middle layer of active biological systems that orchestrate the activities of genes and proteins. Gene network modeling can provide critical information to bridge the gap between causes and effects which is essential to explain the mechanisms underlying disease. Among the network construction tasks, the rewiring of relevant network structure plays critical roles in determining the behavior of diseases. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential dependency networks cannot be constructed by existing knowledge alone, effective incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. Existing studies on protein-protein interactions and biological pathways provide constantly accumulated rich domain knowledge. Though novel incorporation of biological prior knowledge into network learning algorithms can effectively leverage domain knowledge, biological prior knowledge is neither condition-specific nor error-free, only serving as an aggregated source of partially-validated evidence under diverse experimental conditions. Hence, direct incorporation of imperfect and non-specific prior knowledge in specific problems is prone to errors and theoretically problematic.
To address this challenge, we propose a novel mathematical formulation that enables incorporation of prior knowledge into structural learning of biological networks as Gaussian graphical models, utilizing the strengths of both measurement data and prior knowledge. We propose a novel strategy to estimate and control the impact of unavoidable false positives in the prior knowledge that fully exploits the evidence from data while obtains "second opinion" by efficient consultations with prior knowledge. By proposing a significance assessment scheme to detect statistically significant rewiring of the learned differential dependency network, our method can assign edge-specific p-values and specify edge types to indicate one of six biological scenarios. The data-knowledge jointly inferred gene networks are relatively simple to interpret, yet still convey considerable biological information. Experiments on extensive simulation data and comparison with peer methods demonstrate the effectiveness of knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks, leveraging data-driven evidence and existing biological knowledge, while remaining robust to the false positive edges in the prior knowledge. We also made significant efforts in disseminating the developed method tools to the research community. We developed an accompanying R package and Cytoscape plugin to provide both batch processing ability and user-friendly graphic interfaces. With the comprehensive software tools, we apply our method to several practically important biological problems to study how yeast response to stress, to find the origin of ovarian cancer, and to evaluate the drug treatment effectiveness and other broader biological questions. In the yeast stress response study our findings corroborated existing literatures. A network distance measurement is defined based on KDDN and provided novel hypothesis on the origin of high-grade serous ovarian cancer. KDDN is also used in a novel integrated study of network biology and imaging in evaluating drug treatment of brain tumor. Applications to many other problems
also received promising biological results.<br>Ph. D.
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Mellström, Björn. "En studie av zero knowledge-identifikationsprotokoll för smarta kort." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2327.
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<p>Zero knowledge protocols is a lesser known type of protocol that can be used for identification. These protocols are especially designed not to reveal any information during an identification process that can be misused later on, neither by the one who should be convinced of the identity of the user, nor by anyone else that is eavesdropping. Many of these protocols are also especially designed for implementation in smart cards. The more common type of card with a magnetic stripe has during the last few years become more susceptible to attacks since they are easily copied. Smart cards combined with a secure identification protocol has been predicted to be the solution to this problem. Zero knowledge protocols are one of several types of protocols that can be used for this purpose. </p><p>In this thesis a number of zero knowledge protocols are examined that have been presented since the introduction of the concept in the 1980's. In addition to the protocol descriptions information is also given about how to choose parameter values, and what progress and discoveries have been made concerning the security of the protocols. Some assumptions that are easy to overlook in an implementation are also highlighted, and an evaluation of the protocol performances is made. </p><p>The conclusion is that zero knowledge protocols are both efficient and adaptable, while they at the same time provide high security. Because of this it may not be necessary to compromise between these properties even for simpler types of smart cards.</p>
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Thomas, Kathryn. "South African rugby coaches' knowledge of the prevention, identification and management of concussion." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11239.
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Includes bibliographical references.<br>The incidence of concussion injuries is high irrespective of player ability, from professional to semi-professional and schoolboy rugby players. Concussion injuries are considered difficult to diagnose, particularly in an on field environment, and are often under-reported or unrecognised. In the South African setting medical professionals are often not present at practices and matches and coaches are therefore often required to identify and manage concussed players. Previous studies have identified that the risk of concussion may be reduced through coach education and subsequent implementation of skills training and the education of players. The aim of the study is to determine South African rugby coaches’ knowledge of the prevention, identification and management of concussion.
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Ross, Audrey E. "Can Knowledge of Future Public Presentations of Eyewitness Testimonies Obviate Positive Post-Identification Feedback Effects?" Marietta College Honors Theses / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=marhonors1367867665.
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Cece, Esra Nurten 1984. "Metabolite identification in drug discovery : from data to information and from information to knowledge." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/403648.
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Drug metabolism studies provide the opportunity to enhance metabolic properties of new drugs. The overall aims of the drug metabolism studies are to (1.) optimize pharmacokinetic properties of the drug candidates, (2.) characterize the polymorphic enzyme contribution to clearance, and (3.) support the selection of safe drugs with respect to bioactivation potentials.
The ultimate goal in drug metabolism assays in early drug discovery is to translate analytical data to build final knowledge. By contribution of this translation, metabolism scientists can rationalize how structures of new drug compounds could be changed and how metabolic pathways could be better understood. Analytical techniques, such as High Resolution Mass Spectrometry (HRMS), have progressed and now it is possible to generate large datasets through High Throughput Screening (HTS) assays in drug metabolism laboratories. However, the transformation of these data into information and information into knowledge is insufficient. In-depth data inspection is necessary to support the generation of high quality results which are consistent across experiments. For this purpose, innovative software solutions can be utilized to process analytical data. In this respect, by applying standardized and fully automated data evaluation tools, it is possible to (1.) enable comprehensive data analysis, (2.) accelerate structure-based information handling, (3.) eliminate human error and finally (4.) improve chemical features of lead molecules in terms of biotransformation properties.
This thesis research aimed to use a novel automated workflow within HRMS to identify the drug metabolites and their structures. Final results confirmed that this new workflow can be used to translate HRMS data into information, which is required for building useful and ultimate knowledge in drug metabolism.<br>Los estudios de metabolismo de fármacos ofrecen la oportunidad de mejorar las propiedades metabólicas de nuevos fármacos. Los objetivos generales de los estudios de metabolismo de fármacos son: (1.) optimizar las propiedades farmacocinéticas de los fármacos candidatos, (2.) caracterizar la contribución de las enzimas polimórficas a la elimicación y (3.) apoyar la selección de fármacos seguros con respecto a los potenciales bioactivación.
El objetivo final en los ensayos de metabolismo de fármacos es traducir los datos analíticos para construir conocimiento final. Mediante la aportación de esta traducción, los científicos que trabajan en metabolismo pueden racionalizar cómo las estructuras de los nuevos compuestos de fármacos podrían ser cambiadas y cómo las vías metabólicas podrían ser mejor entendidaa. Las técnicas analíticas, como la espectrometría de masas de alta resolución (HRMS), han progresado y ahora es posible generar grandes volúmenes de datos a través de ensayos masivos (HTS) en laboratorios de metabolismo de fármacos. Sin embargo, la transformación de estos datos a información y la información a conocimiento es insuficiente. Es necesario un estudio en profundidad de los datos para ayudar a la generación de resultados de alta calidad que sean consistentes con los experimentos. Para este propósito, las soluciones innovadoras de software pueden ser utilizados con el objetivo de procesar los datos analíticos. A este respecto, mediante la aplicación de datos estandarizados y totalmente automatizados herramientas de evaluación, es posible (1.) permitir el análisis de datos completos, (2.) acelerar el manejo de información basada en la estructura, (3.) eliminar el error humano y finalmente (4.) mejorar las características químicas de las moléculas en términos de sus propiedades metabólicas.
La investigación de esta tesis tuvo el objetivo que utilizar una novedoso y automatizado “sistema de trabajo” dentro HRMS para identificar los metabolitos de compuestos así como sus estructuras. Los resultados finales se confirmaron que se pueden utilizar herramientas de software para convertir los datos en información de HRMS, necesario para la construcción del conocimiento útil en el metabolismo de fármacos
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Correia, Jorge Antonio Pinto. "Identification of essential knowledge co-creation processes for effective organisational transformation of service organisations." Thesis, University of Buckingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436889.
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Cowley, Jonathan Bowes. "The use of knowledge discovery databases in the identification of patients with colorectal cancer." Thesis, University of Hull, 2012. http://hydra.hull.ac.uk/resources/hull:7082.
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Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000. Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country. Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer. This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection. A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP. Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes. The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7. The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients.
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Roberts, Shantale D. "EXPANDING OUR PRESENT KNOWLEDGE OF THE NON-FICTIONAL WORLD: AN ANALYSIS OF TRANSPORTATION AND IDENTIFICATION WITH VICTIMS AND PERPETRATORS." Cleveland State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=csu1529075576461922.
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Bailey, Anastasia Veronica Graham. "The Knowledge Effects of Founders' Human and Social Capital on Entrepreneurship." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1470758829.
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Mirman, Jennifer Lauren. "AN ASSESSMENT OF CURRENT CLINICAL ORTHODONTICS: CLINICIAN KNOWLEDGE, IDENTIFICATION AND TREATMENT PLANNING OF RESTRICTED AIRWAY." Master's thesis, Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/580484.
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Oral Biology<br>M.S.<br>Objectives: The naso- and oropharyngeal airways are influenced by a myriad of factors: jaw shape and position, tongue shape and position, lymphoid tissue, sleep apnea, chronic mouth breathing, and swallowing patterns. It is unknown if the relationships of these factors are recognized and routinely assessed in clinical orthodontics. This cross-sectional study sought to determine the proportion of participating orthodontists whom: 1) Are knowledgeable about airway restriction and its etiology, 2) Learned about these topics in post-graduate orthodontic education, 3) Consider airway restrictions in orthodontic treatment planning. Methods: A survey was administered through an online survey management platform, and sent to the email listings of 2,084 active American Association of Orthodontists (AAO) members. Survey questions are evidence-based and developed from findings in current literature. The questionnaire results were analyzed by coding and cleaning data through SAS 9.3 software. Univariate and bivariate analyses were performed to assess responses. Results: The survey received responses from 117 orthodontists. Most received their orthodontic certification from a two-year program (71.82%). The majority were knowledgeable about tongue adaptations, swallowing mechanisms, mouth breathing, and sleep apnea. Respondents were less confident about the relationship airway patency has with lymphoid tissue and with jaw position. Only half (50.51%) were taught about restricted naso- and oropharyngeal airway in post-graduate orthodontic education. A low majority, 66.32%, reported that they refer for medical consultation to the appropriate clinician before they begin treatment if a patient presents with restricted airway. Conclusions: Although the majority of respondents are knowledgeable about factors that influence airway patency, the survey identified areas in which understanding of and education in certain topics (lymphoid tissue, jaw position) may be lacking. Further emphasis should be placed on these topics to improve patient care. Orthodontics nationwide would benefit from more thorough post graduate orthodontic residency curriculum and general guidelines for clinical management of patients that present with airway obstruction.<br>Temple University--Theses
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Patterson, Frank H. "Fuzzy framework for robust architecture identification in concept selection." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54413.
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An evolving set of modern physics-based, multi-disciplinary conceptual design methods seek to explore the feasibility of a new generation of systems, with new capabilities, capable of missions that conventional vehicles cannot be empirically redesigned to perform. These methods provide a more complete understanding of a concept's design space, forecasting the feasibility of uncertain systems, but are often computationally expensive and time consuming to prepare. This trend creates a unique and critical need to identify a manageable number of capable concept alternatives early in the design process. Ongoing efforts attempting to stretch capability through new architectures, like the U.S. Army's Future Vertical Lift effort and DARPA's Vertical Takeoff and Landing (VTOL) X-plane program highlight this need.
The process of identifying and selecting a concept configuration is often given insufficient attention, especially when a small subset of favorable concept families is not immediately apparent. Commonly utilized methods for concept generation, like filtered morphological analysis, often identify an exponential number of alternatives. Simple approaches to concept selection then rely on designers to identify a relatively small subset of alternatives for comparison through simple methods regularly related to decision matrices (Pugh, TOPSIS, AHP, etc.). More in-depth approaches utilize modeling and simulation to compare concepts with techniques such as stochastic optimization or probabilistic decision making, but a complicated setup limits these approaches to just a discrete few alternatives.
A new framework to identify and select promising, robust concept configurations utilizing fuzzy methods is proposed in this research and applied to the example problem of concept selection for DARPA's VTOL Xplane program. The framework leverages fuzzy systems in conjunction with morphological analysis to assess large design spaces of potential architecture alternatives while capturing the inherent uncertainty and ambiguity in the evaluation of these early concepts. Experiments show how various fuzzy systems can be utilized for evaluating criteria of interest across disparate architectures by modeling expert knowledge as well as simple physics-based data. The models are integrated into a single environment and variations on multi-criteria optimization are tested to demonstrate an ability to identify a non-dominated set of architectural families in a large combinatorial design space. The resulting framework is shown to provide an approach to quickly identify promising concepts in the face of uncertainty early in the design process.An evolving set of modern physics-based, multi-disciplinary conceptual design methods seek to explore the feasibility of a new generation of systems, with new capabilities, capable of missions that conventional vehicles cannot be empirically redesigned to perform. These methods provide a more complete understanding of a concept's design space, forecasting the feasibility of uncertain systems, but are often computationally expensive and time consuming to prepare. This trend creates a unique and critical need to identify a manageable number of capable concept alternatives early in the design process. Ongoing efforts attempting to stretch capability through new architectures, like the U.S. Army's Future Vertical Lift effort and DARPA's Vertical Takeoff and Landing (VTOL) X-plane program highlight this need.
The process of identifying and selecting a concept configuration is often given insufficient attention, especially when a small subset of favorable concept families is not immediately apparent. Commonly utilized methods for concept generation, like filtered morphological analysis, often identify an exponential number of alternatives. Simple approaches to concept selection then rely on designers to identify a relatively small subset of alternatives for comparison through simple methods regularly related to decision matrices (Pugh, TOPSIS, AHP, etc.). More in-depth approaches utilize modeling and simulation to compare concepts with techniques such as stochastic optimization or probabilistic decision making, but a complicated setup limits these approaches to just a discrete few alternatives.
A new framework to identify and select promising, robust concept configurations utilizing fuzzy methods is proposed in this research and applied to the example problem of concept selection for DARPA's VTOL Xplane program. The framework leverages fuzzy systems in conjunction with morphological analysis to assess large design spaces of potential architecture alternatives while capturing the inherent uncertainty and ambiguity in the evaluation of these early concepts. Experiments show how various fuzzy systems can be utilized for evaluating criteria of interest across disparate architectures by modeling expert knowledge as well as simple physics-based data. The models are integrated into a single environment and variations on multi-criteria optimization are tested to demonstrate an ability to identify a non-dominated set of architectural families in a large combinatorial design space. The resulting framework is shown to provide an approach to quickly identify promising concepts in the face of uncertainty early in the design process.
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Chen, Hui. "Identification and classification of shareable tacit knowledge associated with experience in the Chinese software industry sector." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19659.
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The study reported in this thesis aimed to provide an ontology of professional activities in the software industry that require and enable the acquisition of experience and that, in turn, is the basis for tacit knowledge creation. The rationale behind the creation of such an ontology was based on the need to externalise this tacit knowledge and then record such externalisations so that these can be shared and disseminated across organisations through electronic records management. The research problem here is to conciliate highly theoretical principles associated with tacit knowledge and the ill-defined and quasi-colloquial concept of experience into a tool that can be used by more technical and explicit knowledge minded practitioners of electronic records management. The ontology produced and proposed here provides exactly such a bridge, by identifying what aspects of professional and personal experience should be captured and organising these aspects into an explicit classification that can be used to capture the tacit knowledge and codify it into explicit knowledge. Since such ontologies are always closely related to actual contexts of practice, the researcher decided to choose her own national context of China, where she had worked before and had good guarantees of industrial access. This study used a multiple case-study Straussian Grounded Theory inductive approach. Data collection was conducted through semi-structured interviews in order to get direct interaction with practitioners in the field and capture individuals opinions and perceptions, as well as interpret individuals understandings associated with these processes. The interviews were conducted in three different and representative types of company (SMEs, State Owned and Large Private) in an attempt to capture a rich variety of possible contexts in the SW sector in a Chinese context. Data analysis was conducted according to coding the procedures advocated by Grounded Theory, namely: open, axial and selective coding. Data collection and analysis was conducted until the emergent theory reached theoretical saturation. The theory generated identified 218 different codes out of 797 representative quotations. These codes were grouped and organised into a category hierarchy that includes 6 main categories and 31 sub-categories, which are, in turn, represented in the ontology proposed. This emergent theory indicates in a very concise manner that experienced SW development practitioners in China should be able to understand the nature and value of experience in the SW industry, effectively communicate with other stake holders in the SW development process, be able and motivated to actively engage with continuous professional development, be able to share knowledge with peers and the profession at large, effectively work on projects and exhibit a sound professional attitude both internally to their own company and externally to customers, partners and even competitors. This basic theory was then further analysed by applying selective coding. This resulted in a main theory centred on Working in Projects, which was clearly identified as the core activity in the SW Industry reflecting its design and development nature. Directly related with the core category, three other significant categories were identified as enablers: Communication, Knowledge Sharing and Individual Development. Additionally, Understanding the Nature of Experience in the SW Industry and Professional Attitude were identified as drivers for the entire process of reflection, experience acquisition and tacit knowledge construction by the individual practitioners. Finally, as an integral part of any inductive process of research, the final stage in this study was to position the emerged theory in the body of knowledge. This resulted in the understanding that the theory presented in this study bridges two extremely large bodies of literature: employability skills and competencies. Both of these bodies of literature put their emphasis in explicit knowledge concerning skills and competencies that are defined so that they can be measured and assessed. The focus of the theory proposed in this thesis on experience and resulting acquisition of tacit knowledge allows a natural link between the employability skills and competencies in the SW industry that was hitherto lacking in the body of knowledge. The ontology proposed is of interest to academics in the areas of knowledge management, electronic records management and information systems. The same ontology may be of interest to human resources practitioners to select and develop experienced personnel as well as knowledge and information professionals in organisations.
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Thompson, Susan Lynn. "Provider Identification of Hepatitis C Virus (HCV) Risk Factors at Inmate Intake to Prison." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/560731.
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The hepatitis C virus (HCV) disproportionately affects the prison population. Studies demonstrate that healthcare provider knowledge of HCV risk factors is insufficient and many individuals are not aware that they are HCV positive. Early identification of HCV status can prompt early treatment and avoidance of complications that contribute to poor outcomes resulting in chronic disease progression. This doctor of nursing practice (DNP) project addresses provider identification of HCV risk factors at initial inmate intake to prison and whether providers obtained HCV testing based on guidelines from the Centers for Disease Control and Prevention (CDC). The principal investigator (PI) conducted a retrospective medical record review at Arizona State Prison Complex (ASPC) Lewis focusing on initial inmate intake forms identifying two of the CDC risk factors for HCV: drug abuse and tattoos; and ascertaining if a providers ordered a HCV test if inmates had one or both of these risk factors. The PI reviewed 51 randomly selected medical records; 40 records met inclusion criteria of 1) inmates who had an initial inmate intake evaluation occurring from 1 October 2013 to 1 October 2014 and 2) documentation of positive HCV risk factors. Analysis of the records showed a mean inmate age of 26.78 years with a variable racial distribution. The risk factor of tattooing was present in 37 (92.5%) of records reviewed and the risk factor of intravenous drug use (IVDU) was present in 7 (17.5%). Only 4 (10%) records of inmates with positive risk factors had a HCV test ordered by the provider: One physician (n=2) and one nurse practitioner (n=2). This project demonstrated a gap in HCV testing in the presence of risk factors in the inmate population at ASPC Lewis which is consistent with studies in the general population. This study does not identify any reasons for this consistency, but raises questions for future studies focused on provider knowledge, education and the institution of HCV testing protocols. This DNP project provides the foundation for a future full quality improvement Plan-Do-Study-Act based project aimed at educating providers about HCV testing according to CDC (2013a) guidelines and subsequently re-evaluating their HCV test ordering practices.
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Phan, John H. "Biomarker discovery and clinical outcome prediction using knowledge based-bioinformatics." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33855.
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Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. However, identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. We research and investigate the benefits of using knowledge-based algorithms to solve clinical prediction problems. Because we are interested in identifying biomarkers that are also feasible in clinical prediction models, we focus on two analytical components: feature selection and predictive model selection. In addition to data variance, we must also consider the variance of analytical methods. There are many existing feature selection algorithms, each of which may produce different results. Moreover, it is not trivial to identify model parameters that maximize the sensitivity and specificity of clinical prediction. Thus, we introduce a method that uses independently validated biological knowledge to reduce the space of relevant feature selection algorithms and to improve the reliability of clinical predictors. Finally, we implement several functions of this knowledge-based method as a web-based, user-friendly, and standards-compatible software application.
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Osantowski, Teddy Brodie Bowen Mack L. "Effects of inservice training on teachers' knowledge and applied skills related to identification of learning disabilities." Normal, Ill. Illinois State University, 1993. http://wwwlib.umi.com/cr/ilstu/fullcit?p9323740.
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Thesis (Ed. D.)--Illinois State University, 1993.<br>Title from title page screen, viewed February 16, 2006. Dissertation Committee: Mack L. Bowen (chair), Lanny E. Morreau, William C. Rau, Paula J. Smith, Kenneth H. Strand. Includes bibliographical references (leaves 92-100) and abstract. Also available in print.
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Golden, Jonathan Oren. "Analysis of orthographic knowledge and its relationship to naming speed, phonological awareness, and single word identification." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22208.pdf.
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Flowers, David Christopher 1988. "Computational modeling of knowledge and uncertainty in systems biology for drug target identification and protein engineering." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119969.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 72-76).<br>In systems biology, ordinary differential equation models are used frequently to model the dynamics of molecular and cellular systems. These models are parameterized with rate constants and other quantities that are often estimated from empirical data. When the data are insufficient to fully determine the model parameters, the parameter values are unidentifiable, and many parameter sets are consistent with the data. To cope, many studies sample a collection of parameters to represent the uncertainty or simplify the model to remove parameters. Studies rarely verify that their sampling is sufficient or test alternative model simplifications. There is a need for better practices for uncertainty quantification. In this work, I present two case studies demonstrating the use of biochemical models with unidentifiable parameters to make useful predictions. The first study investigates a model of the complement system, a system of circulating proteins involved in immune response, to find promising drug targets for treatment of sepsis. I compared a sampling method to a worst-case search method for quantifying the uncertainty in responses to hypothetical inhibitors and found that the choice of method significantly impacts the results. I identified mechanistic explanations for the observed inhibitor responses that demonstrate limitations of intuition and suggest strategies for further studies. The second study uses a kinetic model of the thiolase and reductase enzymes of the 3-hydroxyacid metabolic pathway to interpret available in vitro data to determine the kinetic changes induced by a mutation in the thiolase. Sampling approaches cannot identify all combinations of rate constants that could have changed according to the data, so I perform a selective enumeration strategy that identifies all feasible combinations by testing only a limited number. The simplest feasible combinations identify three classes of rate constant changes induced by the mutation. I also use a global sensitivity analysis approach to predict which reaction steps are most likely to positively affect the product selectivity ratio of the system. Together, these studies demonstrate that unidentifiable models can be useful if the correct methods are chosen to quantify their uncertainty and serve as examples of how to choose or design these methods.<br>by David C. Flowers.<br>Ph. D.
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Wiradee, Imrattanatrai. "Supporting Entity-oriented Search with Fine-grained Information in Knowledge Graphs." Kyoto University, 2020. http://hdl.handle.net/2433/259074.
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Coursey, Kino High. "The Value of Everything: Ranking and Association with Encyclopedic Knowledge." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12108/.
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This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the PageRank algorithm. WikiRank is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, WikiRank is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, WikiRank is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus allowing for the automatic generation of mapping links between ontologies. The conclusion of this thesis is that the "knowledge access heuristic" is valuable and that a ranking process based on a large encyclopedic resource can form the basis for an extendable general purpose mechanism capable of identifying relevant concepts by association, which in turn can be effectively utilized for enumeration and comparison at a semantic level.
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Pramanik, Saugata. "A Hybrid Knowledge-Based System for Process Plant Fault Diagnosis." Thesis, Indian Institute of Science, 1989. https://etd.iisc.ac.in/handle/2005/83.
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Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained.
However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations.
In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants.
The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components.
The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts.
The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose.
The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.
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Pramanik, Saugata. "A Hybrid Knowledge-Based System for Process Plant Fault Diagnosis." Thesis, Indian Institute of Science, 1989. http://hdl.handle.net/2005/83.
Full textAbstract:
Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained.
However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations.
In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants.
The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components.
The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts.
The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose.
The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.
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Roberts, Alexandra Hannah. "The Coaches’ Eye: Exploring coach decision-making during talent identification." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2391.
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For decades, researchers and practitioners interested in talent identification have discussed the coaches’ eye: the elusive ability that allows some coaches to ‘see’ qualities in an athlete that point to their talent or future potential. While there is significant anecdotal evidence of coaches who possess this ability, there is little empirical research supporting the validity or reliability of the coaches’ eye. Guided by ecological dynamics, this thesis employs mixed methodologies to explore the decision-making that underpins how high-level coaches identify talent in Olympic combat sports. These four studies captured the processes of thirty- four coaches during the talent identification process, exploring and identifying the factors that impact on a coach’s ability to perform this integral task.
A systematic review and meta-synthesis revealed that ‘instinct’ is a primary contributor to coach decision-making during talent identification (TID), allowing coaches to ‘know it when they see it’. Semi-structured interviews with international coaches explored this ‘instinct’ during TID and revealed that coaches require experience, time and knowledge of context in order to identify talent. An instrumental case study corroborated these results, and also found that there is a significant conceptual difference between talent identification and talent selection, in the eyes of this coach. Both studies indicated that coaches likely select athletes based on their capabilities as a coach, not purely on athlete ability or potential. The final study found that nine national-level coaches did not agree on the rankings of talented youth judo athletes after four days. This finding indicates that the coaches’ eye is subjective and confirms the novel findings of the prior studies; namely that coaches require time to get to know athletes, their opinions of the athletes’ talent changed over time, and coaches vary in who they ‘see’ as talented. Finally, two new models are presented: the Coach-Informed Talent Identification Process and a novel model of the Coaches’ Eye in Talent Identification. The experiential coach knowledge gathered in this thesis informed the creation of these models.
This thesis indicates that the coaches’ eye is the lens through which coaches view athletes, using their expertise and experience to interpret the athlete’s raw potential, and the time spent with the athlete and the context of their identification to determine whom they will select into their team. It appears that coaches perceive talent with reference to what they can develop in an athlete; thus, coaches must be involved in the identification and selection of talented athletes. These results indicate that National Sporting Organisations should ensure that coaches are provided with the necessary time, education and guidance to ensure that athlete outcomes are optimised.
This thesis provides an understanding of how the coaches’ eye works during TID and a new understanding of this term. These findings have implications for the ongoing practice and research of talent identification in combat sports, and this work contain recommendations for both coaches and national sporting organisations to improve the confidence, accuracy and reliability of the coaches’ eye when forecasting talent.
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O'Connor, Brendan Harold. "Racial Identification, Knowledge, and the Politics of Everyday Life in an Arizona Science Classroom: A Linguistic Ethnography." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/228119.
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This dissertation is a linguistic ethnography of a high school Astronomy/Oceanography classroom in southern Arizona, where an exceptionally promising, novice, white science teacher and mostly Mexican-American students confronted issues of identity and difference through interactions both related and unrelated to science learning. Through close analysis of video-recorded, naturally-occurring interaction and rich ethnographic description, the study documents how a teacher and students accomplished everyday classroom life, built caring relationships, and pursued scientific inquiry at a time and in a place where nationally- and locally-circulating discourses about immigration and race infused even routine interactions with tension and uncertainty. In their talk, students appropriated elements of racializing discourses, but also used language creatively to "speak back" to commonsense notions about Mexicanness. Careful examination of science-related interactions reveals the participants' negotiation of multiple, intersecting forms of citizenship (i.e., cultural and scientific citizenship) in the classroom, through multidirectional processes of language socialization in which students and the teacher regularly exchanged expert and novice roles. This study offers insight into the continuing relevance of racial, cultural, and linguistic identity to students' experiences of schooling, and sheds new light on classroom discourse, teacher-student relationships, and dimensions of citizenship in science learning, with important implications for teacher preparation and practice.
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Mezhrahid, Julie. "Logiques du Délire : Savoir et Méconnaissance dans la clinique de la Psychose." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM3117.
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L'entreprise de cette recherche doctorale s'articule autour de trois axes de réflexion. Elle a, pour un temps, consisté en la redécouverte freudienne d'une intelligence de la notion de méconnaissance. Dans l’exploration des procédés fondateurs de la psyché et des mécanismes associés, cette méconnaissance apparaît en filigrane tout au long de l'oeuvre du maître de la psychanalyse et sa consistante à la fois structurelle et processuelle est supposée. La visite de l'enseignement de l'héritage de la psychiatrie classique ainsi que l'examen lacanien ont permis, par la suite, le développement du concept de méconnaissance. Il est défini à travers les empreintes corrélatives des mécanismes de reconnaissance et de l'identification, ceci afin de modéliser quatre actes psychiques fondant des méconnaissances singulières selon la prégnance de leur ancrage dans les champs de l'imaginaire et du symbolique. Le délire est alors appréhendé tel un savoir, « Ça-voir » authentique, dont l'efficience de la liaison avec une image signifiante dévoile les qualités de son expression. Un déploiement de nouvelles perspectives théoriques a été un dernier pas, avancées faisant valoir l'existence de logiques délirantes chez des patients en fonction de la particularité d’un discours psychotique, schizophrénique ou paraphrénique. L'argumentation psychopathologique a été engagée dans une intention d'ouvertures thérapeutiques sur la clinique des psychoses<br>All research and works during all these years are gathered in this document based on three main phases. The first phase was to rediscosver Freud’s theory about smart concept of “méconnaissance” meaning "misconstrue" or "misrecognize”. As we have explored the foundations of the psyché and all related mechanisms, this misconstrue appears within all Freud’s work long where both structural and processual components have been evaluated. Following the heritage of classical psychiatry school, and deepening lacanien’s theories and teachings, the second step has allowed to develop and detail the “méconnaissance” concept. According to Lacan’s lessons, identification and acknowledgement are the two key drivers of this concept. To build on them, it is possible to modelize four psychic acts founded on particular “méconnaissance” depending on the close links to the fields of imaginary and symbol. So, delusion could be viewed as a kind of knowledge, a true “ça-voir”, closely connected to a significant image. Last but not least, the third phase dealt with new theoritical perspectives based on the existence of delirious logic for patients according to their specific psychosis, schizophrenia and paraphrenia talkings. Psychopathological arguments were binded in order to identify new open therapeutic areas for clinical psychosis
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Karlsson, Birgitta, and Svantesson Marie Ågestedt. "Skolsköterskans möjlighet att identifiera barnmisshandel : en litteraturgranskning." Thesis, Högskolan i Skövde, Institutionen för vård och natur, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4421.
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Barnmisshandel kan förekomma i alla samhällsklasser, trots att det enligt lag är förbjudet att slå sitt barn. Barnmisshandel brukar delas in i fysisk-, psykisk- och sexuell misshandel. För att kunna identifiera barnmisshandel måste skolsköterskan ha kunskap inom området, vilket saknas idag. Syftet med studien är att beskriva skolsköterskors kunskap och roll i att identifiera barnmisshandel. Studien utfördes som en litteraturgranskning och belyser flera tecken som skolsköterskan bör observera vid misstänkt barnmisshandel. Att det råder kunskapsbrist inom området, både då det gäller att upptäcka, identifiera och rapportera barnmisshandel framkom tydligt i studien. Skolsköterskan är i en bra position för att upptäcka barnmisshandel och hjälpa utsatta barn. Genom hälsosamtalet kan skolsköterskan uppmärksamma barn som kan vara utsatta för våld; begreppet KASAM är angeläget att ha med sig i det hälsofrämjande arbetet. I samtalet med barnet är det viktigt med en bra samtalsmetod. För att i ett tidigt skede uppmärksamma barn som utsätts för misshandel behövs tydliga riktlinjer och rutiner, kontinuerlig och fortlöpande utbildning inom området för att överbrygga den kunskapsbrist som finns.<br>Child abuse can occur in all social classes, even if it´s forbidden to hit the child according to the law. Child abuse usually divides in physical-, psychic- and sexual abuse. To identify maltreatment the school nurse needs knowledge about this field, which is missing today. The aim of the study is to describe school nurses knowledge and function to identify child abuse. The study is a literature review and it illuminates several signs that a school nurse should observe at suspected child abuse. It counsels lack of knowledge about maltreatment, both to describe, identify and report child abuse. The school nurse is in a good position to recognize child abuse and to help exposed children. Through the health conversation the school nurse can observe children that can be exposed to violence; the concept SOC is important in the health promotion. In the conversation with the child it´s important to have good method of conversation. To early observe children that have been exposed for maltreatment it´s a need of clear guidelines and routines, continuous education inside the subject child abuse in able to over bridge the lack of knowledge.
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Youssef, Ingy. "Trust via Common Languages." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469155513.
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Franco, Salvador Marc. "A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/84285.
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Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario.
In this thesis we study the use of knowledge graphs as a cross-domain and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts.
As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way.
The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification.
The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals.<br>El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües.
En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano.
Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje.
Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales.<br>El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges.
En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà.
Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge.
Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals.<br>Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285<br>TESIS
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Weber, Ralf Johannes Maria. "Increased confidence of metabolite identification in high-resolution mass spectra using prior biological and chemical knowledge-based approaches." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1622/.
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Mass spectrometry-based metabolomics aims to study endogenous, low molecular weight metabolites and can be used to examine a variety of biological systems. To substantially increase the accuracy of metabolite identification and increase coverage of the metabolome detected by high-resolution (HR) mass spectrometry I developed, optimised and/or employed several analytical and bioinformatics methods. Biological samples contain thousands of metabolites that are related through specific substrate-product transformations. This prior biological knowledge together with a mass error surface, which represents the mass accuracy of peak differences within mass spectra, were employed to significantly reduce the false positive rate of metabolite identification. To maximise the sensitivity of the Thermo LTQ FT Ultra mass spectrometer, the existing direct-infusion SIM-stitching acquisition parameters (Southam et al., 2007) were reoptimised, yielding a ca. 3-fold increase in sensitivity. Finally, relative isotopic abundance measurements (RIA) using HR direct-infusion MS were characterised on the two most popular Fourier transform MS instruments (FT-ICR and Oribitrap) using the reoptimised SIM-stitching acquisition parameters. Several novel observations regarding RIA measurements were reported. Utilising these RIA characterisations within a putative metabolite identification pipeline increased the number of single true empirical formula assignments compared to using accurate mass alone. To conclude, analytical and bioinformatics methods developed in this thesis have successfully facilitated the putative identification of hundreds of metabolites in several metabolomics studies.
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Yang, Seungwon. "Automatic Identification of Topic Tags from Texts Based on Expansion-Extraction Approach." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/25111.
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Identifying topics of a textual document is useful for many purposes. We can organize the documents by topics in digital libraries. Then, we could browse and search for the documents with specific topics. By examining the topics of a document, we can quickly understand what the document is about. To augment the traditional manual way of topic tagging tasks, which is labor-intensive, solutions using computers have been developed.
This dissertation describes the design and development of a topic identification approach, in this case applied to disaster events. In a sense, this study represents the marriage of research analysis with an engineering effort in that it combines inspiration from Cognitive Informatics with a practical model from Information Retrieval. One of the design constraints, however, is that the Web was used as a universal knowledge source, which was essential in accessing the required information for inferring topics from texts.
Retrieving specific information of interest from such a vast information source was achieved by querying a search engine's application programming interface. Specifically, the information gathered was processed mainly by incorporating the Vector Space Model from the Information Retrieval field. As a proof of concept, we subsequently developed and evaluated a prototype tool, Xpantrac, which is able to run in a batch mode to automatically process text documents. A user interface of Xpantrac also was constructed to support an interactive semi-automatic topic tagging application, which was subsequently assessed via a usability study.
Throughout the design, development, and evaluation of these various study components, we detail how the hypotheses and research questions of this dissertation have been supported and answered. We also present that our overarching goal, which was the identification of topics in a human-comparable way without depending on a large training set or a corpus, has been achieved.<br>Ph. D.
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Idengren, Camilla, and Evelina Johannesson. "Ett tyst rop på hjälp : en litteraturstudie om hur sjuksköterskan kan identifiera barnmisshandel." Thesis, Högskolan Väst, Avd för vårdvetenskap på grundnivå, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-5118.
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Background: Corporal punishment has been banned in Sweden since 1979. Child abuse is known to exist in all cultures and social classes in our society. Therefore the nurse must be aware of this working with children. Aim: The aim of this study was to describe how nurses' can identify signs and symptoms of child abuse. Method: A literature review of ten articles containing six quantitative and four qualitative studies published between the years of 2000 and 2012. A content analysis was performed where similarities emerged in themes and subthemes. Results: The findings were signs and symptoms which concerns physical, psychological and social behaviors seen in child abuse. Additional findings were; an insecurity and lack of knowledge among nurses working with children. Experienced nurses believed to be more confident than less experienced nurses in identifying abused children and adolescents. Conclusion: Nurses' ought to have a holistic and ethical approach towards evaluating child abuse. It's important to have in mind that child abuse appears in great variety in physical, physiological and social aspects. Nurses' expressed the need of clearer guidelines in how to identify child abuse and common risk factors.