15 CFR 921.3 - National Estuarine Research Reserve System biogeographic classification scheme and estuarine...

Code of Federal Regulations, 2011 CFR

2011-01-01

... System biogeographic classification scheme and estuarine typologies. 921.3 Section 921.3 Commerce and... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... classification scheme based on regional variations in the nation's coastal zone has been developed. The...

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

A Computerized English-Spanish Correlation Index to Five Biomedical Library Classification Schemes Based on MeSH*

PubMed Central

Muench, Eugene V.

1971-01-01

A computerized English/Spanish correlation index to five biomedical library classification schemes and a computerized English/Spanish, Spanish/English listings of MeSH are described. The index was accomplished by supplying appropriate classification numbers of five classification schemes (National Library of Medicine; Library of Congress; Dewey Decimal; Cunningham; Boston Medical) to MeSH and a Spanish translation of MeSH The data were keypunched, merged on magnetic tape, and sorted in a computer alphabetically by English and Spanish subject headings and sequentially by classification number. Some benefits and uses of the index are: a complete index to classification schemes based on MeSH terms; a tool for conversion of classification numbers when reclassifying collections; a Spanish index and a crude Spanish translation of five classification schemes; a data base for future applications, e.g., automatic classification. Other classification schemes, such as the UDC, and translations of MeSH into other languages can be added. PMID:5172471

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI).

PubMed

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-06-01

Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research.

Inter-sectoral costs and benefits of mental health prevention: towards a new classification scheme.

PubMed

Drost, Ruben M W A; Paulus, Aggie T G; Ruwaard, Dirk; Evers, Silvia M A A

2013-12-01

Many preventive interventions for mental disorders have costs and benefits that spill over to sectors outside the healthcare sector. Little is known about these "inter-sectoral costs and benefits" (ICBs) of prevention. However, to achieve an efficient allocation of scarce resources, insights on ICBs are indispensable. The main aim was to identify the ICBs related to the prevention of mental disorders and provide a sector-specific classification scheme for these ICBs. Using PubMed, a literature search was conducted for ICBs of mental disorders and related (psycho)social effects. A policy perspective was used to build the scheme's structure, which was adapted to the outcomes of the literature search. In order to validate the scheme's international applicability inside and outside the mental health domain, semi-structured interviews were conducted with (inter)national experts in the broad fields of health promotion and disease prevention. The searched-for items appeared in a total of 52 studies. The ICBs found were classified in one of four sectors: "Education", "Labor and Social Security", "Household and Leisure" or "Criminal Justice System". Psycho(social) effects were placed in a separate section under "Individual and Family". Based on interviews, the scheme remained unadjusted, apart from adding a population-based dimension. This is the first study which offers a sector-specific classification of ICBs. Given the explorative nature of the study, no guidelines on sector-specific classification of ICBs were available. Nevertheless, the classification scheme was acknowledged by an international audience and could therefore provide added value to researchers and policymakers in the field of mental health economics and prevention. The identification and classification of ICBs offers decision makers supporting information on how to optimally allocate scarce resources with respect to preventive interventions for mental disorders. By exploring a new area of research, which has remained largely unexplored until now, the current study has an added value as it may form the basis for the development of a tool which can be used to calculate the ICBs of specific mental health related preventive interventions.

Physiotherapy movement based classification approaches to low back pain: comparison of subgroups through review and developer/expert survey.

PubMed

Karayannis, Nicholas V; Jull, Gwendolen A; Hodges, Paul W

2012-02-20

Several classification schemes, each with its own philosophy and categorizing method, subgroup low back pain (LBP) patients with the intent to guide treatment. Physiotherapy derived schemes usually have a movement impairment focus, but the extent to which other biological, psychological, and social factors of pain are encompassed requires exploration. Furthermore, within the prevailing 'biological' domain, the overlap of subgrouping strategies within the orthopaedic examination remains unexplored. The aim of this study was "to review and clarify through developer/expert survey, the theoretical basis and content of physical movement classification schemes, determine their relative reliability and similarities/differences, and to consider the extent of incorporation of the bio-psycho-social framework within the schemes". A database search for relevant articles related to LBP and subgrouping or classification was conducted. Five dominant movement-based schemes were identified: Mechanical Diagnosis and Treatment (MDT), Treatment Based Classification (TBC), Pathoanatomic Based Classification (PBC), Movement System Impairment Classification (MSI), and O'Sullivan Classification System (OCS) schemes. Data were extracted and a survey sent to the classification scheme developers/experts to clarify operational criteria, reliability, decision-making, and converging/diverging elements between schemes. Survey results were integrated into the review and approval obtained for accuracy. Considerable diversity exists between schemes in how movement informs subgrouping and in the consideration of broader neurosensory, cognitive, emotional, and behavioural dimensions of LBP. Despite differences in assessment philosophy, a common element lies in their objective to identify a movement pattern related to a pain reduction strategy. Two dominant movement paradigms emerge: (i) loading strategies (MDT, TBC, PBC) aimed at eliciting a phenomenon of centralisation of symptoms; and (ii) modified movement strategies (MSI, OCS) targeted towards documenting the movement impairments associated with the pain state. Schemes vary on: the extent to which loading strategies are pursued; the assessment of movement dysfunction; and advocated treatment approaches. A biomechanical assessment predominates in the majority of schemes (MDT, PBC, MSI), certain psychosocial aspects (fear-avoidance) are considered in the TBC scheme, certain neurophysiologic (central versus peripherally mediated pain states) and psychosocial (cognitive and behavioural) aspects are considered in the OCS scheme.

A new classification of glaucomas

PubMed Central

Bordeianu, Constantin-Dan

2014-01-01

Purpose To suggest a new glaucoma classification that is pathogenic, etiologic, and clinical. Methods After discussing the logical pathway used in criteria selection, the paper presents the new classification and compares it with the classification currently in use, that is, the one issued by the European Glaucoma Society in 2008. Results The paper proves that the new classification is clear (being based on a coherent and consistently followed set of criteria), is comprehensive (framing all forms of glaucoma), and helps in understanding the sickness understanding (in that it uses a logical framing system). The great advantage is that it facilitates therapeutic decision making in that it offers direct therapeutic suggestions and avoids errors leading to disasters. Moreover, the scheme remains open to any new development. Conclusion The suggested classification is a pathogenic, etiologic, and clinical classification that fulfills the conditions of an ideal classification. The suggested classification is the first classification in which the main criterion is consistently used for the first 5 to 7 crossings until its differentiation capabilities are exhausted. Then, secondary criteria (etiologic and clinical) pick up the relay until each form finds its logical place in the scheme. In order to avoid unclear aspects, the genetic criterion is no longer used, being replaced by age, one of the clinical criteria. The suggested classification brings only benefits to all categories of ophthalmologists: the beginners will have a tool to better understand the sickness and to ease their decision making, whereas the experienced doctors will have their practice simplified. For all doctors, errors leading to therapeutic disasters will be less likely to happen. Finally, researchers will have the object of their work gathered in the group of glaucoma with unknown or uncertain pathogenesis, whereas the results of their work will easily find a logical place in the scheme, as the suggested classification remains open to any new development. PMID:25246759

A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification.

PubMed

G Caton, Jack; Armitage, Gary; Berglundh, Tord; Chapple, Iain L C; Jepsen, Søren; S Kornman, Kenneth; L Mealey, Brian; Papapanou, Panos N; Sanz, Mariano; S Tonetti, Maurizio

2018-06-01

A classification scheme for periodontal and peri-implant diseases and conditions is necessary for clinicians to properly diagnose and treat patients as well as for scientists to investigate etiology, pathogenesis, natural history, and treatment of the diseases and conditions. This paper summarizes the proceedings of the World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. The workshop was co-sponsored by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) and included expert participants from all over the world. Planning for the conference, which was held in Chicago on November 9 to 11, 2017, began in early 2015. An organizing committee from the AAP and EFP commissioned 19 review papers and four consensus reports covering relevant areas in periodontology and implant dentistry. The authors were charged with updating the 1999 classification of periodontal diseases and conditions and developing a similar scheme for peri-implant diseases and conditions. Reviewers and workgroups were also asked to establish pertinent case definitions and to provide diagnostic criteria to aid clinicians in the use of the new classification. All findings and recommendations of the workshop were agreed to by consensus. This introductory paper presents an overview for the new classification of periodontal and peri-implant diseases and conditions, along with a condensed scheme for each of four workgroup sections, but readers are directed to the pertinent consensus reports and review papers for a thorough discussion of the rationale, criteria, and interpretation of the proposed classification. Changes to the 1999 classification are highlighted and discussed. Although the intent of the workshop was to base classification on the strongest available scientific evidence, lower level evidence and expert opinion were inevitably used whenever sufficient research data were unavailable. The scope of this workshop was to align and update the classification scheme to the current understanding of periodontal and peri-implant diseases and conditions. This introductory overview presents the schematic tables for the new classification of periodontal and peri-implant diseases and conditions and briefly highlights changes made to the 1999 classification. It cannot present the wealth of information included in the reviews, case definition papers, and consensus reports that has guided the development of the new classification, and reference to the consensus and case definition papers is necessary to provide a thorough understanding of its use for either case management or scientific investigation. Therefore, it is strongly recommended that the reader use this overview as an introduction to these subjects. Accessing this publication online will allow the reader to use the links in this overview and the tables to view the source papers (Table ). © 2018 American Academy of Periodontology and European Federation of Periodontology.

A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification.

PubMed

G Caton, Jack; Armitage, Gary; Berglundh, Tord; Chapple, Iain L C; Jepsen, Søren; S Kornman, Kenneth; L Mealey, Brian; Papapanou, Panos N; Sanz, Mariano; S Tonetti, Maurizio

2018-06-01

A classification scheme for periodontal and peri-implant diseases and conditions is necessary for clinicians to properly diagnose and treat patients as well as for scientists to investigate etiology, pathogenesis, natural history, and treatment of the diseases and conditions. This paper summarizes the proceedings of the World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. The workshop was co-sponsored by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) and included expert participants from all over the world. Planning for the conference, which was held in Chicago on November 9 to 11, 2017, began in early 2015. An organizing committee from the AAP and EFP commissioned 19 review papers and four consensus reports covering relevant areas in periodontology and implant dentistry. The authors were charged with updating the 1999 classification of periodontal diseases and conditions and developing a similar scheme for peri-implant diseases and conditions. Reviewers and workgroups were also asked to establish pertinent case definitions and to provide diagnostic criteria to aid clinicians in the use of the new classification. All findings and recommendations of the workshop were agreed to by consensus. This introductory paper presents an overview for the new classification of periodontal and peri-implant diseases and conditions, along with a condensed scheme for each of four workgroup sections, but readers are directed to the pertinent consensus reports and review papers for a thorough discussion of the rationale, criteria, and interpretation of the proposed classification. Changes to the 1999 classification are highlighted and discussed. Although the intent of the workshop was to base classification on the strongest available scientific evidence, lower level evidence and expert opinion were inevitably used whenever sufficient research data were unavailable. The scope of this workshop was to align and update the classification scheme to the current understanding of periodontal and peri-implant diseases and conditions. This introductory overview presents the schematic tables for the new classification of periodontal and peri-implant diseases and conditions and briefly highlights changes made to the 1999 classification. It cannot present the wealth of information included in the reviews, case definition papers, and consensus reports that has guided the development of the new classification, and reference to the consensus and case definition papers is necessary to provide a thorough understanding of its use for either case management or scientific investigation. Therefore, it is strongly recommended that the reader use this overview as an introduction to these subjects. Accessing this publication online will allow the reader to use the links in this overview and the tables to view the source papers (Table 1). © 2018 American Academy of Periodontology and European Federation of Periodontology.

The future of transposable element annotation and their classification in the light of functional genomics - what we can learn from the fables of Jean de la Fontaine?

PubMed

Arensburger, Peter; Piégu, Benoît; Bigot, Yves

2016-01-01

Transposable element (TE) science has been significantly influenced by the pioneering ideas of David Finnegan near the end of the last century, as well as by the classification systems that were subsequently developed. Today, whole genome TE annotation is mostly done using tools that were developed to aid gene annotation rather than to specifically study TEs. We argue that further progress in the TE field is impeded both by current TE classification schemes and by a failure to recognize that TE biology is fundamentally different from that of multicellular organisms. Novel genome wide TE annotation methods are helping to redefine our understanding of TE sequence origins and evolution. We briefly discuss some of these new methods as well as ideas for possible alternative classification schemes. Our hope is to encourage the formation of a society to organize a larger debate on these questions and to promote the adoption of standards for annotation and an improved TE classification.

Branch classification: A new mechanism for improving branch predictor performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, P.Y.; Hao, E.; Patt, Y.

There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

Hiltunen, P; Prince, S J D; Arridge, S

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

OBJECTIVE METEOROLOGICAL CLASSIFICATION SCHEME DESIGNED TO ELUCIDATE OZONE'S DEPENDENCE ON METEOROLOGY

EPA Science Inventory

This paper utilizes a two-stage clustering approach as part of an objective classification scheme designed to elucidate 03's dependence on meteorology. hen applied to ten years (1981-1990) of meteorological data for Birmingham, Alabama, the classification scheme identified seven ...

Development of a methodology for classifying software errors

NASA Technical Reports Server (NTRS)

Gerhart, S. L.

1976-01-01

A mathematical formalization of the intuition behind classification of software errors is devised and then extended to a classification discipline: Every classification scheme should have an easily discernible mathematical structure and certain properties of the scheme should be decidable (although whether or not these properties hold is relative to the intended use of the scheme). Classification of errors then becomes an iterative process of generalization from actual errors to terms defining the errors together with adjustment of definitions according to the classification discipline. Alternatively, whenever possible, small scale models may be built to give more substance to the definitions. The classification discipline and the difficulties of definition are illustrated by examples of classification schemes from the literature and a new study of observed errors in published papers of programming methodologies.

Threshold of toxicological concern values for non-genotoxic effects in industrial chemicals: re-evaluation of the Cramer classification.

PubMed

Kalkhof, H; Herzler, M; Stahlmann, R; Gundert-Remy, U

2012-01-01

The TTC concept employs available data from animal testing to derive a distribution of NOAELs. Taking a probabilistic view, the 5th percentile of the distribution is taken as a threshold value for toxicity. In this paper, we use 824 NOAELs from repeated dose toxicity studies of industrial chemicals to re-evaluate the currently employed TTC values, which have been derived for substances grouped according to the Cramer scheme (Cramer et al. in Food Cosm Toxicol 16:255-276, 1978) by Munro et al. (Food Chem Toxicol 34:829-867, 1996) and refined by Kroes and Kozianowski (Toxicol Lett 127:43-46, 2002), Kroes et al. 2000. In our data set, consisting of 756 NOAELs from 28-day repeated dose testing and 57 NOAELs from 90-days repeated dose testing, the experimental NOAEL had to be extrapolated to chronic TTC using regulatory accepted extrapolation factors. The TTC values derived from our data set were higher than the currently used TTC values confirming the safety of the latter. We analysed the prediction of the Cramer classification by comparing the classification by this tool with the guidance values for classification according to the Globally Harmonised System of classification and labelling of the United Nations (GHS). Nearly 90% of the chemicals were in Cramer class 3 and assumed as highly toxic compared to 22% according to the GHS. The Cramer classification does underestimate the toxicity of chemicals only in 4.6% of the cases. Hence, from a regulatory perspective, the Cramer classification scheme might be applied as it overestimates hazard of a chemical.

Automated structural classification of lipids by machine learning.

PubMed

Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T

2015-03-01

Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI)

PubMed Central

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-01-01

Introduction: Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. Aim: The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. Methods: first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. Results: There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. Conclusion: The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research. PMID:28883671

FIELD TESTS OF GEOGRAPHICALLY-DEPENDENT VS. THRESHOLD-BASED WATERSHED CLASSIFICATION SCHEMES IN THE GREAT LAKES BASIN

EPA Science Inventory

We compared classification schemes based on watershed storage (wetland + lake area/watershed area) and forest fragmentation with a geographically-based classification scheme for two case studies involving 1) Lake Superior tributaries and 2) watersheds of riverine coastal wetlands...

FIELD TESTS OF GEOGRAPHICALLY-DEPENDENT VS. THRESHOLD-BASED WATERSHED CLASSIFICATION SCHEMED IN THE GREAT LAKES BASIN

EPA Science Inventory

We compared classification schemes based on watershed storage (wetland + lake area/watershed area) and forest fragmentation with a geographically-based classification scheme for two case studies involving 1)Lake Superior tributaries and 2) watersheds of riverine coastal wetlands ...

Enriching User-Oriented Class Associations for Library Classification Schemes.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh; Yang, Chyan

2003-01-01

Explores the possibility of adding user-oriented class associations to hierarchical library classification schemes. Analyses a log of book circulation records from a university library in Taiwan and shows that classification schemes can be made more adaptable by analyzing circulation patterns of similar users. (Author/LRW)

[Evaluation of traditional pathological classification at molecular classification era for gastric cancer].

PubMed

Yu, Yingyan

2014-01-01

Histopathological classification is in a pivotal position in both basic research and clinical diagnosis and treatment of gastric cancer. Currently, there are different classification systems in basic science and clinical application. In medical literatures, different classifications are used including Lauren and WHO systems, which have confused many researchers. Lauren classification has been proposed for half a century, but is still used worldwide. It shows many advantages of simple, easy handling with prognostic significance. The WHO classification scheme is better than Lauren classification in that it is continuously being revised according to the progress of gastric cancer, and is always used in the clinical and pathological diagnosis of common scenarios. Along with the progression of genomics, transcriptomics, proteomics, metabolomics researches, molecular classification of gastric cancer becomes the current hot topics. The traditional therapeutic approach based on phenotypic characteristics of gastric cancer will most likely be replaced with a gene variation mode. The gene-targeted therapy against the same molecular variation seems more reasonable than traditional chemical treatment based on the same morphological change.

Classification and overview of research in real-time imaging

NASA Astrophysics Data System (ADS)

Sinha, Purnendu; Gorinsky, Sergey V.; Laplante, Phillip A.; Stoyenko, Alexander D.; Marlowe, Thomas J.

1996-10-01

Real-time imaging has application in areas such as multimedia, virtual reality, medical imaging, and remote sensing and control. Recently, the imaging community has witnessed a tremendous growth in research and new ideas in these areas. To lend structure to this growth, we outline a classification scheme and provide an overview of current research in real-time imaging. For convenience, we have categorized references by research area and application.

15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Biogeographic Classification Scheme I Appendix I to Part 921 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade... Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the Sheepscot...

15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Biogeographic Classification Scheme I Appendix I to Part 921 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade... Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the Sheepscot...

15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Biogeographic Classification Scheme I Appendix I to Part 921 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade... Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the Sheepscot...

15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Biogeographic Classification Scheme I Appendix I to Part 921 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade... Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the Sheepscot...

15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Biogeographic Classification Scheme I Appendix I to Part 921 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade... Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the Sheepscot...

A Classification Methodology and Retrieval Model to Support Software Reuse

DTIC Science & Technology

1988-01-01

Dewey Decimal Classification ( DDC 18), an enumerative scheme, occupies 40 pages [Buchanan 19791. Langridge [19731 states that the facets listed in the...sense of historical importance or wide spread use. The schemes are: Dewey Decimal Classification ( DDC ), Universal Decimal Classification (UDC...Classification Systems ..... ..... 2.3.3 Library Classification__- .52 23.3.1 Dewey Decimal Classification -53 2.33.2 Universal Decimal Classification 55 2333

Classification of close binary systems by Svechnikov

NASA Astrophysics Data System (ADS)

Dryomova, G. N.

The paper presents the historical overview of classification schemes of eclipsing variable stars with the foreground of advantages of the classification scheme by Svechnikov being widely appreciated for Close Binary Systems due to simplicity of classification criteria and brevity.

State of the Art in the Cramer Classification Scheme and ...

EPA Pesticide Factsheets

Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD. Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD.

Generalized Rainich conditions, generalized stress-energy conditions, and the Hawking-Ellis classification

NASA Astrophysics Data System (ADS)

Martín–Moruno, Prado; Visser, Matt

2017-11-01

The (generalized) Rainich conditions are algebraic conditions which are polynomial in the (mixed-component) stress-energy tensor. As such they are logically distinct from the usual classical energy conditions (NEC, WEC, SEC, DEC), and logically distinct from the usual Hawking-Ellis (Segré-Plebański) classification of stress-energy tensors (type I, type II, type III, type IV). There will of course be significant inter-connections between these classification schemes, which we explore in the current article. Overall, we shall argue that it is best to view the (generalized) Rainich conditions as a refinement of the classical energy conditions and the usual Hawking-Ellis classification.

Creating a Canonical Scientific and Technical Information Classification System for NCSTRL+

NASA Technical Reports Server (NTRS)

Tiffany, Melissa E.; Nelson, Michael L.

1998-01-01

The purpose of this paper is to describe the new subject classification system for the NCSTRL+ project. NCSTRL+ is a canonical digital library (DL) based on the Networked Computer Science Technical Report Library (NCSTRL). The current NCSTRL+ classification system uses the NASA Scientific and Technical (STI) subject classifications, which has a bias towards the aerospace, aeronautics, and engineering disciplines. Examination of other scientific and technical information classification systems showed similar discipline-centric weaknesses. Traditional, library-oriented classification systems represented all disciplines, but were too generalized to serve the needs of a scientific and technically oriented digital library. Lack of a suitable existing classification system led to the creation of a lightweight, balanced, general classification system that allows the mapping of more specialized classification schemes into the new framework. We have developed the following classification system to give equal weight to all STI disciplines, while being compact and lightweight.

An evaluation of several different classification schemes - Their parameters and performance. [maximum likelihood decision for crop identification

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.

1979-01-01

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

Characterization of palmprints by wavelet signatures via directional context modeling.

PubMed

Zhang, Lei; Zhang, David

2004-06-01

The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.

A Visual Basic program to plot sediment grain-size data on ternary diagrams

USGS Publications Warehouse

Poppe, L.J.; Eliason, A.H.

2008-01-01

Sedimentologic datasets are typically large and compiled into tables or databases, but pure numerical information can be difficult to understand and interpret. Thus, scientists commonly use graphical representations to reduce complexities, recognize trends and patterns in the data, and develop hypotheses. Of the graphical techniques, one of the most common methods used by sedimentologists is to plot the basic gravel, sand, silt, and clay percentages on equilateral triangular diagrams. This means of presenting data is simple and facilitates rapid classification of sediments and comparison of samples.The original classification scheme developed by Shepard (1954) used a single ternary diagram with sand, silt, and clay in the corners and 10 categories to graphically show the relative proportions among these three grades within a sample. This scheme, however, did not allow for sediments with significant amounts of gravel. Therefore, Shepard's classification scheme was later modified by the addition of a second ternary diagram with two categories to account for gravel and gravelly sediment (Schlee, 1973). The system devised by Folk (1954, 1974)\\ is also based on two triangular diagrams, but it has 21 categories and uses the term mud (defined as silt plus clay). Patterns within the triangles of both systems differ, as does the emphasis placed on gravel. For example, in the system described by Shepard, gravelly sediments have more than 10% gravel; in Folk's system, slightly gravelly sediments have as little as 0.01% gravel. Folk's classification scheme stresses gravel because its concentration is a function of the highest current velocity at the time of deposition as is the maximum grain size of the detritus that is available; Shepard's classification scheme emphasizes the ratios of sand, silt, and clay because they reflect sorting and reworking (Poppe et al., 2005).The program described herein (SEDPLOT) generates verbal equivalents and ternary diagrams to characterize sediment grain-size distributions. It is written in Microsoft Visual Basic 6.0 and provides a window to facilitate program execution. The inputs for the sediment fractions are percentages of gravel, sand, silt, and clay in the Wentworth (1922) grade scale, and the program permits the user to select output in either the Shepard (1954) classification scheme, modified as described above, or the Folk (1954, 1974) scheme. Users select options primarily with mouse-click events and through interactive dialogue boxes. This program is intended as a companion to other Visual Basic software we have developed to process sediment data (Poppe et al., 2003, 2004).

Evaluating Support for the Current Classification of Eukaryotic Diversity

PubMed Central

Parfrey, Laura Wegener; Barbero, Erika; Lasser, Elyse; Dunthorn, Micah; Bhattacharya, Debashish; Patterson, David J; Katz, Laura A

2006-01-01

Perspectives on the classification of eukaryotic diversity have changed rapidly in recent years, as the four eukaryotic groups within the five-kingdom classification—plants, animals, fungi, and protists—have been transformed through numerous permutations into the current system of six “supergroups.” The intent of the supergroup classification system is to unite microbial and macroscopic eukaryotes based on phylogenetic inference. This supergroup approach is increasing in popularity in the literature and is appearing in introductory biology textbooks. We evaluate the stability and support for the current six-supergroup classification of eukaryotes based on molecular genealogies. We assess three aspects of each supergroup: (1) the stability of its taxonomy, (2) the support for monophyly (single evolutionary origin) in molecular analyses targeting a supergroup, and (3) the support for monophyly when a supergroup is included as an out-group in phylogenetic studies targeting other taxa. Our analysis demonstrates that supergroup taxonomies are unstable and that support for groups varies tremendously, indicating that the current classification scheme of eukaryotes is likely premature. We highlight several trends contributing to the instability and discuss the requirements for establishing robust clades within the eukaryotic tree of life. PMID:17194223

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

NASA Astrophysics Data System (ADS)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91 %, while that with the conventional classification scheme is 93.70 %. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08 %, which is much higher than the 87.79 % from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

The classification of phobic disorders.

PubMed

Sheehan, D V; Sheehan, K H

The history of classification of phobic disorders is reviewed. Problems in the ability of current classification schemes to predict, control and describe the relationship between the symptoms and other phenomena are outlined. A new classification of phobic disorders is proposed based on the presence or absence of an endogenous anxiety syndrome with the phobias. The two categories of phobic disorder have a different clinical presentation and course, a different mean age of onset, distribution of age of onset, sex distribution, response to treatment modalities, GSR testing and habituation response. Empirical evidence supporting this proposal is cited. This classification has heuristic merit in guiding research efforts and discussions and in directing the clinician to a simple and practical solution of his patient's phobic disorder.

A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics

PubMed Central

Lee, Y. Tina; Kumaraguru, Senthilkumaran; Jain, Sanjay; Robinson, Stefanie; Helu, Moneer; Hatim, Qais Y.; Rachuri, Sudarsan; Dornfeld, David; Saldana, Christopher J.; Kumara, Soundar

2017-01-01

This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme. In addition to the themes, we discuss a conceptual model that may form the basis for the information necessary for performance evaluations. Finally, we present future challenges in developing robust, performance-measurement systems for real-time, data-intensive enterprises. PMID:28785744

CLASSIFICATION FRAMEWORK FOR COASTAL ECOSYSTEM RESPONSES TO AQUATIC STRESSORS

EPA Science Inventory

Many classification schemes have been developed to group ecosystems based on similar characteristics. To date, however, no single scheme has addressed coastal ecosystem responses to multiple stressors. We developed a classification framework for coastal ecosystems to improve the ...

THE ROLE OF WATERSHED CLASSIFICATION IN DIAGNOSING CAUSES OF BIOLOGICAL IMPAIRMENT

EPA Science Inventory

We compared classification schemes based on watershed storage (wetland + lake area/watershed area) and forest fragmention with a gewographically-based classification scheme for two case studies involving 1) Lake Superior tributaries and 2) watersheds of riverine coastal wetlands ...

Occupant detection using support vector machines with a polynomial kernel function

NASA Astrophysics Data System (ADS)

Destefanis, Eduardo A.; Kienzle, Eberhard; Canali, Luis R.

2000-10-01

The use of air bags in the presence of bad passenger and baby seat positions in car seats can injure or kill these individuals in case of an accident when this device is inflated. A proposed solution is the use of range sensors to detect passenger and baby seat risky positions. Such sensors allow the Airbag inflation to be controlled. This work is concerned with the application of different classification schemes to a real world problem and the optimization of a sensor as a function of the classification performance. The sensor is constructed using a new technology which is called Photo-Mixer-Device (PMD). A systematic analysis of the occupant detection problem was made using real and virtual environments. The challenge is to find the best sensor geometry and to adapt a classification scheme under the current technological constraints. Passenger head position detection is also a desirable issue. A couple of classifiers have been used into a simple configuration to reach this goal. Experiences and results are described.

Classification of diffuse lung diseases: why and how.

PubMed

Hansell, David M

2013-09-01

The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

United States Air Force Summer Research Program 1991. Summer Faculty Research Program (SFRP) Reports. Volume 5A. Wright Laboratory

DTIC Science & Technology

1992-01-09

consolidated into this aniuai report. 14. SUBJECT TERMS IS. NUMBER OF PAGi:S 16. P ’.RCE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19...Tc(Jop), the conductor is perfectly superconducting and carries the entirety of the operating current Iop. This implies no heat generation and P = 0...further found that the code cannot converge as is. See Appendix A. 2. The subject of developing a numerical scheme capable of handling both the incom

Selective classification for improved robustness of myoelectric control under nonideal conditions.

PubMed

Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

2011-06-01

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.

A classification scheme for edge-localized modes based on their probability distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shabbir, A., E-mail: aqsa.shabbir@ugent.be; Max Planck Institute for Plasma Physics, D-85748 Garching; Hornung, G.

We present here an automated classification scheme which is particularly well suited to scenarios where the parameters have significant uncertainties or are stochastic quantities. To this end, the parameters are modeled with probability distributions in a metric space and classification is conducted using the notion of nearest neighbors. The presented framework is then applied to the classification of type I and type III edge-localized modes (ELMs) from a set of carbon-wall plasmas at JET. This provides a fast, standardized classification of ELM types which is expected to significantly reduce the effort of ELM experts in identifying ELM types. Further, themore » classification scheme is general and can be applied to various other plasma phenomena as well.« less

A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers

PubMed Central

Bennett, David A.; Blennow, Kaj; Carrillo, Maria C.; Feldman, Howard H.; Frisoni, Giovanni B.; Hampel, Harald; Jagust, William J.; Johnson, Keith A.; Knopman, David S.; Petersen, Ronald C.; Scheltens, Philip; Sperling, Reisa A.; Dubois, Bruno

2016-01-01

Biomarkers have become an essential component of Alzheimer disease (AD) research and because of the pervasiveness of AD pathology in the elderly, the same biomarkers are used in cognitive aging research. A number of current issues suggest that an unbiased descriptive classification scheme for these biomarkers would be useful. We propose the “A/T/N” system in which 7 major AD biomarkers are divided into 3 binary categories based on the nature of the pathophysiology that each measures. “A” refers to the value of a β-amyloid biomarker (amyloid PET or CSF Aβ42); “T,” the value of a tau biomarker (CSF phospho tau, or tau PET); and “N,” biomarkers of neurodegeneration or neuronal injury ([18F]-fluorodeoxyglucose–PET, structural MRI, or CSF total tau). Each biomarker category is rated as positive or negative. An individual score might appear as A+/T+/N−, or A+/T−/N−, etc. The A/T/N system includes the new modality tau PET. It is agnostic to the temporal ordering of mechanisms underlying AD pathogenesis. It includes all individuals in any population regardless of the mix of biomarker findings and therefore is suited to population studies of cognitive aging. It does not specify disease labels and thus is not a diagnostic classification system. It is a descriptive system for categorizing multidomain biomarker findings at the individual person level in a format that is easy to understand and use. Given the present lack of consensus among AD specialists on terminology across the clinically normal to dementia spectrum, a biomarker classification scheme will have broadest acceptance if it is independent from any one clinically defined diagnostic scheme. PMID:27371494

Realistic Expectations for Rock Identification.

ERIC Educational Resources Information Center

Westerback, Mary Elizabeth; Azer, Nazmy

1991-01-01

Presents a rock classification scheme for use by beginning students. The scheme is based on rock textures (glassy, crystalline, clastic, and organic framework) and observable structures (vesicles and graded bedding). Discusses problems in other rock classification schemes which may produce confusion, misidentification, and anxiety. (10 references)…

A Philosophical Approach to Describing Science Content: An Example From Geologic Classification.

ERIC Educational Resources Information Center

Finley, Fred N.

1981-01-01

Examines how research of philosophers of science may be useful to science education researchers and curriculum developers in the development of descriptions of science content related to classification schemes. Provides examples of concept analysis of two igneous rock classification schemes. (DS)

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts' Law test.

PubMed

Scheme, Erik J; Englehart, Kevin B

2013-07-01

When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law test is proposed as a suitable alternative to using virtual limb environments for evaluating real-time myoelectric control performance. The test is used to compare the selective approach to a state-of-the-art linear discriminant analysis classification based scheme. The framework is shown to obey Fitts' Law for both control schemes, producing linear regression fittings with high coefficients of determination (R(2) > 0.936). Additional performance metrics focused on quality of control are discussed and incorporated in the evaluation. Using this framework the selective classification based scheme is shown to produce significantly higher efficiency and completion rates, and significantly lower overshoot and stopping distances, with no significant difference in throughput.

A Visual Basic program to classify sediments based on gravel-sand-silt-clay ratios

USGS Publications Warehouse

Poppe, L.J.; Eliason, A.H.; Hastings, M.E.

2003-01-01

Nomenclature describing size distributions is important to geologists because grain size is the most basic attribute of sediments. Traditionally, geologists have divided sediments into four size fractions that include gravel, sand, silt, and clay, and classified these sediments based on ratios of the various proportions of the fractions. Definitions of these fractions have long been standardized to the grade scale described by Wentworth (1922), and two main classification schemes have been adopted to describe the approximate relationship between the size fractions.Specifically, according to the Wentworth grade scale gravel-sized particles have a nominal diameter of ⩾2.0 mm; sand-sized particles have nominal diameters from <2.0 mm to ⩾62.5 μm; silt-sized particles have nominal diameters from <62.5 to ⩾4.0 μm; and clay is <4.0 μm. As for sediment classification, most sedimentologists use one of the systems described either by Shepard (1954) or Folk (1954, 1974). The original scheme devised by Shepard (1954) utilized a single ternary diagram with sand, silt, and clay in the corners to graphically show the relative proportions among these three grades within a sample. This scheme, however, does not allow for sediments with significant amounts of gravel. Therefore, Shepard's classification scheme (Fig. 1) was subsequently modified by the addition of a second ternary diagram to account for the gravel fraction (Schlee, 1973). The system devised by Folk (1954, 1974) is also based on two triangular diagrams (Fig. 2), but it has 23 major categories, and uses the term mud (defined as silt plus clay). The patterns within the triangles of both systems differ, as does the emphasis placed on gravel. For example, in the system described by Shepard, gravelly sediments have more than 10% gravel; in Folk's system, slightly gravelly sediments have as little as 0.01% gravel. Folk's classification scheme stresses gravel because its concentration is a function of the highest current velocity at the time of deposition, together with the maximum grain size of the detritus that is available; Shepard's classification scheme emphasizes the ratios of sand, silt, and clay because they reflect sorting and reworking (Poppe et al., 2000).

THE WESTERN LAKE SUPERIOR COMPARATIVE WATERSHED FRAMEWORK: A FIELD TEST OF GEOGRAPHICALLY-DEPENDENT VS. THRESHOLD-BASED GEOGRAPHICALLY-INDEPENDENT CLASSIFICATION

EPA Science Inventory

Stratified random selection of watersheds allowed us to compare geographically-independent classification schemes based on watershed storage (wetland + lake area/watershed area) and forest fragmentation with a geographically-based classification scheme within the Northern Lakes a...

Overview of classification systems in peripheral artery disease.

PubMed

Hardman, Rulon L; Jazaeri, Omid; Yi, J; Smith, M; Gupta, Rajan

2014-12-01

Peripheral artery disease (PAD), secondary to atherosclerotic disease, is currently the leading cause of morbidity and mortality in the western world. While PAD is common, it is estimated that the majority of patients with PAD are undiagnosed and undertreated. The challenge to the treatment of PAD is to accurately diagnose the symptoms and determine treatment for each patient. The varied presentations of peripheral vascular disease have led to numerous classification schemes throughout the literature. Consistent grading of patients leads to both objective criteria for treating patients and a baseline for clinical follow-up. Reproducible classification systems are also important in clinical trials and when comparing medical, surgical, and endovascular treatment paradigms. This article reviews the various classification systems for PAD and advantages to each system.

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

PubMed Central

Santagata, Sandro; Thakkar, Ankita; Ergonul, Ayse; Wang, Bin; Woo, Terri; Hu, Rong; Harrell, J. Chuck; McNamara, George; Schwede, Matthew; Culhane, Aedin C.; Kindelberger, David; Rodig, Scott; Richardson, Andrea; Schnitt, Stuart J.; Tamimi, Rulla M.; Ince, Tan A.

2014-01-01

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors. PMID:24463450

Detailed Quantitative Classifications of Galaxy Morphology

NASA Astrophysics Data System (ADS)

Nair, Preethi

2018-01-01

Understanding the physical processes responsible for the growth of galaxies is one of the key challenges in extragalactic astronomy. The assembly history of a galaxy is imprinted in a galaxy’s detailed morphology. The bulge-to-total ratio of galaxies, the presence or absence of bars, rings, spiral arms, tidal tails etc, all have implications for the past merger, star formation, and feedback history of a galaxy. However, current quantitative galaxy classification schemes are only useful for broad binning. They cannot classify or exploit the wide variety of galaxy structures seen in nature. Therefore, comparisons of observations with theoretical predictions of secular structure formation have only been conducted on small samples of visually classified galaxies. However large samples are needed to disentangle the complex physical processes of galaxy formation. With the advent of large surveys, like the Sloan Digital Sky Survey (SDSS) and the upcoming Large Synoptic Survey Telescope (LSST) and WFIRST, the problem of statistics will be resolved. However, the need for a robust quantitative classification scheme will still remain. Here I will present early results on promising machine learning algorithms that are providing detailed classifications, identifying bars, rings, multi-armed spiral galaxies, and Hubble type.

Towards a Collaborative Intelligent Tutoring System Classification Scheme

ERIC Educational Resources Information Center

Harsley, Rachel

2014-01-01

This paper presents a novel classification scheme for Collaborative Intelligent Tutoring Systems (CITS), an emergent research field. The three emergent classifications of CITS are unstructured, semi-structured, and fully structured. While all three types of CITS offer opportunities to improve student learning gains, the full extent to which these…

Adaptive skin detection based on online training

NASA Astrophysics Data System (ADS)

Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

2007-11-01

Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

Map Classification: A Comparison of Schemes with Special Reference to the Continent of Africa. Occasional Papers, Number 154.

ERIC Educational Resources Information Center

Merrett, Christopher E.

This guide to the theory and practice of map classification begins with a discussion of the filing of maps and the function of map classification based on area and theme as illustrated by four maps of Africa. The description of the various classification systems which follows is divided into book schemes with provision for maps (including Dewey…

Predominant-period site classification for response spectra prediction equations in Italy

USGS Publications Warehouse

Di Alessandro, Carola; Bonilla, Luis Fabian; Boore, David M.; Rovelli, Antonio; Scotti, Oona

2012-01-01

We propose a site‐classification scheme based on the predominant period of the site, as determined from the average horizontal‐to‐vertical (H/V) spectral ratios of ground motion. Our scheme extends Zhao et al. (2006) classifications by adding two classes, the most important of which is defined by flat H/V ratios with amplitudes less than 2. The proposed classification is investigated by using 5%‐damped response spectra from Italian earthquake records. We select a dataset of 602 three‐component analog and digital recordings from 120 earthquakes recorded at 214 seismic stations within a hypocentral distance of 200 km. Selected events are in the moment‐magnitude range 4.0≤Mw≤6.8 and focal depths from a few kilometers to 46 km. We computed H/V ratios for these data and used them to classify each site into one of six classes. We then investigate the impact of this classification scheme on empirical ground‐motion prediction equations (GMPEs) by comparing its performance with that of the conventional rock/soil classification. Although the adopted approach results in only a small reduction of the overall standard deviation, the use of H/V spectral ratios in site classification does capture the signature of sites with flat frequency‐response, as well as deep and shallow‐soil profiles, characterized by long‐ and short‐period resonance, respectively; in addition, the classification scheme is relatively quick and inexpensive, which is an advantage over schemes based on measurements of shear‐wave velocity.

Classification scheme for phenomenological universalities in growth problems in physics and other sciences.

PubMed

Castorina, P; Delsanto, P P; Guiot, C

2006-05-12

A classification in universality classes of broad categories of phenomenologies, belonging to physics and other disciplines, may be very useful for a cross fertilization among them and for the purpose of pattern recognition and interpretation of experimental data. We present here a simple scheme for the classification of nonlinear growth problems. The success of the scheme in predicting and characterizing the well known Gompertz, West, and logistic models, suggests to us the study of a hitherto unexplored class of nonlinear growth problems.

Enhancing Vocabulary Acquisition through Reading: A Hierarchy of Text-Related Exercise Types.

ERIC Educational Resources Information Center

Wesche, M.; Paribakht, T. Sima

This paper describes a classification scheme developed to examine the effects of extensive reading on primary and second language vocabulary acquisition and reports on an experiment undertaken to test the model scheme. The classification scheme represents a hypothesized hierarchy of the degree and type of mental processing required by various…

Using Simulations to Investigate the Longitudinal Stability of Alternative Schemes for Classifying and Identifying Children with Reading Disabilities

ERIC Educational Resources Information Center

Schatschneider, Christopher; Wagner, Richard K.; Hart, Sara A.; Tighe, Elizabeth L.

2016-01-01

The present study employed data simulation techniques to investigate the 1-year stability of alternative classification schemes for identifying children with reading disabilities. Classification schemes investigated include low performance, unexpected low performance, dual-discrepancy, and a rudimentary form of constellation model of reading…

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Classifying aerosol type using in situ surface spectral aerosol optical properties

NASA Astrophysics Data System (ADS)

Schmeisser, Lauren; Andrews, Elisabeth; Ogren, John A.; Sheridan, Patrick; Jefferson, Anne; Sharma, Sangeeta; Kim, Jeong Eun; Sherman, James P.; Sorribas, Mar; Kalapov, Ivo; Arsov, Todor; Angelov, Christo; Mayol-Bracero, Olga L.; Labuschagne, Casper; Kim, Sang-Woo; Hoffer, András; Lin, Neng-Huei; Chia, Hao-Ping; Bergin, Michael; Sun, Junying; Liu, Peng; Wu, Hao

2017-10-01

Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes.Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station.The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.

Multimedia

NASA Technical Reports Server (NTRS)

Kaye, Karen

1993-01-01

Multimedia initiative objectives for the NASA Scientific and Technical Information (STI) program are described. A multimedia classification scheme was developed and the types of non-print media currently in use are inventoried. The NASA STI Program multimedia initiative is driven by a changing user population and technical requirements in the areas of publications, dissemination, and user and management support.

Neuroendocrine neoplasms of the lung: Concepts and terminology.

PubMed

Wick, Mark R; Marchevsky, Alberto M

2015-11-01

Neuroendocrine neoplasms of the lung continue to undergo scrutiny, with respect to the diagnostic terminology recommended for them and details of their clinicopathologic profiles. This overview considers the nosological evolution of such lesions and presents current views on classification schemes that pertain to them. Copyright © 2015 Elsevier Inc. All rights reserved.

The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs.

PubMed

Pulley, Simon; Foster, Ian; Collins, Adrian L

2017-06-01

The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes. The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes. The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0-100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty (a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

A recursive field-normalized bibliometric performance indicator: an application to the field of library and information science.

PubMed

Waltman, Ludo; Yan, Erjia; van Eck, Nees Jan

2011-10-01

Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators). We combine these two ideas in a single indicator, referred to as the recursive mean normalized citation score indicator, and we study the validity of this indicator. Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme. Based on these observations, we advise against the use of indicators in which the idea of normalization based on a field classification scheme and the idea of recursive citation weighing are combined.

Hydrometeorological application of an extratropical cyclone classification scheme in the southern United States

NASA Astrophysics Data System (ADS)

Senkbeil, J. C.; Brommer, D. M.; Comstock, I. J.; Loyd, T.

2012-07-01

Extratropical cyclones (ETCs) in the southern United States are often overlooked when compared with tropical cyclones in the region and ETCs in the northern United States. Although southern ETCs are significant weather events, there is currently not an operational scheme used for identifying and discussing these nameless storms. In this research, we classified 84 ETCs (1970-2009). We manually identified five distinct formation regions and seven unique ETC types using statistical classification. Statistical classification employed the use of principal components analysis and two methods of cluster analysis. Both manual and statistical storm types generally showed positive (negative) relationships with El Niño (La Niña). Manual storm types displayed precipitation swaths consistent with discrete storm tracks which further legitimizes the existence of multiple modes of southern ETCs. Statistical storm types also displayed unique precipitation intensity swaths, but these swaths were less indicative of track location. It is hoped that by classifying southern ETCs into types, that forecasters, hydrologists, and broadcast meteorologists might be able to better anticipate projected amounts of precipitation at their locations.

Classification of DNA nucleotides with transverse tunneling currents

NASA Astrophysics Data System (ADS)

Nyvold Pedersen, Jonas; Boynton, Paul; Di Ventra, Massimiliano; Jauho, Antti-Pekka; Flyvbjerg, Henrik

2017-01-01

It has been theoretically suggested and experimentally demonstrated that fast and low-cost sequencing of DNA, RNA, and peptide molecules might be achieved by passing such molecules between electrodes embedded in a nanochannel. The experimental realization of this scheme faces major challenges, however. In realistic liquid environments, typical currents in tunneling devices are of the order of picoamps. This corresponds to only six electrons per microsecond, and this number affects the integration time required to do current measurements in real experiments. This limits the speed of sequencing, though current fluctuations due to Brownian motion of the molecule average out during the required integration time. Moreover, data acquisition equipment introduces noise, and electronic filters create correlations in time-series data. We discuss how these effects must be included in the analysis of, e.g., the assignment of specific nucleobases to current signals. As the signals from different molecules overlap, unambiguous classification is impossible with a single measurement. We argue that the assignment of molecules to a signal is a standard pattern classification problem and calculation of the error rates is straightforward. The ideas presented here can be extended to other sequencing approaches of current interest.

A Review of Major Nursing Vocabularies and the Extent to Which They Have the Characteristics Required for Implementation in Computer-based Systems

PubMed Central

Henry, Suzanne Bakken; Warren, Judith J.; Lange, Linda; Button, Patricia

1998-01-01

Building on the work of previous authors, the Computer-based Patient Record Institute (CPRI) Work Group on Codes and Structures has described features of a classification scheme for implementation within a computer-based patient record. The authors of the current study reviewed the evaluation literature related to six major nursing vocabularies (the North American Nursing Diagnosis Association Taxonomy 1, the Nursing Interventions Classification, the Nursing Outcomes Classification, the Home Health Care Classification, the Omaha System, and the International Classification for Nursing Practice) to determine the extent to which the vocabularies include the CPRI features. None of the vocabularies met all criteria. The Omaha System, Home Health Care Classification, and International Classification for Nursing Practice each included five features. Criteria not fully met by any systems were clear and non-redundant representation of concepts, administrative cross-references, syntax and grammar, synonyms, uncertainty, context-free identifiers, and language independence. PMID:9670127

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Giger, Maryellen L.; Doi, Kunio; Vyborny, Carl J.; Schmidt, Robert A.; Metz, Charles E.; Wu, Chris Y.; Yin, Fang-Fang; Jiang, Yulei; Huo, Zhimin; Lu, Ping; Zhang, Wei; Ema, Takahiro; Bick, Ulrich; Papaioannou, John; Nagel, Rufus H.

1993-07-01

We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

PubMed

Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

2015-01-01

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

Defining functional biomes and monitoring their change globally.

PubMed

Higgins, Steven I; Buitenwerf, Robert; Moncrieff, Glenn R

2016-11-01

Biomes are important constructs for organizing understanding of how the worlds' major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30-year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture-limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function. © 2016 John Wiley & Sons Ltd.

Etiological classification of ischemic stroke in young patients: a comparative study of TOAST, CCS, and ASCO.

PubMed

Gökçal, Elif; Niftaliyev, Elvin; Asil, Talip

2017-09-01

Analysis of stroke subtypes is important for making treatment decisions and prognostic evaluations. The TOAST classification system is most commonly used, but the CCS and ASCO classification systems might be more useful to identify stroke etiologies in young patients whose strokes have a wide range of different causes. In this manuscript, we aim to compare the differences in subtype classification between TOAST, CCS, and ASCO in young stroke patients. The TOAST, CCS, and ASCO classification schemes were applied to 151 patients with ischemic stroke aged 18-49 years old and the proportion of subtypes classified by each scheme was compared. For comparison, determined etiologies were defined as cases with evident and probable subtypes when using the CCS scheme and cases with grade 1 and 2 subtypes but no other grade 1 subtype when using the ASCO scheme. The McNemar test with Bonferroni correction was used to assess significance. By TOAST, 41.1% of patients' stroke etiology was classified as undetermined etiology, 19.2% as cardioembolic, 13.2% as large artery atherosclerosis, 11.3% as small vessel occlusion, and 15.2% as other causes. Compared with TOAST, both CCS and ASCO assigned fewer patients to the undetermined etiology group (30.5% p < 0.001 and 26.5% p < 0.001, respectively) and assigned more patients to the small vessel occlusion category (19.9%, p < 0.001, and 21.9%, p < 0.001, respectively). Additionally, both schemes assigned more patients to the large artery atherosclerosis group (15.9 and 16.6%, respectively). The proportion of patients assigned to either the cardioembolic or the other causes etiology did not differ significantly between the three schemes. Application of the CCS and ASCO classification schemes in young stroke patients seems feasible, and using both schemes may result in fewer patients being classified as undetermined etiology. New studies with more patients and a prospective design are needed to explore this topic further.

Development and application of a new comprehensive image-based classification scheme for coastal and benthic environments along the southeast Florida continental shelf

NASA Astrophysics Data System (ADS)

Makowski, Christopher

The coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The purpose of this study was to test the hypothesis and answer the research question of whether new parameters of integrating geomorphological components with dominant biological covers could be developed and applied across multiple remote sensing platforms for an innovative way to identify, interpret, and classify diverse coastal and benthic environments along the southeast Florida continental shelf. An ordered manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies.

Social Sciences in Forestry, A Current Selected Bibliography, No. 57. Special Appendix: Theses and Dissertations in Progress.

ERIC Educational Resources Information Center

Schwab, Judith L., Ed.

1982-01-01

Documents which address the interface between forestry and the social sciences comprise this annotated bibliography. A subject-matter classification scheme is used to group publications by subheadings under five major heading: (1) social science applied to forestry at large; (2) applied to forestry's productive agents; (3) applied to forest…

Demonstration of Advanced EMI Models for Live-Site UXO Discrimination at Former Camp Butner, North Carolina

DTIC Science & Technology

2012-05-01

GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7 . PERFORMING ORGANIZATION NAME(S...2.3.3 Classification using template matching ...................................................... 7 2.4 Details of classification schemes... 7 2.4.1 Camp Butner TEMTADS data inversion and classification scheme .......... 9

The Adam Walsh Act: An Examination of Sex Offender Risk Classification Systems.

PubMed

Zgoba, Kristen M; Miner, Michael; Levenson, Jill; Knight, Raymond; Letourneau, Elizabeth; Thornton, David

2016-12-01

This study was designed to compare the Adam Walsh Act (AWA) classification tiers with actuarial risk assessment instruments and existing state classification schemes in their respective abilities to identify sex offenders at high risk to re-offend. Data from 1,789 adult sex offenders released from prison in four states were collected (Minnesota, New Jersey, Florida, and South Carolina). On average, the sexual recidivism rate was approximately 5% at 5 years and 10% at 10 years. AWA Tier 2 offenders had higher Static-99R scores and higher recidivism rates than Tier 3 offenders, and in Florida, these inverse correlations were statistically significant. Actuarial measures and existing state tier systems, in contrast, did a better job of identifying high-risk offenders and recidivists. As well, we examined the distribution of risk assessment scores within and across tier categories, finding that a majority of sex offenders fall into AWA Tier 3, but more than half score low or moderately low on the Static-99R. The results indicate that the AWA sex offender classification scheme is a poor indicator of relative risk and is likely to result in a system that is less effective in protecting the public than those currently implemented in the states studied. © The Author(s) 2015.

Gene expression overlap affects karyotype prediction in pediatric acute lymphoblastic leukemia

DOE PAGES

Martin, S. B.; Mosquera-Caro, M. P.; Potter, J. W.; ...

2007-04-05

Leukemia is the most common childhood malignancy in the United States. Acute lymphoblastic leukemia (ALL) accounts for 75% of new leukemia cases in children. Although the outcome for children with ALL has improved dramatically over the past three decades, 25% of children with ALL still develop recurrent disease. Current risk classification schemes in pediatric ALL use clinical and laboratory parameters such as age and initial white blood cell count, as well as the presence of specific ALL-associated cytogenetic or molecular genetic abnormalities. Stratification based on cytogenetic analysis and molecular genetic detection consider B precursor ALL translocations such as t(12;21)(TEL-AML1), t(1;19)(E2A-PBX1)more » and t(9;22)(BCR-ABL), as well as numerical imbalances such as hyperdiploidy, specific chromosome trisomies or hypodiploidy. Despite such efforts, current diagnosis and risk classification schemes in pediatric ALL remain imprecise. In particular, it is likely that a significant number of higher-risk children are currently overtreated and could be cured with less intensive regimens, resulting in fewer toxicities and long-term side effects. Finally and conversely, a significant number of children in lower-risk categories still relapse and precise means to prospectively identify them have remained elusive.« less

The EpiOcular™ Eye Irritation Test is the Method of Choice for the In Vitro Eye Irritation Testing of Agrochemical Formulations: Correlation Analysis of EpiOcular Eye Irritation Test and BCOP Test Data According to the UN GHS, US EPA and Brazil ANVISA Classification Schemes.

PubMed

Kolle, Susanne N; Rey Moreno, Maria Cecilia; Mayer, Winfried; van Cott, Andrew; van Ravenzwaay, Bennard; Landsiedel, Robert

2015-07-01

The Bovine Corneal Opacity and Permeability (BCOP) test is commonly used for the identification of severe ocular irritants (GHS Category 1), but it is not recommended for the identification of ocular irritants (GHS Category 2). The incorporation of human reconstructed tissue model-based tests into a tiered test strategy to identify ocular non-irritants and replace the Draize rabbit eye irritation test has been suggested (OECD TG 405). The value of the EpiOcular™ Eye Irritation Test (EIT) for the prediction of ocular non-irritants (GHS No Category) has been demonstrated, and an OECD Test Guideline (TG) was drafted in 2014. The purpose of this study was to evaluate whether the BCOP test, in conjunction with corneal histopathology (as suggested for the evaluation of the depth of the injury( and/or the EpiOcular-EIT, could be used to predict the eye irritation potential of agrochemical formulations according to the UN GHS, US EPA and Brazil ANVISA classification schemes. We have assessed opacity, permeability and histopathology in the BCOP assay, and relative tissue viability in the EpiOcular-EIT, for 97 agrochemical formulations with available in vivo eye irritation data. By using the OECD TG 437 protocol for liquids, the BCOP test did not result in sufficient correct predictions of severe ocular irritants for any of the three classification schemes. The lack of sensitivity could be improved somewhat by the inclusion of corneal histopathology, but the relative viability in the EpiOcular-EIT clearly outperformed the BCOP test for all three classification schemes. The predictive capacity of the EpiOcular-EIT for ocular non-irritants (UN GHS No Category) for the 97 agrochemical formulations tested (91% sensitivity, 72% specificity and 82% accuracy for UN GHS classification) was comparable to that obtained in the formal validation exercise underlying the OECD draft TG. We therefore conclude that the EpiOcular-EIT is currently the best in vitro method for the prediction of the eye irritation potential of liquid agrochemical formulations. 2015 FRAME.

A scheme for a flexible classification of dietary and health biomarkers.

PubMed

Gao, Qian; Praticò, Giulia; Scalbert, Augustin; Vergères, Guy; Kolehmainen, Marjukka; Manach, Claudine; Brennan, Lorraine; Afman, Lydia A; Wishart, David S; Andres-Lacueva, Cristina; Garcia-Aloy, Mar; Verhagen, Hans; Feskens, Edith J M; Dragsted, Lars O

2017-01-01

Biomarkers are an efficient means to examine intakes or exposures and their biological effects and to assess system susceptibility. Aided by novel profiling technologies, the biomarker research field is undergoing rapid development and new putative biomarkers are continuously emerging in the scientific literature. However, the existing concepts for classification of biomarkers in the dietary and health area may be ambiguous, leading to uncertainty about their application. In order to better understand the potential of biomarkers and to communicate their use and application, it is imperative to have a solid scheme for biomarker classification that will provide a well-defined ontology for the field. In this manuscript, we provide an improved scheme for biomarker classification based on their intended use rather than the technology or outcomes (six subclasses are suggested: food compound intake biomarkers (FCIBs), food or food component intake biomarkers (FIBs), dietary pattern biomarkers (DPBs), food compound status biomarkers (FCSBs), effect biomarkers, physiological or health state biomarkers). The application of this scheme is described in detail for the dietary and health area and is compared with previous biomarker classification for this field of research.

Model Validation and Site Characterization for Early Deployment MHK Sites and Establishment of Wave Classification Scheme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kilcher, Levi F

Model Validation and Site Characterization for Early Deployment Marine and Hydrokinetic Energy Sites and Establishment of Wave Classification Scheme presentation from from Water Power Technologies Office Peer Review, FY14-FY16.

Mathematical model of blasting schemes management in mining operations in presence of random disturbances

NASA Astrophysics Data System (ADS)

Kazakova, E. I.; Medvedev, A. N.; Kolomytseva, A. O.; Demina, M. I.

2017-11-01

The paper presents a mathematical model of blasting schemes management in presence of random disturbances. Based on the lemmas and theorems proved, a control functional is formulated, which is stable. A universal classification of blasting schemes is developed. The main classification attributes are suggested: the orientation in plan the charging wells rows relatively the block of rocks; the presence of cuts in the blasting schemes; the separation of the wells series onto elements; the sequence of the blasting. The periodic regularity of transition from one Short-delayed scheme of blasting to another is proved.

The Ecohydrological Context of Drought and Classification of Plant Responses

NASA Astrophysics Data System (ADS)

Feng, X.; Ackerly, D.; Dawson, T. E.; Manzoni, S.; Skelton, R. P.; Vico, G.; Thompson, S. E.

2017-12-01

Many recent studies on drought-induced vegetation mortality have explored how plant functional traits, and classifications of such traits along axes of, e.g., isohydry - anisohydry, might contribute to predicting drought survival and recovery. As these studies proliferate, concerns are growing about the consistency and predictive value of such classifications. Here, we outline the basis for a systematic classification of drought strategies that accounts for both environmental conditions and functional traits. We (1) identify drawbacks of exiting isohydricity and trait-based metrics, (2) identify major axes of trait and environmental variation that determine drought mortality pathways (hydraulic failure and carbon starvation) using non-dimensional trait groups, and (3) demonstrate that these trait groupings predict physiological drought outcomes using both measured and synthetic data. In doing so we untangle some confounding effects of environment and trait variations that undermine current classification schemes, outline a pathway to progress towards a general classification of drought vulnerability, and advocate for more careful treatment of the environmental conditions within which plant drought responses occur.

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

PubMed Central

2012-01-01

Background Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. Results Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. Conclusions We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. PMID:22316103

Classification of childhood epilepsies in a tertiary pediatric neurology clinic using a customized classification scheme from the international league against epilepsy 2010 report.

PubMed

Khoo, Teik-Beng

2013-01-01

In its 2010 report, the International League Against Epilepsy Commission on Classification and Terminology had made a number of changes to the organization, terminology, and classification of seizures and epilepsies. This study aims to test the usefulness of this revised classification scheme on children with epilepsies aged between 0 and 18 years old. Of 527 patients, 75.1% only had 1 type of seizure and the commonest was focal seizure (61.9%). A specific electroclinical syndrome diagnosis could be made in 27.5%. Only 2.1% had a distinctive constellation. In this cohort, 46.9% had an underlying structural, metabolic, or genetic etiology. Among the important causes were pre-/perinatal insults, malformation of cortical development, intracranial infections, and neurocutaneous syndromes. However, 23.5% of the patients in our cohort were classified as having "epilepsies of unknown cause." The revised classification scheme is generally useful for pediatric patients. To make it more inclusive and clinically meaningful, some local customizations are required.

Toward an endovascular internal carotid artery classification system.

PubMed

Shapiro, M; Becske, T; Riina, H A; Raz, E; Zumofen, D; Jafar, J J; Huang, P P; Nelson, P K

2014-02-01

Does the world need another ICA classification scheme? We believe so. The purpose of proposed angiography-driven classification is to optimize description of the carotid artery from the endovascular perspective. A review of existing, predominantly surgically-driven classifications is performed, and a new scheme, based on the study of NYU aneurysm angiographic and cross-sectional databases is proposed. Seven segments - cervical, petrous, cavernous, paraophthlamic, posterior communicating, choroidal, and terminus - are named. This nomenclature recognizes intrinsic uncertainty in precise angiographic and cross-sectional localization of aneurysms adjacent to the dural rings, regarding all lesions distal to the cavernous segment as potentially intradural. Rather than subdividing various transitional, ophthalmic, and hypophyseal aneurysm subtypes, as necessitated by their varied surgical approaches and risks, the proposed classification emphasizes their common endovascular treatment features, while recognizing that many complex, trans-segmental, and fusiform aneurysms not readily classifiable into presently available, saccular aneurysm-driven schemes, are being increasingly addressed by endovascular means. We believe this classification may find utility in standardizing nomenclature for outcome tracking, treatment trials and physician communication.

Underwater target classification using wavelet packets and neural networks.

PubMed

Azimi-Sadjadi, M R; Yao, D; Huang, Q; Dobeck, G J

2000-01-01

In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consists of the backscattered signals from six different objects: two mine-like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multiaspect fusion scheme was also adopted in order to further improve the classification performance.

Do thoraco-lumbar spinal injuries classification systems exhibit lower inter- and intra-observer agreement than other fractures classifications?: A comparison using fractures of the trochanteric area of the proximal femur as contrast model.

PubMed

Urrutia, Julio; Zamora, Tomas; Klaber, Ianiv; Carmona, Maximiliano; Palma, Joaquin; Campos, Mauricio; Yurac, Ratko

2016-04-01

It has been postulated that the complex patterns of spinal injuries have prevented adequate agreement using thoraco-lumbar spinal injuries (TLSI) classifications; however, limb fracture classifications have also shown variable agreements. This study compared agreement using two TLSI classifications with agreement using two classifications of fractures of the trochanteric area of the proximal femur (FTAPF). Six evaluators classified the radiographs and computed tomography scans of 70 patients with acute TLSI using the Denis and the new AO Spine thoraco-lumbar injury classifications. Additionally, six evaluators classified the radiographs of 70 patients with FTAPF using the Tronzo and the AO schemes. Six weeks later, all cases were presented in a random sequence for repeat assessment. The Kappa coefficient (κ) was used to determine agreement. Inter-observer agreement: For TLSI, using the AOSpine classification, the mean κ was 0.62 (0.57-0.66) considering fracture types, and 0.55 (0.52-0.57) considering sub-types; using the Denis classification, κ was 0.62 (0.59-0.65). For FTAPF, with the AO scheme, the mean κ was 0.58 (0.54-0.63) considering fracture types and 0.31 (0.28-0.33) considering sub-types; for the Tronzo classification, κ was 0.54 (0.50-0.57). Intra-observer agreement: For TLSI, using the AOSpine scheme, the mean κ was 0.77 (0.72-0.83) considering fracture types, and 0.71 (0.67-0.76) considering sub-types; for the Denis classification, κ was 0.76 (0.71-0.81). For FTAPF, with the AO scheme, the mean κ was 0.75 (0.69-0.81) considering fracture types and 0.45 (0.39-0.51) considering sub-types; for the Tronzo classification, κ was 0.64 (0.58-0.70). Using the main types of AO classifications, inter- and intra-observer agreement of TLSI were comparable to agreement evaluating FTAPF; including sub-types, inter- and intra-observer agreement evaluating TLSI were significantly better than assessing FTAPF. Inter- and intra-observer agreements using the Denis classification were also significantly better than agreement using the Tronzo scheme. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comparative agreement evaluation of two subaxial cervical spine injury classification systems: the AOSpine and the Allen and Ferguson schemes.

PubMed

Urrutia, Julio; Zamora, Tomas; Campos, Mauricio; Yurac, Ratko; Palma, Joaquin; Mobarec, Sebastian; Prada, Carlos

2016-07-01

We performed an agreement study using two subaxial cervical spine classification systems: the AOSpine and the Allen and Ferguson (A&F) classifications. We sought to determine which scheme allows better agreement by different evaluators and by the same evaluator on different occasions. Complete imaging studies of 65 patients with subaxial cervical spine injuries were classified by six evaluators (three spine sub-specialists and three senior orthopaedic surgery residents) using the AOSpine subaxial cervical spine classification system and the A&F scheme. The cases were displayed in a random sequence after a 6-week interval for repeat evaluation. The Kappa coefficient (κ) was used to determine inter- and intra-observer agreement. Inter-observer: considering the main AO injury types, the agreement was substantial for the AOSpine classification [κ = 0.61 (0.57-0.64)]; using AO sub-types, the agreement was moderate [κ = 0.57 (0.54-0.60)]. For the A&F classification, the agreement [κ = 0.46 (0.42-0.49)] was significantly lower than using the AOSpine scheme. Intra-observer: the agreement was substantial considering injury types [κ = 0.68 (0.62-0.74)] and considering sub-types [κ = 0.62 (0.57-0.66)]. Using the A&F classification, the agreement was also substantial [κ = 0.66 (0.61-0.71)]. No significant differences were observed between spine surgeons and orthopaedic residents in the overall inter- and intra-observer agreement, or in the inter- and intra-observer agreement of specific type of injuries. The AOSpine classification (using the four main injury types or at the sub-types level) allows a significantly better agreement than the A&F classification. The A&F scheme does not allow reliable communication between medical professionals.

Functional Basis of Microorganism Classification.

PubMed

Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

2015-08-01

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.

Functional Basis of Microorganism Classification

PubMed Central

Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

2015-01-01

Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent. PMID:26317871

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

NASA Astrophysics Data System (ADS)

Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

2018-02-01

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

The Importance of Temporal and Spatial Vegetation Structure Information in Biotope Mapping Schemes: A Case Study in Helsingborg, Sweden

NASA Astrophysics Data System (ADS)

Gao, Tian; Qiu, Ling; Hammer, Mårten; Gunnarsson, Allan

2012-02-01

Temporal and spatial vegetation structure has impact on biodiversity qualities. Yet, current schemes of biotope mapping do only to a limited extend incorporate these factors in the mapping. The purpose of this study is to evaluate the application of a modified biotope mapping scheme that includes temporal and spatial vegetation structure. A refined scheme was developed based on a biotope classification, and applied to a green structure system in Helsingborg city in southern Sweden. It includes four parameters of vegetation structure: continuity of forest cover, age of dominant trees, horizontal structure, and vertical structure. The major green structure sites were determined by interpretation of panchromatic aerial photographs assisted with a field survey. A set of biotope maps was constructed on the basis of each level of modified classification. An evaluation of the scheme included two aspects in particular: comparison of species richness between long-continuity and short-continuity forests based on identification of woodland continuity using ancient woodland indicators (AWI) species and related historical documents, and spatial distribution of animals in the green space in relation to vegetation structure. The results indicate that (1) the relationship between forest continuity: according to verification of historical documents, the richness of AWI species was higher in long-continuity forests; Simpson's diversity was significantly different between long- and short-continuity forests; the total species richness and Shannon's diversity were much higher in long-continuity forests shown a very significant difference. (2) The spatial vegetation structure and age of stands influence the richness and abundance of the avian fauna and rabbits, and distance to the nearest tree and shrub was a strong determinant of presence for these animal groups. It is concluded that continuity of forest cover, age of dominant trees, horizontal and vertical structures of vegetation should now be included in urban biotope classifications.

Automated 3D Phenotype Analysis Using Data Mining

PubMed Central

Plyusnin, Ilya; Evans, Alistair R.; Karme, Aleksis; Gionis, Aristides; Jernvall, Jukka

2008-01-01

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases. PMID:18320060

Deriving exposure limits

NASA Astrophysics Data System (ADS)

Sliney, David H.

1990-07-01

Historically many different agencies and standards organizations have proposed laser occupational exposure limits (EL1s) or maximum permissible exposure (MPE) levels. Although some safety standards have been limited in scope to manufacturer system safety performance standards or to codes of practice most have included occupational EL''s. Initially in the 1960''s attention was drawn to setting EL''s however as greater experience accumulated in the use of lasers and some accident experience had been gained safety procedures were developed. It became clear by 1971 after the first decade of laser use that detailed hazard evaluation of each laser environment was too complex for most users and a scheme of hazard classification evolved. Today most countries follow a scheme of four major hazard classifications as defined in Document WS 825 of the International Electrotechnical Commission (IEC). The classifications and the associated accessible emission limits (AEL''s) were based upon the EL''s. The EL and AEL values today are in surprisingly good agreement worldwide. There exists a greater range of safety requirements for the user for each class of laser. The current MPE''s (i. e. EL''s) and their basis are highlighted in this presentation. 2. 0

New Course Design: Classification Schemes and Information Architecture.

ERIC Educational Resources Information Center

Weinberg, Bella Hass

2002-01-01

Describes a course developed at St. John's University (New York) in the Division of Library and Information Science that relates traditional classification schemes to information architecture and Web sites. Highlights include functional aspects of information architecture, that is, the way content is structured; assignments; student reactions; and…

Perspectives on current tumor-node-metastasis (TNM) staging of cancers of the colon and rectum.

PubMed

Hu, Huankai; Krasinskas, Alyssa; Willis, Joseph

2011-08-01

Improvements in classifications of cancers based on discovery and validation of important histopathological parameters and new molecular markers continue unabated. Though still not perfect, recent updates of classification schemes in gastrointestinal oncology by the American Joint Commission on Cancer (tumor-node-metastasis [TNM] staging) and the World Health Organization further stratify patients and guide optimization of treatment strategies and better predict patient outcomes. These updates recognize the heterogeneity of patient populations with significant subgrouping of each tumor stage and use of tumor deposits to significantly "up-stage" some cancers; change staging parameters for subsets of IIIB and IIIC cancers; and introduce of several new subtypes of colon carcinomas. By the nature of the process, recent discoveries that are important to improving even routine standards of patient care, especially new advances in molecular medicine, are not incorporated into these systems. Nonetheless, these classifications significantly advance clinical standards and are welcome enhancements to our current methods of cancer reporting. Copyright © 2011 Elsevier Inc. All rights reserved.

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

Enhancing Vocabulary Acquisition Through Reading: A Hierarchy of Text-Related Exercise Types.

ERIC Educational Resources Information Center

Paribakht, T. Sima; Wesche, Marjorie

1996-01-01

Presents a classification scheme for reading-related exercises advocated in English-as-a-Foreign-Language textbooks. The scheme proposes a hierarchy of the degree and type of mental processing required by various vocabulary exercises. The categories of classification are selective attention, recognition, manipulation, interpretation and…

Reconciling Mining with the Conservation of Cave Biodiversity: A Quantitative Baseline to Help Establish Conservation Priorities.

PubMed

Jaffé, Rodolfo; Prous, Xavier; Zampaulo, Robson; Giannini, Tereza C; Imperatriz-Fonseca, Vera L; Maurity, Clóvis; Oliveira, Guilherme; Brandi, Iuri V; Siqueira, José O

2016-01-01

Caves pose significant challenges for mining projects, since they harbor many endemic and threatened species, and must therefore be protected. Recent discussions between academia, environmental protection agencies, and industry partners, have highlighted problems with the current Brazilian legislation for the protection of caves. While the licensing process is long, complex and cumbersome, the criteria used to assign caves into conservation relevance categories are often subjective, with relevance being mainly determined by the presence of obligate cave dwellers (troglobites) and their presumed rarity. However, the rarity of these troglobitic species is questionable, as most remain unidentified to the species level and their habitats and distribution ranges are poorly known. Using data from 844 iron caves retrieved from different speleology reports for the Carajás region (South-Eastern Amazon, Brazil), one of the world's largest deposits of high-grade iron ore, we assess the influence of different cave characteristics on four biodiversity proxies (species richness, presence of troglobites, presence of rare troglobites, and presence of resident bat populations). We then examine how the current relevance classification scheme ranks caves with different biodiversity indicators. Large caves were found to be important reservoirs of biodiversity, so they should be prioritized in conservation programs. Our results also reveal spatial autocorrelation in all the biodiversity proxies assessed, indicating that iron caves should be treated as components of a cave network immersed in the karst landscape. Finally, we show that by prioritizing the conservation of rare troglobites, the current relevance classification scheme is undermining overall cave biodiversity and leaving ecologically important caves unprotected. We argue that conservation efforts should target subterranean habitats as a whole and propose an alternative relevance ranking scheme, which could help simplify the assessment process and channel more resources to the effective protection of overall cave biodiversity.

Comparing ecoregional classifications for natural areas management in the Klamath Region, USA

USGS Publications Warehouse

Sarr, Daniel A.; Duff, Andrew; Dinger, Eric C.; Shafer, Sarah L.; Wing, Michael; Seavy, Nathaniel E.; Alexander, John D.

2015-01-01

We compared three existing ecoregional classification schemes (Bailey, Omernik, and World Wildlife Fund) with two derived schemes (Omernik Revised and Climate Zones) to explore their effectiveness in explaining species distributions and to better understand natural resource geography in the Klamath Region, USA. We analyzed presence/absence data derived from digital distribution maps for trees, amphibians, large mammals, small mammals, migrant birds, and resident birds using three statistical analyses of classification accuracy (Analysis of Similarity, Canonical Analysis of Principal Coordinates, and Classification Strength). The classifications were roughly comparable in classification accuracy, with Omernik Revised showing the best overall performance. Trees showed the strongest fidelity to the classifications, and large mammals showed the weakest fidelity. We discuss the implications for regional biogeography and describe how intermediate resolution ecoregional classifications may be appropriate for use as natural areas management domains.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

NASA Astrophysics Data System (ADS)

Jürgens, Björn; Herrero-Solana, Victor

2017-04-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

A malware detection scheme based on mining format information.

PubMed

Bai, Jinrong; Wang, Junfeng; Zou, Guozhong

2014-01-01

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates.

A Malware Detection Scheme Based on Mining Format Information

PubMed Central

Bai, Jinrong; Wang, Junfeng; Zou, Guozhong

2014-01-01

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates. PMID:24991639

The Nutraceutical Bioavailability Classification Scheme: Classifying Nutraceuticals According to Factors Limiting their Oral Bioavailability.

PubMed

McClements, David Julian; Li, Fang; Xiao, Hang

2015-01-01

The oral bioavailability of a health-promoting dietary component (nutraceutical) may be limited by various physicochemical and physiological phenomena: liberation from food matrices, solubility in gastrointestinal fluids, interaction with gastrointestinal components, chemical degradation or metabolism, and epithelium cell permeability. Nutraceutical bioavailability can therefore be improved by designing food matrices that control their bioaccessibility (B*), absorption (A*), and transformation (T*) within the gastrointestinal tract (GIT). This article reviews the major factors influencing the gastrointestinal fate of nutraceuticals, and then uses this information to develop a new scheme to classify the major factors limiting nutraceutical bioavailability: the nutraceutical bioavailability classification scheme (NuBACS). This new scheme is analogous to the biopharmaceutical classification scheme (BCS) used by the pharmaceutical industry to classify drug bioavailability, but it contains additional factors important for understanding nutraceutical bioavailability in foods. The article also highlights potential strategies for increasing the oral bioavailability of nutraceuticals based on their NuBACS designation (B*A*T*).

Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database.

PubMed

Thompson, Bryony A; Spurdle, Amanda B; Plazzer, John-Paul; Greenblatt, Marc S; Akagi, Kiwamu; Al-Mulla, Fahd; Bapat, Bharati; Bernstein, Inge; Capellá, Gabriel; den Dunnen, Johan T; du Sart, Desiree; Fabre, Aurelie; Farrell, Michael P; Farrington, Susan M; Frayling, Ian M; Frebourg, Thierry; Goldgar, David E; Heinen, Christopher D; Holinski-Feder, Elke; Kohonen-Corish, Maija; Robinson, Kristina Lagerstedt; Leung, Suet Yi; Martins, Alexandra; Moller, Pal; Morak, Monika; Nystrom, Minna; Peltomaki, Paivi; Pineda, Marta; Qi, Ming; Ramesar, Rajkumar; Rasmussen, Lene Juel; Royer-Pokora, Brigitte; Scott, Rodney J; Sijmons, Rolf; Tavtigian, Sean V; Tops, Carli M; Weber, Thomas; Wijnen, Juul; Woods, Michael O; Macrae, Finlay; Genuardi, Maurizio

2014-02-01

The clinical classification of hereditary sequence variants identified in disease-related genes directly affects clinical management of patients and their relatives. The International Society for Gastrointestinal Hereditary Tumours (InSiGHT) undertook a collaborative effort to develop, test and apply a standardized classification scheme to constitutional variants in the Lynch syndrome-associated genes MLH1, MSH2, MSH6 and PMS2. Unpublished data submission was encouraged to assist in variant classification and was recognized through microattribution. The scheme was refined by multidisciplinary expert committee review of the clinical and functional data available for variants, applied to 2,360 sequence alterations, and disseminated online. Assessment using validated criteria altered classifications for 66% of 12,006 database entries. Clinical recommendations based on transparent evaluation are now possible for 1,370 variants that were not obviously protein truncating from nomenclature. This large-scale endeavor will facilitate the consistent management of families suspected to have Lynch syndrome and demonstrates the value of multidisciplinary collaboration in the curation and classification of variants in public locus-specific databases.

Application of a five-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants lodged on the InSiGHT locus-specific database

PubMed Central

Plazzer, John-Paul; Greenblatt, Marc S.; Akagi, Kiwamu; Al-Mulla, Fahd; Bapat, Bharati; Bernstein, Inge; Capellá, Gabriel; den Dunnen, Johan T.; du Sart, Desiree; Fabre, Aurelie; Farrell, Michael P.; Farrington, Susan M.; Frayling, Ian M.; Frebourg, Thierry; Goldgar, David E.; Heinen, Christopher D.; Holinski-Feder, Elke; Kohonen-Corish, Maija; Robinson, Kristina Lagerstedt; Leung, Suet Yi; Martins, Alexandra; Moller, Pal; Morak, Monika; Nystrom, Minna; Peltomaki, Paivi; Pineda, Marta; Qi, Ming; Ramesar, Rajkumar; Rasmussen, Lene Juel; Royer-Pokora, Brigitte; Scott, Rodney J.; Sijmons, Rolf; Tavtigian, Sean V.; Tops, Carli M.; Weber, Thomas; Wijnen, Juul; Woods, Michael O.; Macrae, Finlay; Genuardi, Maurizio

2015-01-01

Clinical classification of sequence variants identified in hereditary disease genes directly affects clinical management of patients and their relatives. The International Society for Gastrointestinal Hereditary Tumours (InSiGHT) undertook a collaborative effort to develop, test and apply a standardized classification scheme to constitutional variants in the Lynch Syndrome genes MLH1, MSH2, MSH6 and PMS2. Unpublished data submission was encouraged to assist variant classification, and recognized by microattribution. The scheme was refined by multidisciplinary expert committee review of clinical and functional data available for variants, applied to 2,360 sequence alterations, and disseminated online. Assessment using validated criteria altered classifications for 66% of 12,006 database entries. Clinical recommendations based on transparent evaluation are now possible for 1,370 variants not obviously protein-truncating from nomenclature. This large-scale endeavor will facilitate consistent management of suspected Lynch Syndrome families, and demonstrates the value of multidisciplinary collaboration for curation and classification of variants in public locus-specific databases. PMID:24362816

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Thompson, D. E.; Slater, G. L.

2005-01-01

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

NASA Astrophysics Data System (ADS)

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

2018-01-01

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

TFM classification and staging of oral submucous fibrosis: A new proposal.

PubMed

Arakeri, Gururaj; Thomas, Deepak; Aljabab, Abdulsalam S; Hunasgi, Santosh; Rai, Kirthi Kumar; Hale, Beverley; Fonseca, Felipe Paiva; Gomez, Ricardo Santiago; Rahimi, Siavash; Merkx, Matthias A W; Brennan, Peter A

2018-04-01

We have evaluated the rationale of existing grading and staging schemes of oral submucous fibrosis (OSMF) based on how they are categorized. A novel classification and staging scheme is proposed. A total of 300 OSMF patients were evaluated for agreement between functional, clinical, and histopathological staging. Bilateral biopsies were assessed in 25 patients to evaluate for any differences in histopathological staging of OSMF in the same mouth. Extent of clinician agreement for categorized staging data was evaluated using Cohen's weighted kappa analysis. Cross-tabulation was performed on categorical grading data to understand the intercorrelation, and the unweighted kappa analysis was used to assess the bilateral grade agreement. Probabilities of less than 0.05 were considered significant. Data were analyzed using SPSS Statistics (version 25.0, IBM, USA). A low agreement was found between all the stages depicting the independent nature of trismus, clinical features, and histopathological components (K = 0.312, 0.167, 0.152) in OSMF. Following analysis, a three-component classification scheme (TFM classification) was developed that describes the severity of each independently, grouping them using a novel three-tier staging scheme as a guide to the treatment plan. The proposed classification and staging could be useful for effective communication, categorization, and for recording data and prognosis, and for guiding treatment plans. Furthermore, the classification considers OSMF malignant transformation in detail. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Discovery of User-Oriented Class Associations for Enriching Library Classification Schemes.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh

2002-01-01

Presents a user-based approach to exploring the possibility of adding user-oriented class associations to hierarchical library classification schemes. Classes not grouped in the same subject hierarchies yet relevant to users' knowledge are obtained by analyzing a log book of a university library's circulation records, using collaborative filtering…

Social Constructivism: Botanical Classification Schemes of Elementary School Children.

ERIC Educational Resources Information Center

Tull, Delena

The assertion that there is a social component to children's construction of knowledge about natural phenomena is supported by evidence from an examination of children's classification schemes for plants. An ethnographic study was conducted with nine sixth grade children in central Texas. The children classified plants in the outdoors, in a…

A Classification Scheme for Career Education Resource Materials.

ERIC Educational Resources Information Center

Koontz, Ronald G.

The introductory section of the paper expresses its purpose: to devise a classification scheme for career education resource material, which will be used to develop the USOE Office of Career Education Resource Library and will be disseminated to interested State departments of education and local school districts to assist them in classifying…

An Alternative Classification Scheme for Teaching Performance Incentives Using a Factor Analytic Approach.

ERIC Educational Resources Information Center

Mertler, Craig A.

This study attempted to (1) expand the dichotomous classification scheme typically used by educators and researchers to describe teaching incentives and (2) offer administrators and teachers an alternative framework within which to develop incentive systems. Elementary, middle, and high school teachers in Ohio rated 10 commonly instituted teaching…

A Classification Scheme for Adult Education. Education Libraries Bulletin, Supplement Twelve.

ERIC Educational Resources Information Center

Greaves, Monica A., Comp.

This classification scheme, based on the 'facet formula' theory of Ranganathan, is designed primarily for the library of the National Institute of Adult Education in London, England. Kinds of persons being educated (educands), methods and problems of education, specific countries, specific organizations, and forms in which the information is…

The family structure of the Mucorales: a synoptic revision based on comprehensive multigene-genealogies.

PubMed

Hoffmann, K; Pawłowska, J; Walther, G; Wrzosek, M; de Hoog, G S; Benny, G L; Kirk, P M; Voigt, K

2013-06-01

The Mucorales (Mucoromycotina) are one of the most ancient groups of fungi comprising ubiquitous, mostly saprotrophic organisms. The first comprehensive molecular studies 11 yr ago revealed the traditional classification scheme, mainly based on morphology, as highly artificial. Since then only single clades have been investigated in detail but a robust classification of the higher levels based on DNA data has not been published yet. Therefore we provide a classification based on a phylogenetic analysis of four molecular markers including the large and the small subunit of the ribosomal DNA, the partial actin gene and the partial gene for the translation elongation factor 1-alpha. The dataset comprises 201 isolates in 103 species and represents about one half of the currently accepted species in this order. Previous family concepts are reviewed and the family structure inferred from the multilocus phylogeny is introduced and discussed. Main differences between the current classification and preceding concepts affects the existing families Lichtheimiaceae and Cunninghamellaceae, as well as the genera Backusella and Lentamyces which recently obtained the status of families along with the Rhizopodaceae comprising Rhizopus, Sporodiniella and Syzygites. Compensatory base change analyses in the Lichtheimiaceae confirmed the lower level classification of Lichtheimia and Rhizomucor while genera such as Circinella or Syncephalastrum completely lacked compensatory base changes.

A Computer Oriented Scheme for Coding Chemicals in the Field of Biomedicine.

ERIC Educational Resources Information Center

Bobka, Marilyn E.; Subramaniam, J.B.

The chemical coding scheme of the Medical Coding Scheme (MCS), developed for use in the Comparative Systems Laboratory (CSL), is outlined and evaluated in this report. The chemical coding scheme provides a classification scheme and encoding method for drugs and chemical terms. Using the scheme complicated chemical structures may be expressed…

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

Maximum likelihood estimation of label imperfections and its use in the identification of mislabeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of estimating label imperfections and the use of the estimation in identifying mislabeled patterns is presented. Expressions for the maximum likelihood estimates of classification errors and a priori probabilities are derived from the classification of a set of labeled patterns. Expressions also are given for the asymptotic variances of probability of correct classification and proportions. Simple models are developed for imperfections in the labels and for classification errors and are used in the formulation of a maximum likelihood estimation scheme. Schemes are presented for the identification of mislabeled patterns in terms of threshold on the discriminant functions for both two-class and multiclass cases. Expressions are derived for the probability that the imperfect label identification scheme will result in a wrong decision and are used in computing thresholds. The results of practical applications of these techniques in the processing of remotely sensed multispectral data are presented.

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme

PubMed Central

Jordan, Alan; Rees, Tony; Gowlett-Holmes, Karen

2015-01-01

Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data generated from imagery includes identification, annotation and quantification of biological subjects and environmental features within an image. To be long-lived and useful beyond their project-specific initial purpose, and to maximize their utility across studies and disciplines, marine imagery data should use a standardised vocabulary of defined terms. This would enable the compilation of regional, national and/or global data sets from multiple sources, contributing to broad-scale management studies and development of automated annotation algorithms. The classification scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project provides such a vocabulary. The CATAMI classification scheme introduces Australian-wide acknowledged, standardised terminology for annotating benthic substrates and biota in marine imagery. It combines coarse-level taxonomy and morphology, and is a flexible, hierarchical classification that bridges the gap between habitat/biotope characterisation and taxonomy, acknowledging limitations when describing biological taxa through imagery. It is fully described, documented, and maintained through curated online databases, and can be applied across benthic image collection methods, annotation platforms and scoring methods. Following release in 2013, the CATAMI classification scheme was taken up by a wide variety of users, including government, academia and industry. This rapid acceptance highlights the scheme’s utility and the potential to facilitate broad-scale multidisciplinary studies of marine ecosystems when applied globally. Here we present the CATAMI classification scheme, describe its conception and features, and discuss its utility and the opportunities as well as challenges arising from its use. PMID:26509918

A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

NASA Astrophysics Data System (ADS)

Gundreddy, Rohith Reddy; Tan, Maxine; Qui, Yuchen; Zheng, Bin

2015-03-01

The purpose of this study is to develop and test a new content-based image retrieval (CBIR) scheme that enables to achieve higher reproducibility when it is implemented in an interactive computer-aided diagnosis (CAD) system without significantly reducing lesion classification performance. This is a new Fourier transform based CBIR algorithm that determines image similarity of two regions of interest (ROI) based on the difference of average regional image pixel value distribution in two Fourier transform mapped images under comparison. A reference image database involving 227 ROIs depicting the verified soft-tissue breast lesions was used. For each testing ROI, the queried lesion center was systematically shifted from 10 to 50 pixels to simulate inter-user variation of querying suspicious lesion center when using an interactive CAD system. The lesion classification performance and reproducibility as the queried lesion center shift were assessed and compared among the three CBIR schemes based on Fourier transform, mutual information and Pearson correlation. Each CBIR scheme retrieved 10 most similar reference ROIs and computed a likelihood score of the queried ROI depicting a malignant lesion. The experimental results shown that three CBIR schemes yielded very comparable lesion classification performance as measured by the areas under ROC curves with the p-value greater than 0.498. However, the CBIR scheme using Fourier transform yielded the highest invariance to both queried lesion center shift and lesion size change. This study demonstrated the feasibility of improving robustness of the interactive CAD systems by adding a new Fourier transform based image feature to CBIR schemes.

Evaluation of effectiveness of wavelet based denoising schemes using ANN and SVM for bearing condition classification.

PubMed

Vijay, G S; Kumar, H S; Srinivasa Pai, P; Sriram, N S; Rao, Raj B K N

2012-01-01

The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher's Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.

Classification of basic facilities for high-rise residential: A survey from 100 housing scheme in Kajang area

NASA Astrophysics Data System (ADS)

Ani, Adi Irfan Che; Sairi, Ahmad; Tawil, Norngainy Mohd; Wahab, Siti Rashidah Hanum Abd; Razak, Muhd Zulhanif Abd

2016-08-01

High demand for housing and limited land in town area has increasing the provision of high-rise residential scheme. This type of housing has different owners but share the same land lot and common facilities. Thus, maintenance works of the buildings and common facilities must be well organized. The purpose of this paper is to identify and classify basic facilities for high-rise residential building hoping to improve the management of the scheme. The method adopted is a survey on 100 high-rise residential schemes that ranged from affordable housing to high cost housing by using a snowball sampling. The scope of this research is within Kajang area, which is rapidly developed with high-rise housing. The objective of the survey is to list out all facilities in every sample of the schemes. The result confirmed that pre-determined 11 classifications hold true and can provide the realistic classification for high-rise residential scheme. This paper proposed for redefinition of facilities provided to create a better management system and give a clear definition on the type of high-rise residential based on its facilities.

Applying the Methodology of the Community College Classification Scheme to the Public Master's Colleges and Universities Sector

ERIC Educational Resources Information Center

Kinkead, J. Clint.; Katsinas, Stephen G.

2011-01-01

This work brings forward the geographically-based classification scheme for the public Master's Colleges and Universities sector. Using the same methodology developed by Katsinas and Hardy (2005) to classify community colleges, this work classifies Master's Colleges and Universities. This work has four major findings and conclusions. First, a…

What's in a Name? A Comparison of Methods for Classifying Predominant Type of Maltreatment

ERIC Educational Resources Information Center

Lau, A.S.; Leeb, R.T.; English, D.; Graham, J.C.; Briggs, E.C.; Brody, K.E.; Marshall, J.M.

2005-01-01

Objective:: The primary aim of the study was to identify a classification scheme, for determining the predominant type of maltreatment in a child's history that best predicts differences in developmental outcomes. Method:: Three different predominant type classification schemes were examined in a sample of 519 children with a history of alleged…

Congenital spinal malformations in small animals.

PubMed

Westworth, Diccon R; Sturges, Beverly K

2010-09-01

Congenital anomalies of the spine are common in small animals. The type of deformity, location, severity, time of onset of associated clinical signs, and progression of neurologic dysfunction varies widely. To promote clearer understanding, the authors present the various spinal malformations using modified human classification schemes and use current widely accepted definitions and terminology. The diagnostic approach, including utilization of advanced imaging, and surgical management is emphasized. Copyright 2010. Published by Elsevier Inc.

Current Practices in Constructing and Evaluating Assurance Cases With Applications to Aviation

NASA Technical Reports Server (NTRS)

Rinehart, David J.; Knight, John C.; Rowanhill, Jonathan

2015-01-01

This report introduces and provides an overview of assurance cases including theory, practice, and evaluation. This report includes a section that introduces the principles, terminology, and history of assurance cases. The core of the report presents twelve example uses of assurance cases from a range of domains, using a novel classification scheme. The report also reviews the state of the art in assurance case evaluation methods.

A risk-based classification scheme for genetically modified foods. II: Graded testing.

PubMed

Chao, Eunice; Krewski, Daniel

2008-12-01

This paper presents a graded approach to the testing of crop-derived genetically modified (GM) foods based on concern levels in a proposed risk-based classification scheme (RBCS) and currently available testing methods. A graded approach offers the potential for more efficient use of testing resources by focusing less on lower concern GM foods, and more on higher concern foods. In this proposed approach to graded testing, products that are classified as Level I would have met baseline testing requirements that are comparable to what is widely applied to premarket assessment of GM foods at present. In most cases, Level I products would require no further testing, or very limited confirmatory analyses. For products classified as Level II or higher, additional testing would be required, depending on the type of the substance, prior dietary history, estimated exposure level, prior knowledge of toxicity of the substance, and the nature of the concern related to unintended changes in the modified food. Level III testing applies only to the assessment of toxic and antinutritional effects from intended changes and is tailored to the nature of the substance in question. Since appropriate test methods are not currently available for all effects of concern, future research to strengthen the testing of GM foods is discussed.

Poisoning by Herbs and Plants: Rapid Toxidromic Classification and Diagnosis.

PubMed

Diaz, James H

2016-03-01

The American Association of Poison Control Centers has continued to report approximately 50,000 telephone calls or 8% of incoming calls annually related to plant exposures, mostly in children. Although the frequency of plant ingestions in children is related to the presence of popular species in households, adolescents may experiment with hallucinogenic plants; and trekkers and foragers may misidentify poisonous plants as edible. Since plant exposures have continued at a constant rate, the objectives of this review were (1) to review the epidemiology of plant poisonings; and (2) to propose a rapid toxidromic classification system for highly toxic plant ingestions for field use by first responders in comparison to current classification systems. Internet search engines were queried to identify and select peer-reviewed articles on plant poisonings using the key words in order to classify plant poisonings into four specific toxidromes: cardiotoxic, neurotoxic, cytotoxic, and gastrointestinal-hepatotoxic. A simple toxidromic classification system of plant poisonings may permit rapid diagnoses of highly toxic versus less toxic and nontoxic plant ingestions both in households and outdoors; direct earlier management of potentially serious poisonings; and reduce costly inpatient evaluations for inconsequential plant ingestions. The current textbook classification schemes for plant poisonings were complex in comparison to the rapid classification system; and were based on chemical nomenclatures and pharmacological effects, and not on clearly presenting toxidromes. Validation of the rapid toxidromic classification system as compared to existing chemical classification systems for plant poisonings will require future adoption and implementation of the toxidromic system by its intended users. Copyright © 2016 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

Classification of infectious bursal disease virus into genogroups.

PubMed

Michel, Linda O; Jackwood, Daral J

2017-12-01

Infectious bursal disease virus (IBDV) causes infectious bursal disease (IBD), an immunosuppressive disease of poultry. The current classification scheme of IBDV is confusing because it is based on antigenic types (variant and classical) as well as pathotypes. Many of the amino acid changes differentiating these various classifications are found in a hypervariable region of the capsid protein VP2 (hvVP2), the major host protective antigen. Data from this study were used to propose a new classification scheme for IBDV based solely on genogroups identified from phylogenetic analysis of the hvVP2 of strains worldwide. Seven major genogroups were identified, some of which are geographically restricted and others that have global dispersion, such as genogroup 1. Genogroup 2 viruses are predominately distributed in North America, while genogroup 3 viruses are most often identified on other continents. Additionally, we have identified a population of genogroup 3 vvIBDV isolates that have an amino acid change from alanine to threonine at position 222 while maintaining other residues conserved in this genogroup (I242, I256 and I294). A222T is an important mutation because amino acid 222 is located in the first of four surface loops of hvVP2. A similar shift from proline to threonine at 222 is believed to play a role in the significant antigenic change of the genogroup 2 IBDV strains, suggesting that antigenic drift may be occurring in genogroup 3, possibly in response to antigenic pressure from vaccination.

A classification scheme for risk assessment methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stamp, Jason Edwin; Campbell, Philip LaRoche

2004-08-01

This report presents a classification scheme for risk assessment methods. This scheme, like all classification schemes, provides meaning by imposing a structure that identifies relationships. Our scheme is based on two orthogonal aspects--level of detail, and approach. The resulting structure is shown in Table 1 and is explained in the body of the report. Each cell in the Table represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that amore » method chosen is optimal for a situation given. This report imposes structure on the set of risk assessment methods in order to reveal their relationships and thus optimize their usage.We present a two-dimensional structure in the form of a matrix, using three abstraction levels for the rows and three approaches for the columns. For each of the nine cells in the matrix we identify the method type by name and example. The matrix helps the user understand: (1) what to expect from a given method, (2) how it relates to other methods, and (3) how best to use it. Each cell in the matrix represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that a method chosen is optimal for a situation given. The matrix, with type names in the cells, is introduced in Table 2 on page 13 below. Unless otherwise stated we use the word 'method' in this report to refer to a 'risk assessment method', though often times we use the full phrase. The use of the terms 'risk assessment' and 'risk management' are close enough that we do not attempt to distinguish them in this report. The remainder of this report is organized as follows. In Section 2 we provide context for this report--what a 'method' is and where it fits. In Section 3 we present background for our classification scheme--what other schemes we have found, the fundamental nature of methods and their necessary incompleteness. In Section 4 we present our classification scheme in the form of a matrix, then we present an analogy that should provide an understanding of the scheme, concluding with an explanation of the two dimensions and the nine types in our scheme. In Section 5 we present examples of each of our classification types. In Section 6 we present conclusions.« less

Research on simulation based material delivery system for an automobile company with multi logistics center

NASA Astrophysics Data System (ADS)

Luo, D.; Guan, Z.; Wang, C.; Yue, L.; Peng, L.

2017-06-01

Distribution of different parts to the assembly lines is significant for companies to improve production. Current research investigates the problem of distribution method optimization of a logistics system in a third party logistic company that provide professional services to an automobile manufacturing case company in China. Current research investigates the logistics leveling the material distribution and unloading platform of the automobile logistics enterprise and proposed logistics distribution strategy, material classification method, as well as logistics scheduling. Moreover, the simulation technology Simio is employed on assembly line logistics system which helps to find and validate an optimization distribution scheme through simulation experiments. Experimental results indicate that the proposed scheme can solve the logistic balance and levels the material problem and congestion of the unloading pattern in an efficient way as compared to the original method employed by the case company.

Classification of instability after reverse shoulder arthroplasty guides surgical management and outcomes.

PubMed

Abdelfattah, Adham; Otto, Randall J; Simon, Peter; Christmas, Kaitlyn N; Tanner, Gregory; LaMartina, Joey; Levy, Jonathan C; Cuff, Derek J; Mighell, Mark A; Frankle, Mark A

2018-04-01

Revision of unstable reverse shoulder arthroplasty (RSA) remains a significant challenge. The purpose of this study was to determine the reliability of a new treatment-guiding classification for instability after RSA, to describe the clinical outcomes of patients stabilized operatively, and to identify those with higher risk of recurrence. All patients undergoing revision for instability after RSA were identified at our institution. Demographic, clinical, radiographic, and intraoperative data were collected. A classification was developed using all identified causes of instability after RSA and allocating them to 1 of 3 defined treatment-guiding categories. Eight surgeons reviewed all data and applied the classification scheme to each case. Interobserver and intraobserver reliability was used to evaluate the classification scheme. Preoperative clinical outcomes were compared with final follow-up in stabilized shoulders. Forty-three revision cases in 34 patients met the inclusion for study. Five patients remained unstable after revision. Persistent instability most commonly occurred in persistent deltoid dysfunction and postoperative acromial fractures but also in 1 case of soft tissue impingement. Twenty-one patients remained stable at minimum 2 years of follow-up and had significant improvement of clinical outcome scores and range of motion. Reliability of the classification scheme showed substantial and almost perfect interobserver and intraobserver agreement among all the participants (κ = 0.699 and κ = 0.851, respectively). Instability after RSA can be successfully treated with revision surgery using the reliable treatment-guiding classification scheme presented herein. However, more understanding is needed for patients with greater risk of recurrent instability after revision surgery. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Classifier fusion for VoIP attacks classification

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

Medulloblastoma: Tumor Biology and Relevance to Treatment and Prognosis Paradigm.

PubMed

Coluccia, Daniel; Figuereido, Carlyn; Isik, Semra; Smith, Christian; Rutka, James T

2016-05-01

Medulloblastoma is a malignant embryonic brain tumor arising in the posterior fossa and typically occurring in pediatric patients. Current multimodal treatment regimes have significantly improved the survival rates; however, a marked heterogeneity in therapy response is observed, and one third of all patients die within 5 years after diagnosis. Large-scale genetic and transcriptome analysis revealed four medulloblastoma subgroups (WNT, SHH, Group 3, and Group 4) associated with different demographic parameters, tumor manifestation, and clinical behavior. Future treatment protocols will integrate molecular classification schemes to evaluate subgroup-specific intensification or de-escalation of adjuvant therapies aimed to increase tumor control and reduce iatrogenic induced morbidity. Furthermore, the identification of genetic drivers allows assessing target therapies in order to increase the chemotherapeutic armamentarium. This review highlights the biology behind the current classification system and elucidates relevant aspects of the disease influencing forthcoming clinical trials.

The impact of endorsing Spitzer's proposed criteria for PTSD in the forthcoming DSM-V on male and female Veterans.

PubMed

Miller, Lyndsey N; Chard, Kathleen M; Schumm, Jeremiah A; O'Brien, Carol

2011-06-01

This study explored differences between Spitzer's proposed model of posttraumatic stress disorder (PTSD) and the current DSM-IV diagnostic classification scheme in 353 Veterans. The majority of Veterans (89%) diagnosed with PTSD as specified in the DSM-IV also met Spitzer's proposed criteria. Veterans who met both DSM-IV and Spitzer's proposed criteria had significantly higher Clinician Administered PTSD Scale severity scores than Veterans only meeting DSM-IV criteria. Logistic regression indicated that being African American and having no comorbid diagnosis of major depressive disorder or history of a substance use disorder were found to predict those Veterans who met current, but not proposed criteria. These findings have important implications regarding proposed changes to the diagnostic classification criteria for PTSD in the forthcoming DSM-V. Copyright © 2011 Elsevier Ltd. All rights reserved.

Twenty five years of beach monitoring in Hong Kong: A re-examination of the beach water quality classification scheme from a comparative and global perspective.

PubMed

Thoe, W; Lee, Olive H K; Leung, K F; Lee, T; Ashbolt, Nicholas J; Yang, Ron R; Chui, Samuel H K

2018-06-01

Hong Kong's beach water quality classification scheme, used effectively for >25 years in protecting public health, was first established in local epidemiology studies during the late 1980s where Escherichia coli (E. coli) was identified as the most suitable faecal indicator bacteria. To review and further substantiate the scheme's robustness, a performance check was carried out to classify water quality of 37 major local beaches in Hong Kong during four bathing seasons (March-October) from 2010 to 2013. Given the enterococci and E. coli data collected, beach classification by the local scheme was found to be in line with the prominent international benchmarks recommended by the World Health Organization and the European Union. Local bacteriological studies over the last 15 years further confirmed that E. coli is the more suitable faecal indicator bacteria than enterococci in the local context. Copyright © 2018 Elsevier Ltd. All rights reserved.

Retinal vessel tortuosity measures and their applications.

PubMed

Kalitzeos, Angelos A; Lip, Gregory Y H; Heitmar, Rebekka

2013-01-01

Structural retinal vascular characteristics, such as vessel calibers, tortuosity and bifurcation angles are increasingly quantified in an objective manner, slowly replacing subjective qualitative disease classification schemes. This paper provides an overview of the current methodologies and calculations used to compute retinal vessel tortuosity. We set out the different parameter calculations and provide an insight into the clinical applications, while critically reviewing its pitfalls and shortcomings. Copyright © 2012 Elsevier Ltd. All rights reserved.

Update on diabetes classification.

PubMed

Thomas, Celeste C; Philipson, Louis H

2015-01-01

This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

Identification of terrain cover using the optimum polarimetric classifier

NASA Technical Reports Server (NTRS)

Kong, J. A.; Swartz, A. A.; Yueh, H. A.; Novak, L. M.; Shin, R. T.

1988-01-01

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow-covered fields is developed using the optimum polarimetric classifier. The covariance matrices for various terrain cover are computed from theoretical models of random medium by evaluating the scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Analytical and Monte Carlo simulated classification errors using the fully polarimetric feature vector are compared with classification based on single features which include the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

A proposed classification scheme for Ada-based software products

NASA Technical Reports Server (NTRS)

Cernosek, Gary J.

1986-01-01

As the requirements for producing software in the Ada language become a reality for projects such as the Space Station, a great amount of Ada-based program code will begin to emerge. Recognizing the potential for varying levels of quality to result in Ada programs, what is needed is a classification scheme that describes the quality of a software product whose source code exists in Ada form. A 5-level classification scheme is proposed that attempts to decompose this potentially broad spectrum of quality which Ada programs may possess. The number of classes and their corresponding names are not as important as the mere fact that there needs to be some set of criteria from which to evaluate programs existing in Ada. An exact criteria for each class is not presented, nor are any detailed suggestions of how to effectively implement this quality assessment. The idea of Ada-based software classification is introduced and a set of requirements from which to base further research and development is suggested.

Exception handling for sensor fusion

NASA Astrophysics Data System (ADS)

Chavez, G. T.; Murphy, Robin R.

1993-08-01

This paper presents a control scheme for handling sensing failures (sensor malfunctions, significant degradations in performance due to changes in the environment, and errant expectations) in sensor fusion for autonomous mobile robots. The advantages of the exception handling mechanism are that it emphasizes a fast response to sensing failures, is able to use only a partial causal model of sensing failure, and leads to a graceful degradation of sensing if the sensing failure cannot be compensated for. The exception handling mechanism consists of two modules: error classification and error recovery. The error classification module in the exception handler attempts to classify the type and source(s) of the error using a modified generate-and-test procedure. If the source of the error is isolated, the error recovery module examines its cache of recovery schemes, which either repair or replace the current sensing configuration. If the failure is due to an error in expectation or cannot be identified, the planner is alerted. Experiments using actual sensor data collected by the CSM Mobile Robotics/Machine Perception Laboratory's Denning mobile robot demonstrate the operation of the exception handling mechanism.

Multidimensional classification of magma types for altered igneous rocks and application to their tectonomagmatic discrimination and igneous provenance of siliciclastic sediments

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Pandarinath, Kailasa; Amezcua-Valdez, Alejandra; Rosales-Rivera, Mauricio; Verma, Sanjeet K.; Quiroz-Ruiz, Alfredo; Armstrong-Altrin, John S.

2017-05-01

A new multidimensional scheme consistent with the International Union of Geological Sciences (IUGS) is proposed for the classification of igneous rocks in terms of four magma types: ultrabasic, basic, intermediate, and acid. Our procedure is based on an extensive database of major element composition of a total of 33,868 relatively fresh rock samples having a multinormal distribution (initial database with 37,215 samples). Multinormally distributed database in terms of log-ratios of samples was ascertained by a new computer program DOMuDaF, in which the discordancy test was applied at the 99.9% confidence level. Isometric log-ratio (ilr) transformation was used to provide overall percent correct classification of 88.7%, 75.8%, 88.0%, and 80.9% for ultrabasic, basic, intermediate, and acid rocks, respectively. Given the known mathematical and uncertainty propagation properties, this transformation could be adopted for routine applications. The incorrect classification was mainly for the "neighbour" magma types, e.g., basic for ultrabasic and vice versa. Some of these misclassifications do not have any effect on multidimensional tectonic discrimination. For an efficient application of this multidimensional scheme, a new computer program MagClaMSys_ilr (MagClaMSys-Magma Classification Major-element based System) was written, which is available for on-line processing on http://tlaloc.ier.unam.mx/index.html. This classification scheme was tested from newly compiled data for relatively fresh Neogene igneous rocks and was found to be consistent with the conventional IUGS procedure. The new scheme was successfully applied to inter-laboratory data for three geochemical reference materials (basalts JB-1 and JB-1a, and andesite JA-3) from Japan and showed that the inferred magma types are consistent with the rock name (basic for basalts JB-1 and JB-1a and intermediate for andesite JA-3). The scheme was also successfully applied to five case studies of older Archaean to Mesozoic igneous rocks. Similar or more reliable results were obtained from existing tectonomagmatic discrimination diagrams when used in conjunction with the new computer program as compared to the IUGS scheme. The application to three case studies of igneous provenance of sedimentary rocks was demonstrated as a novel approach. Finally, we show that the new scheme is more robust for post-emplacement compositional changes than the conventional IUGS procedure.

Classification of proteins: available structural space for molecular modeling.

PubMed

Andreeva, Antonina

2012-01-01

The wealth of available protein structural data provides unprecedented opportunity to study and better understand the underlying principles of protein folding and protein structure evolution. A key to achieving this lies in the ability to analyse these data and to organize them in a coherent classification scheme. Over the past years several protein classifications have been developed that aim to group proteins based on their structural relationships. Some of these classification schemes explore the concept of structural neighbourhood (structural continuum), whereas other utilize the notion of protein evolution and thus provide a discrete rather than continuum view of protein structure space. This chapter presents a strategy for classification of proteins with known three-dimensional structure. Steps in the classification process along with basic definitions are introduced. Examples illustrating some fundamental concepts of protein folding and evolution with a special focus on the exceptions to them are presented.

Regional land cover characterization using Landsat thematic mapper data and ancillary data sources

USGS Publications Warehouse

Vogelmann, James E.; Sohl, Terry L.; Campbell, P.V.; Shaw, D.M.; ,

1998-01-01

As part of the activities of the Multi-Resolution Land Characteristics (MRLC) Interagency Consortium, an intermediate-scale land cover data set is being generated for the conterminous United States. This effort is being conducted on a region-by-region basis using U.S. Standard Federal Regions. To date, land cover data sets have been generated for Federal Regions 3 (Pennsylvania, West Virginia, Virginia, Maryland, and Delaware) and 2 (New York and New Jersey). Classification work is currently under way in Federal Region 4 (the southeastern United States), and land cover mapping activities have been started in Federal Regions 5 (the Great Lakes region) and 1 (New England). It is anticipated that a land cover data set for the conterminous United States will be completed by the end of 1999. A standard land cover classification legend is used, which is analogous to and compatible with other classification schemes. The primary MRLC regional classification scheme contains 23 land cover classes.The primary source of data for the project is the Landsat thematic mapper (TM) sensor. For each region, TM scenes representing both leaf-on and leaf-off conditions are acquired, preprocessed, and georeferenced to MRLC specifications. Mosaicked data are clustered using unsupervised classification, and individual clusters are labeled using aerial photographs. Individual clusters that represent more than one land cover unit are split using spatial modeling with multiple ancillary spatial data layers (most notably, digital elevation model, population, land use and land cover, and wetlands information). This approach yields regional land cover information suitable for a wide array of applications, including landscape metric analyses, land management, land cover change studies, and nutrient and pesticide runoff modeling.

Classifying GRB 170817A/GW170817 in a Fermi duration-hardness plane

NASA Astrophysics Data System (ADS)

Horváth, I.; Tóth, B. G.; Hakkila, J.; Tóth, L. V.; Balázs, L. G.; Rácz, I. I.; Pintér, S.; Bagoly, Z.

2018-03-01

GRB 170817A, associated with the LIGO-Virgo GW170817 neutron-star merger event, lacks the short duration and hard spectrum of a Short gamma-ray burst (GRB) expected from long-standing classification models. Correctly identifying the class to which this burst belongs requires comparison with other GRBs detected by the Fermi GBM. The aim of our analysis is to classify Fermi GRBs and to test whether or not GRB 170817A belongs—as suggested—to the Short GRB class. The Fermi GBM catalog provides a large database with many measured variables that can be used to explore gamma-ray burst classification. We use statistical techniques to look for clustering in a sample of 1298 gamma-ray bursts described by duration and spectral hardness. Classification of the detected bursts shows that GRB 170817A most likely belongs to the Intermediate, rather than the Short GRB class. We discuss this result in light of theoretical neutron-star merger models and existing GRB classification schemes. It appears that GRB classification schemes may not yet be linked to appropriate theoretical models, and that theoretical models may not yet adequately account for known GRB class properties. We conclude that GRB 170817A may not fit into a simple phenomenological classification scheme.

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

USGS Publications Warehouse

Friedel, M.J.; Asch, T.H.; Oden, C.

2012-01-01

The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self-organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

NASA Astrophysics Data System (ADS)

Friedel, M. J.; Asch, T. H.; Oden, C.

2012-08-01

The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot-Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self-organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

Automating the expert consensus paradigm for robust lung tissue classification

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Diffuse Lung Disease in Biopsied Children 2 to 18 Years of Age. Application of the chILD Classification Scheme.

PubMed

Fan, Leland L; Dishop, Megan K; Galambos, Csaba; Askin, Frederic B; White, Frances V; Langston, Claire; Liptzin, Deborah R; Kroehl, Miranda E; Deutsch, Gail H; Young, Lisa R; Kurland, Geoffrey; Hagood, James; Dell, Sharon; Trapnell, Bruce C; Deterding, Robin R

2015-10-01

Children's Interstitial and Diffuse Lung Disease (chILD) is a heterogeneous group of disorders that is challenging to categorize. In previous study, a classification scheme was successfully applied to children 0 to 2 years of age who underwent lung biopsies for chILD. This classification scheme has not been evaluated in children 2 to 18 years of age. This multicenter interdisciplinary study sought to describe the spectrum of biopsy-proven chILD in North America and to apply a previously reported classification scheme in children 2 to 18 years of age. Mortality and risk factors for mortality were also assessed. Patients 2 to 18 years of age who underwent lung biopsies for diffuse lung disease from 12 North American institutions were included. Demographic and clinical data were collected and described. The lung biopsies were reviewed by pediatric lung pathologists with expertise in diffuse lung disease and were classified by the chILD classification scheme. Logistic regression was used to determine risk factors for mortality. A total of 191 cases were included in the final analysis. Number of biopsies varied by center (5-49 biopsies; mean, 15.8) and by age (2-18 yr; mean, 10.6 yr). The most common classification category in this cohort was Disorders of the Immunocompromised Host (40.8%), and the least common was Disorders of Infancy (4.7%). Immunocompromised patients suffered the highest mortality (52.8%). Additional associations with mortality included mechanical ventilation, worse clinical status at time of biopsy, tachypnea, hemoptysis, and crackles. Pulmonary hypertension was found to be a risk factor for mortality but only in the immunocompetent patients. In patients 2 to 18 years of age who underwent lung biopsies for diffuse lung disease, there were far fewer diagnoses prevalent in infancy and more overlap with adult diagnoses. Immunocompromised patients with diffuse lung disease who underwent lung biopsies had less than 50% survival at time of last follow-up.

The Semantic Management of Environmental Resources within the Interoperable Context of the EuroGEOSS: Alignment of GEMET and the GEOSS SBAs

NASA Astrophysics Data System (ADS)

Cialone, Claudia; Stock, Kristin

2010-05-01

EuroGEOSS is a European Commission funded project. It aims at improving a scientific understanding of the complex mechanisms which drive changes affecting our planet, identifying and establishing interoperable arrangements between environmental information systems. These systems would be sustained and operated by organizations with a clear mandate and resources and rendered available following the specifications of already existent frameworks such as GEOSS (the Global Earth Observation System of systems)1 and INSPIRE (the Infrastructure for Spatial Information in the European Community)2. The EuroGEOSS project's infrastructure focuses on three thematic areas: forestry, drought and biodiversity. One of the important activities in the project is the retrieval, parsing and harmonization of the large amount of heterogeneous environmental data available at local, regional and global levels between these strategic areas. The challenge is to render it semantically and technically interoperable in a simple way. An initial step in achieving this semantic and technical interoperability involves the selection of appropriate classification schemes (for example, thesauri, ontologies and controlled vocabularies) to describe the resources in the EuroGEOSS framework. These classifications become a crucial part of the interoperable framework scaffolding because they allow data providers to describe their resources and thus support resource discovery, execution and orchestration of varying levels of complexity. However, at present, given the diverse range of environmental thesauri, controlled vocabularies and ontologies and the large number of resources provided by project participants, the selection of appropriate classification schemes involves a number of considerations. First of all, there is the semantic difficulty of selecting classification schemes that contain concepts that are relevant to each thematic area. Secondly, EuroGEOSS is intended to accommodate a number of existing environmental projects (for example, GEOSS and INSPIRE). This requirement imposes constraints on the selection. Thirdly, the selected classification scheme or group of schemes (if more than one) must be capable of alignment (establishing different kinds of mappings between concepts, hence preserving intact the original knowledge schemes) or merging (the creation of another unique ontology from the original ontological sources) (Pérez-Gómez et al., 2004). Last but not least, there is the issue of including multi-lingual schemes that are based on free, open standards (non-proprietary). Using these selection criteria, we aim to support open and convenient data discovery and exchange for users who speak different languages (particularly the European ones for the broad scopes of EuroGEOSS). In order to support the project, we have developed a solution that employs two classification schemes: the Societal Benefit Areas (SBAs)3: the upper-level environmental categorization developed for the GEOSS project and the GEneral Multilingual Environmental Thesaurus (GEMET)4: a general environmental thesaurus whose conceptual structure has already been integrated with the spatial data themes proposed by the INSPIRE project. The former seems to provide the spatial data keywords relevant to the INSPIRE's Directive (JRC, 2008). In this way, we provide users with a basic set of concepts to support resource description and discovery in the thematic areas while supporting the requirements of INSPIRE and GEOSS. Furthermore, the use of only two classification schemes together with the fact that the SBAs are very general categories while GEMET includes much more detailed, yet still top-level, concepts, makes alignment an achievable task. Alignment was selected over merging because it leaves the existing classification schemes intact and requires only a simple activity of defining mappings from GEMET to the SBAs. In order to accomplish this task we are developing a simple, automated, open-source application to assist thematic experts in defining the mappings between concepts in the two classification schemes. The application will then generate SKOS mappings (exactMatch, closeMatch, broadMatch, narrowMatch, relatedMatch) based on thematic expert selections between the concepts in GEMET with the SBAs (including both the general Societal Benefit Areas and their subcategories). Once these mappings are defined and the SKOS files generated, resource providers will be able to select concepts from either GEMET or the SBAs (or a mixture) to describe their resources, and discovery approaches will support selection of concepts from either classification scheme, also returning results classified using the other scheme. While the focus of our work has been on the SBAs and GEMET, we also plan to provide a method for resource providers to further extend the semantic infrastructure by defining alignments to new classification schemes if these are required to support particular specialized thematic areas that are not covered by GEMET. In this way, the approach is flexible and suited to the general scope of EuroGEOSS, allowing specialists to increase at will the level of semantic quality and specificity of data to the initial infrastructural skeleton of the project. References ____________________________________________ Joint research Centre (JRC), 2008. INSPIRE Metadata Editor User Guide Pérez-Gómez A., Fernandez-Lopez M., Corcho O. Ontological engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web.Spinger: London, 2004

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Approach for a Clinically Useful Comprehensive Classification of Vascular and Neural Aspects of Diabetic Retinal Disease

PubMed Central

Abramoff, Michael D.; Fort, Patrice E.; Han, Ian C.; Jayasundera, K. Thiran; Sohn, Elliott H.; Gardner, Thomas W.

2018-01-01

The Early Treatment Diabetic Retinopathy Study (ETDRS) and other standardized classification schemes have laid a foundation for tremendous advances in the understanding and management of diabetic retinopathy (DR). However, technological advances in optics and image analysis, especially optical coherence tomography (OCT), OCT angiography (OCTa), and ultra-widefield imaging, as well as new discoveries in diabetic retinal neuropathy (DRN), are exposing the limitations of ETDRS and other classification systems to completely characterize retinal changes in diabetes, which we term diabetic retinal disease (DRD). While it may be most straightforward to add axes to existing classification schemes, as diabetic macular edema (DME) was added as an axis to earlier DR classifications, doing so may make these classifications increasingly complicated and thus clinically intractable. Therefore, we propose future research efforts to develop a new, comprehensive, and clinically useful classification system that will identify multimodal biomarkers to reflect the complex pathophysiology of DRD and accelerate the development of therapies to prevent vision-threatening DRD. PMID:29372250

Approach for a Clinically Useful Comprehensive Classification of Vascular and Neural Aspects of Diabetic Retinal Disease.

PubMed

Abramoff, Michael D; Fort, Patrice E; Han, Ian C; Jayasundera, K Thiran; Sohn, Elliott H; Gardner, Thomas W

2018-01-01

The Early Treatment Diabetic Retinopathy Study (ETDRS) and other standardized classification schemes have laid a foundation for tremendous advances in the understanding and management of diabetic retinopathy (DR). However, technological advances in optics and image analysis, especially optical coherence tomography (OCT), OCT angiography (OCTa), and ultra-widefield imaging, as well as new discoveries in diabetic retinal neuropathy (DRN), are exposing the limitations of ETDRS and other classification systems to completely characterize retinal changes in diabetes, which we term diabetic retinal disease (DRD). While it may be most straightforward to add axes to existing classification schemes, as diabetic macular edema (DME) was added as an axis to earlier DR classifications, doing so may make these classifications increasingly complicated and thus clinically intractable. Therefore, we propose future research efforts to develop a new, comprehensive, and clinically useful classification system that will identify multimodal biomarkers to reflect the complex pathophysiology of DRD and accelerate the development of therapies to prevent vision-threatening DRD.

Toward functional classification of neuronal types.

PubMed

Sharpee, Tatyana O

2014-09-17

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species. Copyright © 2014 Elsevier Inc. All rights reserved.

Applying graphics user interface ot group technology classification and coding at the Boeing aerospace company

NASA Astrophysics Data System (ADS)

Ness, P. H.; Jacobson, H.

1984-10-01

The thrust of 'group technology' is toward the exploitation of similarities in component design and manufacturing process plans to achieve assembly line flow cost efficiencies for small batch production. The systematic method devised for the identification of similarities in component geometry and processing steps is a coding and classification scheme implemented by interactive CAD/CAM systems. This coding and classification scheme has led to significant increases in computer processing power, allowing rapid searches and retrievals on the basis of a 30-digit code together with user-friendly computer graphics.

Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function.

PubMed

Rahman, Md Mostafizur; Fattah, Shaikh Anowarul

2017-01-01

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

Reconciling Mining with the Conservation of Cave Biodiversity: A Quantitative Baseline to Help Establish Conservation Priorities

PubMed Central

Prous, Xavier; Zampaulo, Robson; Giannini, Tereza C.; Imperatriz-Fonseca, Vera L.; Maurity, Clóvis; Oliveira, Guilherme; Brandi, Iuri V.; Siqueira, José O.

2016-01-01

Caves pose significant challenges for mining projects, since they harbor many endemic and threatened species, and must therefore be protected. Recent discussions between academia, environmental protection agencies, and industry partners, have highlighted problems with the current Brazilian legislation for the protection of caves. While the licensing process is long, complex and cumbersome, the criteria used to assign caves into conservation relevance categories are often subjective, with relevance being mainly determined by the presence of obligate cave dwellers (troglobites) and their presumed rarity. However, the rarity of these troglobitic species is questionable, as most remain unidentified to the species level and their habitats and distribution ranges are poorly known. Using data from 844 iron caves retrieved from different speleology reports for the Carajás region (South-Eastern Amazon, Brazil), one of the world's largest deposits of high-grade iron ore, we assess the influence of different cave characteristics on four biodiversity proxies (species richness, presence of troglobites, presence of rare troglobites, and presence of resident bat populations). We then examine how the current relevance classification scheme ranks caves with different biodiversity indicators. Large caves were found to be important reservoirs of biodiversity, so they should be prioritized in conservation programs. Our results also reveal spatial autocorrelation in all the biodiversity proxies assessed, indicating that iron caves should be treated as components of a cave network immersed in the karst landscape. Finally, we show that by prioritizing the conservation of rare troglobites, the current relevance classification scheme is undermining overall cave biodiversity and leaving ecologically important caves unprotected. We argue that conservation efforts should target subterranean habitats as a whole and propose an alternative relevance ranking scheme, which could help simplify the assessment process and channel more resources to the effective protection of overall cave biodiversity. PMID:27997576

DISSECT: a new mnemonic-based approach to the categorization of aortic dissection.

PubMed

Dake, M D; Thompson, M; van Sambeek, M; Vermassen, F; Morales, J P

2013-08-01

Classification systems for aortic dissection provide important guides to clinical decision-making, but the relevance of traditional categorization schemes is being questioned in an era when endovascular techniques are assuming a growing role in the management of this frequently complex and catastrophic entity. In recognition of the expanding range of interventional therapies now used as alternatives to conventional treatment approaches, the Working Group on Aortic Diseases of the DEFINE Project developed a categorization system that features the specific anatomic and clinical manifestations of the disease process that are most relevant to contemporary decision-making. The DISSECT classification system is a mnemonic-based approach to the evaluation of aortic dissection. It guides clinicians through an assessment of six critical characteristics that facilitate optimal communication of the most salient details that currently influence the selection of a therapeutic option, including those findings that are key when considering an endovascular procedure, but are not taken into account by the DeBakey or Stanford categorization schemes. The six features of aortic dissection include: duration of disease; intimal tear location; size of the dissected aorta; segmental extent of aortic involvement; clinical complications of the dissection, and thrombus within the aortic false lumen. In current clinical practice, endovascular therapy is increasingly considered as an alternative to medical management or open surgical repair in select cases of type B aortic dissection. Currently, endovascular aortic repair is not used for patients with type A aortic dissection, but catheter-based techniques directed at peripheral branch vessel ischemia that may complicate type A dissection are considered valuable adjunctive interventions, when indicated. The use of a new system for categorization of aortic dissection, DISSECT, addresses the shortcomings of well-known established schemes devised more than 40 years ago, before the introduction of endovascular techniques. It will serve as a guide to support a critical analysis of contemporary therapeutic options and inform management decisions based on specific features of the disease process. Copyright © 2013 European Society for Vascular Surgery. All rights reserved.

Semi-supervised classification tool for DubaiSat-2 multispectral imagery

NASA Astrophysics Data System (ADS)

Al-Mansoori, Saeed

2015-10-01

This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

Chondrule formation, metamorphism, brecciation, an important new primary chondrule group, and the classification of chondrules

NASA Technical Reports Server (NTRS)

Sears, Derek W. G.; Shaoxiong, Huang; Benoit, Paul H.

1995-01-01

The recently proposed compositional classification scheme for meteoritic chondrules divides the chondrules into groups depending on the composition of their two major phases, olivine (or pyroxene) and the mesostasis, both of which are genetically important. The scheme is here applied to discussions of three topics: the petrographic classification of Roosevelt County 075 (the least-metamorphosed H chondrite known), brecciation (an extremely important and ubiquitous process probably experienced by greater than 40% of all unequilibrated ordinary chondrites), and the group A5 chondrules in the least metamorphosed ordinary chondrites which have many similarities to chondrules in the highly metamorphosed 'equilibrated' chondrites. Since composition provides insights into both primary formation properties of the chondruies and the effects of metamorphism on the entire assemblage it is possible to determine the petrographic type of RC075 as 3.1 with unique certainty. Similarly, the near scheme can be applied to individual chondrules without knowledge of the petrographic type of the host chondrite, which makes it especially suitable for studying breccias. Finally, the new scheme has revealed the existence of chondrules not identified by previous techniques and which appear to be extremely important. Like group A1 and A2 chondrules (but unlike group B1 chondrules) the primitive group A5 chondruies did not supercool during formation, but unlike group A1 and A2 chondrules (and like group B1 chondrules) they did not suffer volatile loss and reduction during formation. It is concluded that the compositional classification scheme provides important new insights into the formation and history of chondrules and chondrites which would be overlooked by previous schemes.

Carnegie's New Community Engagement Classification: Affirming Higher Education's Role in Community

ERIC Educational Resources Information Center

Driscoll, Amy

2009-01-01

In 2005, the Carnegie Foundation for the Advancement of Teaching (CFAT) stirred the higher education world with the announcement of a new classification for institutions that engage with community. The classification, community engagement, is the first in a set of planned classification schemes resulting from the foundation's reexamination of the…

Real-time ultrasonic weld evaluation system

NASA Astrophysics Data System (ADS)

Katragadda, Gopichand; Nair, Satish; Liu, Harry; Brown, Lawrence M.

1996-11-01

Ultrasonic testing techniques are currently used as an alternative to radiography for detecting, classifying,and sizing weld defects, and for evaluating weld quality. Typically, ultrasonic weld inspections are performed manually, which require significant operator expertise and time. Thus, in recent years, the emphasis is to develop automated methods to aid or replace operators in critical weld inspections where inspection time, reliability, and operator safety are major issues. During this period, significant advances wee made in the areas of weld defect classification and sizing. Very few of these methods, however have found their way into the market, largely due to the lack of an integrated approach enabling real-time implementation. Also, not much research effort was directed in improving weld acceptance criteria. This paper presents an integrated system utilizing state-of-the-art techniques for a complete automation of the weld inspection procedure. The modules discussed include transducer tracking, classification, sizing, and weld acceptance criteria. Transducer tracking was studied by experimentally evaluating sonic and optical position tracking techniques. Details for this evaluation are presented. Classification is obtained using a multi-layer perceptron. Results from different feature extraction schemes, including a new method based on a combination of time and frequency-domain signal representations are given. Algorithms developed to automate defect registration and sizing are discussed. A fuzzy-logic acceptance criteria for weld acceptance is presented describing how this scheme provides improved robustness compared to the traditional flow-diagram standards.

Classification for Estuarine Ecosystems: A Review and Comparison of Selected Classification Schemes

EPA Science Inventory

Estuarine scientists have devoted considerable effort to classifying coastal, estuarine and marine environments and their watersheds, for a variety of purposes. These classifications group systems with similarities – most often in physical and hydrodynamic properties – in order ...

Comprehensive 4-stage categorization of bicuspid aortic valve leaflet morphology by cardiac MRI in 386 patients.

PubMed

Murphy, I G; Collins, J; Powell, A; Markl, M; McCarthy, P; Malaisrie, S C; Carr, J C; Barker, A J

2017-08-01

Bicuspid aortic valve (BAV) disease is heterogeneous and related to valve dysfunction and aortopathy. Appropriate follow up and surveillance of patients with BAV may depend on correct phenotypic categorization. There are multiple classification schemes, however a need exists to comprehensively capture commissure fusion, leaflet asymmetry, and valve orifice orientation. Our aim was to develop a BAV classification scheme for use at MRI to ascertain the frequency of different phenotypes and the consistency of BAV classification. The BAV classification scheme builds on the Sievers surgical BAV classification, adding valve orifice orientation, partial leaflet fusion and leaflet asymmetry. A single observer successfully applied this classification to 386 of 398 Cardiac MRI studies. Repeatability of categorization was ascertained with intraobserver and interobserver kappa scores. Sensitivity and specificity of MRI findings was determined from operative reports, where available. Fusion of the right and left leaflets accounted for over half of all cases. Partial leaflet fusion was seen in 46% of patients. Good interobserver agreement was seen for orientation of the valve opening (κ = 0.90), type (κ = 0.72) and presence of partial fusion (κ = 0.83, p < 0.0001). Retrospective review of operative notes showed sensitivity and specificity for orientation (90, 93%) and for Sievers type (73, 87%). The proposed BAV classification schema was assessed by MRI for its reliability to classify valve morphology in addition to illustrating the wide heterogeneity of leaflet size, orifice orientation, and commissural fusion. The classification may be helpful in further understanding the relationship between valve morphology, flow derangement and aortopathy.

Centrifuge: rapid and sensitive classification of metagenomic sequences

PubMed Central

Song, Li; Breitwieser, Florian P.

2016-01-01

Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. PMID:27852649

Modern radiosurgical and endovascular classification schemes for brain arteriovenous malformations.

PubMed

Tayebi Meybodi, Ali; Lawton, Michael T

2018-05-04

Stereotactic radiosurgery (SRS) and endovascular techniques are commonly used for treating brain arteriovenous malformations (bAVMs). They are usually used as ancillary techniques to microsurgery but may also be used as solitary treatment options. Careful patient selection requires a clear estimate of the treatment efficacy and complication rates for the individual patient. As such, classification schemes are an essential part of patient selection paradigm for each treatment modality. While the Spetzler-Martin grading system and its subsequent modifications are commonly used for microsurgical outcome prediction for bAVMs, the same system(s) may not be easily applicable to SRS and endovascular therapy. Several radiosurgical- and endovascular-based grading scales have been proposed for bAVMs. However, a comprehensive review of these systems including a discussion on their relative advantages and disadvantages is missing. This paper is dedicated to modern classification schemes designed for SRS and endovascular techniques.

Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers.

PubMed

Mai, Xiaofeng; Liu, Jie; Wu, Xiong; Zhang, Qun; Guo, Changjian; Yang, Yanfu; Li, Zhaohui

2017-02-06

A Stokes-space modulation format classification (MFC) technique is proposed for coherent optical receivers by using a non-iterative clustering algorithm. In the clustering algorithm, two simple parameters are calculated to help find the density peaks of the data points in Stokes space and no iteration is required. Correct MFC can be realized in numerical simulations among PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM and PM-64QAM signals within practical optical signal-to-noise ratio (OSNR) ranges. The performance of the proposed MFC algorithm is also compared with those of other schemes based on clustering algorithms. The simulation results show that good classification performance can be achieved using the proposed MFC scheme with moderate time complexity. Proof-of-concept experiments are finally implemented to demonstrate MFC among PM-QPSK/16QAM/64QAM signals, which confirm the feasibility of our proposed MFC scheme.

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

PubMed

Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

2015-11-01

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

[The establishment, development and application of classification approach of freshwater phytoplankton based on the functional group: a review].

PubMed

Yang, Wen; Zhu, Jin-Yong; Lu, Kai-Hong; Wan, Li; Mao, Xiao-Hua

2014-06-01

Appropriate schemes for classification of freshwater phytoplankton are prerequisites and important tools for revealing phytoplanktonic succession and studying freshwater ecosystems. An alternative approach, functional group of freshwater phytoplankton, has been proposed and developed due to the deficiencies of Linnaean and molecular identification in ecological applications. The functional group of phytoplankton is a classification scheme based on autoecology. In this study, the theoretical basis and classification criterion of functional group (FG), morpho-functional group (MFG) and morphology-based functional group (MBFG) were summarized, as well as their merits and demerits. FG was considered as the optimal classification approach for the aquatic ecology research and aquatic environment evaluation. The application status of FG was introduced, with the evaluation standards and problems of two approaches to assess water quality on the basis of FG, index methods of Q and QR, being briefly discussed.

Addition of Histology to the Paris Classification of Pediatric Crohn Disease Alters Classification of Disease Location.

PubMed

Fernandes, Melissa A; Verstraete, Sofia G; Garnett, Elizabeth A; Heyman, Melvin B

2016-02-01

The aim of the study was to investigate the value of microscopic findings in the classification of pediatric Crohn disease (CD) by determining whether classification of disease changes significantly with inclusion of histologic findings. Sixty patients were randomly selected from a cohort of patients studied at the Pediatric Inflammatory Bowel Disease Clinic at the University of California, San Francisco Benioff Children's Hospital. Two physicians independently reviewed the electronic health records of the included patients to determine the Paris classification for each patient by adhering to present guidelines and then by including microscopic findings. Macroscopic and combined disease location classifications were discordant in 34 (56.6%), with no statistically significant differences between groups. Interobserver agreement was higher in the combined classification (κ = 0.73, 95% confidence interval 0.65-0.82) as opposed to when classification was limited to macroscopic findings (κ = 0.53, 95% confidence interval 0.40-0.58). When evaluating the proximal upper gastrointestinal tract (Paris L4a), the interobserver agreement was better in macroscopic compared with the combined classification. Disease extent classifications differed significantly when comparing isolated macroscopic findings (Paris classification) with the combined scheme that included microscopy. Further studies are needed to determine which scheme provides more accurate representation of disease extent.

The search for structure - Object classification in large data sets. [for astronomers

NASA Technical Reports Server (NTRS)

Kurtz, Michael J.

1988-01-01

Research concerning object classifications schemes are reviewed, focusing on large data sets. Classification techniques are discussed, including syntactic, decision theoretic methods, fuzzy techniques, and stochastic and fuzzy grammars. Consideration is given to the automation of MK classification (Morgan and Keenan, 1973) and other problems associated with the classification of spectra. In addition, the classification of galaxies is examined, including the problems of systematic errors, blended objects, galaxy types, and galaxy clusters.

Classification and reduction of pilot error

NASA Technical Reports Server (NTRS)

Rogers, W. H.; Logan, A. L.; Boley, G. D.

1989-01-01

Human error is a primary or contributing factor in about two-thirds of commercial aviation accidents worldwide. With the ultimate goal of reducing pilot error accidents, this contract effort is aimed at understanding the factors underlying error events and reducing the probability of certain types of errors by modifying underlying factors such as flight deck design and procedures. A review of the literature relevant to error classification was conducted. Classification includes categorizing types of errors, the information processing mechanisms and factors underlying them, and identifying factor-mechanism-error relationships. The classification scheme developed by Jens Rasmussen was adopted because it provided a comprehensive yet basic error classification shell or structure that could easily accommodate addition of details on domain-specific factors. For these purposes, factors specific to the aviation environment were incorporated. Hypotheses concerning the relationship of a small number of underlying factors, information processing mechanisms, and error types types identified in the classification scheme were formulated. ASRS data were reviewed and a simulation experiment was performed to evaluate and quantify the hypotheses.

Integrating multisource imagery and GIS analysis for mapping Bermuda`s benthic habitats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vierros, M.K.

1997-06-01

Bermuda is a group of isolated oceanic situated in the northwest Atlantic Ocean and surrounded by the Sargasso Sea. Bermuda possesses the northernmost coral reefs and mangroves in the Atlantic Ocean, and because of its high population density, both the terrestrial and marine environments are under intense human pressure. Although a long record of scientific research exists, this study is the first attempt to comprehensively map the area`s benthic habitats, despite the need for such a map for resource assessment and management purposes. Multi-source and multi-date imagery were used for producing the habitat map due to lack of a completemore » up-to-date image. Classifications were performed with SPOT data, and the results verified from recent aerial photography and current aerial video, along with extensive ground truthing. Stratification of the image into regions prior to classification reduced the confusing effects of varying water depth. Classification accuracy in shallow areas was increased by derivation of a texture pseudo-channel, while bathymetry was used as a classification tool in deeper areas, where local patterns of zonation were well known. Because of seasonal variation in extent of seagrasses, a classification scheme based on density could not be used. Instead, a set of classes based on the seagrass area`s exposure to the open ocean were developed. The resulting habitat map is currently being assessed for accuracy with promising preliminary results, indicating its usefulness as a basis for future resource assessment studies.« less

An analysis of the synoptic and climatological applicability of circulation type classifications for Ireland

NASA Astrophysics Data System (ADS)

Broderick, Ciaran; Fealy, Rowan

2013-04-01

Circulation type classifications (CTCs) compiled as part of the COST733 Action, entitled 'Harmonisation and Application of Weather Type Classifications for European Regions', are examined for their synoptic and climatological applicability to Ireland based on their ability to characterise surface temperature and precipitation. In all 16 different objective classification schemes, representative of four different methodological approaches to circulation typing (optimization algorithms, threshold based methods, eigenvector techniques and leader algorithms) are considered. Several statistical metrics which variously quantify the ability of CTCs to discretize daily data into well-defined homogeneous groups are used to evaluate and compare different approaches to synoptic typing. The records from 14 meteorological stations located across the island of Ireland are used in the study. The results indicate that while it was not possible to identify a single optimum classification or approach to circulation typing - conditional on the location and surface variables considered - a number of general assertions regarding the performance of different schemes can be made. The findings for surface temperature indicate that that those classifications based on predefined thresholds (e.g. Litynski, GrossWetterTypes and original Lamb Weather Type) perform well, as do the Kruizinga and Lund classification schemes. Similarly for precipitation predefined type classifications return high skill scores, as do those classifications derived using some optimization procedure (e.g. SANDRA, Self Organizing Maps and K-Means clustering). For both temperature and precipitation the results generally indicate that the classifications perform best for the winter season - reflecting the closer coupling between large-scale circulation and surface conditions during this period. In contrast to the findings for temperature, spatial patterns in the performance of classifications were more evident for precipitation. In the case of this variable those more westerly synoptic stations open to zonal airflow and less influenced by regional scale forcings generally exhibited a stronger link with large-scale circulation.

An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.

PubMed

Hazrati, Mehrnaz Kh; Erfanian, Abbas

2008-01-01

This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.

Classification of Instructional Programs: 2000 Edition.

ERIC Educational Resources Information Center

Morgan, Robert L.; Hunt, E. Stephen

This third revision of the Classification of Instructional Programs (CIP) updates and modifies education program classifications, providing a taxonomic scheme that supports the accurate tracking, assessment, and reporting of field of study and program completions activity. This edition has also been adopted as the standard field of study taxonomy…

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Attribution of local climate zones using a multitemporal land use/land cover classification scheme

NASA Astrophysics Data System (ADS)

Wicki, Andreas; Parlow, Eberhard

2017-04-01

Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful.

Fuzzy-Wavelet Based Double Line Transmission System Protection Scheme in the Presence of SVC

NASA Astrophysics Data System (ADS)

Goli, Ravikumar; Shaik, Abdul Gafoor; Tulasi Ram, Sankara S.

2015-06-01

Increasing the power transfer capability and efficient utilization of available transmission lines, improving the power system controllability and stability, power oscillation damping and voltage compensation have made strides and created Flexible AC Transmission (FACTS) devices in recent decades. Shunt FACTS devices can have adverse effects on distance protection both in steady state and transient periods. Severe under reaching is the most important problem of relay which is caused by current injection at the point of connection to the system. Current absorption of compensator leads to overreach of relay. This work presents an efficient method based on wavelet transforms, fault detection, classification and location using Fuzzy logic technique which is almost independent of fault impedance, fault distance and fault inception angle. The proposed protection scheme is found to be fast, reliable and accurate for various types of faults on transmission lines with and without Static Var compensator at different locations and with various incidence angles.

Pattern Recognition Application of Support Vector Machine for Fault Classification of Thyristor Controlled Series Compensated Transmission Lines

NASA Astrophysics Data System (ADS)

Yashvantrai Vyas, Bhargav; Maheshwari, Rudra Prakash; Das, Biswarup

2016-06-01

Application of series compensation in extra high voltage (EHV) transmission line makes the protection job difficult for engineers, due to alteration in system parameters and measurements. The problem amplifies with inclusion of electronically controlled compensation like thyristor controlled series compensation (TCSC) as it produce harmonics and rapid change in system parameters during fault associated with TCSC control. This paper presents a pattern recognition based fault type identification approach with support vector machine. The scheme uses only half cycle post fault data of three phase currents to accomplish the task. The change in current signal features during fault has been considered as discriminatory measure. The developed scheme in this paper is tested over a large set of fault data with variation in system and fault parameters. These fault cases have been generated with PSCAD/EMTDC on a 400 kV, 300 km transmission line model. The developed algorithm has proved better for implementation on TCSC compensated line with its improved accuracy and speed.

Solar photovoltaics: current state and trends

NASA Astrophysics Data System (ADS)

Milichko, V. A.; Shalin, A. S.; Mukhin, I. S.; Kovrov, A. E.; Krasilin, A. A.; Vinogradov, A. V.; Belov, P. A.; Simovski, C. R.

2016-08-01

Basic aspects of current solar photovoltaics (PVs) are reviewed, starting from the recently developed already-on-the-market first-generation solar cells and ending with promising but not yet commercialized third-generation cells and materials possibly leading to new cell designs. The emphasis is on the physical principles of operation of various solar cells, which are divided into several groups according to our classification scheme. To make the picture complete, some technological and economic aspects of the field are discussed. A separate chapter considers antireflection coatings and light-trapping textures — structures which, while not having appeared yet in the PV review literature, are an integral part of the solar cells.

A physical classification scheme for blazars

NASA Astrophysics Data System (ADS)

Landt, Hermine; Padovani, Paolo; Perlman, Eric S.; Giommi, Paolo

2004-06-01

Blazars are currently separated into BL Lacertae objects (BL Lacs) and flat spectrum radio quasars based on the strength of their emission lines. This is performed rather arbitrarily by defining a diagonal line in the Ca H&K break value-equivalent width plane, following Marchã et al. We readdress this problem and put the classification scheme for blazars on firm physical grounds. We study ~100 blazars and radio galaxies from the Deep X-ray Radio Blazar Survey (DXRBS) and 2-Jy radio survey and find a significant bimodality for the narrow emission line [OIII]λ5007. This suggests the presence of two physically distinct classes of radio-loud active galactic nuclei (AGN). We show that all radio-loud AGN, blazars and radio galaxies, can be effectively separated into weak- and strong-lined sources using the [OIII]λ5007-[OII]λ3727 equivalent width plane. This plane allows one to disentangle orientation effects from intrinsic variations in radio-loud AGN. Based on DXRBS, the strongly beamed sources of the new class of weak-lined radio-loud AGN are made up of BL Lacs at the ~75 per cent level, whereas those of the strong-lined radio-loud AGN include mostly (~97 per cent) quasars.

Drinking Water Microbiome as a Screening Tool for ...

EPA Pesticide Factsheets

Many water utilities in the US using chloramine as disinfectant treatment in their distribution systems have experienced nitrification episodes, which detrimentally impact the water quality. A chloraminated drinking water distribution system (DWDS) simulator was operated through four successive operational schemes, including two stable events (SS) and an episode of nitrification (SF), followed by a ‘chlorine burn’ (SR) by switching disinfectant from chloramine to free chlorine. The current research investigated the viability of biological signatures as potential indicators of operational failure and predictors of nitrification in DWDS. For this purpose, we examined the bulk water (BW) bacterial microbiome of a chloraminated DWDS simulator operated through successive operational schemes, including an episode of nitrification. BW data was chosen because sampling of BW in a DWDS by water utility operators is relatively simpler and easier than collecting biofilm samples from underground pipes. The methodology applied a supervised classification machine learning approach (naïve Bayes algorithm) for developing predictive models for nitrification. Classification models were trained with biological datasets (Operational Taxonomic Unit [OTU] and genus-level taxonomic groups) generated using next generation high-throughput technology, and divided into two groups (i.e. binary) of positives and negatives (Failure and Stable, respectively). We also invest

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

DREAM: Classification scheme for dialog acts in clinical research query mediation.

PubMed

Hoxha, Julia; Chandar, Praveen; He, Zhe; Cimino, James; Hanauer, David; Weng, Chunhua

2016-02-01

Clinical data access involves complex but opaque communication between medical researchers and query analysts. Understanding such communication is indispensable for designing intelligent human-machine dialog systems that automate query formulation. This study investigates email communication and proposes a novel scheme for classifying dialog acts in clinical research query mediation. We analyzed 315 email messages exchanged in the communication for 20 data requests obtained from three institutions. The messages were segmented into 1333 utterance units. Through a rigorous process, we developed a classification scheme and applied it for dialog act annotation of the extracted utterances. Evaluation results with high inter-annotator agreement demonstrate the reliability of this scheme. This dataset is used to contribute preliminary understanding of dialog acts distribution and conversation flow in this dialog space. Copyright © 2015 Elsevier Inc. All rights reserved.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Waveform classification and statistical analysis of seismic precursors to the July 2008 Vulcanian Eruption of Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Rodgers, Mel; Smith, Patrick; Pyle, David; Mather, Tamsin

2016-04-01

Understanding the transition between quiescence and eruption at dome-forming volcanoes, such as Soufrière Hills Volcano (SHV), Montserrat, is important for monitoring volcanic activity during long-lived eruptions. Statistical analysis of seismic events (e.g. spectral analysis and identification of multiplets via cross-correlation) can be useful for characterising seismicity patterns and can be a powerful tool for analysing temporal changes in behaviour. Waveform classification is crucial for volcano monitoring, but consistent classification, both during real-time analysis and for retrospective analysis of previous volcanic activity, remains a challenge. Automated classification allows consistent re-classification of events. We present a machine learning (random forest) approach to rapidly classify waveforms that requires minimal training data. We analyse the seismic precursors to the July 2008 Vulcanian explosion at SHV and show systematic changes in frequency content and multiplet behaviour that had not previously been recognised. These precursory patterns of seismicity may be interpreted as changes in pressure conditions within the conduit during magma ascent and could be linked to magma flow rates. Frequency analysis of the different waveform classes supports the growing consensus that LP and Hybrid events should be considered end members of a continuum of low-frequency source processes. By using both supervised and unsupervised machine-learning methods we investigate the nature of waveform classification and assess current classification schemes.

Acute Oral Toxicity of Trimethylolethane Trinitrate (TMETN) in Sprague- Dawley Rats

DTIC Science & Technology

1989-07-01

classification scheme of Hodge and Steiner, these results indicate that TMETN is a slightly toxic compound.1 20. ON-RIBUTION /AVAILABILITY OF ABSTRACT 21. ABSTRACT...the classification scheme of Hodge and Sterner, these results indcate that TMETN is a slightly toxic compound. KEY WORDS: Acute Oral Toxicit-y...Dawley rats and 1027.4 63.7 mg/kg in female Sprague-Dawley rats. These MLD values place TMETN in the "slightly toxic" range by the system of Hodge and

NASA Scope and Subject Category Guide

NASA Technical Reports Server (NTRS)

2011-01-01

This guide provides a simple, effective tool to assist aerospace information analysts and database builders in the high-level subject classification of technical materials. Each of the 76 subject categories comprising the classification scheme is presented with a description of category scope, a listing of subtopics, cross references, and an indication of particular areas of NASA interest. The guide also includes an index of nearly 3,000 specific research topics cross referenced to the subject categories. The portable document format (PDF) version of the guide contains links in the index from each input subject to its corresponding categories. In addition to subject classification, the guide can serve as an aid to searching databases that use the classification scheme, and is also an excellent selection guide for those involved in the acquisition of aerospace literature. The CD-ROM contains both HTML and PDF versions.

A risk-based classification scheme for genetically modified foods. III: Evaluation using a panel of reference foods.

PubMed

Chao, Eunice; Krewski, Daniel

2008-12-01

This paper presents an exploratory evaluation of four functional components of a proposed risk-based classification scheme (RBCS) for crop-derived genetically modified (GM) foods in a concordance study. Two independent raters assigned concern levels to 20 reference GM foods using a rating form based on the proposed RBCS. The four components of evaluation were: (1) degree of concordance, (2) distribution across concern levels, (3) discriminating ability of the scheme, and (4) ease of use. At least one of the 20 reference foods was assigned to each of the possible concern levels, demonstrating the ability of the scheme to identify GM foods of different concern with respect to potential health risk. There was reasonably good concordance between the two raters for the three separate parts of the RBCS. The raters agreed that the criteria in the scheme were sufficiently clear in discriminating reference foods into different concern levels, and that with some experience, the scheme was reasonably easy to use. Specific issues and suggestions for improvements identified in the concordance study are discussed.

Classification of intertrochanteric fractures with computed tomography: a study of intraobserver and interobserver variability and prognostic value.

PubMed

Chapman, Cary B; Herrera, Mauricio F; Binenbaum, Gil; Schweppe, Michael; Staron, Ronald B; Feldman, Frieda; Rosenwasser, Melvin P

2003-09-01

The purpose of this prospective study was to determine the level of interobserver and intraobserver agreement among orthopedic surgeons and radiologists when computed tomography (CT) scans are used with plain radiographs to evaluate intertrochanteric fractures. In addition, the prognostic value of current classifications systems concerning quality of life was evaluated. Sixty-one patients who presented with intertrochanteric fractures received open reduction and internal fixation with compression hip screw. Three orthopedic surgeons and 2 radiologists independently classified the fractures according to 2 systems: Evans-Jensen and AO (Arbeitsgemeinschaft für Osteo-synthesefragen). Fractures were initially graded with plain radiographs and then again in conjunction with CT. Results were analyzed using the (kappa) kappa coefficient. The 36-item Short-Form Health Survey was administered at baseline, 3 months, and 1 year, and results were correlated with fracture grade. Mean kappa coefficients when comparing radiography alone with radiography and CT scan were 0.63 for the AO system and 0.59 for the Evans-Jensen system. Both represent "fair" agreements. Mean overall interobserver kappa coefficients were 0.67 for radiologists and 0.57 for orthopedic surgeons. Radiologists also had higher intraobserver kappa coefficients. No significant relationships were found between follow-up Short Form Health Survey results and intraoperative grading of fractures. When these classification schemes are compared, interobserver agreement does not appear to change dramatically when information from CT scans is added. This may suggest that (1) more data have been provided by CT with greater possibilities for misinterpretation and (2) these classification schemes may not be comprehensive in describing fracture pattern and displacement. Finally, both systems failed to provide any prognostic value.

A new local-global approach for classification.

PubMed

Peres, R T; Pedreira, C E

2010-09-01

In this paper, we propose a new local-global pattern classification scheme that combines supervised and unsupervised approaches, taking advantage of both, local and global environments. We understand as global methods the ones concerned with the aim of constructing a model for the whole problem space using the totality of the available observations. Local methods focus into sub regions of the space, possibly using an appropriately selected subset of the sample. In the proposed method, the sample is first divided in local cells by using a Vector Quantization unsupervised algorithm, the LBG (Linde-Buzo-Gray). In a second stage, the generated assemblage of much easier problems is locally solved with a scheme inspired by Bayes' rule. Four classification methods were implemented for comparison purposes with the proposed scheme: Learning Vector Quantization (LVQ); Feedforward Neural Networks; Support Vector Machine (SVM) and k-Nearest Neighbors. These four methods and the proposed scheme were implemented in eleven datasets, two controlled experiments, plus nine public available datasets from the UCI repository. The proposed method has shown a quite competitive performance when compared to these classical and largely used classifiers. Our method is simple concerning understanding and implementation and is based on very intuitive concepts. Copyright 2010 Elsevier Ltd. All rights reserved.

A novel encoding scheme for effective biometric discretization: Linearly Separable Subcode.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2013-02-01

Separability in a code is crucial in guaranteeing a decent Hamming-distance separation among the codewords. In multibit biometric discretization where a code is used for quantization-intervals labeling, separability is necessary for preserving distance dissimilarity when feature components are mapped from a discrete space to a Hamming space. In this paper, we examine separability of Binary Reflected Gray Code (BRGC) encoding and reveal its inadequacy in tackling interclass variation during the discrete-to-binary mapping, leading to a tradeoff between classification performance and entropy of binary output. To overcome this drawback, we put forward two encoding schemes exhibiting full-ideal and near-ideal separability capabilities, known as Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), respectively. These encoding schemes convert the conventional entropy-performance tradeoff into an entropy-redundancy tradeoff in the increase of code length. Extensive experimental results vindicate the superiority of our schemes over the existing encoding schemes in discretization performance. This opens up possibilities of achieving much greater classification performance with high output entropy.

Fault detection and classification in electrical power transmission system using artificial neural network.

PubMed

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

Interpretation for scales of measurement linking with abstract algebra

PubMed Central

2014-01-01

The Stevens classification of levels of measurement involves four types of scale: “Nominal”, “Ordinal”, “Interval” and “Ratio”. This classification has been used widely in medical fields and has accomplished an important role in composition and interpretation of scale. With this classification, levels of measurements appear organized and validated. However, a group theory-like systematization beckons as an alternative because of its logical consistency and unexceptional applicability in the natural sciences but which may offer great advantages in clinical medicine. According to this viewpoint, the Stevens classification is reformulated within an abstract algebra-like scheme; ‘Abelian modulo additive group’ for “Ordinal scale” accompanied with ‘zero’, ‘Abelian additive group’ for “Interval scale”, and ‘field’ for “Ratio scale”. Furthermore, a vector-like display arranges a mixture of schemes describing the assessment of patient states. With this vector-like notation, data-mining and data-set combination is possible on a higher abstract structure level based upon a hierarchical-cluster form. Using simple examples, we show that operations acting on the corresponding mixed schemes of this display allow for a sophisticated means of classifying, updating, monitoring, and prognosis, where better data mining/data usage and efficacy is expected. PMID:24987515

Interpretation for scales of measurement linking with abstract algebra.

PubMed

Sawamura, Jitsuki; Morishita, Shigeru; Ishigooka, Jun

2014-01-01

THE STEVENS CLASSIFICATION OF LEVELS OF MEASUREMENT INVOLVES FOUR TYPES OF SCALE: "Nominal", "Ordinal", "Interval" and "Ratio". This classification has been used widely in medical fields and has accomplished an important role in composition and interpretation of scale. With this classification, levels of measurements appear organized and validated. However, a group theory-like systematization beckons as an alternative because of its logical consistency and unexceptional applicability in the natural sciences but which may offer great advantages in clinical medicine. According to this viewpoint, the Stevens classification is reformulated within an abstract algebra-like scheme; 'Abelian modulo additive group' for "Ordinal scale" accompanied with 'zero', 'Abelian additive group' for "Interval scale", and 'field' for "Ratio scale". Furthermore, a vector-like display arranges a mixture of schemes describing the assessment of patient states. With this vector-like notation, data-mining and data-set combination is possible on a higher abstract structure level based upon a hierarchical-cluster form. Using simple examples, we show that operations acting on the corresponding mixed schemes of this display allow for a sophisticated means of classifying, updating, monitoring, and prognosis, where better data mining/data usage and efficacy is expected.

A new scheme for urban impervious surface classification from SAR images

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Lin, Hui; Wang, Yunpeng

2018-05-01

Urban impervious surfaces have been recognized as a significant indicator for various environmental and socio-economic studies. There is an increasingly urgent demand for timely and accurate monitoring of the impervious surfaces with satellite technology from local to global scales. In the past decades, optical remote sensing has been widely employed for this task with various techniques. However, there are still a range of challenges, e.g. handling cloud contamination on optical data. Therefore, the Synthetic Aperture Radar (SAR) was introduced for the challenging task because it is uniquely all-time- and all-weather-capable. Nevertheless, with an increasing number of SAR data applied, the methodology used for impervious surfaces classification remains unchanged from the methods used for optical datasets. This shortcoming has prevented the community from fully exploring the potential of using SAR data for impervious surfaces classification. We proposed a new scheme that is comparable to the well-known and fundamental Vegetation-Impervious surface-Soil (V-I-S) model for mapping urban impervious surfaces. Three scenes of fully polarimetric Radsarsat-2 data for the cities of Shenzhen, Hong Kong and Macau were employed to test and validate the proposed methodology. Experimental results indicated that the overall accuracy and Kappa coefficient were 96.00% and 0.8808 in Shenzhen, 93.87% and 0.8307 in Hong Kong and 97.48% and 0.9354 in Macau, indicating the applicability and great potential of the new scheme for impervious surfaces classification using polarimetric SAR data. Comparison with the traditional scheme indicated that this new scheme was able to improve the overall accuracy by up to 4.6% and Kappa coefficient by up to 0.18.

FORUM: A Suggestion for an Improved Vegetation Scheme for Local and Global Mapping and Monitoring.

PubMed

ADAMS

1999-01-01

/ Understanding of global ecological problems is at least partly dependent on clear assessments of vegetation change, and such assessment is always dependent on the use of a vegetation classification scheme. Use of satellite remotely sensed data is the only practical means of carrying out any global-scale vegetation mapping exercise, but if the resulting maps are to be useful to most ecologists and conservationists, they must be closely tied to clearly defined features of vegetation on the ground. Furthermore, much of the mapping that does take place involves more local-scale description of field sites; for purposes of cost and practicality, such studies usually do not involve remote sensing using satellites. There is a need for a single scheme that integrates the smallest to the largest scale in a way that is meaningful to most environmental scientists. Existing schemes are unsatisfactory for this task; they are ambiguous, unnecessarily complex, and their categories do not correspond to common-sense definitions. In response to these problems, a simple structural-physiognomically based scheme with 23 fundamental categories is proposed here for mapping and monitoring on any scale, from local to global. The fundamental categories each subdivide into more specific structural categories for more detailed mapping, but all the categories can be used throughout the world and at any scale, allowing intercomparison between regions. The next stage in the process will be to obtain the views of as many people working in as many different fields as possible, to see whether the proposed scheme suits their needs and how it should be modified. With a few modifications, such a scheme could easily be appended to an existing land cover classification scheme, such as the FAO system, greatly increasing the usefulness and accessability of the results of the landcover classification. KEY WORDS: Vegetation scheme; Mapping; Monitoring; Land cover

What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology.

PubMed

Paschalidou, A K; Kassomenos, P A

2016-01-01

Wildfire management is closely linked to robust forecasts of changes in wildfire risk related to meteorological conditions. This link can be bridged either through fire weather indices or through statistical techniques that directly relate atmospheric patterns to wildfire activity. In the present work the COST-733 classification schemes are applied in order to link wildfires in Greece with synoptic circulation patterns. The analysis reveals that the majority of wildfire events can be explained by a small number of specific synoptic circulations, hence reflecting the synoptic climatology of wildfires. All 8 classification schemes used, prove that the most fire-dangerous conditions in Greece are characterized by a combination of high atmospheric pressure systems located N to NW of Greece, coupled with lower pressures located over the very Eastern part of the Mediterranean, an atmospheric pressure pattern closely linked to the local Etesian winds over the Aegean Sea. During these events, the atmospheric pressure has been reported to be anomalously high, while anomalously low 500hPa geopotential heights and negative total water column anomalies were also observed. Among the various classification schemes used, the 2 Principal Component Analysis-based classifications, namely the PCT and the PXE, as well as the Leader Algorithm classification LND proved to be the best options, in terms of being capable to isolate the vast amount of fire events in a small number of classes with increased frequency of occurrence. It is estimated that these 3 schemes, in combination with medium-range to seasonal climate forecasts, could be used by wildfire risk managers to provide increased wildfire prediction accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

Computer-aided diagnosis of pulmonary diseases using x-ray darkfield radiography

NASA Astrophysics Data System (ADS)

Einarsdóttir, Hildur; Yaroshenko, Andre; Velroyen, Astrid; Bech, Martin; Hellbach, Katharina; Auweter, Sigrid; Yildirim, Önder; Meinel, Felix G.; Eickelberg, Oliver; Reiser, Maximilian; Larsen, Rasmus; Kjær Ersbøll, Bjarne; Pfeiffer, Franz

2015-12-01

In this work we develop a computer-aided diagnosis (CAD) scheme for classification of pulmonary disease for grating-based x-ray radiography. In addition to conventional transmission radiography, the grating-based technique provides a dark-field imaging modality, which utilizes the scattering properties of the x-rays. This modality has shown great potential for diagnosing early stage emphysema and fibrosis in mouse lungs in vivo. The CAD scheme is developed to assist radiologists and other medical experts to develop new diagnostic methods when evaluating grating-based images. The scheme consists of three stages: (i) automatic lung segmentation; (ii) feature extraction from lung shape and dark-field image intensities; (iii) classification between healthy, emphysema and fibrosis lungs. A study of 102 mice was conducted with 34 healthy, 52 emphysema and 16 fibrosis subjects. Each image was manually annotated to build an experimental dataset. System performance was assessed by: (i) determining the quality of the segmentations; (ii) validating emphysema and fibrosis recognition by a linear support vector machine using leave-one-out cross-validation. In terms of segmentation quality, we obtained an overlap percentage (Ω) 92.63  ±  3.65%, Dice Similarity Coefficient (DSC) 89.74  ±  8.84% and Jaccard Similarity Coefficient 82.39  ±  12.62%. For classification, the accuracy, sensitivity and specificity of diseased lung recognition was 100%. Classification between emphysema and fibrosis resulted in an accuracy of 93%, whilst the sensitivity was 94% and specificity 88%. In addition to the automatic classification of lungs, deviation maps created by the CAD scheme provide a visual aid for medical experts to further assess the severity of pulmonary disease in the lung, and highlights regions affected.

Centrifuge: rapid and sensitive classification of metagenomic sequences.

PubMed

Kim, Daehwan; Song, Li; Breitwieser, Florian P; Salzberg, Steven L

2016-12-01

Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. © 2016 Kim et al.; Published by Cold Spring Harbor Laboratory Press.

"Interactive Classification Technology"

NASA Technical Reports Server (NTRS)

deBessonet, Cary

1999-01-01

The investigators are upgrading a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the technologies to be used in automated reasoning and interactive classification systems. The overall goals of the project are: a) the enhancement of the representation language SL to accommodate multiple perspectives and a wider range of meaning; b) the development of a sufficient set of operators to enable the interpreter of SL to handle representations of basic cognitive acts; and c) the development of a default inference scheme to operate over SL notation as it is encoded. As to particular goals the first-year work plan focused on inferencing and.representation issues, including: 1) the development of higher level cognitive/ classification functions and conceptual models for use in inferencing and decision making; 2) the specification of a more detailed scheme of defaults and the enrichment of SL notation to accommodate the scheme; and 3) the adoption of additional perspectives for inferencing.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

Microtopographic characterization of ice-wedge polygon landscape in Barrow, Alaska: a digital map of troughs, rims, centers derived from high resolution (0.25 m) LiDAR data

DOE Data Explorer

Gangodagamage, Chandana; Wullschleger, Stan

2014-07-03

The dataset represents microtopographic characterization of the ice-wedge polygon landscape in Barrow, Alaska. Three microtopographic features are delineated using 0.25 m high resolution digital elevation dataset derived from LiDAR. The troughs, rims, and centers are the three categories in this classification scheme. The polygon troughs are the surface expression of the ice-wedges that are in lower elevations than the interior polygon. The elevated shoulders of the polygon interior immediately adjacent to the polygon troughs are the polygon rims for the low center polygons. In case of high center polygons, these features are the topographic highs. In this classification scheme, both topographic highs and rims are considered as polygon rims. The next version of the dataset will include more refined classification scheme including separate classes for rims ad topographic highs. The interior part of the polygon just adjacent to the polygon rims are the polygon centers.

Dewey Decimal Classification for U. S. Conn: An Advantage?

ERIC Educational Resources Information Center

Marek, Kate

This paper examines the use of the Dewey Decimal Classification (DDC) system at the U. S. Conn Library at Wayne State College (WSC) in Nebraska. Several developments in the last 20 years which have eliminated the trend toward reclassification of academic library collections from DDC to the Library of Congress (LC) classification scheme are…

A Global Classification System for Catchment Hydrology

NASA Astrophysics Data System (ADS)

Woods, R. A.

2004-05-01

It is a shocking state of affairs - there is no underpinning scientific taxonomy of catchments. There are widely used global classification systems for climate, river morphology, lakes and wetlands, but for river catchments there exists only a plethora of inconsistent, incomplete regional schemes. By proceeding without a common taxonomy for catchments, freshwater science has missed one of its key developmental stages, and has leapt from definition of phenomena to experiments, theories and models, without the theoretical framework of a classification. I propose the development of a global hierarchical classification system for physical aspects of river catchments, to help underpin physical science in the freshwater environment and provide a solid foundation for classification of river ecosystems. Such a classification scheme can open completely new vistas in hydrology: for example it will be possible to (i) rationally transfer experimental knowledge of hydrological processes between basins anywhere in the world, provided they belong to the same class; (ii) perform meaningful meta-analyses in order to reconcile studies that show inconsistent results (iii) generate new testable hypotheses which involve locations worldwide.

Guidelines for a priori grouping of species in hierarchical community models

USGS Publications Warehouse

Pacifici, Krishna; Zipkin, Elise; Collazo, Jaime; Irizarry, Julissa I.; DeWan, Amielle A.

2014-01-01

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process borrows from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Heuristic pattern correction scheme using adaptively trained generalized regression neural networks.

PubMed

Hoya, T; Chambers, J A

2001-01-01

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

PubMed

Shangguan, Wei; Dai, YongJiu; García-Gutiérrez, Carlos; Yuan, Hua

2014-01-01

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

Log-ratio transformed major element based multidimensional classification for altered High-Mg igneous rocks

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Quiroz-Ruiz, Alfredo

2016-12-01

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High-Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High-Mg and 32,948 "Common") relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log-ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High-Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High-Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High-Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%-100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass-balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

Adaptive video-based vehicle classification technique for monitoring traffic.

DOT National Transportation Integrated Search

2015-08-01

This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order : to classify the vehicles from video, based on Federal Highway Administration (FHWA)s recommended vehicle : classification scheme....

Modern classification and outcome predictors of surgery in patients with brain arteriovenous malformations.

PubMed

Tayebi Meybodi, Ali; Lawton, Michael T

2018-02-23

Brain arteriovenous malformations (bAVM) are challenging lesions. Part of this challenge stems from the infinite diversity of these lesions regarding shape, location, anatomy, and physiology. This diversity has called on a variety of treatment modalities for these lesions, of which microsurgical resection prevails as the mainstay of treatment. As such, outcome prediction and managing strategy mainly rely on unraveling the nature of these complex tangles and ways each lesion responds to various therapeutic modalities. This strategy needs the ability to decipher each lesion through accurate and efficient categorization. Therefore, classification schemes are essential parts of treatment planning and outcome prediction. This article summarizes different surgical classification schemes and outcome predictors proposed for bAVMs.

A Systematic Survey of Star Formation with the ORION MIDEX Mission

NASA Astrophysics Data System (ADS)

Scowen, P.; Morse, J.; Beasley, M.; Hester, J.; Windhorst, R.; Desch, S.; Jansen, R.; Calzetti, D.; Padgett, D.; Hartigan, P.; Oey, S.; Bally, J.; Gallagher, J.; O'Connell, R.; Kennicutt, R.; Lauer, T.

2004-05-01

The ORION MIDEX mission is a 1.2m UV-visual observatory orbiting at L2 that will conduct the first-ever high spatial resolution survey of a statistically significant sample of visible star-forming environments in the Solar neighborhood in emission lines and continuum. This survey will be used to characterize the star and planet forming environments within 2.5 kpc of the Sun, infer global properties and star formation history in these regions, understand how the environment influences the process of star and planet formation, and develop a classification scheme for star forming regions incorporating the earlier results. Based on these findings we will then conduct a similar high spatial resolution survey of large portions of the Magellanic Clouds, applying the classification scheme from local star forming environments to analogous regions in nearby galaxies, extending the classification scheme to regions that do not have nearby analogs but are common in external galaxies. The results from the local survey will allow us to infer characteristics of low mass star forming environments in the Magellanic Clouds, study the spatial distribution of star forming environments and analyze stellar population photometry to trace star formation history. Finally we will image a representative sample of external galaxies using the same filters used to characterize nearby star formation regions. We will map the distribution of star forming region type as a function of galactic environment for galaxies out to 5 Mpc to infer the distribution and history of low-mass star formation over galactic scales, characterize the stellar content and star formation history of galaxies, and relate these results to the current star forming environments in these galaxies. Ultimately we intend to use these diagnostics to extrapolate to star formation environments in the higher redshift Universe. We will also present an update on the technology development, project planning and operations for the proposed mission.

Space-based Observations of Star Formation using ORION: THE MIDEX

NASA Astrophysics Data System (ADS)

Scowen, P.; Morse, J.; Beasley, M.; Hester, J.; Windhorst, R.; Jansen, R.; Lauer, T.; Danielson, E.; Sepulveda, C.; Olarte, G.; ORION MIDEX Science Team

2003-12-01

The ORION MIDEX mission is a 1.2m UV-visual observatory orbiting at L2 that will conduct the first-ever high spatial resolution survey of a statistically significant sample of visible star-forming environments in the Solar neighborhood in emission lines and continuum. This survey will be used to characterize the star and planet forming environments within 2.5 kpc of the Sun, infer global properties and star formation history in these regions, understand how the environment influences the process of star and planet formation, and develop a classification scheme for star forming regions incorporating the earlier results. Based on these findings we will then conduct a similar high spatial resolution survey of large portions of the Magellanic Clouds, applying the classification scheme from local star forming environments to analogous regions in nearby galaxies, extending the classification scheme to regions that do not have nearby analogs but are common in external galaxies. The results from the local survey will allow us to infer characteristics of low mass star forming environments in the Magellanic Clouds, study the spatial distribution of star forming environments and analyze stellar population photometry to trace star formation history. Finally we will image a representative sample of external galaxies using the same filters used to characterize nearby star formation regions. We will map the distribution of star forming region type as a function of galactic environment for galaxies out to 5 Mpc to infer the distribution and history of low-mass star formation over galactic scales, characterize the stellar content and star formation history of galaxies, and relate these results to the current star forming environments in these galaxies. Ultimately we intend to use these diagnostics to extrapolate to star formation environments in the higher redshift Universe. We will also present details on technology development, project planning and operations for the proposed mission.

ORION: Hierarchical Space-based Observations of Star Formation, From Near to Far

NASA Astrophysics Data System (ADS)

Scowen, P. A.; Morse, J. A.; Beasley, M.; Veach, T.; ORION Science Team

2005-12-01

The ORION MIDEX mission is a 1.2m UV-visual observatory orbiting at L2 that will conduct the first-ever high spatial resolution survey of a statistically significant sample of visible star-forming environments in the Solar neighborhood in emission lines and continuum. This survey will be used to characterize the star and planet forming environments within 2.5 kpc of the Sun, infer global properties and star formation history in these regions, understand how the environment influences the process of star and planet formation, and develop a classification scheme for star forming regions incorporating the earlier results. Based on these findings we will then conduct a similar high spatial resolution survey of large portions of the Magellanic Clouds, applying the classification scheme from local star forming environments to analogous regions in nearby galaxies, extending the classification scheme to regions that do not have nearby analogs but are common in external galaxies. The results from the local survey will allow us to infer characteristics of low mass star forming environments in the Magellanic Clouds, study the spatial distribution of star forming environments and analyze stellar population photometry to trace star formation history. Finally we will image a representative sample of external galaxies using the same filters used to characterize nearby star formation regions. We will map the distribution of star forming region type as a function of galactic environment for galaxies out to 5 Mpc to infer the distribution and history of low-mass star formation over galactic scales, characterize the stellar content and star formation history of galaxies, and relate these results to the current star forming environments in these galaxies. Ultimately we intend to use these diagnostics to extrapolate to star formation environments in the higher redshift Universe. We will also present details on technology development, project planning and operations for the proposed mission.

A Systematic Survey of Star Formation with the ORION MIDEX Mission

NASA Astrophysics Data System (ADS)

Scowen, P.; Morse, J.; Beasley, M.; Hester, J.; Windhorst, R.; Desch, S.; Jansen, R.; Calzetti, D.; Padgett, D.; Hartigan, P.; Oey, S.; Bally, J.; Gallagher, J.; O'Connell, R.; Kennicutt, R.; Lauer, T.; McCaughrean, M.

2004-12-01

The ORION MIDEX mission is a 1.2m UV-visual observatory orbiting at L2 that will conduct the first-ever high spatial resolution survey of a statistically significant sample of visible star-forming environments in the Solar neighborhood in emission lines and continuum. This survey will be used to characterize the star and planet forming environments within 2.5 kpc of the Sun, infer global properties and star formation history in these regions, understand how the environment influences the process of star and planet formation, and develop a classification scheme for star forming regions incorporating the earlier results. Based on these findings we will then conduct a similar high spatial resolution survey of large portions of the Magellanic Clouds, applying the classification scheme from local star forming environments to analogous regions in nearby galaxies, extending the classification scheme to regions that do not have nearby analogs but are common in external galaxies. The results from the local survey will allow us to infer characteristics of low mass star forming environments in the Magellanic Clouds, study the spatial distribution of star forming environments and analyze stellar population photometry to trace star formation history. Finally we will image a representative sample of external galaxies using the same filters used to characterize nearby star formation regions. We will map the distribution of star forming region type as a function of galactic environment for galaxies out to 5 Mpc to infer the distribution and history of low-mass star formation over galactic scales, characterize the stellar content and star formation history of galaxies, and relate these results to the current star forming environments in these galaxies. Ultimately we intend to use these diagnostics to extrapolate to star formation environments in the higher redshift Universe. We will also present an update on the technology development, project planning and operations for the proposed mission.

Mucinous tumours of appendix and ovary: an overview and evaluation of current practice.

PubMed

Rouzbahman, Marjan; Chetty, Runjan

2014-03-01

Mucinous lesions of the appendix and ovary are commonly encountered in routine practice. There are several published classification schemes for appendiceal mucinous neoplasms with resultant inconsistent use of terms and clinical doubt. While nomenclature is more settled with regards to ovarian mucinous neoplasms, the difficulty here lies with distinguishing primary from secondary mucinous tumours. This review highlights the terminology and nomenclature for appendiceal mucinous tumours, the relationship with ovarian mucinous neoplasms and pseudomyxoma peritonei, and the features that assist in separating primary from secondary ovarian mucinous tumours.

Just-in-time coding of the problem list in a clinical environment.

PubMed Central

Warren, J. J.; Collins, J.; Sorrentino, C.; Campbell, J. R.

1998-01-01

Clinically useful problem lists are essential to the CPR. Providing a terminology that is standardized and understood by all clinicians is a major challenge. UNMC has developed a lexicon to support their problem list. Using a just-in-time coding strategy, the lexicon is maintained and extended prospectively in a dynamic clinical environment. The terms in the lexicon are mapped to ICD-9-CM, NANDA, and SNOMED International classification schemes. Currently, the lexicon contains 12,000 terms. This process of development and maintenance of the lexicon is described. PMID:9929226

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

The World Health Organization 2016 classification of testicular germ cell tumours: a review and update from the International Society of Urological Pathology Testis Consultation Panel.

PubMed

Williamson, Sean R; Delahunt, Brett; Magi-Galluzzi, Cristina; Algaba, Ferran; Egevad, Lars; Ulbright, Thomas M; Tickoo, Satish K; Srigley, John R; Epstein, Jonathan I; Berney, Daniel M

2017-02-01

Since the last World Health Organization (WHO) classification scheme for tumours of the urinary tract and male genital organs, there have been a number of advances in the understanding, classification, immunohistochemistry and genetics of testicular germ cell tumours. The updated 2016 draft classification was discussed at an International Society of Urological Pathology Consultation on Testicular and Penile Cancer. This review addresses the main updates to germ cell tumour classification. Major changes include a pathogenetically derived classification using germ cell neoplasia in situ (GCNIS) as a new name for the precursor lesion, and the distinction of prepubertal tumours (non-GCNIS-derived) from postpubertal-type tumours (GCNIS-derived), acknowledging the existence of rare benign prepubertal-type teratomas in the postpubertal testis. Spermatocytic tumour is adopted as a replacement for spermatocytic seminoma, to avoid potential confusion with the unrelated usual seminoma. The spectrum of trophoblastic tumours arising in the setting of testicular germ cell tumour continues to expand, to include epithelioid and placental site trophoblastic tumours analogous to those of the gynaecological tract. Currently, reporting of anaplasia (seminoma or spermatocytic tumour) or immaturity (teratoma) is not required, as these do not have demonstrable prognostic importance. In contrast, overgrowth of a teratomatous component (somatic-type malignancy) and sarcomatous change in spermatocytic tumour indicate more aggressive behaviour, and should be reported. © 2016 John Wiley & Sons Ltd.

Etiologic classification of TIA and minor stroke by A-S-C-O and causative classification system as compared to TOAST reduces the proportion of patients categorized as cause undetermined.

PubMed

Desai, Jamsheed A; Abuzinadah, Ahmad R; Imoukhuede, Oje; Bernbaum, Manya L; Modi, Jayesh; Demchuk, Andrew M; Coutts, Shelagh B

2014-01-01

The assortment of patients based on the underlying pathophysiology is central to preventing recurrent stroke after a transient ischemic attack and minor stroke (TIA-MS). The causative classification of stroke (CCS) and the A-S-C-O (A for atherosclerosis, S for small vessel disease, C for Cardiac source, O for other cause) classification schemes have recently been developed. These systems have not been specifically applied to the TIA-MS population. We hypothesized that both CCS and A-S-C-O would increase the proportion of patients with a definitive etiologic mechanism for TIA-MS as compared with TOAST. Patients were analyzed from the CATCH study. A single-stroke physician assigned all patients to an etiologic subtype using published algorithms for TOAST, CCS and ASCO. We compared the proportions in the various categories for each classification scheme and then the association with stroke progression or recurrence was assessed. TOAST, CCS and A-S-C-O classification schemes were applied in 469 TIA-MS patients. When compared to TOAST both CCS (58.0 vs. 65.3%; p < 0.0001) and ASCO grade 1 or 2 (37.5 vs. 65.3%; p < 0.0001) assigned fewer patients as cause undetermined. CCS had increased assignment of cardioembolism (+3.8%, p = 0.0001) as compared with TOAST. ASCO grade 1 or 2 had increased assignment of cardioembolism (+8.5%, p < 0.0001), large artery atherosclerosis (+14.9%, p < 0.0001) and small artery occlusion (+4.3%, p < 0.0001) as compared with TOAST. Compared with CCS, using ASCO resulted in a 20.5% absolute reduction in patients assigned to the 'cause undetermined' category (p < 0.0001). Patients who had multiple high-risk etiologies either by CCS or ASCO classification or an ASCO undetermined classification had a higher chance of having a recurrent event. Both CCS and ASCO schemes reduce the proportion of TIA and minor stroke patients classified as 'cause undetermined.' ASCO resulted in the fewest patients classified as cause undetermined. Stroke recurrence after TIA-MS is highest in patients with multiple high-risk etiologies or cryptogenic stroke classified by ASCO. © 2014 S. Karger AG, Basel.

Development of a Hazard Classification Scheme for Substances Used in the Fraudulent Adulteration of Foods.

PubMed

Everstine, Karen; Abt, Eileen; McColl, Diane; Popping, Bert; Morrison-Rowe, Sara; Lane, Richard W; Scimeca, Joseph; Winter, Carl; Ebert, Andrew; Moore, Jeffrey C; Chin, Henry B

2018-01-01

Food fraud, the intentional misrepresentation of the true identity of a food product or ingredient for economic gain, is a threat to consumer confidence and public health and has received increased attention from both regulators and the food industry. Following updates to food safety certification standards and publication of new U.S. regulatory requirements, we undertook a project to (i) develop a scheme to classify food fraud-related adulterants based on their potential health hazard and (ii) apply this scheme to the adulterants in a database of 2,970 food fraud records. The classification scheme was developed by a panel of experts in food safety and toxicology from the food industry, academia, and the U.S. Food and Drug Administration. Categories and subcategories were created through an iterative process of proposal, review, and validation using a subset of substances known to be associated with the fraudulent adulteration of foods. Once developed, the scheme was applied to the adulterants in the database. The resulting scheme included three broad categories: 1, potentially hazardous adulterants; 2, adulterants that are unlikely to be hazardous; and 3, unclassifiable adulterants. Categories 1 and 2 consisted of seven subcategories intended to further define the range of hazard potential for adulterants. Application of the scheme to the 1,294 adulterants in the database resulted in 45% of adulterants classified in category 1 (potentially hazardous). Twenty-seven percent of the 1,294 adulterants had a history of causing consumer illness or death, were associated with safety-related regulatory action, or were classified as allergens. These results reinforce the importance of including a consideration of food fraud-related adulterants in food safety systems. This classification scheme supports food fraud mitigation efforts and hazard identification as required in the U.S. Food Safety Modernization Act Preventive Controls Rules.

Functional traits, convergent evolution, and periodic tables of niches.

PubMed

Winemiller, Kirk O; Fitzgerald, Daniel B; Bower, Luke M; Pianka, Eric R

2015-08-01

Ecology is often said to lack general theories sufficiently predictive for applications. Here, we examine the concept of a periodic table of niches and feasibility of niche classification schemes from functional trait and performance data. Niche differences and their influence on ecological patterns and processes could be revealed effectively by first performing data reduction/ordination analyses separately on matrices of trait and performance data compiled according to logical associations with five basic niche 'dimensions', or aspects: habitat, life history, trophic, defence and metabolic. Resultant patterns then are integrated to produce interpretable niche gradients, ordinations and classifications. Degree of scheme periodicity would depend on degrees of niche conservatism and convergence causing species clustering across multiple niche dimensions. We analysed a sample data set containing trait and performance data to contrast two approaches for producing niche schemes: species ordination within niche gradient space, and niche categorisation according to trait-value thresholds. Creation of niche schemes useful for advancing ecological knowledge and its applications will depend on research that produces functional trait and performance datasets directly related to niche dimensions along with criteria for data standardisation and quality. As larger databases are compiled, opportunities will emerge to explore new methods for data reduction, ordination and classification. © 2015 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Using dual classifications in the development of avian wetland indices of biological integrity for wetlands in West Virginia, USA.

PubMed

Veselka, Walter; Anderson, James T; Kordek, Walter S

2010-05-01

Considerable resources are being used to develop and implement bioassessment methods for wetlands to ensure that "biological integrity" is maintained under the United States Clean Water Act. Previous research has demonstrated that avian composition is susceptible to human impairments at multiple spatial scales. Using a site-specific disturbance gradient, we built avian wetland indices of biological integrity (AW-IBI) specific to two wetland classification schemes, one based on vegetative structure and the other based on the wetland's position in the landscape and sources of water. The resulting class-specific AW-IBI was comprised of one to four metrics that varied in their sensitivity to the disturbance gradient. Some of these metrics were specific to only one of the classification schemes, whereas others could discriminate varying levels of disturbance regardless of classification scheme. Overall, all of the derived biological indices specific to the vegetative structure-based classes of wetlands had a significant relation with the disturbance gradient; however, the biological index derived for floodplain wetlands exhibited a more consistent response to a local disturbance gradient. We suspect that the consistency of this response is due to the inherent nature of the connectivity of available habitat in floodplain wetlands.

A Critical Review of Mode of Action (MOA) Assignment ...

EPA Pesticide Factsheets

There are various structure-based classification schemes to categorize chemicals based on mode of action (MOA) which have been applied for both eco and human health toxicology. With increasing calls to assess thousands of chemicals, some of which have little available information other than structure, clear understanding how each of these MOA schemes was devised, what information they are based on, and the limitations of each approach is critical. Several groups are developing low-tier methods to more easily classify or assess chemicals, using approaches such as the ecological threshold of concern (eco-TTC) and chemical-activity. Evaluation of these approaches and determination of their domain of applicability is partly dependent on the MOA classification that is used. The most commonly used MOA classification schemes for ecotoxicology include Verhaar and Russom (included in ASTER), both of which are used to predict acute aquatic toxicity MOA. Verhaar is a QSAR-based system that classifies chemicals into one of 4 classes, with a 5th class specified for those chemicals that are not classified in the other 4. ASTER/Russom includes 8 classifications: narcotics (3 groups), oxidative phosphorylation uncouplers, respiratory inhibitors, electrophiles/proelectrophiles, AChE inhibitors, or CNS seizure agents. Other methodologies include TEST (Toxicity Estimation Software Tool), a computational chemistry-based application that allows prediction to one of 5 broad MOA

Global land cover mapping: a review and uncertainty analysis

USGS Publications Warehouse

Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

2014-01-01

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

PubMed

Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

2016-01-01

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification.

PubMed

Pozo-Aguilar, Jorge O; Monroy-Martínez, Verónica; Díaz, Daniel; Barrios-Palacios, Jacqueline; Ramos, Celso; Ulloa-García, Armando; García-Pillado, Janet; Ruiz-Ordaz, Blanca H

2014-12-11

Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification. A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated. The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur. The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009 WHO classification showed greater sensitivity to identify SD in real time. Timely identification of SD enables accurate early decisions, allowing proper management of health resources for the benefit of patients at risk for SD. This is possible based on the 2009 WHO classification.

COMPARISON OF GEOGRAPHIC CLASSIFICATION SCHEMES FOR MID-ATLANTIC STREAM FISH ASSEMBLAGES

EPA Science Inventory

Understanding the influence of geographic factors in structuring fish assemblages is crucial to developing a comprehensive assessment of stream conditions. We compared the classification strengths (CS) of geographic groups (ecoregions and catchments), stream order, and groups bas...

Sorting Potatoes for Miss Bonner.

ERIC Educational Resources Information Center

Herreid, Clyde Freeman

1998-01-01

Discusses the basis of a classification scheme for types of case studies. Four major classification headings are identified: (1) individual assignment; (2) lecture; (3) discussion; and (4) small group activities. Describes each heading from the point of view of several teaching methods. (DDR)

SOM Classification of Martian TES Data

NASA Technical Reports Server (NTRS)

Hogan, R. C.; Roush, T. L.

2002-01-01

A classification scheme based on unsupervised self-organizing maps (SOM) is described. Results from its application to the ASU mineral spectral database are presented. Applications to the Martian Thermal Emission Spectrometer data are discussed. Additional information is contained in the original extended abstract.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Classification of extraterrestrial civilizations

NASA Astrophysics Data System (ADS)

Tang, Tong B.; Chang, Grace

1991-06-01

A scheme of classification of extraterrestrial intelligence (ETI) communities based on the scope of energy accessible to the civilization in question is proposed as an alternative to the Kardeshev (1964) scheme that includes three types of civilization, as determined by their levels of energy expenditure. The proposed scheme includes six classes: (1) a civilization that runs essentially on energy exerted by individual beings or by domesticated lower life forms, (2) harnessing of natural sources on planetary surface with artificial constructions, like water wheels and wind sails, (3) energy from fossils and fissionable isotopes, mined beneath the planet surface, (4) exploitation of nuclear fusion on a large scale, whether on the planet, in space, or from primary solar energy, (5) extensive use of antimatter for energy storage, and (6) energy from spacetime, perhaps via the action of naked singularities.

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

NASA Astrophysics Data System (ADS)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.

A Job Classification Scheme for Health Manpower

PubMed Central

Weiss, Jeffrey H.

1968-01-01

The Census Bureau's occupational classification scheme and concept of the “health services industry” are inadequate tools for analysis of the changing job structure of health manpower. In an attempt to remedy their inadequacies, a new analytical framework—drawing upon the work of James Scoville on the job content of the U.S. economy—was devised. The first stage in formulating this new framework was to determine which jobs should be considered health jobs. The overall health care job family was designed to encompass jobs in which the primary technical focus or function is oriented toward the provision of health services. There are two dimensions to the job classification scheme presented here. The first describes each job in terms of job content; relative income data and minimum education and training requirements were employed as surrogate measures. By this means, health care jobs were grouped by three levels of job content: high, medium, and low. The other dimension describes each job in terms of its technical focus or function; by this means, health care jobs were grouped into nine job families. PMID:5673666

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

NASA Astrophysics Data System (ADS)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

A Classification Scheme for Analyzing Mobile Apps Used to Prevent and Manage Disease in Late Life

PubMed Central

Wang, Aiguo; Lu, Xin; Chen, Hongtu; Li, Changqun; Levkoff, Sue

2014-01-01

Background There are several mobile apps that offer tools for disease prevention and management among older adults, and promote health behaviors that could potentially reduce or delay the onset of disease. A classification scheme that categorizes apps could be useful to both older adult app users and app developers. Objective The objective of our study was to build and evaluate the effectiveness of a classification scheme that classifies mobile apps available for older adults in the “Health & Fitness” category of the iTunes App Store. Methods We constructed a classification scheme for mobile apps according to three dimensions: (1) the Precede-Proceed Model (PPM), which classifies mobile apps in terms of predisposing, enabling, and reinforcing factors for behavior change; (2) health care process, specifically prevention versus management of disease; and (3) health conditions, including physical health and mental health. Content analysis was conducted by the research team on health and fitness apps designed specifically for older adults, as well as those applicable to older adults, released during the months of June and August 2011 and August 2012. Face validity was assessed by a different group of individuals, who were not related to the study. A reliability analysis was conducted to confirm the accuracy of the coding scheme of the sample apps in this study. Results After applying sample inclusion and exclusion criteria, a total of 119 apps were included in the study sample, of which 26/119 (21.8%) were released in June 2011, 45/119 (37.8%) in August 2011, and 48/119 (40.3%) in August 2012. Face validity was determined by interviewing 11 people, who agreed that this scheme accurately reflected the nature of this application. The entire study sample was successfully coded, demonstrating satisfactory inter-rater reliability by two independent coders (95.8% initial concordance and 100% concordance after consensus was reached). The apps included in the study sample were more likely to be used for the management of disease than prevention of disease (109/119, 91.6% vs 15/119, 12.6%). More apps contributed to physical health rather than mental health (81/119, 68.1% vs 47/119, 39.5%). Enabling apps (114/119, 95.8%) were more common than reinforcing (20/119, 16.8%) or predisposing apps (10/119, 8.4%). Conclusions The findings, including face validity and inter-rater reliability, support the integrity of the proposed classification scheme for categorizing mobile apps for older adults in the “Health and Fitness” category available in the iTunes App Store. Using the proposed classification system, older adult app users would be better positioned to identify apps appropriate for their needs, and app developers would be able to obtain the distributions of available mobile apps for health-related concerns of older adults more easily. PMID:25098687

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

PubMed

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

Evaluation of several schemes for classification of remotely sensed data: Their parameters and performance. [Foster County, North Dakota; Grant County, Kansas; Iroquois County, Illinois, Tippecanoe County, Indiana; and Pottawattamie and Shelby Counties, Iowa

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.; Akiyama, T. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Data sets for corn, soybeans, winter wheat, and spring wheat were used to evaluate the following schemes for crop identification: (1) per point Gaussian maximum classifier; (2) per point sum of normal densities classifiers; (3) per point linear classifier; (4) per point Gaussian maximum likelihood decision tree classifiers; and (5) texture sensitive per field Gaussian maximum likelihood classifier. Test site location and classifier both had significant effects on classification accuracy of small grains; classifiers did not differ significantly in overall accuracy, with the majority of the difference among classifiers being attributed to training method rather than to the classification algorithm applied. The complexity of use and computer costs for the classifiers varied significantly. A linear classification rule which assigns each pixel to the class whose mean is closest in Euclidean distance was the easiest for the analyst and cost the least per classification.

ERTS-1 data applications to Minnesota forest land use classification

NASA Technical Reports Server (NTRS)

Sizer, J. E. (Principal Investigator); Eller, R. G.; Meyer, M. P.; Ulliman, J. J.

1973-01-01

The author has identified the following significant results. Color-combined ERTS-1 MSS spectral slices were analyzed to determine the maximum (repeatable) level of meaningful forest resource classification data visually attainable by skilled forest photointerpreters for the following purposes: (1) periodic updating of the Minnesota Land Management Information System (MLMIS) statewide computerized land use data bank, and (2) to provide first-stage forest resources survey data for large area forest land management planning. Controlled tests were made of two forest classification schemes by experienced professional foresters with special photointerpretation training and experience. The test results indicate it is possible to discriminate the MLMIS forest class from the MLMIS nonforest classes, but that it is not possible, under average circumstances, to further stratify the forest classification into species components with any degree of reliability with ERTS-1 imagery. An ongoing test of the resulting classification scheme involves the interpretation, and mapping, of the south half of Itasca County, Minnesota, with ERTS-1 imagery. This map is undergoing field checking by on the ground field cooperators, whose evaluation will be completed in the fall of 1973.

A fast and efficient segmentation scheme for cell microscopic image.

PubMed

Lebrun, G; Charrier, C; Lezoray, O; Meurie, C; Cardot, H

2007-04-27

Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mainzer, A.; Masiero, J.; Hand, E.

The NEOWISE data set offers the opportunity to study the variations in albedo for asteroid classification schemes based on visible and near-infrared observations for a large sample of minor planets. We have determined the albedos for nearly 1900 asteroids classified by the Tholen, Bus, and Bus-DeMeo taxonomic classification schemes. We find that the S-complex spans a broad range of bright albedos, partially overlapping the low albedo C-complex at small sizes. As expected, the X-complex covers a wide range of albedos. The multiwavelength infrared coverage provided by NEOWISE allows determination of the reflectivity at 3.4 and 4.6 {mu}m relative to themore » visible albedo. The direct computation of the reflectivity at 3.4 and 4.6 {mu}m enables a new means of comparing the various taxonomic classes. Although C, B, D, and T asteroids all have similarly low visible albedos, the D and T types can be distinguished from the C and B types by examining their relative reflectance at 3.4 and 4.6 {mu}m. All of the albedo distributions are strongly affected by selection biases against small, low albedo objects, as all objects selected for taxonomic classification were chosen according to their visible light brightness. Due to these strong selection biases, we are unable to determine whether or not there are correlations between size, albedo, and space weathering. We argue that the current set of classified asteroids makes any such correlations difficult to verify. A sample of taxonomically classified asteroids drawn without significant albedo bias is needed in order to perform such an analysis.« less

A methodology to address mixed AGN and starlight contributions in emission line galaxies found in the RESOLVE survey and ECO catalog

NASA Astrophysics Data System (ADS)

Richardson, Chris T.; Kannappan, Sheila; Bittner, Ashley; Isaac, Rohan; RESOLVE

2017-01-01

We present a novel methodology for modeling emission line galaxy samples that span the entire BPT diagram. Our methodology has several advantages over current modeling schemes: the free variables in the model are identical for both AGN and SF galaxies; these free variables are more closely linked to observable galaxy properties; and the ionizing spectra including an AGN and starlight are handled self-consistently rather than empirically. We show that our methodology is capable of fitting the vast majority of SDSS galaxies that fall within the traditional regions of galaxy classification on the BPT diagram. We also present current results for relaxing classification boundaries and extending our galaxies into the dwarf regime, using the REsolved Spectroscopy of a Local VolumE (RESOLVE) survey and the Environmental COntext (ECO) catalog, with special attention to compact blue E/S0s. We compare this methodology to PCA decomposition of the spectra. This work is supported by National Science Foundation awards AST-0955368 and CISE/ACI-1156614.

A new classification scheme for treating blunt aortic injury.

PubMed

Starnes, Benjamin W; Lundgren, Rachel S; Gunn, Martin; Quade, Samantha; Hatsukami, Thomas S; Tran, Nam T; Mokadam, Nahush; Aldea, Gabriel

2012-01-01

There are numerous questions about the treatment of blunt aortic injury (BAI), including the management of small intimal tears, what injury characteristics are predictive of death from rupture, and which patients actually need intervention. We used our experience in treating BAI during the past decade to create a classification scheme based on radiographic and clinical data and to provide clear treatment guidelines. The records of patients admitted with BAI from 1999 to 2008 were retrospectively reviewed. Patients with a radiographically or operatively confirmed diagnosis (echocardiogram, computed tomography, or angiography) of BAI were included. We created a classification system based on the presence or absence of an aortic external contour abnormality, defined as an alteration in the symmetric, round shape of the aorta: (1) intimal tear (IT)-absence of aortic external contour abnormality and intimal defect and/or thrombus of <10 mm in length or width; (2) large intimal flap (LIF)-absence of aortic external contour abnormality and intimal defect and/or thrombus of ≥10 mm in length or width; (3) pseudoaneurysm-presence of aortic external contour abnormality and contained rupture; (4) rupture-presence of aortic external contour abnormality and free contrast extravasation or hemothorax at thoracotomy. We identified 140 patients with BAI. Most injuries were pseudoaneurysm (71%) at the isthmus (70%), 16.4% had an IT, 5.7% had a LIF, and 6.4% had a rupture. Survival rates by classification were IT, 87%; LIF, 100%; pseudoaneurysm, 76%; and rupture, 11% (one patient). Of the ITs, LIFs, and pseudoaneurysms treated nonoperatively, none worsened, and 65% completely healed. No patient with an IT or LIF died. Most patients with ruptures lost vital signs before presentation or in the emergency department and did not survive. Hypotension before or at hospital presentation and size of the periaortic hematoma at the level of the aortic arch predicted likelihood of death from BAI. As a result of this new classification scheme, no patient without an external aortic contour abnormality died of their BAI. ITs can be managed nonoperatively. BAI patients with rupture will die, and resources could be prioritized elsewhere. Those with LIFs do well, and currently, most at our institution are treated with a stent graft. If a pseudoaneurysm is going to rupture, it does so early. Hematoma at the arch on computed tomography scan and hypotension before or at arrival help to predict which pseudoaneurysms need urgent repair. Copyright © 2012 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Use of physically-based models and Soil Taxonomy to identify soil moisture classes: Problems and proposals

NASA Astrophysics Data System (ADS)

Bonfante, A.; Basile, A.; de Mascellis, R.; Manna, P.; Terribile, F.

2009-04-01

Soil classification according to Soil Taxonomy include, as fundamental feature, the estimation of soil moisture regime. The term soil moisture regime refers to the "presence or absence either of ground water or of water held at a tension of less than 1500 kPa in the soil or in specific horizons during periods of the year". In the classification procedure, defining of the soil moisture control section is the primary step in order to obtain the soil moisture regimes classification. Currently, the estimation of soil moisture regimes is carried out through simple calculation schemes, such as Newhall and Billaux models, and only in few cases some authors suggest the use of different more complex models (i.e., EPIC) In fact, in the Soil Taxonomy, the definition of the soil moisture control section is based on the wetting front position in two different conditions: the upper boundary is the depth to which a dry soil will be moistened by 2.5 cm of water within 24 hours and the lower boundary is the depth to which a dry soil will be moistened by 7.5 cm of water within 48 hours. Newhall, Billaux and EPIC models don't use physical laws to describe soil water flows, but they use a simple bucket-like scheme where the soil is divided into several compartments and water moves, instantly, only downward when the field capacity is achieved. On the other side, a large number of one-dimensional hydrological simulation models (SWAP, Cropsyst, Hydrus, MACRO, etc..) are available, tested and successfully used. The flow is simulated according to pressure head gradients through the numerical solution of the Richard's equation. These simulation models can be fruitful used to improve the study of soil moisture regimes. The aims of this work are: (i) analysis of the soil moisture control section concept by a physically based model (SWAP); (ii) comparison of the classification obtained in five different Italian pedoclimatic conditions (Mantova and Lodi in northern Italy; Salerno, Benevento and Caserta in southern Italy) applying the classical models (Newhall e Billaux) and the physically-based models (CropSyst e SWAP), The results have shown that the Soil Taxonomy scheme for the definition of the soil moisture regime is unrealistic for the considered Mediterranean soil hydrological conditions. In fact, the same classifications arise irrespective of the soil type. In this respect some suggestions on how modified the section control boundaries were formulated. Keywords: Soil moisture regimes, Newhall, Swap, Soil Taxonomy

Sensitivity and Specificity of the World Health Organization Dengue Classification Schemes for Severe Dengue Assessment in Children in Rio de Janeiro

PubMed Central

Macedo, Gleicy A.; Gonin, Michelle Luiza C.; Pone, Sheila M.; Cruz, Oswaldo G.; Nobre, Flávio F.; Brasil, Patrícia

2014-01-01

Background The clinical definition of severe dengue fever remains a challenge for researchers in hyperendemic areas like Brazil. The ability of the traditional (1997) as well as the revised (2009) World Health Organization (WHO) dengue case classification schemes to detect severe dengue cases was evaluated in 267 children admitted to hospital with laboratory-confirmed dengue. Principal Findings Using the traditional scheme, 28.5% of patients could not be assigned to any category, while the revised scheme categorized all patients. Intensive therapeutic interventions were used as the reference standard to evaluate the ability of both the traditional and revised schemes to detect severe dengue cases. Analyses of the classified cases (n = 183) demonstrated that the revised scheme had better sensitivity (86.8%, P<0.001), while the traditional scheme had better specificity (93.4%, P<0.001) for the detection of severe forms of dengue. Conclusions/Significance This improved sensitivity of the revised scheme allows for better case capture and increased ICU admission, which may aid pediatricians in avoiding deaths due to severe dengue among children, but, in turn, it may also result in the misclassification of the patients' condition as severe, reflected in the observed lower positive predictive value (61.6%, P<0.001) when compared with the traditional scheme (82.6%, P<0.001). The inclusion of unusual dengue manifestations in the revised scheme has not shifted the emphasis from the most important aspects of dengue disease and the major factors contributing to fatality in this study: shock with consequent organ dysfunction. PMID:24777054

Sensitivity and specificity of the World Health Organization dengue classification schemes for severe dengue assessment in children in Rio de Janeiro.

PubMed

Macedo, Gleicy A; Gonin, Michelle Luiza C; Pone, Sheila M; Cruz, Oswaldo G; Nobre, Flávio F; Brasil, Patrícia

2014-01-01

The clinical definition of severe dengue fever remains a challenge for researchers in hyperendemic areas like Brazil. The ability of the traditional (1997) as well as the revised (2009) World Health Organization (WHO) dengue case classification schemes to detect severe dengue cases was evaluated in 267 children admitted to hospital with laboratory-confirmed dengue. Using the traditional scheme, 28.5% of patients could not be assigned to any category, while the revised scheme categorized all patients. Intensive therapeutic interventions were used as the reference standard to evaluate the ability of both the traditional and revised schemes to detect severe dengue cases. Analyses of the classified cases (n = 183) demonstrated that the revised scheme had better sensitivity (86.8%, P<0.001), while the traditional scheme had better specificity (93.4%, P<0.001) for the detection of severe forms of dengue. This improved sensitivity of the revised scheme allows for better case capture and increased ICU admission, which may aid pediatricians in avoiding deaths due to severe dengue among children, but, in turn, it may also result in the misclassification of the patients' condition as severe, reflected in the observed lower positive predictive value (61.6%, P<0.001) when compared with the traditional scheme (82.6%, P<0.001). The inclusion of unusual dengue manifestations in the revised scheme has not shifted the emphasis from the most important aspects of dengue disease and the major factors contributing to fatality in this study: shock with consequent organ dysfunction.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

The reliability of axis V of the multiaxial classification scheme.

PubMed

van Goor-Lambo, G

1987-07-01

In a reliability study concerning axis V (abnormal psychosocial situations) of the Multiaxial classification scheme for psychiatric disorders in childhood and adolescence, it was found that the level of agreement in scoring was adequate for only 2 out of 12 categories. A proposal for a modification of axis V was made, including a differentiation and regrouping of the categories and an adjustment of the descriptions in the glossary. With this modification of axis V another reliability study was carried out, in which the level of agreement in scoring was adequate for 12 out of 16 categories.

Analysis of DSN software anomalies

NASA Technical Reports Server (NTRS)

Galorath, D. D.; Hecht, H.; Hecht, M.; Reifer, D. J.

1981-01-01

A categorized data base of software errors which were discovered during the various stages of development and operational use of the Deep Space Network DSN/Mark 3 System was developed. A study team identified several existing error classification schemes (taxonomies), prepared a detailed annotated bibliography of the error taxonomy literature, and produced a new classification scheme which was tuned to the DSN anomaly reporting system and encapsulated the work of others. Based upon the DSN/RCI error taxonomy, error data on approximately 1000 reported DSN/Mark 3 anomalies were analyzed, interpreted and classified. Next, error data are summarized and histograms were produced highlighting key tendencies.

Nosology, ontology and promiscuous realism.

PubMed

Binney, Nicholas

2015-06-01

Medics may consider worrying about their metaphysics and ontology to be a waste of time. I will argue here that this is not the case. Promiscuous realism is a metaphysical position which holds that multiple, equally valid, classification schemes should be applied to objects (such as patients) to capture different aspects of their complex and heterogeneous nature. As medics at the bedside may need to capture different aspects of their patients' problems, they may need to use multiple classification schemes (multiple nosologies), and thus consider adopting a different metaphysics to the one commonly in use. © 2014 John Wiley & Sons, Ltd.

Understanding Homicide-Suicide.

PubMed

Knoll, James L

2016-12-01

Homicide-suicide is the phenomenon in which an individual kills 1 or more people and commits suicide. Research on homicide-suicide has been hampered by a lack of an accepted classification scheme and reliance on media reports. Mass murder-suicide is gaining increasing attention particularly in the United States. This article reviews the research and literature on homicide-suicide, proposing a standard classification scheme. Preventive methods are discussed and sociocultural factors explored. For a more accurate and complete understanding of homicide-suicide, it is argued that future research should use the full psychological autopsy approach, to include collateral interviews. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessment of skeletal maturity in scoliosis patients to determine clinical management: a new classification scheme using distal radius and ulna radiographs.

PubMed

Luk, Keith D K; Saw, Lim Beng; Grozman, Samuel; Cheung, Kenneth M C; Samartzis, Dino

2014-02-01

Assessment of skeletal maturity in patients with adolescent idiopathic scoliosis (AIS) is important to guide clinical management. Understanding growth peak and cessation is crucial to determine clinical observational intervals, timing to initiate or end bracing therapy, and when to instrument and fuse. The commonly used clinical or radiologic methods to assess skeletal maturity are still deficient in predicting the growth peak and cessation among adolescents, and bone age is too complicated to apply. To address these concerns, we describe a new distal radius and ulna (DRU) classification scheme to assess skeletal maturity. A prospective study. One hundred fifty young, female AIS patients with hand x-rays and no previous history of spine surgery from a single institute were assessed. Radius and ulna plain radiographs, and various anthropomorphic parameters were assessed. We identified various stages of radius and ulna epiphysis maturity, which were graded as R1-R11 for the radius and U1-U9 for the ulna. The bone age, development of sexual characteristics, standing height, sitting height, arm span, radius length, and tibia length were studied prospectively at each stage of these epiphysis changes. Standing height, sitting height, and arm span growth were at their peak during stages R7 (mean, 11.4 years old) and U5 (mean, 11.0 years old). The long bone growths also demonstrated a common peak at R7 and U5. Cessation of height and arm span growth was noted after stages R10 (mean, 15.6 years old) and U9 (mean, 17.3 years old). The new DRU classification is a practical and easy-to-use scheme that can provide skeletal maturation status. This classification scheme provides close relationship with adolescent growth spurt and cessation of growth. This classification may have a tremendous utility in improving clinical-decision making in the conservative and operative management of scoliosis patients. Copyright © 2014 Elsevier Inc. All rights reserved.

National Library of Medicine Classification: A Scheme for the Shelf Arrangement of Books in a Field of Medicine and Its Related Sciences. Fourth Edition.

ERIC Educational Resources Information Center

Wiggins, Emilie, Ed.

Outlined is the National Library of Medicine classification system for medicine and related sciences. In this system each preclinical science, such as human anatomy, biochemistry or pathology, and each medical subject, such as infectious diseases or pediatrics, receives a two-letter classification. Under each of these main headings numbered minor…

Human Factors Engineering. Student Supplement,

DTIC Science & Technology

1981-08-01

a job TASK TAXONOMY A classification scheme for the different levels of activities in a system, i.e., job - task - sub-task, etc. TASK-AN~ALYSIS...with the classification of learning objectives by learning category so as to identify learningPhas III guidelines necessary for optimum learning to...correct. .4... .the sequencing of all dependent tasks. .1.. .the classification of learning objectives by learning category and the Identification of

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

The anti-hepatitis drug use effect and inventory management optimization from the perspective of hospital drug supply chain.

PubMed

Liu, Zhanyu

2017-09-01

By analyzing the current hospital anti hepatitis drug use, dosage, indications and drug resistance, this article studied the drug inventory management and cost optimization. The author used drug utilization evaluation method, analyzed the amount and kind distribution of anti hepatitis drugs and made dynamic monitoring of inventory. At the same time, the author puts forward an effective scheme of drug classification management, uses the ABC classification method to classify the drugs according to the average daily dose of drugs, and implements the automatic replenishment plan. The design of pharmaceutical services supply chain includes drug procurement platform, warehouse management system and connect to the hospital system through data exchange. Through the statistical analysis of drug inventory, we put forward the countermeasures of drug logistics optimization. The results showed that drug replenishment plan can effectively improve drugs inventory efficiency.

Family Traits of Galaxies: From the Tuning Fork to a Physical Classification in a Multi-Wavelength Context

NASA Astrophysics Data System (ADS)

Rampazzo, Roberto; D'Onofrio, Mauro; Zaggia, Simone; Elmegreen, Debra M.; Laurikainen, Eija; Duc, Pierre-Alain; Gallart, Carme; Fraix-Burnet, Didier

At the time of the Great Debate nebulæ where recognized to have different morphologies and first classifications, sometimes only descriptive, have been attempted. A review of these early classification systems are well documented by the Allan Sandage's review in 2005 (Sandage 2005). This review emphasized the debt, in term of continuity of forms of spiral galaxies, due by the Hubble's classification scheme to the Reynold's systems proposed in 1920 (Reynolds, 1920).

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Taxonomy and Classification Scheme for Artificial Space Objects

DTIC Science & Technology

2013-09-01

filter UVB and spectroscopic measurements) and albedo (including polarimetry ). Earliest classifications of asteroids [17] were based on the filter...similarities of the asteroid colors to K0 to K2V stars. The first more complete asteroid taxonomy was based on a synthesis of polarimetry , radiometry, and

A Critical Review of Mode of Action (MOA) Assignment Classifications for Ecotoxicology

EPA Science Inventory

There are various structure-based classification schemes to categorize chemicals based on mode of action (MOA) which have been applied for both eco and human health toxicology. With increasing calls to assess thousands of chemicals, some of which have little available informatio...

Solar wind classification from a machine learning perspective

NASA Astrophysics Data System (ADS)

Heidrich-Meisner, V.; Wimmer-Schweingruber, R. F.

2017-12-01

It is a very well known fact that the ubiquitous solar wind comes in at least two varieties, the slow solar wind and the coronal hole wind. The simplified view of two solar wind types has been frequently challenged. Existing solar wind categorization schemes rely mainly on different combinations of the solar wind proton speed, the O and C charge state ratios, the Alfvén speed, the expected proton temperature and the specific proton entropy. In available solar wind classification schemes, solar wind from stream interaction regimes is often considered either as coronal hole wind or slow solar wind, although their plasma properties are different compared to "pure" coronal hole or slow solar wind. As shown in Neugebauer et al. (2016), even if only two solar wind types are assumed, available solar wind categorization schemes differ considerably for intermediate solar wind speeds. Thus, the decision boundary between the coronal hole and the slow solar wind is so far not well defined.In this situation, a machine learning approach to solar wind classification can provide an additional perspective.We apply a well-known machine learning method, k-means, to the task of solar wind classification in order to answer the following questions: (1) How many solar wind types can reliably be identified in our data set comprised of ten years of solar wind observations from the Advanced Composition Explorer (ACE)? (2) Which combinations of solar wind parameters are particularly useful for solar wind classification?Potential subtypes of slow solar wind are of particular interest because they can provide hints of respective different source regions or release mechanisms of slow solar wind.

Video Games: Instructional Potential and Classification.

ERIC Educational Resources Information Center

Nawrocki, Leon H.; Winner, Janet L.

1983-01-01

Intended to provide a framework and impetus for future investigations of video games, this paper summarizes activities investigating the instructional use of such games, observations by the authors, and a proposed classification scheme and a paradigm to assist in the preliminary selection of instructional video games. Nine references are listed.…

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

Mode of Action (MOA) Assignment Classifications for Ecotoxicology: Evaluation of Available Methods

EPA Science Inventory

There are various structure-based classification schemes to categorize chemicals based on mode of action (MOA) which have been applied for both eco and human toxicology. With increasing calls to assess 1000s of chemicals, some of which have little available information other tha...

Surveillance system and method having an operating mode partitioned fault classification model

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

Structure D'Ensemble, Multiple Classification, Multiple Seriation and Amount of Irrelevant Information

ERIC Educational Resources Information Center

Hamel, B. Remmo; Van Der Veer, M. A. A.

1972-01-01

A significant positive correlation between multiple classification was found, in testing 65 children aged 6 to 8 years, at the stage of concrete operations. This is interpreted as support for the existence of a structure d'ensemble of operational schemes in the period of concrete operations. (Authors)

Objectification of Orthodontic Treatment Needs: Does the Classification of Malocclusions or a History of Orthodontic Treatment Matter?

PubMed

Kozanecka, Anna; Sarul, Michał; Kawala, Beata; Antoszewska-Smith, Joanna

2016-01-01

Orthodontic classifications make it possible to give an accurate diagnosis but do not indicate an objective orthodontic treatment need. In order to evaluate the need for treatment, it is necessary to use such indicators as the IOTN. The aim of the study was to find (i) relationships between individual diagnosis and objective recommendations for treatment and (ii) an answer to the question whether and which occlusal anomalies play an important role in the objectification of treatment needs. Two hundred three 18-year-old adolescents (104 girls, 99 boys) were examined. In order to recognize occlusal anomalies, the classifications proposed by Orlik-Grzybowska and Ackerman-Proffit were used. The occlusal anomalies were divided into three categories: belonging to both classifications, typical of OrlikGrzybowska classification and typical of Ackerman-Proffit classification. In order to determine the objective need for orthodontic treatment, the Dental Health Component (DHC) of the IOTN was used. The occurrence of the following malocclusions covered by both classifications, namely abnormal overjet, crossbite and Angle's class, had a statistically significant (p < 0.05) impact on an increase of treatment needs in the subjects (DHC > 3). As for the classification by Orlik-Grzybowska, dental malpositions and canine class significantly affected the need for orthodontic treatment, while in the case of the Ackerman-Proffit scheme, it was asymmetry and crowding. There was no statistically significant correlation between past orthodontic treatment and current orthodontic treatment need. IOTN may be affected by a greater number of occlusal anomalies than it was assumed. Orthodontic treatment received in the past slightly reduces the need for treatment in 18-year-olds.

Cluster analysis based on dimensional information with applications to feature selection and classification

NASA Technical Reports Server (NTRS)

Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

1974-01-01

A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

GRB 060614: a Fake Short Gamma-Ray Burst

NASA Astrophysics Data System (ADS)

Caito, L.; Bernardini, M. G.; Bianco, C. L.; Dainotti, M. G.; Guida, R.; Ruffini, R.

2008-05-01

The explosion of GRB 060614 produced a deep break in the GRB scenario and opened new horizons of investigation because it can't be traced back to any traditional scheme of classification. In fact, it has features both of long bursts and of short bursts and, above all, it is the first case of long duration near GRB without any bright Ib/c associated Supernova. We will show that, in our canonical GRB scenario [1], this ``anomalous'' situation finds a natural interpretation and allows us to discuss a possible variation to the traditional classification scheme, introducing the distinction between ``genuine'' and ``fake'' short bursts.

GRB 060614: a progress report

NASA Astrophysics Data System (ADS)

Caito, L.; Bernardini, M. G.; Bianco, C. L.; Dainotti, M. G.; Guida, R.; Ruffini, R.

2008-01-01

The explosion of GRB 060614, detected by the Swift satellite, produced a deep break in the GRB scenario opening new horizons of investigation, because it can't be traced back to any traditional scheme of classification. In fact, it manifests peculiarities both of long bursts and of short bursts. Above all, it is the first case of long duration near GRB without any bright Ib/c associated Supernova. We will show that, in our canonical GRB scenario ([l]), this ``anomalous'' situation finds a natural interpretation and allows us to discuss a possible variation to the traditional classification scheme, introducing the distinction between ``genuine'' and ``fake'' short bursts.

Planetree health information services: public access to the health information people want.

PubMed Central

Cosgrove, T L

1994-01-01

In July 1981, the Planetree Health Resource Center opened on the San Francisco campus of California Pacific Medical Center (Pacific Presbyterian Medical Center). Planetree was founded on the belief that access to information can empower people and help them face health and medical challenges. The Health Resource Center was created to provide medical library and health information resources to the general public. Over the last twelve years, Planetree has tried to develop a consumer health library collection and information service that is responsive to the needs and interests of a diverse public. In an effort to increase accessibility to the medical literature, a consumer health library classification scheme was created for the organization of library materials. The scheme combines the specificity and sophistication of the National Library of Medicine classification scheme with the simplicity of common lay terminology. PMID:8136762

User oriented ERTS-1 images. [vegetation identification in Canada through image enhancement

NASA Technical Reports Server (NTRS)

Shlien, S.; Goodenough, D.

1974-01-01

Photographic reproduction of ERTS-1 images are capable of displaying only a portion of the total information available from the multispectral scanner. Methods are being developed to generate ERTS-1 images oriented towards special users such as agriculturists, foresters, and hydrologists by applying image enhancement techniques and interactive statistical classification schemes. Spatial boundaries and linear features can be emphasized and delineated using simple filters. Linear and nonlinear transformations can be applied to the spectral data to emphasize certain ground information. An automatic classification scheme was developed to identify particular ground cover classes such as fallow, grain, rape seed or various vegetation covers. The scheme applies the maximum likelihood decision rule to the spectral information and classifies the ERTS-1 image on a pixel by pixel basis. Preliminary results indicate that the classifier has limited success in distinguishing crops, but is well adapted for identifying different types of vegetation.

Current challenges in autonomous vehicle development

NASA Astrophysics Data System (ADS)

Connelly, J.; Hong, W. S.; Mahoney, R. B., Jr.; Sparrow, D. A.

2006-05-01

The field of autonomous vehicles is a rapidly growing one, with significant interest from both government and industry sectors. Autonomous vehicles represent the intersection of artificial intelligence (AI) and robotics, combining decision-making with real-time control. Autonomous vehicles are desired for use in search and rescue, urban reconnaissance, mine detonation, supply convoys, and more. The general adage is to use robots for anything dull, dirty, dangerous or dumb. While a great deal of research has been done on autonomous systems, there are only a handful of fielded examples incorporating machine autonomy beyond the level of teleoperation, especially in outdoor/complex environments. In an attempt to assess and understand the current state of the art in autonomous vehicle development, a few areas where unsolved problems remain became clear. This paper outlines those areas and provides suggestions for the focus of science and technology research. The first step in evaluating the current state of autonomous vehicle development was to develop a definition of autonomy. A number of autonomy level classification systems were reviewed. The resulting working definitions and classification schemes used by the authors are summarized in the opening sections of the paper. The remainder of the report discusses current approaches and challenges in decision-making and real-time control for autonomous vehicles. Suggested research focus areas for near-, mid-, and long-term development are also presented.

Updating Allergy and/or Hypersensitivity Diagnostic Procedures in the WHO ICD-11 Revision.

PubMed

Tanno, Luciana Kase; Calderon, Moises A; Li, James; Casale, Thomas; Demoly, Pascal

2016-01-01

The classification of allergy and/or hypersensitivity conditions for the World Health Organization (WHO) International Classification of Diseases (ICD)-11 provides the appropriate corresponding codes for allergic diseases, assuming that the final diagnosis is correct. This classification should be linked to in vitro and in vivo diagnostic procedures. Considering the impact for our specialty, we decided to review the codification of these procedures into the ICD aiming to have a baseline and to suggest changes and/or submit new proposals. For that, we prepared a list of the relevant allergy and/or hypersensitivity diagnostic procedures that health care professionals are dealing with on a daily basis. This was based on the main current guidelines and selected all possible and relevant corresponding terms from the ICD-10 (2015 version) and the ICD-11 β phase foundation (June 2015 version). More than 90% of very specific and important diagnostic procedures currently used by the allergists' community on a daily basis are missing. We observed that some concepts usually used by the allergist community on a daily basis are not fully recognized by other specialties. The whole scheme and the correspondence in the ICD-10 (2015 version) and ICD-11 foundation (June 2015 version) provided us a big picture of the missing or imprecise terms and how they are scattered in the current ICD-11 framework, allowing us to submit new proposals to increase the visibility of the allergy and/or hypersensitivity conditions and diagnostic procedures. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. All rights reserved.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Maslanik, J. A.; Key, J. R.

1987-01-01

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation.

PubMed

Olives, Casey; Pagano, Marcello; Deitchler, Megan; Hedt, Bethany L; Egge, Kari; Valadez, Joseph J

2009-04-01

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67x3 (67 clusters of three observations) and a 33x6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67x3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis.

Classification of Palmprint Using Principal Line

NASA Astrophysics Data System (ADS)

Prasad, Munaga V. N. K.; Kumar, M. K. Pramod; Sharma, Kuldeep

In this paper, a new classification scheme for palmprint is proposed. Palmprint is one of the reliable physiological characteristics that can be used to authenticate an individual. Palmprint classification provides an important indexing mechanism in a very large palmprint database. Here, the palmprint database is initially categorized into two groups, right hand group and left hand group. Then, each group is further classified based on the distance traveled by principal line i.e. Heart Line During pre processing, a rectangular Region of Interest (ROI) in which only heart line is present, is extracted. Further, ROI is divided into 6 regions and depending upon the regions in which the heart line traverses the palmprint is classified accordingly. Consequently, our scheme allows 64 categories for each group forming a total number of 128 possible categories. The technique proposed in this paper includes only 15 such categories and it classifies not more than 20.96% of the images into a single category.

Classification of topological phonons in linear mechanical metamaterials

PubMed Central

Süsstrunk, Roman

2016-01-01

Topological phononic crystals, alike their electronic counterparts, are characterized by a bulk–edge correspondence where the interior of a material dictates the existence of stable surface or boundary modes. In the mechanical setup, such surface modes can be used for various applications such as wave guiding, vibration isolation, or the design of static properties such as stable floppy modes where parts of a system move freely. Here, we provide a classification scheme of topological phonons based on local symmetries. We import and adapt the classification of noninteracting electron systems and embed it into the mechanical setup. Moreover, we provide an extensive set of examples that illustrate our scheme and can be used to generate models in unexplored symmetry classes. Our work unifies the vast recent literature on topological phonons and paves the way to future applications of topological surface modes in mechanical metamaterials. PMID:27482105

Restoration of Wavelet-Compressed Images and Motion Imagery

DTIC Science & Technology

2004-01-01

SECURITY CLASSIFICATION OF REPORT UNCLASSIFIED 18. SECURITY CLASSIFICATION OF THIS PAGE UNCLASSIFIED 19. SECURITY CLASSIFICATION...images is that they are global translates of each other, where 29 the global motion parameters are known. In a very simple sense , these five images form...Image Proc., vol. 1, Oct. 2001, pp. 185–188. [2] J. W. Woods and T. Naveen, “A filter based bit allocation scheme for subband compresion of HDTV,” IEEE

Cross-mapping the ICNP with NANDA, HHCC, Omaha System and NIC for unified nursing language system development. International Classification for Nursing Practice. International Council of Nurses. North American Nursing Diagnosis Association. Home Health Care Classification. Nursing Interventions Classification.

PubMed

Hyun, S; Park, H A

2002-06-01

Nursing language plays an important role in describing and defining nursing phenomena and nursing actions. There are numerous vocabularies describing nursing diagnoses, interventions and outcomes in nursing. However, the lack of a standardized unified nursing language is considered a problem for further development of the discipline of nursing. In an effort to unify the nursing languages, the International Council of Nurses (ICN) has proposed the International Classification for Nursing Practice (ICNP) as a unified nursing language system. The purpose of this study was to evaluate the inclusiveness and expressiveness of the ICNP terms by cross-mapping them with the existing nursing terminologies, specifically the North American Nursing Diagnosis Association (NANDA) taxonomy I, the Omaha System, the Home Health Care Classification (HHCC) and the Nursing Interventions Classification (NIC). Nine hundred and seventy-four terms from these four classifications were cross-mapped with the ICNP terms. This was performed in accordance with the Guidelines for Composing a Nursing Diagnosis and Guidelines for Composing a Nursing Intervention, which were suggested by the ICNP development team. An expert group verified the results. The ICNP Phenomena Classification described 87.5% of the NANDA diagnoses, 89.7% of the HHCC diagnoses and 72.7% of the Omaha System problem classification scheme. The ICNP Action Classification described 79.4% of the NIC interventions, 80.6% of the HHCC interventions and 71.4% of the Omaha System intervention scheme. The results of this study suggest that the ICNP has a sound starting structure for a unified nursing language system and can be used to describe most of the existing terminologies. Recommendations for the addition of terms to the ICNP are provided.

Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

NASA Astrophysics Data System (ADS)

Weller, Andrew F.; Harris, Anthony J.; Ware, J. Andrew; Jarvis, Paul S.

2006-11-01

The classification of sedimentary organic matter (OM) images can be improved by determining the saliency of image analysis (IA) features measured from them. Knowing the saliency of IA feature measurements means that only the most significant discriminating features need be used in the classification process. This is an important consideration for classification techniques such as artificial neural networks (ANNs), where too many features can lead to the 'curse of dimensionality'. The classification scheme adopted in this work is a hybrid of morphologically and texturally descriptive features from previous manual classification schemes. Some of these descriptive features are assigned to IA features, along with several others built into the IA software (Halcon) to ensure that a valid cross-section is available. After an image is captured and segmented, a total of 194 features are measured for each particle. To reduce this number to a more manageable magnitude, the SPSS AnswerTree Exhaustive CHAID (χ 2 automatic interaction detector) classification tree algorithm is used to establish each measurement's saliency as a classification discriminator. In the case of continuous data as used here, the F-test is used as opposed to the published algorithm. The F-test checks various statistical hypotheses about the variance of groups of IA feature measurements obtained from the particles to be classified. The aim is to reduce the number of features required to perform the classification without reducing its accuracy. In the best-case scenario, 194 inputs are reduced to 8, with a subsequent multi-layer back-propagation ANN recognition rate of 98.65%. This paper demonstrates the ability of the algorithm to reduce noise, help overcome the curse of dimensionality, and facilitate an understanding of the saliency of IA features as discriminators for sedimentary OM classification.

Mechanical properties of human atherosclerotic intima tissue.

PubMed

Akyildiz, Ali C; Speelman, Lambert; Gijsen, Frank J H

2014-03-03

Progression and rupture of atherosclerotic plaques in coronary and carotid arteries are the key processes underlying myocardial infarctions and strokes. Biomechanical stress analyses to compute mechanical stresses in a plaque can potentially be used to assess plaque vulnerability. The stress analyses strongly rely on accurate representation of the mechanical properties of the plaque components. In this review, the composition of intima tissue and how this changes during plaque development is discussed from a mechanical perspective. The plaque classification scheme of the American Heart Association is reviewed and plaques originating from different vascular territories are compared. Thereafter, an overview of the experimental studies on tensile and compressive plaque intima properties are presented and the results are linked to the pathology of atherosclerotic plaques. This overview revealed a considerable variation within studies, and an enormous dispersion between studies. Finally, the implications of the dispersion in experimental data on the clinical applications of biomechanical plaque modeling are presented. Suggestions are made on mechanical testing protocol for plaque tissue and on using a standardized plaque classification scheme. This review identifies the current status of knowledge on plaque mechanical properties and the future steps required for a better understanding of the plaque type specific material properties. With this understanding, biomechanical plaque modeling may eventually provide essential support for clinical plaque risk stratification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Looking at Citations: Using Corpora in English for Academic Purposes.

ERIC Educational Resources Information Center

Thompson, Paul; Tribble, Chris

2001-01-01

Presents a classification scheme and the results of applying this scheme to the coding of academic texts in a corpus. The texts are doctoral theses from agricultural botany and agricultural economics departments. Results lead to a comparison of the citation practices of writers in different disciplines and the different rhetorical practices of…

An unsupervised classification technique for multispectral remote sensing data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Cummings, R. E.

1973-01-01

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Unsupervised classification of earth resources data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Jayroe, R. R., Jr.; Cummings, R. E.

1972-01-01

A new clustering technique is presented. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by existing supervised maximum liklihood classification technique.

Investigating the DSM-5 severity specifiers based on thinness for adults with anorexia nervosa.

PubMed

Reas, Deborah Lynn; Rø, Øyvind

2017-08-01

The DSM-5 severity classification scheme for adults with anorexia nervosa (AN) is based upon current body mass index (BMI; kg/m 2 ). This study examined the utility of the DSM-5 severity specifiers for adults with AN in relation to core cognitive and behavioral features of eating pathology and associated psychosocial impairment. A clinical sample of 146 adult AN patients (140 women, 6 men) were categorized using DSM-5 current BMI severity specifiers and assessed with the Eating Disorder Examination-Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA). A total of 34 (23.3%) patients were categorized as mild (>=17.0 BMI), 35 (24.0%) as moderate (16-16.99 BMI), 32 (21.9%) as severe (15-15.99 BMI), and 45 (30.8%) as extreme (<15 BMI). No significant group differences were found for age, CIA and EDE-Q global or subscale scores, frequency of laxative use, self-induced vomiting, binge eating, or excessive exercise. This study found little empirical evidence to support the utility of DSM-5 severity rating scheme to differentiate adults with AN in terms of core eating disorder pathology or associated psychosocial impairment. © 2017 Wiley Periodicals, Inc.

Classification of cryocoolers

NASA Technical Reports Server (NTRS)

Walker, G.

1985-01-01

A great diversity of methods and mechanisms were devised to effect cryogenic refrigeration. The basic parameters and considerations affecting the selection of a particular system are reviewed. A classification scheme for mechanical cryocoolers is presented. An important distinguishing feature is the incorporation or not of a regenerative heat exchanger, of valves, and of the method for achieving a pressure variation.

A satellite rainfall retrieval technique over northern Algeria based on the probability of rainfall intensities classification from MSG-SEVIRI

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2016-09-01

In this paper, an algorithm based on the probability of rainfall intensities classification for rainfall estimation from Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) has been developed. The classification scheme uses various spectral parameters of SEVIRI that provide information about cloud top temperature and optical and microphysical cloud properties. The presented method is developed and trained for the north of Algeria. The calibration of the method is carried out using as a reference rain classification fields derived from radar for rainy season from November 2006 to March 2007. Rainfall rates are assigned to rain areas previously identified and classified according to the precipitation formation processes. The comparisons between satellite-derived precipitation estimates and validation data show that the developed scheme performs reasonably well. Indeed, the correlation coefficient presents a significant level (r:0.87). The values of POD, POFD and FAR are 80%, 13% and 25%, respectively. Also, for a rainfall estimation of about 614 mm, the RMSD, Bias, MAD and PD indicate 102.06(mm), 2.18(mm), 68.07(mm) and 12.58, respectively.

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Using two classification schemes to develop vegetation indices of biological integrity for wetlands in West Virginia, USA.

PubMed

Veselka, Walter; Rentch, James S; Grafton, William N; Kordek, Walter S; Anderson, James T

2010-11-01

Bioassessment methods for wetlands, and other bodies of water, have been developed worldwide to measure and quantify changes in "biological integrity." These assessments are based on a classification system, meant to ensure appropriate comparisons between wetland types. Using a local site-specific disturbance gradient, we built vegetation indices of biological integrity (Veg-IBIs) based on two commonly used wetland classification systems in the USA: One based on vegetative structure and the other based on a wetland's position in a landscape and sources of water. The resulting class-specific Veg-IBIs were comprised of 1-5 metrics that varied in their sensitivity to the disturbance gradient (R2=0.14-0.65). Moreover, the sensitivity to the disturbance gradient increased as metrics from each of the two classification schemes were combined (added). Using this information to monitor natural and created wetlands will help natural resource managers track changes in biological integrity of wetlands in response to anthropogenic disturbance and allows the use of vegetative communities to set ecological performance standards for mitigation banks.

Pāhoehoe, `a`ā, and block lava: an illustrated history of the nomenclature

NASA Astrophysics Data System (ADS)

Harris, Andrew J. L.; Rowland, Scott K.; Villeneuve, Nicolas; Thordarson, Thor

2017-01-01

Lava flows occur worldwide, and throughout history, various cultures (and geologists) have described flows based on their surface textures. As a result, surface morphology-based nomenclature schemes have been proposed in most languages to aid in the classification and distinction of lava surface types. One of the first to be published was likely the nine-class, Italian-language description-based classification proposed by Mario Gemmellaro in 1858. By far, the most commonly used terms to describe lava surfaces today are not descriptive but, instead, are merely words, specifically the Hawaiian words `a`ā (rough brecciated basalt lava) and pāhoehoe (smooth glassy basalt lava), plus block lava (thick brecciated lavas that are typically more silicic than basalt). `A`ā and pāhoehoe were introduced into the Western geological vocabulary by American geologists working in Hawai`i during the 1800s. They and other nineteenth century geologists proposed formal lava-type classification schemes for scientific use, and most of them used the Hawaiian words. In 1933, Ruy Finch added the third lava type, block lava, to the classification scheme, with the tripartite system being formalized in 1953 by Gordon Macdonald. More recently, particularly since the 1980s and based largely on studies of lava flow interiors, a number of sub-types and transitional forms of all three major lava types have been defined. This paper reviews the early history of the development of the pāhoehoe, `a`ā, and block lava-naming system and presents a new descriptive classification so as to break out the three parental lava types into their many morphological sub-types.

TFOS DEWS II Definition and Classification Report.

PubMed

Craig, Jennifer P; Nichols, Kelly K; Akpek, Esen K; Caffery, Barbara; Dua, Harminder S; Joo, Choun-Ki; Liu, Zuguo; Nelson, J Daniel; Nichols, Jason J; Tsubota, Kazuo; Stapleton, Fiona

2017-07-01

The goals of the TFOS DEWS II Definition and Classification Subcommittee were to create an evidence-based definition and a contemporary classification system for dry eye disease (DED). The new definition recognizes the multifactorial nature of dry eye as a disease where loss of homeostasis of the tear film is the central pathophysiological concept. Ocular symptoms, as a broader term that encompasses reports of discomfort or visual disturbance, feature in the definition and the key etiologies of tear film instability, hyperosmolarity, and ocular surface inflammation and damage were determined to be important for inclusion in the definition. In the light of new data, neurosensory abnormalities were also included in the definition for the first time. In the classification of DED, recent evidence supports a scheme based on the pathophysiology where aqueous deficient and evaporative dry eye exist as a continuum, such that elements of each are considered in diagnosis and management. Central to the scheme is a positive diagnosis of DED with signs and symptoms, and this is directed towards management to restore homeostasis. The scheme also allows consideration of various related manifestations, such as non-obvious disease involving ocular surface signs without related symptoms, including neurotrophic conditions where dysfunctional sensation exists, and cases where symptoms exist without demonstrable ocular surface signs, including neuropathic pain. This approach is not intended to override clinical assessment and judgment but should prove helpful in guiding clinical management and research. Copyright © 2017 Elsevier Inc. All rights reserved.

Applications of the U.S. Geological Survey's global land cover product

USGS Publications Warehouse

Reed, B.

1997-01-01

The U.S. Geological Survey (USGS), in partnership with several international agencies and universities, has produced a global land cover characteristics database. The land cover data were created using multitemporal analysis of advanced very high resolution radiometer satellite images in conjunction with other existing geographic data. A translation table permits the conversion of the land cover classes into several conventional land cover schemes that are used by ecosystem modelers, climate modelers, land management agencies, and other user groups. The alternative classification schemes include Global Ecosystems, the Biosphere Atmosphere Transfer Scheme, the Simple Biosphere, the USGS Anderson Level 2, and the International Geosphere Biosphere Programme. The distribution system for these data is through the World Wide Web (the web site address is: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html) or by magnetic media upon special request The availability of the data over the World Wide Web, in conjunction with the flexible database structure, allows easy data access to a wide range of users. The web site contains a user registration form that allows analysis of the diverse applications of large-area land cover data. Currently, applications are divided among mapping (20 percent), conservation (30 percent), and modeling (35 percent).

Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines

PubMed Central

Swetapadma, Aleena; Yadav, Anamika

2015-01-01

Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance. PMID:26413088

Trends and concepts in fern classification.

PubMed

Christenhusz, Maarten J M; Chase, Mark W

2014-03-01

Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called 'fern allies' (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is sister to all other vascular plants, whereas the whisk ferns (Psilotaceae), often included in the lycopods or believed to be associated with the first vascular plants, are sister to Ophioglossaceae and thus belong to the fern clade. The horsetails (Equisetaceae) are also members of the fern clade (sometimes inappropriately called 'monilophytes'), but, within that clade, their placement is still uncertain. Leptosporangiate ferns are better understood, although deep relationships within this group are still unresolved. Earlier, almost all leptosporangiate ferns were placed in a single family (Polypodiaceae or Dennstaedtiaceae), but these families have been redefined to narrower more natural entities. Concluding this paper, a classification is presented based on our current understanding of relationships of fern and lycopod clades. Major changes in our understanding of these families are highlighted, illustrating issues of classification in relation to convergent evolution and false homologies. Problems with the current classification and groups that still need study are pointed out. A summary phylogenetic tree is also presented. A new classification in which Aspleniaceae, Cyatheaceae, Polypodiaceae and Schizaeaceae are expanded in comparison with the most recent classifications is presented, which is a modification of those proposed by Smith et al. (2006, 2008) and Christenhusz et al. (2011). These classifications are now finding a wider acceptance and use, and even though a few amendments are made based on recently published results from molecular analyses, we have aimed for a stable family and generic classification of ferns.

A Classification Scheme for Glaciological AVA Responses

NASA Astrophysics Data System (ADS)

Booth, A.; Emir, E.

2014-12-01

A classification scheme is proposed for amplitude vs. angle (AVA) responses as an aid to the interpretation of seismic reflectivity in glaciological research campaigns. AVA responses are a powerful tool in characterising the material properties of glacier ice and its substrate. However, before interpreting AVA data, careful true amplitude processing is required to constrain basal reflectivity and compensate amplitude decay mechanisms, including anelastic attenuation and spherical divergence. These fundamental processing steps can be difficult to design in cases of noisy data, e.g. where a target reflection is contaminated by surface wave energy (in the case of shallow glaciers) or by energy reflected from out of the survey plane. AVA methods have equally powerful usage in estimating the fluid fill of potential hydrocarbon reservoirs. However, such applications seldom use true amplitude data and instead consider qualitative AVA responses using a well-defined classification scheme. Such schemes are often defined in terms of the characteristics of best-fit responses to the observed reflectivity, e.g. the intercept (I) and gradient (G) of a linear approximation to the AVA data. The position of the response on a cross-plot of I and G then offers a diagnostic attribute for certain fluid types. We investigate the advantages in glaciology of emulating this practice, and develop a cross-plot based on the 3-term Shuey AVA approximation (using I, G, and a curvature term C). Model AVA curves define a clear lithification trend: AVA responses to stiff (lithified) substrates fall discretely into one quadrant of the cross-plot, with positive I and negative G, whereas those to fluid-rich substrates plot diagonally opposite (in the negative I and positive G quadrant). The remaining quadrants are unoccupied by plausible single-layer responses and may therefore be diagnostic of complex thin-layer reflectivity, and the magnitude and polarity of the C term serves as a further indicator of fluid content. The use of the AVA cross-plot is explored for seismic data from European Arctic glaciers, including Storglaciären and Midtre Lovénbreen, with additional examples from other published sources. The classification scheme should provide a useful reference for the initial assessment of a glaciological AVA response.

Hidden secrets of deformation: Impact-induced compaction within a CV chondrite

NASA Astrophysics Data System (ADS)

Forman, L. V.; Bland, P. A.; Timms, N. E.; Collins, G. S.; Davison, T. M.; Ciesla, F. J.; Benedix, G. K.; Daly, L.; Trimby, P. W.; Yang, L.; Ringer, S. P.

2016-10-01

The CV3 Allende is one of the most extensively studied meteorites in worldwide collections. It is currently classified as S1-essentially unshocked-using the classification scheme of Stöffler et al. (1991), however recent modelling suggests the low porosity observed in Allende indicates the body should have undergone compaction-related deformation. In this study, we detail previously undetected evidence of impact through use of Electron Backscatter Diffraction mapping to identify deformation microstructures in chondrules, AOAs and matrix grains. Our results demonstrate that forsterite-rich chondrules commonly preserve crystal-plastic microstructures (particularly at their margins); that low-angle boundaries in deformed matrix grains of olivine have a preferred orientation; and that disparities in deformation occur between chondrules, surrounding and non-adjacent matrix grains. We find heterogeneous compaction effects present throughout the matrix, consistent with a highly porous initial material. Given the spatial distribution of these crystal-plastic deformation microstructures, we suggest that this is evidence that Allende has undergone impact-induced compaction from an initially heterogeneous and porous parent body. We suggest that current shock classifications (Stöffler et al., 1991) relying upon data from chondrule interiors do not constrain the complete shock history of a sample.

An analysis of USSPACECOM's space surveillance network sensor tasking methodology

NASA Astrophysics Data System (ADS)

Berger, Jeff M.; Moles, Joseph B.; Wilsey, David G.

1992-12-01

This study provides the basis for the development of a cost/benefit assessment model to determine the effects of alterations to the Space Surveillance Network (SSN) on orbital element (OE) set accuracy. It provides a review of current methods used by NORAD and the SSN to gather and process observations, an alternative to the current Gabbard classification method, and the development of a model to determine the effects of observation rate and correction interval on OE set accuracy. The proposed classification scheme is based on satellite J2 perturbations. Specifically, classes were established based on mean motion, eccentricity, and inclination since J2 perturbation effects are functions of only these elements. Model development began by creating representative sensor observations using a highly accurate orbital propagation model. These observations were compared to predicted observations generated using the NORAD Simplified General Perturbation (SGP4) model and differentially corrected using a Bayes, sequential estimation, algorithm. A 10-run Monte Carlo analysis was performed using this model on 12 satellites using 16 different observation rate/correction interval combinations. An ANOVA and confidence interval analysis of the results show that this model does demonstrate the differences in steady state position error based on varying observation rate and correction interval.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Benchmarking protein classification algorithms via supervised cross-validation.

PubMed

Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

2008-04-24

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Sampling for area estimation: A comparison of full-frame sampling with the sample segment approach. [Kansas

NASA Technical Reports Server (NTRS)

Hixson, M. M.; Bauer, M. E.; Davis, B. J.

1979-01-01

The effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of LANDSAT MSS data was investigated. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plants. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

The Evolution of Complex Microsurgical Midface Reconstruction: A Classification Scheme and Reconstructive Algorithm.

PubMed

Alam, Daniel; Ali, Yaseen; Klem, Christopher; Coventry, Daniel

2016-11-01

Orbito-malar reconstruction after oncological resection represents one of the most challenging facial reconstructive procedures. Until the last few decades, rehabilitation was typically prosthesis based with a limited role for surgery. The advent of microsurgical techniques allowed large-volume tissue reconstitution from a distant donor site, revolutionizing the potential approaches to these defects. The authors report a novel surgery-based algorithm and a classification scheme for complete midface reconstruction with a foundation in the Gillies principles of like-to-like reconstruction and with a significant role of computer-aided virtual planning. With this approach, the authors have been able to achieve significantly better patient outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Introduction to the Apollo collections: Part 2: Lunar breccias

NASA Technical Reports Server (NTRS)

Mcgee, P. E.; Simonds, C. H.; Warner, J. L.; Phinney, W. C.

1979-01-01

Basic petrographic, chemical and age data for a representative suite of lunar breccias are presented for students and potential lunar sample investigators. Emphasis is on sample description and data presentation. Samples are listed, together with a classification scheme based on matrix texture and mineralogy and the nature and abundance of glass present both in the matrix and as clasts. A calculus of the classification scheme, describes the characteristic features of each of the breccia groups. The cratering process which describes the sequence of events immediately following an impact event is discussed, especially the thermal and material transport processes affecting the two major components of lunar breccias (clastic debris and fused material).

Robust Transmission of H.264/AVC Streams Using Adaptive Group Slicing and Unequal Error Protection

NASA Astrophysics Data System (ADS)

Thomos, Nikolaos; Argyropoulos, Savvas; Boulgouris, Nikolaos V.; Strintzis, Michael G.

2006-12-01

We present a novel scheme for the transmission of H.264/AVC video streams over lossy packet networks. The proposed scheme exploits the error-resilient features of H.264/AVC codec and employs Reed-Solomon codes to protect effectively the streams. A novel technique for adaptive classification of macroblocks into three slice groups is also proposed. The optimal classification of macroblocks and the optimal channel rate allocation are achieved by iterating two interdependent steps. Dynamic programming techniques are used for the channel rate allocation process in order to reduce complexity. Simulations clearly demonstrate the superiority of the proposed method over other recent algorithms for transmission of H.264/AVC streams.

The Why, What, and Impact of GPA at Oxford Brookes University

ERIC Educational Resources Information Center

Andrews, Matthew

2016-01-01

This paper examines the introduction at Oxford Brookes University of a Grade Point Average (GPA) scheme alongside the traditional honours degree classification. It considers the reasons for the introduction of GPA, the way in which the scheme was implemented, and offers an insight into the impact of GPA at Brookes. Finally, the paper considers…

Research on computer virus database management system

NASA Astrophysics Data System (ADS)

Qi, Guoquan

2011-12-01

The growing proliferation of computer viruses becomes the lethal threat and research focus of the security of network information. While new virus is emerging, the number of viruses is growing, virus classification increasing complex. Virus naming because of agencies' capture time differences can not be unified. Although each agency has its own virus database, the communication between each other lacks, or virus information is incomplete, or a small number of sample information. This paper introduces the current construction status of the virus database at home and abroad, analyzes how to standardize and complete description of virus characteristics, and then gives the information integrity, storage security and manageable computer virus database design scheme.

Acute erythroid leukemia as defined in the World Health Organization classification is a rare and pathogenetically heterogeneous disease.

PubMed

Kasyan, Armen; Medeiros, L Jeffrey; Zuo, Zhuang; Santos, Favio P; Ravandi-Kashani, Farhad; Miranda, Roberto; Vadhan-Raj, Saroj; Koeppen, Hartmut; Bueso-Ramos, Carlos E

2010-08-01

The diagnostic criteria for acute erythroid leukemia have been controversial since this disease was initially described. Using the current World Health Organization classification criteria, we retrospectively reviewed cases of acute myeloid leukemia or myelodysplastic syndrome in which erythroid precursors were >or=50% of the bone marrow nucleated cell population and the diagnosis of erythroleukemia was considered using older classification schemes. We collected 90 cases and separated them into four diagnostic groups: acute erythroid leukemia, erythroleukemia or erythroid/myeloid type (n=20); acute myeloid leukemia with myelodysplasia-related changes (n=22); therapy-related acute myeloid leukemia (n=32); and refractory anemia with excess blasts and preceding or concurrent history of erythropoietin therapy for anemia (n=16). Patients with acute erythroid leukemia were the youngest patient group and had the best overall survival. There was a statistically significant difference in overall survival between patients with acute erythroid leukemia versus acute myeloid leukemia with myelodysplasia-related changes (P=0.003) and between patients with acute erythroid leukemia versus therapy-related acute myeloid leukemia (P<0.0001). The presence of complex cytogenetic abnormalities (>3) was the only statistically significant independent variable that adversely affected survival in the acute erythroid leukemia group. Monosomy 5/del(5q) and monosomy 7/del(7q) were overrepresented in the context of complex chromosomal abnormalities. Our data suggest that acute erythroid leukemia, as currently defined in the World Health Organization classification, has become a rare disease. A majority of the cases reported previously as erythroleukemia are now classified as other entities. In addition, our data suggest that the current definition of acute erythroid leukemia, erythroleukemia type remains heterogeneous. One subset of acute erythroid leukemia patients has relatively low blast counts and are diploid. The prognosis of this patient subset is relatively good. The other subset has cytogenetic abnormalities similar to those in myelodysplastic syndromes and a poor prognosis.

Histological, molecular and functional subtypes of breast cancers

PubMed Central

Malhotra, Gautam K; Zhao, Xiangshan; Band, Hamid

2010-01-01

Increased understanding of the molecular heterogeneity that is intrinsic to the various subtypes of breast cancer will likely shape the future of breast cancer diagnosis, prognosis and treatment. Advances in the field over the last several decades have been remarkable and have clearly translated into better patient care as evidenced by the earlier detection, better prognosis and new targeted therapies. There have been two recent advances in the breast cancer research field that have lead to paradigm shifts: first, the identification of intrinsic breast tumor subtypes, which has changed the way we think about breast cancer and second, the recent characterization of cancer stem cells (CSCs), which are suspected to be responsible for tumor initiation, recurrence and resistance to therapy. These findings have opened new exciting avenues to think about breast cancer therapeutic strategies. While these advances constitute major paradigm shifts within the research realm, the clinical arena has yet to adopt and apply our understanding of the molecular basis of the disease to early diagnosis, prognosis and therapy of breast cancers. Here, we will review the current clinical approach to classification of breast cancers, newer molecular-based classification schemes and potential future of biomarkers representing a functional classification of breast cancer. PMID:21057215

Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection

NASA Astrophysics Data System (ADS)

Suprijanto; Azhari; Juliastuti, E.; Septyvergy, A.; Setyagar, N. P. P.

2016-03-01

Osteoporosis is a degenerative disease characterized by low Bone Mineral Density (BMD). Currently, a BMD level is determined by Dual Energy X-ray Absorptiometry (DXA) at the lumbar vertebrae and femur. Previous studies reported that dental panoramic radiography image has potential information for early osteoporosis detection. This work reported alternative scheme, that consists of the determination of the Region of Interest (ROI) the condyle mandibular in the image as biomarker and feature extraction from ROI and classification of bone conditions. The minimum value of intensity in the cavity area is used to compensate an offset on the ROI. For feature extraction, the fraction of intensity values in the ROI that represent high bone density and the ROI total area is perfomed. The classification will be evaluated from the ability of each feature and its combinations for the BMD detection in 2 classes (normal and abnormal), with the artificial neural network method. The evaluation system used 105 panoramic image data from menopause women which consist of 36 training data and 69 test data that were divided into 2 classes. The 2 classes of classification obtained 88.0% accuracy rate and 88.0% sensitivity rate.

Regional assessment of lake ecological states using Landsat: A classification scheme for alkaline-saline, flamingo lakes in the East African Rift Valley

NASA Astrophysics Data System (ADS)

Tebbs, E. J.; Remedios, J. J.; Avery, S. T.; Rowland, C. S.; Harper, D. M.

2015-08-01

In situ reflectance measurements and Landsat satellite imagery were combined to develop an optical classification scheme for alkaline-saline lakes in the Eastern Rift Valley. The classification allows the ecological state and consequent value, in this case to Lesser Flamingos, to be determined using Landsat satellite imagery. Lesser Flamingos depend on a network of 15 alkaline-saline lakes in East African Rift Valley, where they feed by filtering cyanobacteria and benthic diatoms from the lakes' waters. The classification developed here was based on a decision tree which used the reflectance in Landsat ETM+ bands 2-4 to assign one of six classes: low phytoplankton biomass; suspended sediment-dominated; microphytobenthos; high cyanobacterial biomass; cyanobacterial scum and bleached cyanobacterial scum. The classification accuracy was 77% when verified against in situ measurements. Classified imagery and timeseries were produced for selected lakes, which show the different ecological behaviours of these complex systems. The results have highlighted the importance to flamingos of the food resources offered by the extremely remote Lake Logipi. This study has demonstrated the potential of high spatial resolution, low spectral resolution sensors for providing ecologically valuable information at a regional scale, for alkaline-saline lakes and similar hypereutrophic inland waters.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Wittgenstein's philosophy and a dimensional approach to the classification of mental disorders -- a preliminary scheme.

PubMed

Mackinejad, Kioumars; Sharifi, Vandad

2006-01-01

In this paper the importance of Wittgenstein's philosophical ideas for the justification of a dimensional approach to the classification of mental disorders is discussed. Some of his basic concepts in his Philosophical Investigations, such as 'family resemblances', 'grammar' and 'language-game' and their relations to the concept of mental disorder are explored.

Classification Scheme for Items in CAAT.

ERIC Educational Resources Information Center

Epstein, Marion G.

In planning the development of the system for computer assisted assembly of tests, it was agreed at the outset that one of the basic requirements for the successful initiation of any such system would be the development of a detailed item content classification system. The design of the system for classifying item content is a key element in…

An investigation into the potential of low head hydro power in Northern Ireland for the production of electricity

NASA Astrophysics Data System (ADS)

Redpath, David; Ward, Michael J.

2017-07-01

The maximum exploitable potential for low head hydroelectric sites (gross head≤10 m) in Northern Ireland (NI) was determined as 12.07 MW using a simple payback analysis for 304 potential sites investigated to derive a classification scheme in terms of economic viability. A techno-economic analysis with validated numerical models from previous research estimated the capital investment required for the development of a hydroelectric plant, using the low head Michell-Banki cross flow turbine, for the 304 sites investigated. The number of potentially viable sites in NI for low head hydro ranged from 198 to 286 with an estimated installed capacity ranging from 11.95 to 12.05 MW. Sites with a limited installed capacity were not economically viable unless increased government support in the form of longer term (25-50 years) low interest loans as well as the current (Renewables Obligations Certificates) Renewables Obligation Certificates scheme is provided and sustained.

Mutual information-based analysis of JPEG2000 contexts.

PubMed

Liu, Zhen; Karam, Lina J

2005-04-01

Context-based arithmetic coding has been widely adopted in image and video compression and is a key component of the new JPEG2000 image compression standard. In this paper, the contexts used in JPEG2000 are analyzed using the mutual information, which is closely related to the compression performance. We first show that, when combining the contexts, the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same. Given I, the initial number of contexts, and F, the final desired number of contexts, there are S(I, F) possible context classification schemes where S(I, F) is called the Stirling number of the second kind. The optimal classification scheme is the one that gives the maximum mutual information. Instead of using an exhaustive search, the optimal classification scheme can be obtained through a modified generalized Lloyd algorithm with the relative entropy as the distortion metric. For binary arithmetic coding, the search complexity can be reduced by using dynamic programming. Our experimental results show that the JPEG2000 contexts capture the correlations among the wavelet coefficients very well. At the same time, the number of contexts used as part of the standard can be reduced without loss in the coding performance.

A Rapid Approach to Modeling Species-Habitat Relationships

NASA Technical Reports Server (NTRS)

Carter, Geoffrey M.; Breinger, David R.; Stolen, Eric D.

2005-01-01

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Psychological Features and Their Relationship to Movement-Based Subgroups in People Living With Low Back Pain.

PubMed

Karayannis, Nicholas V; Jull, Gwendolen A; Nicholas, Michael K; Hodges, Paul W

2018-01-01

To determine the distribution of higher psychological risk features within movement-based subgroups for people with low back pain (LBP). Cross-sectional observational study. Participants were recruited from physiotherapy clinics and community advertisements. Measures were collected at a university outpatient-based physiotherapy clinic. People (N=102) seeking treatment for LBP. Participants were subgrouped according to 3 classification schemes: Mechanical Diagnosis and Treatment (MDT), Treatment-Based Classification (TBC), and O'Sullivan Classification (OSC). Questionnaires were used to categorize low-, medium-, and high-risk features based on depression, anxiety, and stress (Depression, Anxiety, and Stress Scale-21 Items); fear avoidance (Fear-Avoidance Beliefs Questionnaire); catastrophizing and coping (Pain-Related Self-Symptoms Scale); and self-efficacy (Pain Self-Efficacy Questionnaire). Psychological risk profiles were compared between movement-based subgroups within each scheme. Scores across all questionnaires revealed that most patients had low psychological risk profiles, but there were instances of higher (range, 1%-25%) risk profiles within questionnaire components. The small proportion of individuals with higher psychological risk scores were distributed between subgroups across TBC, MDT, and OSC schemes. Movement-based subgrouping alone cannot inform on individuals with higher psychological risk features. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects

NASA Technical Reports Server (NTRS)

Kettig, R. L.

1975-01-01

A method of classification of digitized multispectral images is developed and experimentally evaluated on actual earth resources data collected by aircraft and satellite. The method is designed to exploit the characteristic dependence between adjacent states of nature that is neglected by the more conventional simple-symmetric decision rule. Thus contextual information is incorporated into the classification scheme. The principle reason for doing this is to improve the accuracy of the classification. For general types of dependence this would generally require more computation per resolution element than the simple-symmetric classifier. But when the dependence occurs in the form of redundance, the elements can be classified collectively, in groups, therby reducing the number of classifications required.

Murmur intensity in adult dogs with pulmonic and subaortic stenosis reflects disease severity.

PubMed

Caivano, D; Dickson, D; Martin, M; Rishniw, M

2018-03-01

The aims of this study were to determine whether murmur intensity in adult dogs with pulmonic stenosis or subaortic stenosis reflects echocardiographic disease severity and to determine whether a six-level murmur grading scheme provides clinical advantages over a four-level scheme. In this retrospective multi-investigator study on adult dogs with pulmonic stenosis or subaortic stenosis, murmur intensity was compared to echocardiographically determined pressure gradient across the affected valve. Disease severity, based on pressure gradients, was assessed between sequential murmur grades to identify redundancy in classification. A simplified four-level murmur intensity classification scheme ('soft', 'moderate', 'loud', 'palpable') was evaluated. In total, 284 dogs (153 with pulmonic stenosis, 131 with subaortic stenosis) were included; 55 dogs had soft, 59 had moderate, 72 had loud and 98 had palpable murmurs. 95 dogs had mild stenosis, 46 had moderate stenosis, and 143 had severe stenosis. No dogs with soft murmurs of either pulmonic or subaortic stenosis had transvalvular pressure gradients greater than 50 mmHg. Dogs with loud or palpable murmurs mostly, but not always, had severe stenosis. Stenosis severity increased with increasing murmur intensity. The traditional six-level murmur grading scheme provided no additional clinical information than the four-level descriptive murmur grading scheme. A simplified descriptive four-level murmur grading scheme differentiated stenosis severity without loss of clinical information, compared to the traditional six-level scheme. Soft murmurs in dogs with pulmonic or subaortic stenosis are strongly indicative of mild lesions. Loud or palpable murmurs are strongly suggestive of severe stenosis. © 2017 British Small Animal Veterinary Association.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

The conundrum of juvenile psoriatic arthritis.

PubMed

Ravelli, Angelo; Consolaro, Alessandro; Schiappapietra, Benedetta; Martini, Alberto

2015-01-01

Juvenile psoriatic arthritis (JPsA) has provided paediatric rheumatologists with a controversial topic for many years. The principal area of contention centres on the discordance between its treatment as a single diagnostic category in current classification schemes and the demonstration of its heterogeneous nature. A further point of debate is the distinctiveness of JPsA as an entity. Owing to these uncertainties, the concept of JPsA has evolved over the years and there have been several changes in its definition and diagnostic criteria. Recently, strong evidence has been provided that the spectrum of JPsA include at least two distinct subgroups, one that has the same characteristics as early-onset ANA-positive JIA, and another that is part of the spectrum of spondyloarthropathies and resembles the forms of psoriatic arthritis in adults that belong to the same disease family. These findings call for a revision of the classification of childhood arthritis, that refutes the assumptions that children with JPsA constitute a single homogeneous population and that JPsA should be considered an individual disease entity.

The Evolving Classification of Pulmonary Hypertension.

PubMed

Foshat, Michelle; Boroumand, Nahal

2017-05-01

- An explosion of information on pulmonary hypertension has occurred during the past few decades. The perception of this disease has shifted from purely clinical to incorporate new knowledge of the underlying pathology. This transfer has occurred in light of advancements in pathophysiology, histology, and molecular medical diagnostics. - To update readers about the evolving understanding of the etiology and pathogenesis of pulmonary hypertension and to demonstrate how pathology has shaped the current classification. - Information presented at the 5 World Symposia on pulmonary hypertension held since 1973, with the last meeting occurring in 2013, was used in this review. - Pulmonary hypertension represents a heterogeneous group of disorders that are differentiated based on differences in clinical, hemodynamic, and histopathologic features. Early concepts of pulmonary hypertension were largely influenced by pharmacotherapy, hemodynamic function, and clinical presentation of the disease. The initial nomenclature for pulmonary hypertension segregated the clinical classifications from pathologic subtypes. Major restructuring of this disease classification occurred between the first and second symposia, which was the first to unite clinical and pathologic information in the categorization scheme. Additional changes were introduced in subsequent meetings, particularly between the third and fourth World Symposia meetings, when additional pathophysiologic information was gained. Discoveries in molecular diagnostics significantly progressed the understanding of idiopathic pulmonary arterial hypertension. Continued advancements in imaging modalities, mechanistic pathogenicity, and molecular biomarkers will enable physicians to define pulmonary hypertension phenotypes based on the pathobiology and allow for treatment customization.

A risk-based classification scheme for genetically modified foods. I: Conceptual development.

PubMed

Chao, Eunice; Krewski, Daniel

2008-12-01

The predominant paradigm for the premarket assessment of genetically modified (GM) foods reflects heightened public concern by focusing on foods modified by recombinant deoxyribonucleic acid (rDNA) techniques, while foods modified by other methods of genetic modification are generally not assessed for safety. To determine whether a GM product requires less or more regulatory oversight and testing, we developed and evaluated a risk-based classification scheme (RBCS) for crop-derived GM foods. The results of this research are presented in three papers. This paper describes the conceptual development of the proposed RBCS that focuses on two categories of adverse health effects: (1) toxic and antinutritional effects, and (2) allergenic effects. The factors that may affect the level of potential health risks of GM foods are identified. For each factor identified, criteria for differentiating health risk potential are developed. The extent to which a GM food satisfies applicable criteria for each factor is rated separately. A concern level for each category of health effects is then determined by aggregating the ratings for the factors using predetermined aggregation rules. An overview of the proposed scheme is presented, as well as the application of the scheme to a hypothetical GM food.

Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation

PubMed Central

Olives, Casey; Pagano, Marcello; Deitchler, Megan; Hedt, Bethany L; Egge, Kari; Valadez, Joseph J

2009-01-01

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67×3 (67 clusters of three observations) and a 33×6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67×3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis. PMID:20011037

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Classifying quantum entanglement through topological links

NASA Astrophysics Data System (ADS)

Quinta, Gonçalo M.; André, Rui

2018-04-01

We propose an alternative classification scheme for quantum entanglement based on topological links. This is done by identifying a nonrigid ring to a particle, attributing the act of cutting and removing a ring to the operation of tracing out the particle, and associating linked rings to entangled particles. This analogy naturally leads us to a classification of multipartite quantum entanglement based on all possible distinct links for a given number of rings. To determine all different possibilities, we develop a formalism that associates any link to a polynomial, with each polynomial thereby defining a distinct equivalence class. To demonstrate the use of this classification scheme, we choose qubit quantum states as our example of physical system. A possible procedure to obtain qubit states from the polynomials is also introduced, providing an example state for each link class. We apply the formalism for the quantum systems of three and four qubits and demonstrate the potential of these tools in a context of qubit networks.

Integrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions

USGS Publications Warehouse

Stoker, Jason M.; Cochrane, Mark A.; Roy, David P.

2013-01-01

With the acquisition of lidar data for over 30 percent of the US, it is now possible to assess the three-dimensional distribution of features at the national scale. This paper integrates over 350 billion lidar points from 28 disparate datasets into a national-scale database and evaluates if height above ground is an important variable in the context of other nationalscale layers, such as the US Geological Survey National Land Cover Database and the US Environmental Protection Agency ecoregions maps. While the results were not homoscedastic and the available data did not allow for a complete height census in any of the classes, it does appear that where lidar data were used, there were detectable differences in heights among many of these national classification schemes. This study supports the hypothesis that there were real, detectable differences in heights in certain national-scale classification schemes, despite height not being a variable used in any of the classification routines.

Healthy and Unhealthy Perfectionists among Academically Gifted Chinese Students in Hong Kong: Do Different Classification Schemes Make a Difference?

ERIC Educational Resources Information Center

Chan, David W.

2010-01-01

This study investigated the identification and distribution of perfectionist types with a sample of 111 academically gifted Chinese students aged 17 to 20 in Hong Kong. Three approaches to classification were employed. Apart from the direct questioning approach, the rational approach and the clustering approach classified students using their…

Application of a hierarchical habitat unit classification system: stream habitat and salmonid distribution in Ward Creek, southeast Alaska.

Treesearch

M.D. Bryant; B.E. Wright; B.J. Davies

1992-01-01

A hierarchical classification system separating stream habitat into habitat units defined by stream morphology and hydrology was used in a pre-enhancement stream survey. The system separates habitat units into macrounits, mesounits, and micro- units and includes a separate evaluation of instream cover that also uses the hierarchical scheme. This paper presents an...

Identification and classification of known and putative antimicrobial compounds produced by a wide variety of Bacillales species.

PubMed

Zhao, Xin; Kuipers, Oscar P

2016-11-07

Gram-positive bacteria of the Bacillales are important producers of antimicrobial compounds that might be utilized for medical, food or agricultural applications. Thanks to the wide availability of whole genome sequence data and the development of specific genome mining tools, novel antimicrobial compounds, either ribosomally- or non-ribosomally produced, of various Bacillales species can be predicted and classified. Here, we provide a classification scheme of known and putative antimicrobial compounds in the specific context of Bacillales species. We identify and describe known and putative bacteriocins, non-ribosomally synthesized peptides (NRPs), polyketides (PKs) and other antimicrobials from 328 whole-genome sequenced strains of 57 species of Bacillales by using web based genome-mining prediction tools. We provide a classification scheme for these bacteriocins, update the findings of NRPs and PKs and investigate their characteristics and suitability for biocontrol by describing per class their genetic organization and structure. Moreover, we highlight the potential of several known and novel antimicrobials from various species of Bacillales. Our extended classification of antimicrobial compounds demonstrates that Bacillales provide a rich source of novel antimicrobials that can now readily be tapped experimentally, since many new gene clusters are identified.

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

NASA Technical Reports Server (NTRS)

Fayne, Jessica V.; Bolten, John D.; Doyle, Colin S.; Fuhrmann, Sven; Rice, Matthew T.; Houser, Paul R.; Lakshmi, Venkat

2017-01-01

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the -75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Creating a Taxonomy of Local Boards of Health Based on Local Health Departments’ Perspectives

PubMed Central

Shah, Gulzar H.; Sotnikov, Sergey; Leep, Carolyn J.; Ye, Jiali; Van Wave, Timothy W.

2017-01-01

Objectives To develop a local board of health (LBoH) classification scheme and empirical definitions to provide a coherent framework for describing variation in the LBoHs. Methods This study is based on data from the 2015 Local Board of Health Survey, conducted among a nationally representative sample of local health department administrators, with 394 responses. The classification development consisted of the following steps: (1) theoretically guided initial domain development, (2) mapping of the survey variables to the proposed domains, (3) data reduction using principal component analysis and group consensus, and (4) scale development and testing for internal consistency. Results The final classification scheme included 60 items across 6 governance function domains and an additional domain—LBoH characteristics and strengths, such as meeting frequency, composition, and diversity of information sources. Application of this classification strongly supports the premise that LBoHs differ in their performance of governance functions and in other characteristics. Conclusions The LBoH taxonomy provides an empirically tested standardized tool for classifying LBoHs from the viewpoint of local health department administrators. Future studies can use this taxonomy to better characterize the impact of LBoHs. PMID:27854524

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

Trends and concepts in fern classification

PubMed Central

Christenhusz, Maarten J. M.; Chase, Mark W.

2014-01-01

Background and Aims Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. Scope An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Key Results Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called ‘fern allies’ (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is sister to all other vascular plants, whereas the whisk ferns (Psilotaceae), often included in the lycopods or believed to be associated with the first vascular plants, are sister to Ophioglossaceae and thus belong to the fern clade. The horsetails (Equisetaceae) are also members of the fern clade (sometimes inappropriately called ‘monilophytes’), but, within that clade, their placement is still uncertain. Leptosporangiate ferns are better understood, although deep relationships within this group are still unresolved. Earlier, almost all leptosporangiate ferns were placed in a single family (Polypodiaceae or Dennstaedtiaceae), but these families have been redefined to narrower more natural entities. Conclusions Concluding this paper, a classification is presented based on our current understanding of relationships of fern and lycopod clades. Major changes in our understanding of these families are highlighted, illustrating issues of classification in relation to convergent evolution and false homologies. Problems with the current classification and groups that still need study are pointed out. A summary phylogenetic tree is also presented. A new classification in which Aspleniaceae, Cyatheaceae, Polypodiaceae and Schizaeaceae are expanded in comparison with the most recent classifications is presented, which is a modification of those proposed by Smith et al. (2006, 2008) and Christenhusz et al. (2011). These classifications are now finding a wider acceptance and use, and even though a few amendments are made based on recently published results from molecular analyses, we have aimed for a stable family and generic classification of ferns. PMID:24532607

Evaluation of Video Image Analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions.

PubMed

Rius-Vilarrasa, E; Bünger, L; Maltin, C; Matthews, K R; Roehe, R

2009-05-01

The Meat and Livestock Commission's (MLC) EUROP classification based scheme and Video Image Analysis (VIA) system were compared in their ability to predict weights of primal carcass joints. A total of 443 commercial lamb carcasses under 12 months of age and mixed gender were selected by their cold carcass weight (CCW), conformation and fat scores. Lamb carcasses were classified for conformation and fatness, scanned by the VIA system and dissected into primal joints of leg, chump, loin, breast and shoulder. After adjustment for CCW, the estimation of primal joints using MLC EUROP scores showed high coefficients of determination (R(2)) in the range of 0.82-0.99. The use of VIA always resulted in equal or higher R(2). The precision measured as root mean square error (RMSE) was 27% (leg), 13% (chump), 1% (loin), 11% (breast), 5% (shoulders) and 13% (total primals) higher using VIA than MLC carcass information. Adjustment for slaughter day and gender effects indicated that estimations of primal joints using MLC EUROP scores were more sensitive to these factors than using VIA. This was consistent with an increase in stability of the prediction model of 28%, 11%, 2%, 12%, 6% and 14% for leg, chump, loin, breast and shoulder and total primals, respectively, using VIA compared to MLC EUROP scores. Consequently, VIA was capable of improving the prediction of primal meat yields compared to the current MLC EUROP carcass classification scheme used in the UK abattoirs.

A prototype of mammography CADx scheme integrated to imaging quality evaluation techniques

NASA Astrophysics Data System (ADS)

Schiabel, Homero; Matheus, Bruno R. N.; Angelo, Michele F.; Patrocínio, Ana Claudia; Ventura, Liliane

2011-03-01

As all women over the age of 40 are recommended to perform mammographic exams every two years, the demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical practice testing, with results comparable to others reported in literature.

Divorcing Strain Classification from Species Names.

PubMed

Baltrus, David A

2016-06-01

Confusion about strain classification and nomenclature permeates modern microbiology. Although taxonomists have traditionally acted as gatekeepers of order, the numbers of, and speed at which, new strains are identified has outpaced the opportunity for professional classification for many lineages. Furthermore, the growth of bioinformatics and database-fueled investigations have placed metadata curation in the hands of researchers with little taxonomic experience. Here I describe practical challenges facing modern microbial taxonomy, provide an overview of complexities of classification for environmentally ubiquitous taxa like Pseudomonas syringae, and emphasize that classification can be independent of nomenclature. A move toward implementation of relational classification schemes based on inherent properties of whole genomes could provide sorely needed continuity in how strains are referenced across manuscripts and data sets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Circulation Type Classifications and their nexus to Van Bebber's storm track Vb

NASA Astrophysics Data System (ADS)

Hofstätter, M.; Chimani, B.

2012-04-01

Circulation Type Classifications (CTCs) are tools to identify repetitive and predominantly stationary patterns of the atmospheric circulation over a certain area, with the purpose to enable the recognition of specific characteristics in surface climate variables. On the other hand storm tracks can be used to identify similar types of synoptic events from a non-stationary, kinematic perspective. Such a storm track classification for Europe has been done in the late 19th century by Van Bebber (1882, 1891), from which the famous type Vb and Vc/d remained up to the present day because of to their association with major flooding events like in August 2002 in Europe. In this work a systematic tracking procedure has been developed, to determine storm track types and their characteristics especially for the Eastern Alpine Region in the period 1961-2002, using ERA40 and ERAinterim reanalysis. The focus thereby is on cyclone tracks of type V as suggested by van Bebber and congeneric types. This new catalogue is used as a reference to verify the hypothesis of a certain coherence of storm track Vb with certain circulation types (e.g. Fricke and Kaminski, 2002). Selected objective and subjective classification schemes from the COST733 action (http://cost733.met.no/, Phillip et al. 2010) are used therefore, as well as the manual classification from ZAMG (Lauscher 1972 and 1985), in which storm track Vb has been classified explicitly on a daily base since 1948. The latter scheme should prove itself as a valuable and unique data source in that issue. Results show that not less than 146 storm tracks are identified as Vb between 1961 and 2002, whereas only three events could be found from literature, pointing to big subjectivity and preconception in the issue of Vb storm tracks. The annual number of Vb storm tracks do not show any significant trend over the last 42 years, but large variations from year to year. Circulation type classification CAP27 (Cluster Analysis of Principal Components) is the best performing, fully objective scheme tested herein, showing the power to discriminate Vb events. Most of the other fully objective schemes do by far not perform as well. Largest skill in that issue can be seen from the subjective/manual CTCs, proving themselves to enhance relevant synoptic phenomena instead of emphasizing mathematic criteria in the classification. The hypothesis of Fricke and Kaminsky can definitely be supported by this work: Vb storm tracks are included in one or the other stationary circulation pattern, but to which extent depends on the specific characteristics of the CTC in question.

Maxillectomy defects: a suggested classification scheme.

PubMed

Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

2013-06-01

The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2003)

NASA Astrophysics Data System (ADS)

Skiff, A. B.

2003-07-01

This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000, and these serve as a secondary identifier. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or bulk spectral energy distributions. The catalogue includes for the first time results from many large-scale objective-prism spectral surveys done at Case, Stockholm, Crimea, Abastumani, and elsewhere. The stars in these surveys were usually identified only on charts or by other indirect means, and have been overlooked heretofore because of the difficulty in recovering the stars. More complete results from these separate publications, including notes and identifications, have been made available to the CDS, and are kept at the Lowell Observatory ftp area (ftp://ftp.lowell.edu/pub/bas/starcats). Not all of these stars are present in SIMBAD. As a 'living catalogue', an attempt will be made to keep up with current literature, and to extend the indexing of citations back in time. (1 data file).

Mapping forest types in Worcester County, Maryland, using LANDSAT data

NASA Technical Reports Server (NTRS)

Burtis, J., Jr.; Witt, R. G.

1981-01-01

The feasibility of mapping Level 2 forest cover types for a county-sized area on Maryland's Eastern Shore was demonstrated. A Level 1 land use/land cover classification was carried out for all of Worcester County as well. A June 1978 LANDSAT scene was utilized in a classification which employed two software packages on different computers (IDIMS on an HP 3000 and ASTEP-II on a Univac 1108). A twelve category classification scheme was devised for the study area. Resulting products include black and white line printer maps, final color coded classification maps, digitally enhanced color imagery and tabulated acreage statistics for all land use and land cover types.

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

2015-12-01

This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.

Use of Enzyme Tests in Characterization and Identification of Aerobic and Facultatively Anaerobic Gram-Positive Cocci

PubMed Central

Bascomb, Shoshana; Manafi, Mammad

1998-01-01

The contribution of enzyme tests to the accurate and rapid routine identification of gram-positive cocci is introduced. The current taxonomy of the genera of aerobic and facultatively anaerobic cocci based on genotypic and phenotypic characterization is reviewed. The clinical and economic importance of members of these taxa is briefly summarized. Tables summarizing test schemes and kits available for the identification of staphylococci, enterococci, and streptococci on the basis of general requirements, number of tests, number of taxa, test classes, and completion times are discussed. Enzyme tests included in each scheme are compared on the basis of their synthetic moiety. The current understanding of the activity of enzymes important for classification and identification of the major groups, methods of testing, and relevance to the ease and speed of identification are reviewed. Publications describing the use of different identification kits are listed, and overall identification successes and problems are discussed. The relationships between the results of conventional biochemical and rapid enzyme tests are described and considered. The use of synthetic substrates for the detection of glycosidases and peptidases is reviewed, and the advantages of fluorogenic synthetic moieties are discussed. The relevance of enzyme tests to accurate and meaningful rapid routine identification is discussed. PMID:9564566

Spin orbit torque based electronic neuron

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Abhronil, E-mail: asengup@purdue.edu; Choday, Sri Harsha; Kim, Yusung

2015-04-06

A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis, i.e., an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of themore » neuron input and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward artificial neural network based on the SOT electronic neuron shows that it consumes ∼3× lower power than a 45 nm digital CMOS implementation, while reaching ∼80% accuracy in the classification of 100 images of handwritten digits from the MNIST dataset.« less

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

A classification of open Gaussian dynamics

NASA Astrophysics Data System (ADS)

Grimmer, Daniel; Brown, Eric; Kempf, Achim; Mann, Robert B.; Martín-Martínez, Eduardo

2018-06-01

We introduce a classification scheme for the generators of bosonic open Gaussian dynamics, providing instructive diagrams description for each type of dynamics. Using this classification, we discuss the consequences of imposing complete positivity on Gaussian dynamics. In particular, we show that non-symplectic operations must be active to allow for complete positivity. In addition, non-symplectic operations can, in fact, conserve the volume of phase space only if the restriction of complete positivity is lifted. We then discuss the implications for the relationship between information and energy flows in open quantum mechanics.

Contemplating case mix: A primer on case mix classification and management.

PubMed

Costa, Andrew P; Poss, Jeffery W; McKillop, Ian

2015-01-01

Case mix classifications are the frameworks that underlie many healthcare funding schemes, including the so-called activity-based funding. Now more than ever, Canadian healthcare administrators are evaluating case mix-based funding and deciphering how they will influence their organization. Case mix is a topic fraught with technical jargon and largely relegated to government agencies or private industries. This article provides an abridged review of case mix classification as well as its implications for management in healthcare. © 2015 The Canadian College of Health Leaders.

A three-parameter asteroid taxonomy

NASA Technical Reports Server (NTRS)

Tedesco, Edward F.; Williams, James G.; Matson, Dennis L.; Veeder, Glenn J.; Gradie, Jonathan C.

1989-01-01

Broadband U, V, and x photometry together with IRAS asteroid albedos have been used to construct an asteroid classification system. The system is based on three parameters (U-V and v-x color indices and visual geometric albedo), and it is able to place 96 percent of the present sample of 357 asteroids into 11 taxonomic classes. It is noted that all but one of these classes are analogous to those previously found using other classification schemes. The algorithm is shown to account for the observational uncertainties in each of the classification parameters.

The Transition Metal-Like Reactivity of Low Oxidation State s- and p-Block Compounds

DTIC Science & Technology

2017-10-20

equivalent (e.g. Scheme 5),16 and the most active group 14 metal hydrides used for the, sometimes reversible, hydrometallation of unactivated alkenes...not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1.  REPORT DATE (DD-MM-YYYY)      20-10...Main Group Chemistry 16.  SECURITY CLASSIFICATION OF: 17.  LIMITATION OF       ABSTRACT SAR 18.  NUMBER        OF        PAGES               16  19a

Fifty Years of the Index to Dental Literature: A Critical Appraisal

PubMed Central

1971-01-01

The year 1971 marks the fiftieth anniversary of the Index to Dental Literature. The Index had a slow and stormy birth, with twenty-three years of hard work put in until the first volume was issued. The first Index is described and the changes in its contents and format are traced through the years until its production in 1965 by the National Library of Medicine. The current Index is analyzed with attention paid to nomenclature, classification scheme, quality of the index entries and cross references. The results of a survey of regular users of the Index are interpreted, and suggestions gleaned for the improvement of this most useful tool in dental research. Images PMID:4947815

CANDELS Visual Classifications: Scheme, Data Release, and First Results

NASA Technical Reports Server (NTRS)

Kartaltepe, Jeyhan S.; Mozena, Mark; Kocevski, Dale; McIntosh, Daniel H.; Lotz, Jennifer; Bell, Eric F.; Faber, Sandy; Ferguson, Henry; Koo, David; Bassett, Robert;

Site classification of Indian strong motion network using response spectra ratios

NASA Astrophysics Data System (ADS)

Chopra, Sumer; Kumar, Vikas; Choudhury, Pallabee; Yadav, R. B. S.

2018-03-01

In the present study, we tried to classify the Indian strong motion sites spread all over Himalaya and adjoining region, located on varied geological formations, based on response spectral ratio. A total of 90 sites were classified based on 395 strong motion records from 94 earthquakes recorded at these sites. The magnitude of these earthquakes are between 2.3 and 7.7 and the hypocentral distance for most of the cases is less than 50 km. The predominant period obtained from response spectral ratios is used to classify these sites. It was found that the shape and predominant peaks of the spectra at these sites match with those in Japan, Italy, Iran, and at some of the sites in Europe and the same classification scheme can be applied to Indian strong motion network. We found that the earlier schemes based on description of near-surface geology, geomorphology, and topography were not able to capture the effect of sediment thickness. The sites are classified into seven classes (CL-I to CL-VII) with varying predominant periods and ranges as proposed by Alessandro et al. (Bull Seismol Soc Am 102:680-695 2012). The effect of magnitudes and hypocentral distances on the shape and predominant peaks were also studied and found to be very small. The classification scheme is robust and cost-effective and can be used in region-specific attenuation relationships for accounting local site effect.

Reclassification: Rationale and Problems; Proceedings of a Conference on Reclassification held at the Center of Adult Education, University of Maryland, College Park, April 4 to 6, 1968.

ERIC Educational Resources Information Center

Perreault, Jean M., Ed.

Several factors are involved in the decision to reclassify library collections and several problems and choices must be faced. The discussion of four classification schemes (Dewey Decimal, Library of Congress, Library of Congress subject-headings and Universal Decimal Classification) involved in the choices concerns their structure, currency,…

Mandatory, Preferred, or Discretionary: How the Classification of Domestic Violence Warrantless Arrest Laws Impacts Their Estimated Effects on Intimate Partner Homicide

ERIC Educational Resources Information Center

Zeoli, April M.; Norris, Alexis; Brenner, Hannah

2011-01-01

Warrantless arrest laws for domestic violence (DV) are generally classified as discretionary, preferred, or mandatory, based on the level of power accorded to police in deciding whether to arrest. However, there is a lack of consensus in the literature regarding how each state's law should be categorized. Using three classification schemes, this…

Formalizing Resources for Planning

NASA Technical Reports Server (NTRS)

Bedrax-Weiss, Tania; McGann, Conor; Ramakrishnan, Sailesh

2003-01-01

In this paper we present a classification scheme which circumscribes a large class of resources found in the real world. Building on the work of others we also define key properties of resources that allow formal expression of the proposed classification. Furthermore, operations that change the state of a resource are formalized. Together, properties and operations go a long way in formalizing the representation and reasoning aspects of resources for planning.

CANDELS Visual Classifications: Scheme, Data Release, and First Results

NASA Astrophysics Data System (ADS)

Kartaltepe, Jeyhan S.; Mozena, Mark; Kocevski, Dale; McIntosh, Daniel H.; Lotz, Jennifer; Bell, Eric F.; Faber, Sandy; Ferguson, Harry; Koo, David; Bassett, Robert; Bernyk, Maksym; Blancato, Kirsten; Bournaud, Frederic; Cassata, Paolo; Castellano, Marco; Cheung, Edmond; Conselice, Christopher J.; Croton, Darren; Dahlen, Tomas; de Mello, Duilia F.; DeGroot, Laura; Donley, Jennifer; Guedes, Javiera; Grogin, Norman; Hathi, Nimish; Hilton, Matt; Hollon, Brett; Koekemoer, Anton; Liu, Nick; Lucas, Ray A.; Martig, Marie; McGrath, Elizabeth; McPartland, Conor; Mobasher, Bahram; Morlock, Alice; O'Leary, Erin; Peth, Mike; Pforr, Janine; Pillepich, Annalisa; Rosario, David; Soto, Emmaris; Straughn, Amber; Telford, Olivia; Sunnquist, Ben; Trump, Jonathan; Weiner, Benjamin; Wuyts, Stijn; Inami, Hanae; Kassin, Susan; Lani, Caterina; Poole, Gregory B.; Rizer, Zachary

2015-11-01

We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H < 24.5 involving the dedicated efforts of over 65 individual classifiers. Once completed, we expect to have detailed morphological classifications for over 50,000 galaxies spanning 0 < z < 4 over all the fields, with classifications from 3 to 5 independent classifiers for each galaxy. Here, we present our detailed visual classification scheme, which was designed to cover a wide range of CANDELS science goals. This scheme includes the basic Hubble sequence types, but also includes a detailed look at mergers and interactions, the clumpiness of galaxies, k-corrections, and a variety of other structural properties. In this paper, we focus on the first field to be completed—GOODS-S, which has been classified at various depths. The wide area coverage spanning the full field (wide+deep+ERS) includes 7634 galaxies that have been classified by at least three different people. In the deep area of the field, 2534 galaxies have been classified by at least five different people at three different depths. With this paper, we release to the public all of the visual classifications in GOODS-S along with the Perl/Tk GUI that we developed to classify galaxies. We present our initial results here, including an analysis of our internal consistency and comparisons among multiple classifiers as well as a comparison to the Sérsic index. We find that the level of agreement among classifiers is quite good (>70% across the full magnitude range) and depends on both the galaxy magnitude and the galaxy type, with disks showing the highest level of agreement (>50%) and irregulars the lowest (<10%). A comparison of our classifications with the Sérsic index and rest-frame colors shows a clear separation between disk and spheroid populations. Finally, we explore morphological k-corrections between the V-band and H-band observations and find that a small fraction (84 galaxies in total) are classified as being very different between these two bands. These galaxies typically have very clumpy and extended morphology or are very faint in the V-band.

Detection of Coccolithophore Blooms in Ocean Color Satellite Imagery: a Generalized Approach for Use with Multiple Sensors

NASA Technical Reports Server (NTRS)

Moore, Timothy; Dowell, Mark; Franz, Bryan A.

2012-01-01

A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cellsmL and 43,000 and 78,000 lithsmL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 19972010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemi- sphere with values of 2.00 106 km2 and 0.75 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75106 km2 is dominated by contributions from the Southern Ocean.

Generalized interpretation scheme for arbitrary HR InSAR image pairs

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten

2013-10-01

Land cover classification of remote sensing imagery is an important topic of research. For example, different applications require precise and fast information about the land cover of the imaged scenery (e.g., disaster management and change detection). Focusing on high resolution (HR) spaceborne remote sensing imagery, the user has the choice between passive and active sensor systems. Passive systems, such as multispectral sensors, have the disadvantage of being dependent from weather influences (fog, dust, clouds, etc.) and time of day, since they work in the visible part of the electromagnetic spectrum. Here, active systems like Synthetic Aperture Radar (SAR) provide improved capabilities. As an interactive method analyzing HR InSAR image pairs, the CovAmCohTM method was introduced in former studies. CovAmCoh represents the joint analysis of locality (coefficient of variation - Cov), backscatter (amplitude - Am) and temporal stability (coherence - Coh). It delivers information on physical backscatter characteristics of imaged scene objects or structures and provides the opportunity to detect different classes of land cover (e.g., urban, rural, infrastructure and activity areas). As example, railway tracks are easily distinguishable from other infrastructure due to their characteristic bluish coloring caused by the gravel between the sleepers. In consequence, imaged objects or structures have a characteristic appearance in CovAmCoh images which allows the development of classification rules. In this paper, a generalized interpretation scheme for arbitrary InSAR image pairs using the CovAmCoh method is proposed. This scheme bases on analyzing the information content of typical CovAmCoh imagery using the semisupervised k-means clustering. It is shown that eight classes model the main local information content of CovAmCoh images sufficiently and can be used as basis for a classification scheme.

Moraine-dammed lake failures in Patagonia and assessment of outburst susceptibility in the Baker Basin

NASA Astrophysics Data System (ADS)

Iribarren Anacona, P.; Norton, K. P.; Mackintosh, A.

2014-07-01

Glacier retreat since the Little Ice Age has resulted in the development or expansion of hundreds of glacial lakes in Patagonia. Some of these lakes have produced large (≥106 m3) Glacial Lake Outburst Floods (GLOFs) damaging inhabited areas. GLOF hazard studies in Patagonia have been mainly based on the analysis of short-term series (≤50 years) of flood data and until now no attempt has been made to identify the relative susceptibility of lakes to failure. Power schemes and associated infrastructure are planned for Patagonian basins that have historically been affected by GLOFs, and we now require a thorough understanding of the characteristics of dangerous lakes in order to assist with hazard assessment and planning. In this paper, the conditioning factors of 16 outbursts from moraine dammed lakes in Patagonia were analysed. These data were used to develop a classification scheme designed to assess outburst susceptibility, based on image classification techniques, flow routine algorithms and the Analytical Hierarchy Process. This scheme was applied to the Baker Basin, Chile, where at least 7 moraine-dammed lakes have failed in historic time. We identified 386 moraine-dammed lakes in the Baker Basin of which 28 were classified with high or very high outburst susceptibility. Commonly, lakes with high outburst susceptibility are in contact with glaciers and have moderate (>8°) to steep (>15°) dam outlet slopes, akin to failed lakes in Patagonia. The proposed classification scheme is suitable for first-order GLOF hazard assessments in this region. However, rapidly changing glaciers in Patagonia make detailed analysis and monitoring of hazardous lakes and glaciated areas upstream from inhabited areas or critical infrastructure necessary, in order to better prepare for hazards emerging from an evolving cryosphere.

Moraine-dammed lake failures in Patagonia and assessment of outburst susceptibility in the Baker Basin

NASA Astrophysics Data System (ADS)

Iribarren Anacona, P.; Norton, K. P.; Mackintosh, A.

2014-12-01

Glacier retreat since the Little Ice Age has resulted in the development or expansion of hundreds of glacial lakes in Patagonia. Some of these lakes have produced large (≥ 106 m3) Glacial Lake Outburst Floods (GLOFs) damaging inhabited areas. GLOF hazard studies in Patagonia have been mainly based on the analysis of short-term series (≤ 50 years) of flood data and until now no attempt has been made to identify the relative susceptibility of lakes to failure. Power schemes and associated infrastructure are planned for Patagonian basins that have historically been affected by GLOFs, and we now require a thorough understanding of the characteristics of dangerous lakes in order to assist with hazard assessment and planning. In this paper, the conditioning factors of 16 outbursts from moraine-dammed lakes in Patagonia were analysed. These data were used to develop a classification scheme designed to assess outburst susceptibility, based on image classification techniques, flow routine algorithms and the Analytical Hierarchy Process. This scheme was applied to the Baker Basin, Chile, where at least seven moraine-dammed lakes have failed in historic time. We identified 386 moraine-dammed lakes in the Baker Basin of which 28 were classified with high or very high outburst susceptibility. Commonly, lakes with high outburst susceptibility are in contact with glaciers and have moderate (> 8°) to steep (> 15°) dam outlet slopes, akin to failed lakes in Patagonia. The proposed classification scheme is suitable for first-order GLOF hazard assessments in this region. However, rapidly changing glaciers in Patagonia make detailed analysis and monitoring of hazardous lakes and glaciated areas upstream from inhabited areas or critical infrastructure necessary, in order to better prepare for hazards emerging from an evolving cryosphere.

Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.

PubMed

Cai, Hongmin; Peng, Yanxia; Ou, Caiwen; Chen, Minsheng; Li, Li

2014-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported. The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination. Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively. Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

Mapping Neglected Swimming Pools from Satellite Data for Urban Vector Control

NASA Astrophysics Data System (ADS)

Barker, C. M.; Melton, F. S.; Reisen, W. K.

2010-12-01

Neglected swimming pools provide suitable breeding habit for mosquitoes, can contain thousands of mosquito larvae, and present both a significant nuisance and public health risk due to their inherent proximity to urban and suburban populations. The rapid increase and sustained rate of foreclosures in California associated with the recent recession presents a challenge for vector control districts seeking to identify, treat, and monitor neglected pools. Commercial high resolution satellite imagery offers some promise for mapping potential neglected pools, and for mapping pools for which routine maintenance has been reestablished. We present progress on unsupervised classification techniques for mapping both neglected pools and clean pools using high resolution commercial satellite data and discuss the potential uses and limitations of this data source in support of vector control efforts. An unsupervised classification scheme that utilizes image segmentation, band thresholds, and a change detection approach was implemented for sample regions in Coachella Valley, CA and the greater Los Angeles area. Comparison with field data collected by vector control personal was used to assess the accuracy of the estimates. The results suggest that the current system may provide some utility for early detection, or cost effective and time efficient annual monitoring, but additional work is required to address spectral and spatial limitations of current commercial satellite sensors for this purpose.

A cancelable biometric scheme based on multi-lead ECGs.

PubMed

Peng-Tzu Chen; Shun-Chi Wu; Jui-Hsuan Hsieh

2017-07-01

Biometric technologies offer great advantages over other recognition methods, but there are concerns that they may compromise the privacy of individuals. In this paper, an electrocardiogram (ECG)-based cancelable biometric scheme is proposed to relieve such concerns. In this scheme, distinct biometric templates for a given beat bundle are constructed via "subspace collapsing." To determine the identity of any unknown beat bundle, the multiple signal classification (MUSIC) algorithm, incorporating a "suppression and poll" strategy, is adopted. Unlike the existing cancelable biometric schemes, knowledge of the distortion transform is not required for recognition. Experiments with real ECGs from 285 subjects are presented to illustrate the efficacy of the proposed scheme. The best recognition rate of 97.58 % was achieved under the test condition N train = 10 and N test = 10.

A proposed radiographic classification scheme for congenital thoracic vertebral malformations in brachycephalic "screw-tailed" dog breeds.

PubMed

Gutierrez-Quintana, Rodrigo; Guevar, Julien; Stalin, Catherine; Faller, Kiterie; Yeamans, Carmen; Penderis, Jacques

2014-01-01

Congenital vertebral malformations are common in brachycephalic "screw-tailed" dog breeds such as French bulldogs, English bulldogs, Boston terriers, and pugs. The aim of this retrospective study was to determine whether a radiographic classification scheme developed for use in humans would be feasible for use in these dog breeds. Inclusion criteria were hospital admission between September 2009 and April 2013, neurologic examination findings available, diagnostic quality lateral and ventro-dorsal digital radiographs of the thoracic vertebral column, and at least one congenital vertebral malformation. Radiographs were retrieved and interpreted by two observers who were unaware of neurologic status. Vertebral malformations were classified based on a classification scheme modified from a previous human study and a consensus of both observers. Twenty-eight dogs met inclusion criteria (12 with neurologic deficits, 16 with no neurologic deficits). Congenital vertebral malformations affected 85/362 (23.5%) of thoracic vertebrae. Vertebral body formation defects were the most common (butterfly vertebrae 6.6%, ventral wedge-shaped vertebrae 5.5%, dorsal hemivertebrae 0.8%, and dorso-lateral hemivertebrae 0.5%). No lateral hemivertebrae or lateral wedge-shaped vertebrae were identified. The T7 vertebra was the most commonly affected (11/28 dogs), followed by T8 (8/28 dogs) and T12 (8/28 dogs). The number and type of vertebral malformations differed between groups (P = 0.01). Based on MRI, dorsal, and dorso-lateral hemivertebrae were the cause of spinal cord compression in 5/12 (41.6%) of dogs with neurologic deficits. Findings indicated that a modified human radiographic classification system of vertebral malformations is feasible for use in future studies of brachycephalic "screw-tailed" dogs. © 2014 American College of Veterinary Radiology.

Automated Classification of Thermal Infrared Spectra Using Self-organizing Maps

NASA Technical Reports Server (NTRS)

Roush, Ted L.; Hogan, Robert

2006-01-01

Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-organizing maps (SOM) we have developed. The SUM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into appropriate regions. This is prefonned by defining dosed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the therrnal IR.

Childhood interstitial lung diseases: an 18-year retrospective analysis.

PubMed

Soares, Jennifer J; Deutsch, Gail H; Moore, Paul E; Fazili, Mohammad F; Austin, Eric D; Brown, Rebekah F; Sokolow, Andrew G; Hilmes, Melissa A; Young, Lisa R

2013-10-01

Childhood interstitial lung diseases (ILD) occur in a variety of clinical contexts. Advances in the understanding of disease pathogenesis and use of standardized terminology have facilitated increased case ascertainment. However, as all studies have been performed at specialized referral centers, the applicability of these findings to general pulmonary practice has been uncertain. The objective of this study was to determine the historical occurrence of childhood ILD to provide information reflecting general pediatric pulmonary practice patterns. Childhood ILD cases seen at Vanderbilt Children's Hospital from 1994 to 2011 were retrospectively reviewed and classified according to the current pediatric diffuse lung disease histopathologic classification system. A total of 93 cases were identified, of which 91.4% were classifiable. A total of 68.8% (64/93) of subjects underwent lung biopsy in their evaluations. The largest classification categories were disorders related to systemic disease processes (24.7%), disorders of the immunocompromised host (24.7%), and disorders more prevalent in infancy (22.6%). Eight cases of neuroendocrine cell hyperplasia of infancy (NEHI) were identified, including 5 that were previously unrecognized before this review. Our findings demonstrate the general scope of childhood ILD and that these cases present within a variety of pediatric subspecialties. Retrospective review was valuable in recognizing more recently described forms of childhood ILD. As a significant portion of cases were classifiable based on clinical, genetic, and/or radiographic criteria, we urge greater consideration to noninvasive diagnostic approaches and suggest modification to the current childhood ILD classification scheme to accommodate the increasing number of cases diagnosed without lung biopsy.

Comparative Genomics of Erwinia amylovora and Related Erwinia Species—What do We Learn?

PubMed Central

Zhao, Youfu; Qi, Mingsheng

2011-01-01

Erwinia amylovora, the causal agent of fire blight disease of apples and pears, is one of the most important plant bacterial pathogens with worldwide economic significance. Recent reports on the complete or draft genome sequences of four species in the genus Erwinia, including E. amylovora, E. pyrifoliae, E. tasmaniensis, and E. billingiae, have provided us near complete genetic information about this pathogen and its closely-related species. This review describes in silico subtractive hybridization-based comparative genomic analyses of eight genomes currently available, and highlights what we have learned from these comparative analyses, as well as genetic and functional genomic studies. Sequence analyses reinforce the assumption that E. amylovora is a relatively homogeneous species and support the current classification scheme of E. amylovora and its related species. The potential evolutionary origin of these Erwinia species is also proposed. The current understanding of the pathogen, its virulence mechanism and host specificity from genome sequencing data is summarized. Future research directions are also suggested. PMID:24710213

A novel fractal image compression scheme with block classification and sorting based on Pearson's correlation coefficient.

PubMed

Wang, Jianji; Zheng, Nanning

2013-09-01

Fractal image compression (FIC) is an image coding technology based on the local similarity of image structure. It is widely used in many fields such as image retrieval, image denoising, image authentication, and encryption. FIC, however, suffers from the high computational complexity in encoding. Although many schemes are published to speed up encoding, they do not easily satisfy the encoding time or the reconstructed image quality requirements. In this paper, a new FIC scheme is proposed based on the fact that the affine similarity between two blocks in FIC is equivalent to the absolute value of Pearson's correlation coefficient (APCC) between them. First, all blocks in the range and domain pools are chosen and classified using an APCC-based block classification method to increase the matching probability. Second, by sorting the domain blocks with respect to APCCs between these domain blocks and a preset block in each class, the matching domain block for a range block can be searched in the selected domain set in which these APCCs are closer to APCC between the range block and the preset block. Experimental results show that the proposed scheme can significantly speed up the encoding process in FIC while preserving the reconstructed image quality well.

Infant Mortality: Development of a Proposed Update to the Dollfus Classification of Infant Deaths

PubMed Central

Dove, Melanie S.; Minnal, Archana; Damesyn, Mark; Curtis, Michael P.

2015-01-01

Objective Identifying infant deaths with common underlying causes and potential intervention points is critical to infant mortality surveillance and the development of prevention strategies. We constructed an International Classification of Diseases 10th Revision (ICD-10) parallel to the Dollfus cause-of-death classification scheme first published in 1990, which organized infant deaths by etiology and their amenability to prevention efforts. Methods Infant death records for 1996, dual-coded to the ICD Ninth Revision (ICD-9) and ICD-10, were obtained from the CDC public-use multiple-cause-of-death file on comparability between ICD-9 and ICD-10. We used the underlying cause of death to group 27,821 infant deaths into the nine categories of the ICD-9-based update to Dollfus' original coding scheme, published by Sowards in 1999. Comparability ratios were computed to measure concordance between ICD versions. Results The Dollfus classification system updated with ICD-10 codes had limited agreement with the 1999 modified classification system. Although prematurity, congenital malformations, Sudden Infant Death Syndrome, and obstetric conditions were the first through fourth most common causes of infant death under both systems, most comparability ratios were significantly different from one system to the other. Conclusion The Dollfus classification system can be adapted for use with ICD-10 codes to create a comprehensive, etiology-based profile of infant deaths. The potential benefits of using Dollfus logic to guide perinatal mortality reduction strategies, particularly to maternal and child health programs and other initiatives focused on improving infant health, warrant further examination of this method's use in perinatal mortality surveillance. PMID:26556935

Classification of gravity-flow deposits and their significance for unconventional petroleum exploration, with a case study from the Triassic Yanchang Formation (southern Ordos Basin, China)

NASA Astrophysics Data System (ADS)

Fan, Aiping; Yang, Renchao; (Tom) van Loon, A. J.; Yin, Wei; Han, Zuozhen; Zavala, Carlos

2018-08-01

The ongoing exploration for shale oil and gas has focused sedimentological research on the transport and deposition mechanisms of fine-grained sediments, and more specifically on fine-grained mass-flow deposits. It appears, however, that no easily applicable classification scheme for gravity-flow deposits exists, and that such classifications almost exclusively deal with sandy and coarser sediments. Since the lack of a good classification system for fine-grained gravity flow deposits hampers scientific communication and understanding, we propose a classification scheme on the basis of the mud content in combination with the presumed transport mechanism. This results in twelve types of gravity-flow deposits. In order to show the practical applicability of this classification system, we apply it to the Triassic lacustrine Yanchang Formation in the southern Ordos Basin (China), which contains numerous slumps, debris-flows deposits, turbidites and hyperpycnites. The slumps and debrites occur mostly close to a delta front, and the turbidites and hyperpycnites extend over large areas from the delta slopes into the basin plain. The case study shows that (1) mud cannot only be transported but also deposited under active hydrodynamic conditions; (2) fine-grained gravity-flow constitute a significant part of the lacustrine mudstones and shales; (3) muddy gravity flows are important for the transport and deposition of clastic particles, clay minerals and organic matter, and thus are important mechanisms involved in the generation of hydrocarbons, also largely determining the reservoir capability for unconventional petroleum.

Treatment outcomes of saddle nose correction.

PubMed

Hyun, Sang Min; Jang, Yong Ju

2013-01-01

Many valuable classification schemes for saddle nose have been suggested that integrate clinical deformity and treatment; however, there is no consensus regarding the most suitable classification and surgical method for saddle nose correction. To present clinical characteristics and treatment outcome of saddle nose deformity and to propose a modified classification system to better characterize the variety of different saddle nose deformities. The retrospective study included 91 patients who underwent rhinoplasty for correction of saddle nose from April 1, 2003, through December 31, 2011, with a minimum follow-up of 8 months. Saddle nose was classified into 4 types according to a modified classification. Aesthetic outcomes were classified as excellent, good, fair, or poor. Patients underwent minor cosmetic concealment by dorsal augmentation (n = 8) or major septal reconstruction combined with dorsal augmentation (n = 83). Autologous costal cartilages were used in 40 patients (44%), and homologous costal cartilages were used in 5 patients (6%). According to postoperative assessment, 29 patients had excellent, 42 patients had good, 18 patients had fair, and 2 patients had poor aesthetic outcomes. No statistical difference in surgical outcome according to saddle nose classification was observed. Eight patients underwent revision rhinoplasty, owing to recurrence of saddle, wound infection, or warping of the costal cartilage for dorsal augmentation. We introduce a modified saddle nose classification scheme that is simpler and better able to characterize different deformities. Among 91 patients with saddle nose, 20 (22%) had unsuccessful outcomes (fair or poor) and 8 (9%) underwent subsequent revision rhinoplasty. Thus, management of saddle nose deformities remains challenging. 4.

Changing Patient Classification System for Hospital Reimbursement in Romania

PubMed Central

Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian

2010-01-01

Aim To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Methods Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). Results The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians’ knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Conclusion Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care. PMID:20564769

Changing patient classification system for hospital reimbursement in Romania.

PubMed

Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian

2010-06-01

To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians' knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case-mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case-mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care.

In-vivo determination of chewing patterns using FBG and artificial neural networks

NASA Astrophysics Data System (ADS)

Pegorini, Vinicius; Zen Karam, Leandro; Rocha Pitta, Christiano S.; Ribeiro, Richardson; Simioni Assmann, Tangriani; Cardozo da Silva, Jean Carlos; Bertotti, Fábio L.; Kalinowski, Hypolito J.; Cardoso, Rafael

2015-09-01

This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.

Automated source classification of new transient sources

NASA Astrophysics Data System (ADS)

Oertel, M.; Kreikenbohm, A.; Wilms, J.; DeLuca, A.

2017-10-01

The EXTraS project harvests the hitherto unexplored temporal domain information buried in the serendipitous data collected by the European Photon Imaging Camera (EPIC) onboard the ESA XMM-Newton mission since its launch. This includes a search for fast transients, missed by standard image analysis, and a search and characterization of variability in hundreds of thousands of sources. We present an automated classification scheme for new transient sources in the EXTraS project. The method is as follows: source classification features of a training sample are used to train machine learning algorithms (performed in R; randomForest (Breiman, 2001) in supervised mode) which are then tested on a sample of known source classes and used for classification.

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

USGS Publications Warehouse

Rey, Sergio J.; Stephens, Philip A.; Laura, Jason R.

2017-01-01

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.

Development of a Procurement Task Classification Scheme.

DTIC Science & Technology

1987-12-01

Office of Sci- entific Research, Arlington, Virginia, January 1970. Tornow , Walter W . and Pinto, Patrick R. "The Development of a Man- agerial Job...classification. [Ref. 4:271 -. 20 6 %° w Numerical taxonomy proponents hold [Ref. 4:271, ... that the relationships of contiguity and similarity should be...solving. 22 W i * These primitive categories are based on a sorting of learning pro- cesses into classes that have obvious differences at the

USCS and the USDA Soil Classification System: Development of a Mapping Scheme

DTIC Science & Technology

2015-03-01

important to human daily living. A variety of disciplines (geology, agriculture, engineering, etc.) require a sys- tematic categorization of soil, detailing...it is often important to also con- sider parameters that indicate soil strength. Two important properties used for engineering-related problems are...that many textural clas- sification systems were developed to meet specifics needs. In agriculture, textural classification is used to determine crop

Revealing how different spinors can be: The Lounesto spinor classification

NASA Astrophysics Data System (ADS)

Hoff da Silva, J. M.; Cavalcanti, R. T.

2017-11-01

This paper aims to give a coordinate-based introduction to the so-called Lounesto spinorial classification scheme. Among other results, it has evinced classes of spinors which fail to satisfy Dirac equation. The underlying idea and the central aspects of such spinorial categorization are introduced in an argumentative basis, after which we delve into a commented account on recent results obtained from (and within) this branch of research.

Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

NASA Astrophysics Data System (ADS)

Pastor, M. A.; Casado, M. J.

2012-10-01

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes.

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

PubMed

Furlanello, Cesare; Serafini, Maria; Merler, Stefano; Jurman, Giuseppe

2003-11-06

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images.

PubMed

Shanthi, C; Pappa, N

2017-05-01

Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A novel fully automatic scheme for fiducial marker-based alignment in electron tomography.

PubMed

Han, Renmin; Wang, Liansan; Liu, Zhiyong; Sun, Fei; Zhang, Fa

2015-12-01

Although the topic of fiducial marker-based alignment in electron tomography (ET) has been widely discussed for decades, alignment without human intervention remains a difficult problem. Specifically, the emergence of subtomogram averaging has increased the demand for batch processing during tomographic reconstruction; fully automatic fiducial marker-based alignment is the main technique in this process. However, the lack of an accurate method for detecting and tracking fiducial markers precludes fully automatic alignment. In this paper, we present a novel, fully automatic alignment scheme for ET. Our scheme has two main contributions: First, we present a series of algorithms to ensure a high recognition rate and precise localization during the detection of fiducial markers. Our proposed solution reduces fiducial marker detection to a sampling and classification problem and further introduces an algorithm to solve the parameter dependence of marker diameter and marker number. Second, we propose a novel algorithm to solve the tracking of fiducial markers by reducing the tracking problem to an incomplete point set registration problem. Because a global optimization of a point set registration occurs, the result of our tracking is independent of the initial image position in the tilt series, allowing for the robust tracking of fiducial markers without pre-alignment. The experimental results indicate that our method can achieve an accurate tracking, almost identical to the current best one in IMOD with half automatic scheme. Furthermore, our scheme is fully automatic, depends on fewer parameters (only requires a gross value of the marker diameter) and does not require any manual interaction, providing the possibility of automatic batch processing of electron tomographic reconstruction. Copyright © 2015 Elsevier Inc. All rights reserved.

Indexed variation graphs for efficient and accurate resistome profiling.

PubMed

Rowe, Will P M; Winn, Martyn D

2018-05-14

Antimicrobial resistance remains a major threat to global health. Profiling the collective antimicrobial resistance genes within a metagenome (the "resistome") facilitates greater understanding of antimicrobial resistance gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools. Our method combines a variation graph representation of gene sets with an LSH Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, GROOT, and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 minutes using a single CPU. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. GROOT is written in Go and is available at https://github.com/will-rowe/groot (MIT license). will.rowe@stfc.ac.uk. Supplementary data are available at Bioinformatics online.

The Libraries of Rio.

ERIC Educational Resources Information Center

Foster, Barbara

1988-01-01

Describes aspects of several libraries in Rio de Janeiro. Topics covered include library policies, budgets, periodicals and books in the collections, classification schemes used, and literary areas of interest to patrons. (6 references) (CLB)

Relevance popularity: A term event model based feature selection scheme for text classification.

PubMed

Feng, Guozhong; An, Baiguo; Yang, Fengqin; Wang, Han; Zhang, Libiao

2017-01-01

Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.

Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals

NASA Technical Reports Server (NTRS)

Campbell, Janet W.

1998-01-01

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

Evaluation of management measures of software development. Volume 1: Analysis summary

NASA Technical Reports Server (NTRS)

Page, J.; Card, D.; Mcgarry, F.

1982-01-01

The conceptual model, the data classification scheme, and the analytic procedures are explained. The analytic results are summarized and specific software measures for collection and monitoring are recommended.

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Synthesis and size classification of metal oxide nanoparticles for biomedical applications

NASA Astrophysics Data System (ADS)

Atsumi, Takashi; Jeyadevan, Balachandran; Sato, Yoshinori; Tamura, Kazuchika; Aiba, Setsuya; Tohji, Kazuyuki

2004-12-01

Magnetic nanoparticles are considered for biomedical applications, such as the medium in magnetic resonance imaging, hyperthermia, drug delivery, and for the purification or classification of DNA or virus. The performance of magnetic nanoparticles in biomedical application such as hyperthermia depends very much on the magnetic properties, size and size distribution. We briefly described the basic idea behind their use in drug delivery, magnetic separation and hyperthermia and discussed the prerequisite properties magnetic particles for biomedical applications. Finally reported the synthesis and classification scheme to prepare magnetite (Fe3O4) nanoparticles with narrow size distribution for magnetic fluid hyperthermia.

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone

NASA Technical Reports Server (NTRS)

Harwood, P. (Principal Investigator); Finley, R.; Mcculloch, S.; Marphy, D.; Hupp, B.

1976-01-01

The author has identified the following significant results. Image interpretation mapping techniques were successfully applied to test site 5, an area with a semi-arid climate. The land cover/land use classification required further modification. A new program, HGROUP, added to the ADP classification schedule provides a convenient method for examining the spectral similarity between classes. This capability greatly simplifies the task of combining 25-30 unsupervised subclasses into about 15 major classes that approximately correspond to the land use/land cover classification scheme.

Significance of clustering and classification applications in digital and physical libraries

NASA Astrophysics Data System (ADS)

Triantafyllou, Ioannis; Koulouris, Alexandros; Zervos, Spiros; Dendrinos, Markos; Giannakopoulos, Georgios

2015-02-01

Applications of clustering and classification techniques can be proved very significant in both digital and physical (paper-based) libraries. The most essential application, document classification and clustering, is crucial for the content that is produced and maintained in digital libraries, repositories, databases, social media, blogs etc., based on various tags and ontology elements, transcending the traditional library-oriented classification schemes. Other applications with very useful and beneficial role in the new digital library environment involve document routing, summarization and query expansion. Paper-based libraries can benefit as well since classification combined with advanced material characterization techniques such as FTIR (Fourier Transform InfraRed spectroscopy) can be vital for the study and prevention of material deterioration. An improved two-level self-organizing clustering architecture is proposed in order to enhance the discrimination capacity of the learning space, prior to classification, yielding promising results when applied to the above mentioned library tasks.

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

An efficient scheme for automatic web pages categorization using the support vector machine

NASA Astrophysics Data System (ADS)

Bhalla, Vinod Kumar; Kumar, Neeraj

2016-07-01

In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.

Hydrological Climate Classification: Can We Improve on Köppen-Geiger?

NASA Astrophysics Data System (ADS)

Knoben, W.; Woods, R. A.; Freer, J. E.

2017-12-01

Classification is essential in the study of complex natural systems, yet hydrology so far has no formal way to structure the climate forcing which underlies hydrologic response. Various climate classification systems can be borrowed from other disciplines but these are based on different organizing principles than a hydrological classification might use. From gridded global data we calculate a gridded aridity index, an aridity seasonality index and a rain-vs-snow index, which we use to cluster global locations into climate groups. We then define the membership degree of nearly 1100 catchments to each of our climate groups based on each catchment's climate and investigate the extent to which streamflow responses within each climate group are similar. We compare this climate classification approach with the often-used Köppen-Geiger classification, using statistical tests based on streamflow signature values. We find that three climate indices are sufficient to distinguish 18 different climate types world-wide. Climates tend to change gradually in space and catchments can thus belong to multiple climate groups, albeit with different degrees of membership. Streamflow responses within a climate group tend to be similar, regardless of the catchments' geographical proximity. A Wilcoxon two-sample test based on streamflow signature values for each climate group shows that the new classification can distinguish different flow regimes using this classification scheme. The Köppen-Geiger approach uses 29 climate classes but is less able to differentiate streamflow regimes. Climate forcing exerts a strong control on typical hydrologic response and both change gradually in space. This makes arbitrary hard boundaries in any classification scheme difficult to defend. Any hydrological classification should thus acknowledge these gradual changes in forcing. Catchment characteristics (soil or vegetation type, land use, etc) can vary more quickly in space than climate does, which can explain streamflow differences between geographically close locations. Summarizing, this work shows that hydrology needs its own way to structure climate forcing, acknowledging that climates vary gradually on a global scale and explicitly including those climate aspects that drive seasonal changes in hydrologic regimes.

Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment.

PubMed

Rojas, Veronica; Hirshfield, Kim M; Ganesan, Shridar; Rodriguez-Rodriguez, Lorna

2016-12-15

Epithelial ovarian cancer is a highly heterogeneous disease characterized by multiple histological subtypes. Molecular diversity has been shown to occur within specific histological subtypes of epithelial ovarian cancer, between different tumors of an individual patient, as well as within individual tumors. Recent advances in the molecular characterization of epithelial ovarian cancer tumors have provided the basis for a simplified classification scheme in which these cancers are classified as either type I or type II tumors, and these two categories have implications regarding disease pathogenesis and prognosis. Molecular analyses, primarily based on next-generation sequencing, otherwise known as high-throughput sequencing, are allowing for further refinement of ovarian cancer classification, facilitating the elucidation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemoresistance. Potential therapeutic targets have been identified from recent molecular profiling studies of these tumors, and the effectiveness and safety of a number of specific targeted therapies have been evaluated or are currently being studied for the treatment of women with this disease.

Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment

PubMed Central

Rojas, Veronica; Hirshfield, Kim M.; Ganesan, Shridar; Rodriguez-Rodriguez, Lorna

2016-01-01

Epithelial ovarian cancer is a highly heterogeneous disease characterized by multiple histological subtypes. Molecular diversity has been shown to occur within specific histological subtypes of epithelial ovarian cancer, between different tumors of an individual patient, as well as within individual tumors. Recent advances in the molecular characterization of epithelial ovarian cancer tumors have provided the basis for a simplified classification scheme in which these cancers are classified as either type I or type II tumors, and these two categories have implications regarding disease pathogenesis and prognosis. Molecular analyses, primarily based on next-generation sequencing, otherwise known as high-throughput sequencing, are allowing for further refinement of ovarian cancer classification, facilitating the elucidation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemoresistance. Potential therapeutic targets have been identified from recent molecular profiling studies of these tumors, and the effectiveness and safety of a number of specific targeted therapies have been evaluated or are currently being studied for the treatment of women with this disease. PMID:27983698

Types of Crude Oil

EPA Pesticide Factsheets

The petroleum industry often classifies these types by geographical source, but the classification scheme here is more useful in a spill cleanup scenario. It indicates general toxicity, physical state, and changes caused by time and weathering.

Transport on Riemannian manifold for functional connectivity-based classification.

PubMed

Ng, Bernard; Dressler, Martin; Varoquaux, Gaël; Poline, Jean Baptiste; Greicius, Michael; Thirion, Bertrand

2014-01-01

We present a Riemannian approach for classifying fMRI connectivity patterns before and after intervention in longitudinal studies. A fundamental difficulty with using connectivity as features is that covariance matrices live on the positive semi-definite cone, which renders their elements inter-related. The implicit independent feature assumption in most classifier learning algorithms is thus violated. In this paper, we propose a matrix whitening transport for projecting the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. We show on real data that our approach provides significantly higher classification accuracy than directly using Pearson's correlation. We further propose a non-parametric scheme for identifying significantly discriminative connections from classifier weights. Using this scheme, a number of neuroanatomically meaningful connections are found, whereas no significant connections are detected with pure permutation testing.

VTOL shipboard letdown guidance system analysis

NASA Technical Reports Server (NTRS)

Phatak, A. V.; Karmali, M. S.

1983-01-01

Alternative letdown guidance strategies are examined for landing of a VTOL aircraft onboard a small aviation ship under adverse environmental conditions. Off line computer simulation of shipboard landing task is utilized for assessing the relative merits of the proposed guidance schemes. The touchdown performance of a nominal constant rate of descent (CROD) letdown strategy serves as a benchmark for ranking the performance of the alternative letdown schemes. Analysis of ship motion time histories indicates the existence of an alternating sequence of quiescent and rough motions called lulls and swells. A real time algorithms lull/swell classification based upon ship motion pattern features is developed. The classification algorithm is used to command a go/no go signal to indicate the initiation and termination of an acceptable landing window. Simulation results show that such a go/no go pattern based letdown guidance strategy improves touchdown performance.

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

[Who benefits from systemic therapy with a reflecting team?].

PubMed

Höger, C; Temme, M; Geiken, G

1994-03-01

In an evaluation study we investigated the effectiveness of the reflecting team approach compared to eclectic child psychiatric treatment in an outpatient setting and the indications for each type of treatment. The relationship between treatment outcome and diagnostic data obtained with the Multi-axial Classification Scheme was examined in 22 families treated with the reflecting team approach and in a second group of families matched on all important sociodemographic and diagnostic variables but receiving eclectic treatment. No difference was found between the two groups regarding symptom improvement or changes in family functioning. Regarding satisfaction with treatment, the reflecting team approach was superior to the eclectic modality. In the reflecting team group parental mental disorder and inadequate intra-familial communication (according to the new fifth axis of the Multi-axial Classification Scheme) had a negative effect on outcome.

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

NASA Astrophysics Data System (ADS)

He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

2018-04-01

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

A Support Vector Machine-Based Gender Identification Using Speech Signal

NASA Astrophysics Data System (ADS)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Synthesis of Potential Trypanocides

DTIC Science & Technology

1987-12-01

0188 Ia. REPORT SECURITY CLASSIFICATION 1b RESTRICTIVE MARKINGS Unclassified 2a. SECURITY CLASSIFICATION AUTHORITY 3 . DISTRIBUTION /AVAILABILITY OF...and the phenyl ring, ring structures 2 and 3 , introduction of a -CH:CII- group between the phenyl ring and its 4’-substituent, ring structure 4...imidazole (9, 15) thiazole (11) and pyridine (12-14) into ether-linked and vinyl-linked structures. 3 t % SCHEME 1 HETEROAROMATIC RINGS OHw Ch3 CH3 +1 N% f

Some Complexity Results About Packet Radio Networks

DTIC Science & Technology

1983-03-01

divsio-muti-cces)schemes foP TD ^12A 1 660 SECURITY CLASSIFICATION OF THIS PAGE (ft* 60 IAD SZCURTY CLASSIFICATION Of THIS PAGE(Whan Data Entoted) -other...8217-21-. bbd bd a aK (a) (b) b-d b 0~~ C C (c) (d) Fig. 1. Situations in a PRN for which (c,d) conflicts with (a,b). -22- 12 3 3m 3m+1- 3m+2 Fig. 2. A

A framework for classification of prokaryotic protein kinases.

PubMed

Tyagi, Nidhi; Anamika, Krishanpal; Srinivasan, Narayanaswamy

2010-05-26

Overwhelming majority of the Serine/Threonine protein kinases identified by gleaning archaeal and eubacterial genomes could not be classified into any of the well known Hanks and Hunter subfamilies of protein kinases. This is owing to the development of Hanks and Hunter classification scheme based on eukaryotic protein kinases which are highly divergent from their prokaryotic homologues. A large dataset of prokaryotic Serine/Threonine protein kinases recognized from genomes of prokaryotes have been used to develop a classification framework for prokaryotic Ser/Thr protein kinases. We have used traditional sequence alignment and phylogenetic approaches and clustered the prokaryotic kinases which represent 72 subfamilies with at least 4 members in each. Such a clustering enables classification of prokaryotic Ser/Thr kinases and it can be used as a framework to classify newly identified prokaryotic Ser/Thr kinases. After series of searches in a comprehensive sequence database we recognized that 38 subfamilies of prokaryotic protein kinases are associated to a specific taxonomic level. For example 4, 6 and 3 subfamilies have been identified that are currently specific to phylum proteobacteria, cyanobacteria and actinobacteria respectively. Similarly subfamilies which are specific to an order, sub-order, class, family and genus have also been identified. In addition to these, we also identify organism-diverse subfamilies. Members of these clusters are from organisms of different taxonomic levels, such as archaea, bacteria, eukaryotes and viruses. Interestingly, occurrence of several taxonomic level specific subfamilies of prokaryotic kinases contrasts with classification of eukaryotic protein kinases in which most of the popular subfamilies of eukaryotic protein kinases occur diversely in several eukaryotes. Many prokaryotic Ser/Thr kinases exhibit a wide variety of modular organization which indicates a degree of complexity and protein-protein interactions in the signaling pathways in these microbes.

An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.

PubMed

Valderrama-Landeros, L; Flores-de-Santiago, F; Kovacs, J M; Flores-Verdugo, F

2017-12-14

Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had  the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.

High-order asynchrony-tolerant finite difference schemes for partial differential equations

NASA Astrophysics Data System (ADS)

Aditya, Konduri; Donzis, Diego A.

2017-12-01

Synchronizations of processing elements (PEs) in massively parallel simulations, which arise due to communication or load imbalances between PEs, significantly affect the scalability of scientific applications. We have recently proposed a method based on finite-difference schemes to solve partial differential equations in an asynchronous fashion - synchronization between PEs is relaxed at a mathematical level. While standard schemes can maintain their stability in the presence of asynchrony, their accuracy is drastically affected. In this work, we present a general methodology to derive asynchrony-tolerant (AT) finite difference schemes of arbitrary order of accuracy, which can maintain their accuracy when synchronizations are relaxed. We show that there are several choices available in selecting a stencil to derive these schemes and discuss their effect on numerical and computational performance. We provide a simple classification of schemes based on the stencil and derive schemes that are representative of different classes. Their numerical error is rigorously analyzed within a statistical framework to obtain the overall accuracy of the solution. Results from numerical experiments are used to validate the performance of the schemes.

Classifying the hierarchy of nonlinear-Schrödinger-equation rogue-wave solutions.

PubMed

Kedziora, David J; Ankiewicz, Adrian; Akhmediev, Nail

2013-07-01

We present a systematic classification for higher-order rogue-wave solutions of the nonlinear Schrödinger equation, constructed as the nonlinear superposition of first-order breathers via the recursive Darboux transformation scheme. This hierarchy is subdivided into structures that exhibit varying degrees of radial symmetry, all arising from independent degrees of freedom associated with physical translations of component breathers. We reveal the general rules required to produce these fundamental patterns. Consequently, we are able to extrapolate the general shape for rogue-wave solutions beyond order 6, at which point accuracy limitations due to current standards of numerical generation become non-negligible. Furthermore, we indicate how a large set of irregular rogue-wave solutions can be produced by hybridizing these fundamental structures.

Dynamic Computation Offloading for Low-Power Wearable Health Monitoring Systems.

PubMed

Kalantarian, Haik; Sideris, Costas; Mortazavi, Bobak; Alshurafa, Nabil; Sarrafzadeh, Majid

2017-03-01

The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices. We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy. By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%. We demonstrate that computation offloading can be applied to real-time monitoring systems, and yields significant power savings. Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.

Assessment of current virotherapeutic application schemes: “hit hard and early” versus “killing softly”?

PubMed Central

Ruf, Benjamin; Lauer, Ulrich M

2015-01-01

Over the past two decades, a considerable amount of oncolytic vector families has entered numerous clinical trials. However, to this date, the field has not yet been able to come to a common understanding regarding the best possible ways to administer oncolytic viruses to cancer patients. This is mainly due to the fact that so far clinical trials being designed for head-to-head comparisons (such as using two different virotherapeutics originating from two distinct virus families being applied via identical routes in the same types of cancer) are still missing. Hence, there is no consent (i) on the best route of virotherapeutics administration (e.g., systemic versus intratumoral), (ii) on the virus dosages to be applied, (iii) on dosing intervals, and (iv) on the numbers of repetitive courses of virus administration. As the detailed comparison of clinical virotherapy trial regimens is time-consuming and complex, we here present an overview of current state-of-the-art virotherapeutic application schemes. Notably, our comprehensive assessment culminates in raising two rough classifications of virotherapeutic strategies, i.e., “hit hard and early” versus “killing softly”. In order to find out which one of these two gross alternatives might be most successful for each and every tumor entity, we here suggest the implementation of phase 1/2 studies, which primarily aim at a repetitive sampling and analysis of tumor samples in cancer patients treated with oncolytic viruses reading out (i) virus-specific, (ii) tumor-specific as well as (iii) immunotherapeutic parameters. On this basis, a rational design of significantly improved virotherapeutic application schemes should be possible in the future. PMID:27119110

Elucidation of molecular kinetic schemes from macroscopic traces using system identification

PubMed Central

González-Maeso, Javier; Sealfon, Stuart C.; Galocha-Iragüen, Belén; Brezina, Vladimir

2017-01-01

Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and recording events at the molecular level, this is still possible only in especially favorable cases. Therefore the development of methods to extract the dynamics and relationships between the different lower-level (microscopic) processes from the overall (macroscopic) response remains a crucial challenge in the understanding of many aspects of physiology. Here we have devised a hybrid computational-analytical method to accomplish this task, the SYStems-based MOLecular kinetic scheme Extractor (SYSMOLE). SYSMOLE utilizes system-identification input-output analysis to obtain a transfer function between the stimulus and the overall cellular response in the Laplace-transformed domain. It then derives a Markov-chain state molecular kinetic scheme uniquely associated with the transfer function by means of a classification procedure and an analytical step that imposes general biological constraints. We first tested SYSMOLE with synthetic data and evaluated its performance in terms of its rate of convergence to the correct molecular kinetic scheme and its robustness to noise. We then examined its performance on real experimental traces by analyzing macroscopic calcium-current traces elicited by membrane depolarization. SYSMOLE derived the correct, previously known molecular kinetic scheme describing the activation and inactivation of the underlying calcium channels and correctly identified the accepted mechanism of action of nifedipine, a calcium-channel blocker clinically used in patients with cardiovascular disease. Finally, we applied SYSMOLE to study the pharmacology of a new class of glutamate antipsychotic drugs and their crosstalk mechanism through a heteromeric complex of G protein-coupled receptors. Our results indicate that our methodology can be successfully applied to accurately derive molecular kinetic schemes from experimental macroscopic traces, and we anticipate that it may be useful in the study of a wide variety of biological systems. PMID:28192423

Unsupervised classification of remote multispectral sensing data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

Update and validation of the Society for Vascular Surgery wound, ischemia, and foot infection threatened limb classification system.

PubMed

Mills, Joseph L

2014-03-01

The diagnosis of critical limb ischemia, first defined in 1982, was intended to delineate a patient cohort with a threatened limb and at risk for amputation due to severe peripheral arterial disease. The influence of diabetes and its associated neuropathy on the pathogenesis-threatened limb was an excluded comorbidity, despite its known contribution to amputation risk. The Fontaine and Rutherford classifications of limb ischemia severity have also been used to predict amputation risk and the likelihood of tissue healing. The dramatic increase in the prevalence of diabetes mellitus and the expanding techniques of arterial revascularization has prompted modification of peripheral arterial disease classification schemes to improve outcomes analysis for patients with threatened limbs. The diabetic patient with foot ulceration and infection is at risk for limb loss, with abnormal arterial perfusion as only one determinant of outcome. The wound extent and severity of infection also impact the likelihood of limb loss. To better predict amputation risk, the Society for Vascular Surgery Lower Extremity Guidelines Committee developed a classification of the threatened lower extremity that reflects these important clinical considerations. Risk stratification is based on three major factors that impact amputation risk and clinical management: wound, ischemia, and foot infection. This classification scheme is relevant to the patient with critical limb ischemia because many are also diabetic. Implementation of the wound, ischemia, and foot infection classification system in critical limb ischemia patients is recommended and should assist the clinician in more meaningful analysis of outcomes for various forms of wound and arterial revascularizations procedures required in this challenging, patient population. Copyright © 2014 Elsevier Inc. All rights reserved.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

A Unified Classification Framework for FP, DP and CP Data at X-Band in Southern China

NASA Astrophysics Data System (ADS)

Xie, Lei; Zhang, Hong; Li, Hhongzhong; Wang, Chao

2015-04-01

The main objective of this paper is to introduce an unified framework for crop classification in Southern China using data in fully polarimetric (FP), dual-pol (DP) and compact polarimetric (CP) modes. The TerraSAR-X data acquired over the Leizhou Peninsula, South China are used in our experiments. The study site involves four main crops (rice, banana, sugarcane eucalyptus). Through exploring the similarities between data in these three modes, a knowledge-based characteristic space is created and the unified framework is presented. The overall classification accuracies for data in the FP, coherent HH/VV are about 95%, and is about 91% in CP modes, which suggests that the proposed classification scheme is effective and promising. Compared with the Wishart Maximum Likelihood (ML) classifier, the proposed method exhibits higher classification accuracy.

Speech Enhancement based on the Dominant Classification Between Speech and Noise Using Feature Data in Spectrogram of Observation Signal

NASA Astrophysics Data System (ADS)

Nomura, Yukihiro; Lu, Jianming; Sekiya, Hiroo; Yahagi, Takashi

This paper presents a speech enhancement using the classification between the dominants of speech and noise. In our system, a new classification scheme between the dominants of speech and noise is proposed. The proposed classifications use the standard deviation of the spectrum of observation signal in each band. We introduce two oversubtraction factors for the dominants of speech and noise, respectively. And spectral subtraction is carried out after the classification. The proposed method is tested on several noise types from the Noisex-92 database. From the investigation of segmental SNR, Itakura-Saito distance measure, inspection of spectrograms and listening tests, the proposed system is shown to be effective to reduce background noise. Moreover, the enhanced speech using our system generates less musical noise and distortion than that of conventional systems.

Wilderness ecology: a method of sampling and summarizing data for plant community classification.

Treesearch

Lewis F. Ohmann; Robert R. Ream

1971-01-01

Presents a flexible sampling scheme that researchers and land managers may use in surveying and classifying plant communities of forest lands. Includes methods, data sheets, and computer summarization printouts.

Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

NASA Astrophysics Data System (ADS)

Andreadis, Ioannis; Sevastianos, Chatzistergos; George, Spyrou; Konstantina, Nikita

2017-11-01

Computer aided diagnosis (CAD x ) approaches are developed towards the effective discrimination between benign and malignant clusters of microcalcifications. Different sources of information are exploited, such as features extracted from the image analysis of the region of interest, features related to the location of the cluster inside the breast, age of the patient and descriptors provided by the radiologists while performing their diagnostic task. A series of different CAD x schemes are implemented, each of which uses a different category of features and adopts a variety of machine learning algorithms and alternative image processing techniques. A novel framework is introduced where these independent diagnostic components are properly combined according to features critical to a radiologist in an attempt to identify the most appropriate CAD x schemes for the case under consideration. An open access database (Digital Database of Screening Mammography (DDSM)) has been elaborated to construct a large dataset with cases of varying subtlety, in order to ensure the development of schemes with high generalization ability, as well as extensive evaluation of their performance. The obtained results indicate that the proposed framework succeeds in improving the diagnostic procedure, as the achieved overall classification performance outperforms all the independent single diagnostic components, as well as the radiologists that assessed the same cases, in terms of accuracy, sensitivity, specificity and area under the curve following receiver operating characteristic analysis.

PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish.

PubMed

Nendza, Monika; Kühne, Ralph; Lombardo, Anna; Strempel, Sebastian; Schüürmann, Gerrit

2018-03-01

Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour. Copyright © 2017 Elsevier B.V. All rights reserved.

Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography

PubMed Central

Chen, Sheng; Suzuki, Kenji

2014-01-01

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially. PMID:23193306

Computerized detection of lung nodules by means of "virtual dual-energy" radiography.

PubMed

Chen, Sheng; Suzuki, Kenji

2013-02-01

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of "virtual dual-energy" (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated "extremely subtle" or "very subtle" by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.

Medical image enhancement using resolution synthesis

NASA Astrophysics Data System (ADS)

Wong, Tak-Shing; Bouman, Charles A.; Thibault, Jean-Baptiste; Sauer, Ken D.

2011-03-01

We introduce a post-processing approach to improve the quality of CT reconstructed images. The scheme is adapted from the resolution-synthesis (RS)1 interpolation algorithm. In this approach, we consider the input image, scanned at a particular dose level, as a degraded version of a high quality image scanned at a high dose level. Image enhancement is achieved by predicting the high quality image by classification based linear regression. To improve the robustness of our scheme, we also apply the minimum description length principle to determine the optimal number of predictors to use in the scheme, and the ridge regression to regularize the design of the predictors. Experimental results show that our scheme is effective in reducing the noise in images reconstructed from filtered back projection without significant loss of image details. Alternatively, our scheme can also be applied to reduce dose while maintaining image quality at an acceptable level.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

A comprehensive catalogue and classification of human thermal climate indices

NASA Astrophysics Data System (ADS)

de Freitas, C. R.; Grigorieva, E. A.

2015-01-01

The very large number of human thermal climate indices that have been proposed over the past 100 years or so is a manifestation of the perceived importance within the scientific community of the thermal environment and the desire to quantify it. Schemes used differ in approach according to the number of variables taken into account, the rationale employed, the relative sophistication of the underlying body-atmosphere heat exchange theory and the particular design for application. They also vary considerably in type and quality, as well as in several other aspects. Reviews appear in the literature, but they cover a limited number of indices. A project that produces a comprehensive documentation, classification and overall evaluation of the full range of existing human thermal climate indices has never been attempted. This paper deals with documentation and classification. A subsequent report will focus on evaluation. Here a comprehensive register of 162 thermal indices is assembled and a sorting scheme devised that groups them according to eight primary classification classes. It is the first stage in a project to organise and evaluate the full range of all human thermal climate indices. The work, when completed, will make it easier for users to reflect on the merits of all available thermal indices. It will be simpler to locate and compare indices and decide which is most appropriate for a particular application or investigation.

Construction of an Yucatec Maya soil classification and comparison with the WRB framework

PubMed Central

2010-01-01

Background Mayas living in southeast Mexico have used soils for millennia and provide thus a good example for understanding soil-culture relationships and for exploring the ways indigenous people name and classify the soils of their territory. This paper shows an attempt to organize the Maya soil knowledge into a soil classification scheme and compares the latter with the World Reference Base for Soil Resources (WRB). Methods Several participative soil surveys were carried out in the period 2000-2009 with the help of bilingual Maya-Spanish-speaking farmers. A multilingual soil database was built with 315 soil profile descriptions. Results On the basis of the diagnostic soil properties and the soil nomenclature used by Maya farmers, a soil classification scheme with a hierarchic, dichotomous and open structure was constructed, organized in groups and qualifiers in a fashion similar to that of the WRB system. Maya soil properties were used at the same categorical levels as similar diagnostic properties are used in the WRB system. Conclusions The Maya soil classification (MSC) is a natural system based on key properties, such as relief position, rock types, size and quantity of stones, color of topsoil and subsoil, depth, water dynamics, and plant-supporting processes. The MSC addresses the soil properties of surficial and subsurficial horizons, and uses plant communities as qualifier in some cases. The MSC is more accurate than the WRB for classifying Leptosols. PMID:20152047

Construction of an Yucatec Maya soil classification and comparison with the WRB framework.

PubMed

Bautista, Francisco; Zinck, J Alfred

2010-02-13

Mayas living in southeast Mexico have used soils for millennia and provide thus a good example for understanding soil-culture relationships and for exploring the ways indigenous people name and classify the soils of their territory. This paper shows an attempt to organize the Maya soil knowledge into a soil classification scheme and compares the latter with the World Reference Base for Soil Resources (WRB). Several participative soil surveys were carried out in the period 2000-2009 with the help of bilingual Maya-Spanish-speaking farmers. A multilingual soil database was built with 315 soil profile descriptions. On the basis of the diagnostic soil properties and the soil nomenclature used by Maya farmers, a soil classification scheme with a hierarchic, dichotomous and open structure was constructed, organized in groups and qualifiers in a fashion similar to that of the WRB system. Maya soil properties were used at the same categorical levels as similar diagnostic properties are used in the WRB system. The Maya soil classification (MSC) is a natural system based on key properties, such as relief position, rock types, size and quantity of stones, color of topsoil and subsoil, depth, water dynamics, and plant-supporting processes. The MSC addresses the soil properties of surficial and subsurficial horizons, and uses plant communities as qualifier in some cases. The MSC is more accurate than the WRB for classifying Leptosols.

A comprehensive catalogue and classification of human thermal climate indices.

PubMed

de Freitas, C R; Grigorieva, E A

2015-01-01

The very large number of human thermal climate indices that have been proposed over the past 100 years or so is a manifestation of the perceived importance within the scientific community of the thermal environment and the desire to quantify it. Schemes used differ in approach according to the number of variables taken into account, the rationale employed, the relative sophistication of the underlying body-atmosphere heat exchange theory and the particular design for application. They also vary considerably in type and quality, as well as in several other aspects. Reviews appear in the literature, but they cover a limited number of indices. A project that produces a comprehensive documentation, classification and overall evaluation of the full range of existing human thermal climate indices has never been attempted. This paper deals with documentation and classification. A subsequent report will focus on evaluation. Here a comprehensive register of 162 thermal indices is assembled and a sorting scheme devised that groups them according to eight primary classification classes. It is the first stage in a project to organise and evaluate the full range of all human thermal climate indices. The work, when completed, will make it easier for users to reflect on the merits of all available thermal indices. It will be simpler to locate and compare indices and decide which is most appropriate for a particular application or investigation.

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

PubMed

Lu, Na; Li, Tengfei; Ren, Xiaodong; Miao, Hongyu

2017-06-01

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform (FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.

Evidences in Neurological Surgery and a Cutting Edge Classification of the Trigeminocardiac Reflex: A Systematic Review.

PubMed

Leon-Ariza, Daniel S; Leon-Ariza, Juan S; Nangiana, Jasvinder; Grau, Gabriel Vargas; Leon-Sarmiento, Fidias E; Quiñones-Hinojosa, Alfredo

2018-06-05

The trigeminocardiac reflex (TCR) is characterized by bradycardia, decrease of main arterial blood pressure (MABP), and sometimes, asystole during surgery. We critically reviewed TCR studies and devised a novel classification scheme for assessing the reflex. and Methods: A comprehensive systematic literature review was performed using PubMed, MEDLINE, Web of Science, EMBASE, and Scielo databases. Eligible studies were extracted based on stringent inclusion and exclusion criteria. Statistical analyses were used to assess cardiovascular variables. TCR was classified according to morphophysiological aspects involved with reflex elicitation. 575 patients were included in this study. TCR was found in 8.9% of patients. The reflex was more often triggered by interventions made within the anterior cranial fossa. The maxillary branch (type II in the new classification) was the most prevalent nerve branch found to trigger the TCR. Heart rate (HR) and mean arterial blood pressure (MABP) were similarly altered (p = 0.06; F = 0.3912809), covaried with age (p = 0.012; F = 9.302), and inversely correlated to each other (r = -0.27). TCR is a critical cardiovascular phenomenon that must be quickly identified, efficiently classified, and should trigger vigilance. Prompt therapeutic measures during neurosurgical procedures should be carefully addressed to avoid unwanted complications. Accurate categorization using the new classification scheme will help to improve understanding and guide the management of TCR in the perioperative period. Copyright © 2018 Elsevier Inc. All rights reserved.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Ancestral State Reconstruction Reveals Rampant Homoplasy of Diagnostic Morphological Characters in Urticaceae, Conflicting with Current Classification Schemes

PubMed Central

Wu, Zeng-Yuan; Milne, Richard I.; Chen, Chia-Jui; Liu, Jie; Wang, Hong; Li, De-Zhu

2015-01-01

Urticaceae is a family with more than 2000 species, which contains remarkable morphological diversity. It has undergone many taxonomic reorganizations, and is currently the subject of further systematic studies. To gain more resolution in systematic studies and to better understand the general patterns of character evolution in Urticaceae, based on our previous phylogeny including 169 accessions comprising 122 species across 47 Urticaceae genera, we examined 19 diagnostic characters, and analysed these employing both maximum-parsimony and maximum-likelihood approaches. Our results revealed that 16 characters exhibited multiple state changes within the family, with ten exhibiting >eight changes and three exhibiting between 28 and 40. Morphological synapomorphies were identified for many clades, but the diagnostic value of these was often limited due to reversals within the clade and/or homoplasies elsewhere. Recognition of the four clades comprising the family at subfamily level can be supported by a small number carefully chosen defining traits for each. Several non-monophyletic genera appear to be defined only by characters that are plesiomorphic within their clades, and more detailed work would be valuable to find defining traits for monophyletic clades within these. Some character evolution may be attributed to adaptive evolution in Urticaceae due to shifts in habitat or vegetation type. This study demonstrated the value of using phylogeny to trace character evolution, and determine the relative importance of morphological traits for classification. PMID:26529598

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Malware distributed collection and pre-classification system using honeypot technology

NASA Astrophysics Data System (ADS)

Grégio, André R. A.; Oliveira, Isabela L.; Santos, Rafael D. C.; Cansian, Adriano M.; de Geus, Paulo L.

2009-04-01

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes.

Interpolation Hermite Polynomials For Finite Element Method

NASA Astrophysics Data System (ADS)

Gusev, Alexander; Vinitsky, Sergue; Chuluunbaatar, Ochbadrakh; Chuluunbaatar, Galmandakh; Gerdt, Vladimir; Derbov, Vladimir; Góźdź, Andrzej; Krassovitskiy, Pavel

2018-02-01

We describe a new algorithm for analytic calculation of high-order Hermite interpolation polynomials of the simplex and give their classification. A typical example of triangle element, to be built in high accuracy finite element schemes, is given.

Fundamental Concepts in the Teaching of Chemistry.

ERIC Educational Resources Information Center

Loeffler, Paul A.

1989-01-01

Presented is a simple, unified approach to chemical nomenclature which employs the distinction between the terms chemical substance and chemical species. The classification of matter and chemical nomenclature are used as examples to illustrate this scheme. (CW)

Environmental classification scheme for Pontis.

DOT National Transportation Integrated Search

1994-01-01

In an effort to comply with the federal mandate for bridge management systems, many states have chosen to implement an existing system rather than develop their own. One such system is Pontis, the network-level bridge management system developed thro...

Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

NASA Technical Reports Server (NTRS)

Messmore, J.; Copeland, G. E.; Levy, G. F.

1975-01-01

This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95%), and progress is being made towards identifying the mapped spectral classes.

Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

NASA Technical Reports Server (NTRS)

Messmore, J.; Copeland, G. E.; Levy, G. F.

1975-01-01

This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95 percent), and progress is being made towards identifying the mapped spectral classes.

Complex versus simple models: ion-channel cardiac toxicity prediction.

PubMed

Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

The role of man in flight experiment payload missions. Volume 1: Results

NASA Technical Reports Server (NTRS)

Malone, T. B.

1973-01-01

It is pointed out that a controversy exists concerning the required role of man, and his attendant skills and levels of skills, for Sortie Lab operations. As a result, a study was conducted to generate a taxonomy of candidate crew roles which would: (1) be applicable across all experiments, and (2) be usable for Sortie scientists and engineers in determination of level of skill as well as type of skill. Nine basic roles were identified in the study, and the tasks associated with each were developed from a functional description of a generalized in-flight experiment. The functional analysis comprised the baseline for establishment of crew roles, with roles being defined as combinations of tasks, associated skills, and knowledges. A role classification scheme was developed in which the functions and tasks identified were allocated to each of the nine role types. This classification scheme is presented together with the significant results of the study.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Spatial variations in US poverty: beyond metropolitan and non-metropolitan.

PubMed

Wang, Man; Kleit, Rachel Garshick; Cover, Jane; Fowler, Christopher S

2012-01-01

Because poverty in rural and urban areas of the US often has different causes, correlates and solutions, effective anti-poverty policies depend on a thorough understanding of the ruralness or urbanness of specific places. This paper compares several widely used classification schemes and the varying magnitudes of poverty that they reveal in the US. The commonly used ‘metropolitan/non-metropolitan’ distinction obscures important socioeconomic differences among metropolitan areas, making our understanding of the geography of poverty imprecise. Given the number and concentration of poor people living in mixed-rural and rural counties in metropolitan regions, researchers and policy-makers need to pay more nuanced attention to the opportunities and constraints such individuals face. A cross-classification of the Office of Management and Budget’s metro system with a nuanced RUDC scheme is the most effective for revealing the geographical complexities of poverty within metropolitan areas.

Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-01-01

This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level- 2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset- 255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested scheme offers better performance than other existing schemes in terms of accuracy and number of features. The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset- 255, an accuracy of 99.45% is achieved. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Nationwide classification of forest types of India using remote sensing and GIS.

PubMed

Reddy, C Sudhakar; Jha, C S; Diwakar, P G; Dadhwal, V K

2015-12-01

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Revisiting the taxonomical classification of Porcine Circovirus type 2 (PCV2): still a real challenge.

PubMed

Franzo, Giovanni; Cortey, Martí; Olvera, Alex; Novosel, Dinko; Castro, Alessandra Marnie Martins Gomes De; Biagini, Philippe; Segalés, Joaquim; Drigo, Michele

2015-08-28

PCV2 has emerged as one of the most devastating viral infections of swine farming, causing a relevant economic impact due to direct losses and control strategies expenses. Epidemiological and experimental studies have evidenced that genetic diversity is potentially affecting the virulence of PVC2. The growing number of PCV2 complete genomes and partial sequences available at GenBank questioned the accepted PCV2 classification. Nine hundred seventy five PCV2 complete genomes and 1,270 ORF2 sequences available from GenBank were subjected to recombination, PASC and phylogenetic analyses and results were used for comparison with previous classification scheme. The outcome of these analyses favors the recognition of four genotypes on the basis of ORF2 sequences, namely PCV2a, PCV2b, PCV2c and PCV2d-mPCV2b. To deal with the difficulty of founding an unambiguous classification and accounting the impossibility to define a p-distance cut-off, a set of reference sequences that could be used in further phylogenetic studies for PCV2 genotyping was established. Being aware that extensive phylogenetic analyses are time-consuming and often impracticable during routine diagnostic activity, ORF2 nucleotide positions adequately conserved in the reference sequences were identified and reported to allow a quick genotype differentiation. Globally, the present work provides an updated scenario of PCV2 genotypes distribution and, based on the limits of the previous classification criteria, proposes new rapid and effective schemes for differentiating the four defined PCV2 genotypes.

The uncertainty in the radon hazard classification of areas as a function of the number of measurements.

PubMed

Friedmann, H; Baumgartner, A; Gruber, V; Kaineder, H; Maringer, F J; Ringer, W; Seidel, C

2017-07-01

The administration in many countries demands a classification of areas concerning their radon risk taking into account the requirements of the EU Basic Safety Standards. The wide variation of indoor radon concentrations in an area which is caused by different house construction, different living style and different geological situations introduces large uncertainties for any classification scheme. Therefore, it is of importance to estimate the size of the experimental coefficient of variation (relative standard deviation) of the parameter which is used to classify an area. Besides the time period of measurement it is the number of measurements which strongly influences this uncertainty and it is important to find a compromise between the economic possibilities and the needed confidence level. Some countries do not use pure measurement results for the classification of areas but use derived quantities, usually called radon potential, which should reduce the influence of house construction, living style etc. and should rather represent the geological situation of an area. Here, radon indoor measurements in nearly all homes in three municipalities and its conversion into a radon potential were used to determine the uncertainty of the mean radon potential of an area as a function of the number of investigated homes. It could be shown that the coefficient of variation scales like 1/√n with n the number of measured dwellings. The question how to deal with uncertainties when using a classification scheme for the radon risk is discussed and a general procedure is proposed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography.

PubMed

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

2009-04-01

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments.

PubMed

Socoró, Joan Claudi; Alías, Francesc; Alsina-Pagès, Rosa Ma

2017-10-12

One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement.

Generic revision of the ant subfamily Dorylinae (Hymenoptera, Formicidae)

PubMed Central

Borowiec, Marek L.

2016-01-01

Abstract The generic classification of the ant subfamily Dorylinae is revised, with the aim of facilitating identification of easily-diagnosable monophyletic genera. The new classification is based on recent molecular phylogenetic evidence and a critical reappraisal of doryline morphology. New keys and diagnoses based on workers and males are provided, along with reviews of natural history and phylogenetic relationships, distribution maps, and a list of valid species for each lineage. Twenty-eight genera (27 extant and 1 extinct) are recognized within the subfamily, an increase from 20 in the previous classification scheme. Species classified in the polyphyletic Cerapachys and Sphinctomyrmex prior to this publication are here distributed among 9 and 3 different genera, respectively. Amyrmex and Asphinctanilloides are synonymized under Leptanilloides and the currently recognized subgenera are synonymized for Dorylus. No tribal classification is proposed for the subfamily, but several apparently monophyletic genus-groups are discussed. Valid generic names recognized here include: Acanthostichus (= Ctenopyga), Aenictogiton, Aenictus (= Paraenictus, Typhlatta), Cerapachys (= Ceratopachys), Cheliomyrmex, Chrysapace gen. rev., Cylindromyrmex (= Holcoponera, Hypocylindromyrmex, Metacylindromyrmex), Dorylus (= Alaopone syn. n., Anomma syn. n., Cosmaecetes, Dichthadia syn. n., Rhogmus syn. n., Shuckardia, Sphecomyrmex, Sphegomyrmex, Typhlopone syn. n.), Eburopone gen. n., Eciton (= Camptognatha, Holopone, Mayromyrmex), Eusphinctus gen. rev., Labidus (= Nycteresia, Pseudodichthadia), Leptanilloides (= Amyrmex syn. n., Asphinctanilloides syn. n.), Lioponera gen. rev. (= Neophyracaces syn. n., Phyracaces syn. n.), Lividopone, Neivamyrmex (= Acamatus, Woitkowskia), Neocerapachys gen. n., Nomamyrmex, Ooceraea gen. rev. (= Cysias syn. n.), Parasyscia gen. rev., †Procerapachys, Simopone, Sphinctomyrmex, Syscia gen. rev., Tanipone, Vicinopone, Yunodorylus gen. rev., Zasphinctus gen. rev. (= Aethiopopone syn. n., Nothosphinctus syn. n.). PMID:27559303

CrossLink: a novel method for cross-condition classification of cancer subtypes.

PubMed

Ma, Chifeng; Sastry, Konduru S; Flore, Mario; Gehani, Salah; Al-Bozom, Issam; Feng, Yusheng; Serpedin, Erchin; Chouchane, Lotfi; Chen, Yidong; Huang, Yufei

2016-08-22

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

PubMed

Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

2016-07-01

Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological categories predict renal or patient survival. Age, renal function and proteinuria at presentation, histopathology, and infectious complications constitute the main outcome predictors and should be considered for individualized management.

A two-tier atmospheric circulation classification scheme for the European-North Atlantic region

NASA Astrophysics Data System (ADS)

Guentchev, Galina S.; Winkler, Julie A.

A two-tier classification of large-scale atmospheric circulation was developed for the European-North-Atlantic domain. The classification was constructed using a combination of principal components and k-means cluster analysis applied to reanalysis fields of mean sea-level pressure for 1951-2004. Separate classifications were developed for the winter, spring, summer, and fall seasons. For each season, the two classification tiers were identified independently, such that the definition of one tier does not depend on the other tier having already been defined. The first tier of the classification is comprised of supertype patterns. These broad-scale circulation classes are useful for generalized analyses such as investigations of the temporal trends in circulation frequency and persistence. The second, more detailed tier consists of circulation types and is useful for numerous applied research questions regarding the relationships between large-scale circulation and local and regional climate. Three to five supertypes and up to 19 circulation types were identified for each season. An intuitive nomenclature scheme based on the physical entities (i.e., anomaly centers) which dominate the specific patterns was used to label each of the supertypes and types. Two example applications illustrate the potential usefulness of a two-tier classification. In the first application, the temporal variability of the supertypes was evaluated. In general, the frequency and persistence of supertypes dominated by anticyclonic circulation increased during the study period, whereas the supertypes dominated by cyclonic features decreased in frequency and persistence. The usefulness of the derived circulation types was exemplified by an analysis of the circulation associated with heat waves and cold spells reported at several cities in Bulgaria. These extreme temperature events were found to occur with a small number of circulation types, a finding that can be helpful in understanding past variability and projecting future changes in the occurrence of extreme weather and climate events.

In situ measurements of angular-dependent light scattering by aerosols over the contiguous United States

NASA Astrophysics Data System (ADS)

Reed Espinosa, W.; Vanderlei Martins, J.; Remer, Lorraine A.; Puthukkudy, Anin; Orozco, Daniel; Dolgos, Gergely

2018-03-01

This work provides a synopsis of aerosol phase function (F11) and polarized phase function (F12) measurements made by the Polarized Imaging Nephelometer (PI-Neph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F11 and -F12/F11 for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular-scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 % recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.

Does the World Health Organization histological classification predict outcomes after thymomectomy? Results of a multicentre study on 750 patients.

PubMed

Guerrera, Francesco; Rendina, Erino Angelo; Venuta, Federico; Margaritora, Stefano; Ciccone, Anna Maria; Novellis, Pierluigi; Novero, Domenico; Anile, Marco; Bora, Giulia; Rena, Ottavio; Casadio, Caterina; Mussi, Alfredo; Evangelista, Andrea; Ruffini, Enrico; Lucchi, Marco; Filosso, Pier Luigi

2015-07-01

The World Health Organization (WHO) thymoma histological classification clinical value remains a controversy. In this study, we evaluated its prognostic significance in patients with thymoma treated with radical intent. Six high-volume Italian Thoracic Surgery Institutions collaborated with their own retrospective anonymized datasets. Demographic, clinical, pathological and treatment data were examined. A WHO histological classification (WHO-HC) collapsed scheme (A/AB and B1/B2 types merged) was proposed and compared with the traditional one. Predictors of survival were assessed using a Cox model with shared frailty. Competing-risk regression models were performed to identify the association between individual factors and freedom from recurrence. Between 1990 and 2011, 750 thymomas were operated on in participating centres. Myasthenia gravis was observed in 363 (48%) patients. A complete resection was achieved in 676 (91%) cases. One hundred and nine patients (15%) had a WHO-HC A type, 166 (22%) AB, 179 (24%) B1, 158 (21%) B2 and 135 (18%) B3. The rate of 5-year OS and cumulative incidence of recurrence for all cases was 91% and 0.11, respectively. Five-year survival rates by WHO-HC in the collapsed scheme were A/AB 93%, early-B 90% and advanced-B 85%. Masaoka stage only was demonstrated to be an independent predictor for survival and recurrence. The WHO-collapsed scheme showed a trend in influencing recurrence overall survival development (hazard ratio: 1.32; P = 0.16). Our results show evidence of lack of significance by WHO-HC in influencing prognosis, even though the proposed collapsed scheme revealed a fair stratification of risk to relapses and better correlation with patients' clinical characteristics. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Application of Snyder-Dolan classification scheme to the selection of "orthogonal" columns for fast screening of illicit drugs and impurity profiling of pharmaceuticals--I. Isocratic elution.

PubMed

Fan, Wenzhe; Zhang, Yu; Carr, Peter W; Rutan, Sarah C; Dumarey, Melanie; Schellinger, Adam P; Pritts, Wayne

2009-09-18

Fourteen judiciously selected reversed phase columns were tested with 18 cationic drug solutes under the isocratic elution conditions advised in the Snyder-Dolan (S-D) hydrophobic subtraction method of column classification. The standard errors (S.E.) of the least squares regressions of logk' vs. logk'(REF) were obtained for a given column against a reference column and used to compare and classify columns based on their selectivity. The results are consistent with those obtained with a study of the 16 test solutes recommended by Snyder and Dolan. To the extent these drugs are representative, these results show that the S-D classification scheme is also generally applicable to pharmaceuticals under isocratic conditions. That is, those columns judged to be similar based on the 16 S-D solutes were similar based on the 18 drugs; furthermore those columns judged to have significantly different selectivities based on the 16 S-D probes appeared to be quite different for the drugs as well. Given that the S-D method has been used to classify more than 400 different types of reversed phases the extension to cationic drugs is a significant finding.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

PubMed Central

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. PMID:26346558

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.