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Sample records for machining feature relationship

  1. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Science.gov (United States)

    Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

    2015-04-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  2. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Directory of Open Access Journals (Sweden)

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  3. Elementary epistemological features of machine intelligence

    OpenAIRE

    Horvat, Marko

    2008-01-01

    Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of in...

  4. The Machine / Job Features Mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Alef, M. [KIT, Karlsruhe; Cass, T. [CERN; Keijser, J. J. [NIKHEF, Amsterdam; McNab, A. [Manchester U.; Roiser, S. [CERN; Schwickerath, U. [CERN; Sfiligoi, I. [Fermilab

    2017-11-22

    Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

  5. Machine parameters and characteristic features

    International Nuclear Information System (INIS)

    Le Duff, J.

    1979-01-01

    The design and operating characteristics of LEP are presented. Its probable performance, possible improvements and cost are discussed and some comparisons are drawn with machines currently in operation. (W.D.L.)

  6. The machine/job features mechanism

    Science.gov (United States)

    Alef, M.; Cass, T.; Keijser, J. J.; McNab, A.; Roiser, S.; Schwickerath, U.; Sfiligoi, I.

    2017-10-01

    Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

  7. Machine learning spatial geometry from entanglement features

    Science.gov (United States)

    You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang

    2018-02-01

    Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).

  8. Realistic Free-Spins Features Increase Preference for Slot Machines.

    Science.gov (United States)

    Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

    2017-06-01

    Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

  9. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

    The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

  10. Critical feature analysis of a radiotherapy machine

    International Nuclear Information System (INIS)

    Rae, Andrew; Jackson, Daniel; Ramanan, Prasad; Flanz, Jay; Leyman, Didier

    2005-01-01

    The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An 'assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code

  11. Blue gum gaming machine: an evaluation of responsible gambling features.

    Science.gov (United States)

    Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

    2014-09-01

    Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

  12. A prototype feature system for feature retrieval using relationships

    Science.gov (United States)

    Choi, J.; Usery, E.L.

    2009-01-01

    Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.

  13. Simultaneous feature selection and classification via Minimax Probability Machine

    Directory of Open Access Journals (Sweden)

    Liming Yang

    2010-12-01

    Full Text Available This paper presents a novel method for simultaneous feature selection and classification by incorporating a robust L1-norm into the objective function of Minimax Probability Machine (MPM. A fractional programming framework is derived by using a bound on the misclassification error involving the mean and covariance of the data. Furthermore, the problems are solved by the Quadratic Interpolation method. Experiments show that our methods can select fewer features to improve the generalization compared to MPM, which illustrates the effectiveness of the proposed algorithms.

  14. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

    International Nuclear Information System (INIS)

    Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

    2010-01-01

    Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

  15. Exploring the relationship between fractal features and bacterial essential genes

    International Nuclear Information System (INIS)

    Yu Yong-Ming; Yang Li-Cai; Zhao Lu-Lu; Liu Zhi-Ping; Zhou Qian

    2016-01-01

    Essential genes are indispensable for the survival of an organism in optimal conditions. Rapid and accurate identifications of new essential genes are of great theoretical and practical significance. Exploring features with predictive power is fundamental for this. Here, we calculate six fractal features from primary gene and protein sequences and then explore their relationship with gene essentiality by statistical analysis and machine learning-based methods. The models are applied to all the currently available identified genes in 27 bacteria from the database of essential genes (DEG). It is found that the fractal features of essential genes generally differ from those of non-essential genes. The fractal features are used to ascertain the parameters of two machine learning classifiers: Naïve Bayes and Random Forest. The area under the curve (AUC) of both classifiers show that each fractal feature is satisfactorily discriminative between essential genes and non-essential genes individually. And, although significant correlations exist among fractal features, gene essentiality can also be reliably predicted by various combinations of them. Thus, the fractal features analyzed in our study can be used not only to construct a good essentiality classifier alone, but also to be significant contributors for computational tools identifying essential genes. (paper)

  16. Fault diagnosis of rotating machine by isometric feature mapping

    International Nuclear Information System (INIS)

    Zhang, Yun; Li, Benwei; Wang, Lin; Wang, Wen; Wang, Zibin

    2013-01-01

    Principal component analysis (PCA) and linear discriminate analysis (LDA) are well-known linear dimensionality reductions for fault classification. However, since they are linear methods, they perform not well for high-dimensional data that has the nonlinear geometric structure. As kernel extension of PCA, Kernel PCA is used for nonlinear fault classification. However, the performance of Kernel PCA largely depends on its kernel function which can only be empirically selected from finite candidates. Thus, a novel rotating machine fault diagnosis approach based on geometrically motivated nonlinear dimensionality reduction named isometric feature mapping (Isomap) is proposed. The approach can effectively extract the intrinsic nonlinear manifold features embedded in high-dimensional fault data sets. Experimental results with rotor and rolling bearing data show that the proposed approach overcomes the flaw of conventional fault pattern recognition approaches and obviously improves the fault classification performance.

  17. Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

    Science.gov (United States)

    Ghosh, Samiran; Wang, Yazhen

    2015-02-01

    The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations which lie close to the boundary of the classifier function. However when the number of observations are not very large (small n ) but the number of dimensions/features are large (large p ), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of an useful fraction of the available features may result in huge data compression. In this paper we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie (2005) in the context of import vector machine (IVM), to select an optimal sub-dimensional model to build the final classifier with sufficient accuracy.

  18. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2017-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  19. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

    Science.gov (United States)

    Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

  20. Non-binding relationship between visual features

    Directory of Open Access Journals (Sweden)

    Dragan eRangelov

    2014-10-01

    Full Text Available The answer as to how visual attributes processed in different brain loci at different speeds are bound together to give us our unitary experience of the visual world remains unknown. In this study we investigated whether bound representations arise, as commonly assumed, through physiological interactions between cells in the visual areas. In a focal attentional task in which correct responses from either bound or unbound representations were possible, participants discriminated the colour or orientation of briefly presented single bars. On the assumption that representations of the two attributes are bound, the accuracy of reporting the colour and orientation should co-vary. By contrast, if the attributes are not mandatorily bound, the accuracy of reporting the two attributes should be independent. The results of our psychophysical studies reported here supported the latter, non-binding, relationship between visual features, suggesting that binding does not necessarily occur even under focal attention. We propose a task-contingent binding mechanism, postulating that binding occurs at late, post-perceptual, stages through the intervention of memory.

  1. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

    NARCIS (Netherlands)

    Malta, Tathiane M.; Sokolov, Artem; Gentles, Andrew J.; Burzykowski, Tomasz; Poisson, Laila; Weinstein, John N.; Kamińska, Bożena; Huelsken, Joerg; Omberg, Larsson; Gevaert, Olivier; Colaprico, Antonio; Czerwińska, Patrycja; Mazurek, Sylwia; Mishra, Lopa; Heyn, Holger; Krasnitz, Alex; Godwin, Andrew K.; Lazar, Alexander J.; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Gonzalez, Ana Maria Angulo; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Pinero, Edna M.Mora; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz; Stuart, Joshua M.; Hoadley, Katherine A.; Laird, Peter W.; Noushmehr, Houtan; Wiznerowicz, Maciej

    2018-01-01

    Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR)

  2. A Novel Approach for Automatic Machining Feature Recognition with Edge Blend Feature

    OpenAIRE

    Keong Chen Wong; Yusof Yusri

    2017-01-01

    This paper presents an algorithm for efficiently recognizing and determining the convexity of an edge blend feature. The algorithm first recognizes all of the edge blend features from the Boundary Representation of a part; then a series of convexity test have been run on the recognized edge blend features. The novelty of the presented algorithm lies in, instead of each recognized blend feature is suppressed as most of researchers did, the recognized blend features of this research are gone th...

  3. Accuracy of locating circular features using machine vision

    Science.gov (United States)

    Sklair, Cheryl W.; Hoff, William A.; Gatrell, Lance B.

    1992-03-01

    The ability to automatically locate objects using vision is a key technology for flexible, intelligent robotic operations. The vision task is facilitated by placing optical targets or markings in advance on the objects to be located. A number of researchers have advocated the use of circular target features as the features that can be most accurately located. This paper describes extensive analysis on circle centroid accuracy using both simulations and laboratory measurements. The work was part of an effort to design a video positioning sensor for NASA's Flight Telerobotic Servicer that would meet accuracy requirements. We have analyzed the main contributors to centroid error and have classified them into the following: (1) spatial quantization errors, (2) errors due to signal noise and random timing errors, (3) surface tilt errors, and (4) errors in modeling camera geometry. It is possible to compensate for the errors in (3) given an estimate of the tilt angle, and the errors from (4) by calibrating the intrinsic camera attributes. The errors in (1) and (2) cannot be compensated for, but they can be measured and their effects reduced somewhat. To characterize these error sources, we measured centroid repeatability under various conditions, including synchronization method, signal-to-noise ratio, and frequency attenuation. Although these results are specific to our video system and equipment, they provide a reference point that should be a characteristic of typical CCD cameras and digitization equipment.

  4. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

    Science.gov (United States)

    Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

    2018-02-01

    Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  6. Machine-assisted discovery of relationships in astronomy

    Science.gov (United States)

    Graham, Matthew J.; Djorgovski, S. G.; Mahabal, Ashish A.; Donalek, Ciro; Drake, Andrew J.

    2013-05-01

    High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focusing on an evolutionary programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships - the Hertzsprung-Russell diagram and the Fundamental Plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as the Large Synoptic Survey Telescope (LSST). We find that comparable results to more familiar techniques, such as decision trees, are achievable. Finally, we consider the reality of the relationships discovered and how this can be used for feature selection and extraction.

  7. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jia Uddin

    2014-01-01

    Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

  8. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

    Science.gov (United States)

    Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

    2015-04-01

    With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  9. Learning features for tissue classification with the classification restricted Boltzmann machine

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2014-01-01

    Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convo...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

  10. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

    Science.gov (United States)

    Jegadeeshwaran, R.; Sugumaran, V.

    2015-02-01

    Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

  11. A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P

    2015-02-01

    The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Towards Semantic Analysis of Training-Learning Relationships within Human-Machine Interactions

    DEFF Research Database (Denmark)

    Badie, Farshad

    2016-01-01

    In this article First-Order Predicate Logic (FOL) is employed for analysing some relationships between human beings and machines. Based on FOL, I will be conceptually and logically concerned with semantic analysis of training-learning relationships in human-machine interaction. The central focus...

  13. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  14. Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

    Science.gov (United States)

    Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

    2015-06-01

    Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

  15. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  16. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    Science.gov (United States)

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  17. Application of the Disruption Predictor Feature Developer to developing a machine-portable disruption predictor

    Science.gov (United States)

    Parsons, Matthew; Tang, William; Feibush, Eliot

    2016-10-01

    Plasma disruptions pose a major threat to the operation of tokamaks which confine a large amount of stored energy. In order to effectively mitigate this damage it is necessary to predict an oncoming disruption with sufficient warning time to take mitigative action. Machine learning approaches to this problem have shown promise but require further developments to address (1) the need for machine-portable predictors and (2) the availability of multi-dimensional signal inputs. Here we demonstrate progress in these two areas by applying the Disruption Predictor Feature Developer to data from JET and NSTX, and discuss topics of focus for ongoing work in support of ITER. The author is also supported under the Fulbright U.S. Student Program as a graduate student in the department of Nuclear, Plasma and Radiological Engineering at the University of Illinois at Urbana-Champaign.

  18. Pipeline leakage recognition based on the projection singular value features and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)

    2010-07-01

    The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.

  19. Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

    Science.gov (United States)

    Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

    2018-05-01

    Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

  20. Relationship between body mass index and fibromyalgia features.

    Science.gov (United States)

    Yunus, Muhammad B; Arslan, Sule; Aldag, Jean C

    2002-01-01

    to evaluate the relationship between body mass index (BMI) and features of the fibromyalgia syndrome (FMS). 211 female patients with FMS seen consecutively in our rheumatology clinic were analyzed. Spearman correlation was used. Further, FMS features were compared at different levels of BMI (kg/m2), e.g., or = 25.00 (normal vs overweight). P value of BMI and age (pBMI and education (pBMI (pBMI in FMS with a trend towards significance for fatigue and TP. Weight loss may improve physical functioning in this disorder.

  1. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  2. Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

    Directory of Open Access Journals (Sweden)

    Kuan-Cheng Lin

    2015-01-01

    Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

  3. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

    2011-01-01

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  4. Towards human behavior recognition based on spatio temporal features and support vector machines

    Science.gov (United States)

    Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

    2017-03-01

    Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

  5. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

    Science.gov (United States)

    Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

    2017-07-01

    Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  7. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

    Science.gov (United States)

    Calvo, Roque; D’Amato, Roberto; Gómez, Emilio; Domingo, Rosario

    2016-01-01

    The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included. PMID:27690052

  8. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

    Directory of Open Access Journals (Sweden)

    Roque Calvo

    2016-09-01

    Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.

  9. Feature recognition and detection for ancient architecture based on machine vision

    Science.gov (United States)

    Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

    2018-03-01

    Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

  10. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

    Directory of Open Access Journals (Sweden)

    Alim Samat

    2016-03-01

    Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

  11. Specific Features of Chip Making and Work-piece Surface Layer Formation in Machining Thermal Coatings

    Directory of Open Access Journals (Sweden)

    V. M. Yaroslavtsev

    2016-01-01

    Full Text Available A wide range of unique engineering structural and performance properties inherent in metallic composites characterizes wear- and erosion-resistant high-temperature coatings made by thermal spraying methods. This allows their use both in manufacturing processes to enhance the wear strength of products, which have to operate under the cyclic loading, high contact pressures, corrosion and high temperatures and in product renewal.Thermal coatings contribute to the qualitative improvement of the technical level of production and product restoration using the ceramic composite materials. However, the possibility to have a significantly increased product performance, reduce their factory labour hours and materials/output ratio in manufacturing and restoration is largely dependent on the degree of the surface layer quality of products at their finishing stage, which is usually provided by different kinds of machining.When machining the plasma-sprayed thermal coatings, a removing process of the cut-off layer material is determined by its distinctive features such as a layered structure, high internal stresses, low ductility material, high tendency to the surface layer strengthening and rehardening, porosity, high abrasive properties, etc. When coatings are machined these coating properties result in specific characteristics of chip formation and conditions for formation of the billet surface layer.The chip formation of plasma-sprayed coatings was studied at micro-velocities using an experimental tool-setting microscope-based setup, created in BMSTU. The setup allowed simultaneous recording both the individual stages (phases of the chip formation process and the operating force factors.It is found that formation of individual chip elements comes with the multiple micro-cracks that cause chipping-off the small particles of material. The emerging main crack in the cut-off layer of material leads to separation of the largest chip element. Then all the stages

  12. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (αtexture features.

  13. Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

    Science.gov (United States)

    Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

    2017-01-01

    Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  14. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  15. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  16. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

    Science.gov (United States)

    Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

    2017-06-09

    Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Extending the features of RBMK refuelling machine simulator with a training tool based on virtual reality

    International Nuclear Information System (INIS)

    Khoudiakov, M.; Slonimsky, V.; Mitrofanov, S.

    2004-01-01

    include a training methodology, simulation models/ malfunctions and VR-models to support the maintenance personnel. That work is to be based on a design and creation of a multi-machine computer complex, software and information support (Data base) development, and developing anew and/or up-grade the technology system models and training support methodology. The paper gives the background for developing the training system, the features and the structure of the system in addition to the current status in the development process. The final system will be delivered to LNPP in November 2004. (Author)

  18. Features of the Development Strategy Customer Relationship Management

    Directory of Open Access Journals (Sweden)

    I. M. Gurova

    2017-01-01

    Full Text Available Purpose: in the current economic situation one of the main conditions of competitiveness of the company is its customer focus. In this regard, the most promising strategy for strengthening the stability of the organization is the management of customer relationships.The presented article explores the process of developing a customer relationship management strategy. This is a crucial, fundamental stage, from the very outcome of which the very possibility of achieving the organization's global goal depends to a large extent. Therefore, the development of a strategy requires deep reflection and qualitative elaboration. The aim of this work is to identify specific features of development of strategy of customer relationship management through the systematization of theoretical and methodological framework and a detailed consideration of the algorithm of this process. Methods: the methodology of the study is based on the application of universal scientific methods of analysis and synthesis, analogy and modeling. In the process of collecting and collating data used content analysis, comparative analysis, methods of induction and deduction, system approach. Functional, strategic and economic analysis, methods of formalization, forecasting and expert assessments are used to generalize information and solve assigned tasks. Results: in the process of the study, a common algorithm for creating a customer relationship management strategy was developed. In particular, its main stages have been identified; the processes of solving the most important problems have been singled out and studied in detail. In addition, the article substantiates the basic theoretical and methodological guidelines, as well as the practical aspects of development of this type of strategy. Conclusions and Relevance: the main feature of development of the strategy of customer relationship management is the focus on the value characteristics of the analyzed elements. This is due to

  19. EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

    OpenAIRE

    Cukic, Milena; Pokrajac, David; Stokic, Miodrag; Simic, slobodan; Radivojevic, Vlada; Ljubisavljevic, Milos

    2018-01-01

    Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linea...

  20. Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

    Science.gov (United States)

    2013-01-01

    Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction

  1. Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

    Science.gov (United States)

    Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

    2014-09-24

    This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

  2. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Directory of Open Access Journals (Sweden)

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  3. Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

    Directory of Open Access Journals (Sweden)

    Mingyu Shao

    Full Text Available MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

  4. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

    Science.gov (United States)

    Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

    2016-10-18

    Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

  5. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

    Science.gov (United States)

    Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

    2016-04-01

    This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

  6. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Alessandra Caggiano

    2018-03-01

    Full Text Available Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA is proposed. PCA allowed to identify a smaller number of features (k = 2 features, the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax was achieved, with predicted values very close to the measured tool wear values.

  7. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

    Science.gov (United States)

    Caggiano, Alessandra

    2018-03-09

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

  8. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

    Science.gov (United States)

    2018-01-01

    Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

  9. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

    2018-03-01

    Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

  10. Cross-cutting Relationships of Surface Features on Europa

    Science.gov (United States)

    1997-01-01

    This image of Jupiter's moon Europa shows a very complex terrain of ridges and fractures. The absence of large craters and the low number of small craters indicates that this surface is geologically young. The relative ages of the ridges can be determined by using the principle of cross-cutting relationships; i.e. older features are cross-cut by younger features. Using this principle, planetary geologists are able to unravel the sequence of events in this seemingly chaotic terrain to unfold Europa's unique geologic history.The spacecraft Galileo obtained this image on February 20, 1997. The area covered in this image is approximately 11 miles (18 kilometers) by 8.5 miles (14 kilometers) across, near 15 North, 273 West. North is toward the top of the image, with the sun illuminating from the right.The Jet Propulsion Laboratory, Pasadena, CA manages the mission for NASA's Office of Space Science, Washington, DC.This image and other images and data received from Galileo are posted on the World Wide Web, on the Galileo mission home page at URL http://galileo.jpl.nasa.gov. Background information and educational context for the images can be found at URL http://www.jpl.nasa.gov/galileo/sepo

  11. Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

    OpenAIRE

    Wei-Jong Yang; Wei-Hau Du; Pau-Choo Chang; Jar-Ferr Yang; Pi-Hsia Hung

    2017-01-01

    The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an importan...

  12. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

    Directory of Open Access Journals (Sweden)

    Arif Mehmood

    2017-01-01

    Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

  13. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.

    Science.gov (United States)

    Madero Orozco, Hiram; Vergara Villegas, Osslan Osiris; Cruz Sánchez, Vianey Guadalupe; Ochoa Domínguez, Humberto de Jesús; Nandayapa Alfaro, Manuel de Jesús

    2015-02-12

    Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx

  14. Machine medical ethics

    CERN Document Server

    Pontier, Matthijs

    2015-01-01

    The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

  15. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

    Science.gov (United States)

    Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

    2018-02-01

    In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Features of measurement and processing of vibration signals registered on the moving parts of electrical machines

    OpenAIRE

    Gyzhko, Yuri

    2011-01-01

    Measurement and processing of vibration signals registered on the moving parts of the electrical machines using the diagnostic information-measuring system that uses Bluetooth wireless standard for the transmission of the measured data from moving parts of electrical machine is discussed.

  17. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  18. A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

    Directory of Open Access Journals (Sweden)

    Fabio Boi

    2016-12-01

    Full Text Available Bidirectional brain-machine interfaces (BMIs establish a two-way direct communication link4 between the brain and the external world. A decoder translates recorded neural activity into motor5 commands and an encoder delivers sensory information collected from the environment directly6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky7 external devices. However, the clinical support of patients with severe motor and sensory deficits8 requires compact, low-power, and fully implantable systems that can decode neural signals to9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits.12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn13 to decode neural signals recorded from the brain into motor outputs controlling the movements14 of an external device. The modularity of the BMI allowed us to tune the individual components15 of the setup without modifying the whole system. In this paper we present the features of16 this modular BMI, and describe how we configured the network of spiking neuron circuits to17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is21 mature enough for the development of BMI modules that are sufficiently low-power and compact,22 while being highly computationally powerful and adaptive.

  19. A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

    Science.gov (United States)

    Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

    2016-01-01

    Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

  20. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  1. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  2. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    2017-09-01

    Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

  3. The relationship between migraine headache and asthma features.

    Science.gov (United States)

    Dirican, Nigar; Demirci, Seden; Cakir, Munire

    2017-06-01

    Migraine and asthma are comorbid chronic disorders with episodic attacks thought to involve inflammatory and neurological mechanisms. The objective of the present study is to investigate the relationship of asthma features between the asthma patients with migraine and those without migraine headache. A cross-sectional study was conducted from October 2015 to June 2016. Physician-diagnosed asthma patients aged 18 years and above were included. Demographic data, pulmonary function test and treatment of asthma were recorded. Asthma control was assessed using the asthma control test (ACT) and asthma control questionnaire (ACQ). The diagnosis of migraine was made by the neurologist with face-to face examinations based on the International Classification of Headache Disorders, third edition beta (ICHD-III-beta) criteria. Data about the age at onset, frequency of headache attacks, duration of headache attack, the presence of aura, and severity of headache were recorded. The severity of headache was evaluated using visual analogue scale (VAS). Overall 121 asthma patients were included in this study. Migraine was found to be present in 32 (26.4%) of patients. No statistically significant difference was found between asthma group and asthma with migraine groups in terms of pulmonary function test parameters. The mean ACT score in asthma with migraine patients group was significantly lower than the asthma groups. Morever, in the group asthma with migraine, a negative significant correlations were found between ACT scores with VAS scores. This study demonstrates that migraine headache may be associated with poor asthma control. On the other hand, it should not be forgotten that ACT is a subjective test and can be affected from by many clinical parameters.

  4. Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

    Science.gov (United States)

    Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

    2016-05-15

    Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

    Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

  6. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

    Science.gov (United States)

    2013-01-01

    Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313

  7. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  8. Using machine learning to classify image features from canine pelvic radiographs

    DEFF Research Database (Denmark)

    McEvoy, Fintan; Amigo Rubio, Jose Manuel

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

  9. Multi-script handwritten character recognition : Using feature descriptors and machine learning

    NARCIS (Netherlands)

    Surinta, Olarik

    2016-01-01

    Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

  10. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    International Nuclear Information System (INIS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  11. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  12. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Science.gov (United States)

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  13. Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM

    Directory of Open Access Journals (Sweden)

    Enda Esyudha Pratama

    2015-12-01

    Full Text Available Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM. Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect, dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF, document frequency (DF, information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

  14. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    Science.gov (United States)

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

    Science.gov (United States)

    Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

    2014-01-01

    Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

  16. Brain cells in the avian 'prefrontal cortex' code for features of slot-machine-like gambling.

    Directory of Open Access Journals (Sweden)

    Damian Scarf

    2011-01-01

    Full Text Available Slot machines are the most common and addictive form of gambling. In the current study, we recorded from single neurons in the 'prefrontal cortex' of pigeons while they played a slot-machine-like task. We identified four categories of neurons that coded for different aspects of our slot-machine-like task. Reward-Proximity neurons showed a linear increase in activity as the opportunity for a reward drew near. I-Won neurons fired only when the fourth stimulus of a winning (four-of-a-kind combination was displayed. I-Lost neurons changed their firing rate at the presentation of the first nonidentical stimulus, that is, when it was apparent that no reward was forthcoming. Finally, Near-Miss neurons also changed their activity the moment it was recognized that a reward was no longer available, but more importantly, the activity level was related to whether the trial contained one, two, or three identical stimuli prior to the display of the nonidentical stimulus. These findings not only add to recent neurophysiological research employing simulated gambling paradigms, but also add to research addressing the functional correspondence between the avian NCL and primate PFC.

  17. Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

    Directory of Open Access Journals (Sweden)

    Nurul Arneida Husin

    2012-04-01

    Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

  18. Smoke composition and predicting relationships for international commercial cigarettes smoked with three machine-smoking conditions.

    Science.gov (United States)

    Counts, M E; Morton, M J; Laffoon, S W; Cox, R H; Lipowicz, P J

    2005-04-01

    The study objectives were to determine the effects of smoking machine puffing parameters on mainstream smoke composition and to express those effects as predicting relationships. Forty-eight commercial Philip Morris USA and Philip Morris International cigarettes from international markets and the 1R4F reference cigarette were machine-smoked using smoking conditions defined by the International Organization of Standardization (ISO), the Massachusetts Department of Public Health (MDPH), and Health Canada (HC). Cigarette tobacco fillers were analyzed for nitrate, nicotine, tobacco-specific nitrosamines (TSNA), and ammonia. Mainstream yields for tar and 44 individual smoke constituents and "smoke pH" were determined. Cigarette constituent yields typically increased in the order ISOrelationships were developed between ISO tar and ISO, MDPH, and HC constituent yields and between MDPH tar and HC tar and respective smoking condition yields. MDPH and HC constituent yields could be predicted with similar reliability using ISO tar or the corresponding smoking-condition tar. The reliability of the relationships varied from strong to weak, depending on particular constituents. Weak predicting relationships for nitrogen oxides and TSNA's, for example, were improved with inclusion of tobacco filler composition factors. "Smoke pH" was similar for all cigarettes at any one smoking condition, and overall marginally lower at HC conditions than at ISO or MDPH conditions.

  19. A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features

    Science.gov (United States)

    Kotani, Katsunori; Yoshimi, Takehiko; Isahara, Hitoshi

    2011-01-01

    The present paper introduces and evaluates a readability measurement method designed for learners of EFL (English as a foreign language). The proposed readability measurement method (a regression model) estimates the text readability based on linguistic features, such as lexical, syntactic and discourse features. Text readability refers to the…

  20. The Role of Auditory Features Within Slot-Themed Social Casino Games and Online Slot Machine Games.

    Science.gov (United States)

    Bramley, Stephanie; Gainsbury, Sally M

    2015-12-01

    Over the last few years playing social casino games has become a popular entertainment activity. Social casino games are offered via social media platforms and mobile apps and resemble gambling activities. However, social casino games are not classified as gambling as they can be played for free, outcomes may not be determined by chance, and players receive no monetary payouts. Social casino games appear to be somewhat similar to online gambling activities in terms of their visual and auditory features, but to date little research has investigated the cross over between these games. This study examines the auditory features of slot-themed social casino games and online slot machine games using a case study design. An example of each game type was played on three separate occasions during which, the auditory features (i.e., music, speech, sound effects, and the absence of sound) within the games were logged. The online slot-themed game was played in demo mode. This is the first study to provide a qualitative account of the role of auditory features within a slot-themed social casino game and an online slot machine game. Our results found many similarities between how sound is utilised within the two games. Therefore the sounds within these games may serve functions including: setting the scene for gaming, creating an image, demarcating space, interacting with visual features, prompting players to act, communicating achievements to players, providing reinforcement, heightening player emotions and the gaming experience. As a result this may reduce the ability of players to make a clear distinction between these two activities, which may facilitate migration between games.

  1. Sensory experience modifies feature map relationships in visual cortex

    Science.gov (United States)

    Cloherty, Shaun L; Hughes, Nicholas J; Hietanen, Markus A; Bhagavatula, Partha S

    2016-01-01

    The extent to which brain structure is influenced by sensory input during development is a critical but controversial question. A paradigmatic system for studying this is the mammalian visual cortex. Maps of orientation preference (OP) and ocular dominance (OD) in the primary visual cortex of ferrets, cats and monkeys can be individually changed by altered visual input. However, the spatial relationship between OP and OD maps has appeared immutable. Using a computational model we predicted that biasing the visual input to orthogonal orientation in the two eyes should cause a shift of OP pinwheels towards the border of OD columns. We then confirmed this prediction by rearing cats wearing orthogonally oriented cylindrical lenses over each eye. Thus, the spatial relationship between OP and OD maps can be modified by visual experience, revealing a previously unknown degree of brain plasticity in response to sensory input. DOI: http://dx.doi.org/10.7554/eLife.13911.001 PMID:27310531

  2. The Features of Rent Relationships in Conditions of Innovative Economy

    Directory of Open Access Journals (Sweden)

    Patlatoy Oleksandr Ye.

    2017-07-01

    Full Text Available The article is concerned with studying the economic content of the categories of «rent» and «quasi-rent», classification of their main forms arising in terms of relationships in the science and innovation sphere, and analyzing the specificity of formation of these incomes in conditions of contemporary innovation economy. The content of the category of «rent» as a form of income arising from the relationship of at least three entities has been revised; in the case of a scientific-innovation rent, it is the holder of a patent or a license holder, an industrial capitalist or an employee who creates a new or improved product; in some cases, the fourth entity of rent may be a hired researcher. In this relationship, the additional income earned by entrepreneurs in the event of commercial success has been defined as technological (innovation quasi-rent, which can change inversely proportional to the scientific-innovation rent. It has been proved that countries with innovation economies, acting as the main recipients of scientific-innovation rents, may concede to the technological quasi-rent of the less developed countries, in particular, to China.

  3. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Maolong Xi

    2016-01-01

    Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  4. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Science.gov (United States)

    Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

    2016-01-01

    This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

  5. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

    Science.gov (United States)

    Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

    2014-12-01

    Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Nonlinear features of the longitudinal instability for high-current machines

    International Nuclear Information System (INIS)

    Hofmann, I.; Boine-Frankenheim, O.

    1999-01-01

    We present results from experiments at the GSI machines as well as computer simulation for space charge dominated coasting beams (below transition). It is found that for the high-current machines presently under discussion the actual challenge lies in the nonlinear regime. Experiments are in good agreement with theory and simulation in the linear regime; for the nonlinear regime and long-time evolution rsp. saturation our experimental results show good agreement in some aspects, like wave steepening. To analyze the final momentum distribution we still depend on simulation, which shows that the behavior differs substantially, depending on whether the working point in the impedance plane lies close to the real (resistive dominated) or imaginary (space charge dominated) axis, or in between. For the space-charge-dominated regime (Re Z<< Im Z) it is found by computer simulation that for currents far above the Keil-Schnell threshold self-stabilization occurs by formation of a momentum tail, hence linear instability criteria can be practically ignored. It is shown here that the global impedance distribution is of crucial importance

  7. PSB LLRF: new features for machine studies and operation in the PSB 2016 run

    CERN Document Server

    Angoletta, M E

    2017-01-01

    A new digital Low-Level RF (LLRF) system has beensuccessfully deployed on the four PS Booster (PSB) ringsin June 2014, after the Long-Shutdown 1 (LS1). Althoughonly recently deployed, several new features for machinestudies and operation have already been required and im-plemented. This note provides an overview of the main fea-tures deployed for the 2016 PSB run and of their results

  8. In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

    Science.gov (United States)

    Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

    2018-02-01

    We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

  9. Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

    Directory of Open Access Journals (Sweden)

    Mustafa Serter Uzer

    2013-01-01

    Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

  10. Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

    Directory of Open Access Journals (Sweden)

    Tiannan Ma

    2016-12-01

    Full Text Available Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM. The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

  11. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  12. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    Science.gov (United States)

    Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

  13. Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

    Science.gov (United States)

    Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

    2012-02-01

    It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

  14. Man-machine interface in a submarine command and weapon control system: features and design experience

    Directory of Open Access Journals (Sweden)

    Johan H. Aas

    1989-01-01

    Full Text Available Important man-machine interface (MMI issues concerning a submarine command and weapon control system (CWCS such as crew organization, automation level and decision support are discussed in this paper. Generic submarine CWCS functions and operating conditions are outlined. Detailed, dynamic and real-time prototypes were used to support the MMI design. The prototypes are described and experience with detailed prototyping is discussed. Some of the main interaction principles are summarized and a restricted example of the resulting design is given. Our design experience and current work have been used to outline future perspectives of MMI design in naval CWCSs. The need for both formal and experimental approaches is emphasized.

  15. Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

    Science.gov (United States)

    Webb, Samuel J; Hanser, Thierry; Howlin, Brendan; Krause, Paul; Vessey, Jonathan D

    2014-03-25

    A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query. Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.

  16. [General features of the patient-physician relationship].

    Science.gov (United States)

    Baeza, H; Bueno, G

    1997-03-01

    The communication between physicians and patients is often deficient. Little time is devoted to it and the patient receives scanty information with a low emotional content. Some features of our medicine can explain this situation. The rationalist and mechanistic biological model, allows to study only those things that can be undertaken with the scientific method. Psychological, social and spiritual aspects are surpassed. It only looks at material aspects of people, limiting the communication. Patients express their symptoms in an emotional way, with multiple beliefs and fears. The physician converts them to a precise, scientific, measurable and rational medical logical type. This language is not understood by patients, generating hesitancy in the communication. The paternalism is based in the power that physicians have over patients. We give knowledge and ask the patient to subordinate and accept our power. The patient loses his moral right to be informed, to ask, to have doubts or to disagree. Our personal communication is almost always formal, unemotional and with no explanations, further limiting communication.

  17. Cytokine profiles in localized scleroderma and relationship to clinical features.

    Science.gov (United States)

    Kurzinski, Katherine; Torok, Kathryn S

    2011-08-01

    Localized scleroderma (LS) is a disfiguring autoimmune disease of the skin and underlying tissue that mainly affects the pediatric population. Inflammation of the tissue leads to fibrosis and atrophy, causing physical and psychological disability that can continue throughout childhood into adulthood. Available therapies for LS have had variable effects and are associated with morbidity themselves. A better understanding of the pathophysiology of LS, especially during the active inflammatory phase, would lead to more directed and efficacious therapies. As in systemic sclerosis (SSc), the other form of scleroderma, T-helper (Th) cells and their associated cytokines have been suggested to contribute significantly to the pathophysiology of LS supported by the presence of cytokines from these lineages in the sera and tissue of LS patients. It is postulated that the imbalance between Th1/Th2/Th17 cell subsets drives inflammation in the early stages of disease (Th1 and Th17 predominant) and fibrosis in the later stages of scleroderma (Th2 predominant). We review the available experimental data regarding cytokines in LS and compare them to available clinical disease severity and activity features. This provides the platform to launch further investigations into the role of select cytokines in the pathogenesis of LS and to provide directed therapeutic options in the future. Published by Elsevier Ltd.

  18. Practical data mining and machine learning for optics applications: introduction to the feature issue.

    Science.gov (United States)

    Abdulla, Ghaleb; Awwal, Abdul; Borne, Kirk; Ho, Tin Kam; Vestrand, W Thomas

    2011-08-01

    Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.

  19. On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

    Science.gov (United States)

    Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

    2018-03-01

    Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

  20. Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

    International Nuclear Information System (INIS)

    Li Jiazhong; Liu Huanxiang; Yao Xiaojun; Liu Mancang; Hu Zhide; Fan Botao

    2007-01-01

    The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors

  1. Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

    Science.gov (United States)

    Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

    2014-01-01

    Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

  2. An analysis of feature relevance in the classification of astronomical transients with machine learning methods

    Science.gov (United States)

    D'Isanto, A.; Cavuoti, S.; Brescia, M.; Donalek, C.; Longo, G.; Riccio, G.; Djorgovski, S. G.

    2016-04-01

    The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.

  3. Predicting DNA binding proteins using support vector machine with hybrid fractal features.

    Science.gov (United States)

    Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo

    2014-02-21

    DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. © 2013 The Authors. Published by Elsevier Ltd All rights reserved.

  4. Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

    Science.gov (United States)

    Barbosa, Eduardo J Mortani; Lanclus, Maarten; Vos, Wim; Van Holsbeke, Cedric; De Backer, William; De Backer, Jan; Lee, James

    2018-02-19

    Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV 1 ) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV 1 of >10% compared to baseline FEV 1 post LTx. Multifactor analysis correlated declining FEV 1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. The FEV 1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV 1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. ML utilizing qCT could discern distinct mechanisms driving FEV 1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  5. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2015-11-01

    Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

  6. New features of current-driven low-frequency instabilities in a Q-machine plasma

    International Nuclear Information System (INIS)

    Dimitriu, Dan-Gheorghe; Ignatescu, Valerian; Lozneanu, Erzilia; Sanduloviciu, Mircea; Ionita, Codrina; Schrittwieser, Roman Wolfgang

    2001-01-01

    Among the instabilities in a low-density magnetized plasma column, the electrostatic ion-cyclotron instability (EICI) and the potential relaxation instability (PRI) are the best known and most thoroughly investigated. Both instabilities are excited by drawing an electron current parallel to the magnetic field towards a circular collector (CO), which is inserted into the plasma column perpendicular to the axis. For the PRI, the radius of CO must be considerably larger than the ion gyroradius so that the ion trajectories can be approximated as one-dimensional. For the EICI, the radius of CO must be considerably smaller than that of the plasma column, but also larger than one ion gyroradius. A transition from the PRI into the EICI was reported earlier. A certain range of CO radii was found where both instabilities could be excited simultaneously. We report on a strong modulation of the EICI by the PRI, obtained for gradually increasing the CO bias, with the EICI appearing at first, and later the PRI. The EICI frequency was about four times larger than that of the PRI. The modulation not only affects the amplitude but also the frequency of the EICI. This leads to the formation of sidebands in the spectrum around f EICI with a frequency difference equal to ± f PRI . In addition, we find that the EICI frequency depends not only on the magnetic field strength but also on the CO current. Our data also show a strong non-linear dependence of the PRI frequency on the magnetic field strength. To explain these features, we propose a new phenomenological model, which is able to clarify the role of complex space charge configurations for low frequency instabilities in a low-density magnetized plasma column. (authors)

  7. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    Science.gov (United States)

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  8. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lili Chen

    2017-01-01

    Full Text Available Preterm birth (PTB is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT and extreme learning machine (ELM. For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs using empirical mode decomposition (EMD. Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  9. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

    Science.gov (United States)

    Chen, Lili; Hao, Yaru

    2017-01-01

    Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  10. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Science.gov (United States)

    Song, Jiangning; Li, Fuyi; Takemoto, Kazuhiro; Haffari, Gholamreza; Akutsu, Tatsuya; Chou, Kuo-Chen; Webb, Geoffrey I

    2018-04-14

    Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence-structure-function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence-structure-function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations

  11. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  12. The features of family relationship experience, style of parental bonding and relationship with family members of convicts

    Directory of Open Access Journals (Sweden)

    Daiva Karkockienė

    2016-01-01

    Full Text Available The aim of this article is to reveal the features of family relationship, style of parental bonding and relationship with family members of convicts. The tasks of the research: 1 to analyse the relationships experienced in families of convicts and the subjectively perceived style of parental bonding in their childhood; 2 to assess the relationships of convicts (men and women with their families; 3 to compare the attachment styles of convicts analysing different close relationships (with parents, relatives, partner or a close friend. The research was carried out in Panevėžys Correctional Facility and Lukiškės Remand Prison – Closed Prison. In total, the research involved 63 subjects, out of whom 33 were men and 30 women. The female subjects were 18–64 years old, males – 18–45 years old. The following tools were used: Parental Bonding Instrument (Parker G. et al., 1997, Familial Relationship Quality Measure (Ryan & Willits, 2007, Experiences in Close Relationships-Revised (ECR – RS; Fraley, Waller, & Brennan, 2000 and demographic questionnaire. The findings have showed that were no statistically significant differences with regard to gender were established assessing the subjectively perceived style of parental bonding, satisfaction with familial relationships and the attachment style in different close relationships. Both male and female subjects attributed the subjectively perceived upbringing style of a father to “overprotection”, that of a mother – to “care”. The attachment style of males characterised as “avoidance” is insignificantly higher than females, whereas the “anxiety” style of attachment in samples of males and females showed almost no differences. A positive relationship was established between the satisfaction with experienced familial relationships and the “caring” style of upbringing of both parents. Satisfaction with familial relationships positively correlates with the importance of

  13. Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

    Science.gov (United States)

    Jaspers, Arne; Beéck, Tim Op De; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

    2017-12-28

    Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over two seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models' performance on predicting the reported RPE values for future training sessions was compared to the naive baseline's performance. Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring.

  14. Relationship between Chinese Learning Motivation types and demographic features among Danish Students

    DEFF Research Database (Denmark)

    Zhang, Chun

    The purpose of this study is to investigate the relationship between Chinese learning motivation types and the various demographic features among students at lower and upper secondary schools in Denmark. The basis of the analysis is survey data collected in Denmark from 204 students from 6 upper......) in mind, the motivational types in Chinese learning demonstrate the distinct features of the context. Theoretical and pedagogical implications for the findings are discussed....

  15. A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

    Directory of Open Access Journals (Sweden)

    Friehs Karl

    2008-10-01

    Full Text Available Abstract Background Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature

  16. Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yanjun Zhang

    2014-01-01

    Full Text Available This paper employed the clinical Polysomnographic (PSG data, mainly including all-night Electroencephalogram (EEG, Electrooculogram (EOG and Electromyogram (EMG signals of subjects, and adopted the American Academy of Sleep Medicine (AASM clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM were learned and the multi-kernel FSVM (MK-FSVM was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

  17. A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

    Science.gov (United States)

    Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling

    2017-02-01

    Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R 2 ) of the whole training set (training and prediction) was 0.924, and the R 2 of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q 2 value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.

  18. Students' Demand for Smartphones: Structural Relationships of Product Features, Brand Name, Product Price and Social Infuence

    Science.gov (United States)

    Suki, Norazah Mohd

    2013-01-01

    Purpose: The study aims to examine structural relationships of product features, brand name, product price and social influence with demand for Smartphones among Malaysian students'. Design/methodology/approach: Data collected from 320 valid pre-screened university students studying at the pubic higher learning institution in Federal Territory of…

  19. Detection of relationships among multi-modal brain imaging meta-features via information flow.

    Science.gov (United States)

    Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

    2018-01-15

    Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

  20. Relationship of goat milk flow emission variables with milking routine, milking parameters, milking machine characteristics and goat physiology.

    Science.gov (United States)

    Romero, G; Panzalis, R; Ruegg, P

    2017-11-01

    The aim of this paper was to study the relationship between milk flow emission variables recorded during milking of dairy goats with variables related to milking routine, goat physiology, milking parameters and milking machine characteristics, to determine the variables affecting milking performance and help the goat industry pinpoint farm and milking practices that improve milking performance. In total, 19 farms were visited once during the evening milking. Milking parameters (vacuum level (VL), pulsation ratio and pulsation rate, vacuum drop), milk emission flow variables (milking time, milk yield, maximum milk flow (MMF), average milk flow (AVMF), time until 500 g/min milk flow is established (TS500)), doe characteristics of 8 to 10 goats/farm (breed, days in milk and parity), milking practices (overmilking, overstripping, pre-lag time) and milking machine characteristics (line height, presence of claw) were recorded on every farm. The relationships between recorded variables and farm were analysed by a one-way ANOVA analysis. The relationships of milk yield, MMF, milking time and TS500 with goat physiology, milking routine, milking parameters and milking machine design were analysed using a linear mixed model, considering the farm as the random effect. Farm was significant (Pfarms, being similar to those recommended in scientific studies. Few milking parameters and milking machine characteristics affected the tested variables: average vacuum level only showed tendency on MMF, and milk pipeline height on TS500. Milk yield (MY) was mainly affected by parity, as the interaction of days in milk with parity was also significant. Milking time was mainly affected by milk yield and breed. Also significant were parity, the interaction of days in milk with parity and overstripping, whereas overmilking showed a slight tendency. We concluded that most of the studied variables were mainly related to goat physiology characteristics, as the effects of milking parameters and

  1. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

    Science.gov (United States)

    Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

    2015-10-01

    Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley

  2. Feature Fatigue, IT Fashion and IT Consumerization - Is There a Relationship?

    Directory of Open Access Journals (Sweden)

    Luiz Antonio Slongo

    2015-12-01

    Full Text Available Based on the concepts of Feature Fatigue, IT Fashion and IT Consumerization, this paper aims to investigate the relationships between them answering two questions: (1 does the phenomenon of IT Fashion result in Feature Fatigue? (2 Will the concept of Feature Fatigue cause the same effect when looking from the point of view of the IT Consumerization in the corporate environment? The research addresses these questions through two techniques: a laddering and a survey. Albeit tenuously, the results provide evidence that consumption motivated by IT Fashion leads to Feature Fatigue. This study contributes to management research by attempting at the phenomenon described from a multidisciplinary perspective, also contributing to management practice, specifically for marketing managers trying to understand the experiences and expectations of consumers, and also for IT managers engaged in the design of governance policies regarding the use of personal devices by employees in this context.

  3. SU-F-R-17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Yu, V; Ruan, D; Nguyen, D; Kaprealian, T; Chin, R; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

    2016-06-15

    Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image-sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre-contrast, T1 post-contrast, T2-Flair, and T2-TSE images. Eight Gray-level co-occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum-Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, along with an adjacent non-recurrent region-of-interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial-basis-function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm{sup 3} up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm{sup 3} was demonstrated at 96 days prior, followed by significant increase to 1.57 cm{sup 3} at 42 days prior. Visualization of the classified volume shows the merging of recurrence-susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable

  4. A machine learning approach for predicting the relationship between energy resources and economic development

    Science.gov (United States)

    Cogoljević, Dušan; Alizamir, Meysam; Piljan, Ivan; Piljan, Tatjana; Prljić, Katarina; Zimonjić, Stefan

    2018-04-01

    The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach.

  5. Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different Working Conditions

    Directory of Open Access Journals (Sweden)

    Dong Wang

    2015-01-01

    Full Text Available Gears are widely used in gearbox to transmit power from one shaft to another. Gear crack is one of the most frequent gear fault modes found in industry. Identification of different gear crack levels is beneficial in preventing any unexpected machine breakdown and reducing economic loss because gear crack leads to gear tooth breakage. In this paper, an intelligent fault diagnosis method for identification of different gear crack levels under different working conditions is proposed. First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. To reduce the dimensionality of the extracted statistical features and generate new significant low-dimensional statistical features, a simple and effective method called principal component analysis is used. To further improve identification accuracies of different gear crack levels under different working conditions, support vector machine is employed. Three experiments are investigated to show the superiority of the proposed method. Comparisons with other existing gear crack level identification methods are conducted. The results show that the proposed method has the highest identification accuracies among all existing methods.

  6. [Relationship of motor deficits and imaging features in metastatic epidural spinal cord compression].

    Science.gov (United States)

    Liu, Shu-Bin; Liu, Yao-Sheng; Li, Ding-Feng; Fan, Hai-Tao; Huai, Jian-Ye; Guo, Jun; Wang, Lei; Liu, Cheng; Zhang, Ping; Cui, Qiu; Jiang, Wei-Hao; Cao, Yun-Cen; Jiang, Ning; Sui, Jia-Hong; Zhang, Bin; Zhou, Jiu

    2010-06-15

    To explore the relationship of motor deficits of the lower extremities with the imaging features of malignant spinal cord compression (MESCCs). From July 2006 through December 2008, 56 successive MESCC patients were treated at our department. All were evaluated by magnetic resonance imaging and computed tomography and were scored according to motor deficits Frankel grading on admission. Imaging assessment factors of main involved vertebrae were level of vertebral metastatic location, epidural space involvement, vertebral body involvement, lamina involvement, posterior protrusion of posterior wall, pedicle involvement, continuity of main involved vertebrae, fracture of anterior column, fracture of posterior wall, location in upper thoracic spine and/or cervicothoracic junction. Occurrence was the same between paralytic state of MESCCs and epidural space involvement of imaging features. Multiple regression equation showed that paralytic state had a linear regression relationship with imaging factors of lamina involvement (X1), posterior protrusion of posterior wall (X2), location in upper thoracic spine and/or cervicothoracic junction (X7) of main involved vertebrae. The optimal regression equation of paralytic state (Y) and imaging feature (X) was Y = -0.009 +0.639X, + 0.149X, +0.282X. Lamina involvement of main involved vertebrae has a greatest influence upon paralytic state of MESCC patients. Imaging factors of lamina involvement, posterior protrusion of posterior wall, location in upper thoracic spine and/or cervicothoracic junction of main involved vertebrae can predict the paralytic state of MESCC patients. MESCC with lamina involvement is more easily encroached on epidural space.

  7. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    Energy Technology Data Exchange (ETDEWEB)

    Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

    2016-06-15

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  8. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    International Nuclear Information System (INIS)

    Ogden, K; O’Dwyer, R; Bradford, T; Cussen, L

    2016-01-01

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  9. Apollo 15 clastic materials and their relationship to local geologic features

    Science.gov (United States)

    Fruchter, J. S.; Stoeser, J. W.; Lindstrom, M. M.; Goles, G. G.

    1973-01-01

    Ninety sub-samples of Apollo 15 materials have been analyzed by instrumental neutron activation analysis techniques for as many as 21 elements. Soil and soil breccia compositions show considerable variation from station to station although at any given station the soils and soil breccias were compositionally very similar to one another. Mixing model calculations show that the station-to-station variations can be related to important local geologic features. These features include the Apennine Front, Hadley Rille and the ray from the craters Aristillus or Autolycus. Compositional similarities between soils and soil breccias at the Apollo 15 site indicate that the breccias and soils are related in some fundamental way, although the exact nature of this relationship is not yet fully understood.

  10. Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2017-04-01

    The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms

  11. Relationship between udder morphology traits, alveolar and cisternal milk compartments and machine milking performances of dairy camels (Camelus dromedarius

    Directory of Open Access Journals (Sweden)

    M. Ayadi

    2013-07-01

    Full Text Available A total of 22 dairy dromedary camels under intensive conditions in late lactation (275±24 days were used to study the relationship between external and internal udder morphology and machine milking performances. Measurements of udder and teat morphology were obtained immediately before milking and in duplicate. Individual milk yield, lag time and total milking time were recorded during milking, and milk samples were collected and analyzed for milk composition thereafter. Cisternal and alveolar milk volumes and composition were evaluated at 9 h milking interval. Results revealed that dairy camels had well developed udders and milk veins, with medium sized teats. On average, milk yield as well as milk fat and protein contents were 4.80±0.50 L d-1, 2.61±0.16% and 3.08±0.05%, respectively. The low fat values observed indicated incomplete milk letdown during machine milking. Lag time, and total milking time were 3.0±0.3, and 120.0±8.9s, on average, respectively. Positive correlations (p<0.05 were observed between milk yield and udder depth (r=0.37, distance between teats (r=0.57 and milk vein diameter (r=0.28, while a negative correlation was found with udder height (r=-0.25, p<0.05. Cisternal milk accounted for 11% of the total udder milk. Positive correlations were observed between total milk yield and volume of alveolar milk (r=0.98; p<0.001 as well as with volume of cisternal milk (r=0.63, p<0.05. Despite the low udder milk storage capacity observed in dairy camels, our study concluded that the evaluated dromedary sample had adequate udder morphology for machine milking. Finally, positive relationships were detected between milk yield and udder morphology traits of dairy camels.

  12. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  13. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  14. Twitter location (sometimes matters: Exploring the relationship between georeferenced tweet content and nearby feature classes

    Directory of Open Access Journals (Sweden)

    Stefan Hahmann

    2014-12-01

    Full Text Available In this paper, we investigate whether microblogging texts (tweets produced on mobile devices are related to the geographical locations where they were posted. For this purpose, we correlate tweet topics to areas. In doing so, classified points of interest from OpenStreetMap serve as validation points. We adopted the classification and geolocation of these points to correlate with tweet content by means of manual, supervised, and unsupervised machine learning approaches. Evaluation showed the manual classification approach to be highest quality, followed by the supervised method, and that the unsupervised classification was of low quality. We found that the degree to which tweet content is related to nearby points of interest depends upon topic (that is, upon the OpenStreetMap category. A more general synthesis with prior research leads to the conclusion that the strength of the relationship of tweets and their geographic origin also depends upon geographic scale (where smaller scale correlations are more significant than those of larger scale.

  15. Relationship of child abuse with personality features and high risk behaviors in adolescents

    Directory of Open Access Journals (Sweden)

    Mehdi Ghezelseflo

    2015-05-01

    Full Text Available Background: Children are one of the most vulnerable groups of the society and are constantly threatened by different people in their family or society. The aim of this study was investigating the correlation of child abuse with personality features and high risk behavior in high school students of Islamshahr, Iran. Methods: This study cross-sectional analytical was conducted on the high school girls and boys of Islamshahr in spring 2014.528 students were selected by cluster random sampling among 4 high schools (two female and two male high schools. Childhood trauma questionnaire, NEO-Five Factor Inventory and Youth Risk-Taking Scale were used for data collection. Data were analyzed by independence t-test, Pearson's correlation coefficient and multiple linear regression. Results: The results of independence t-test indicated significant differences between girls and boys in terms of child abuse and high risk experience (t=-2.16,p=0.03 and t=-5.03, P=0.001, respectively. Also, the results demonstrated a significant relationship between child abuse and personality characteristics, high risk behavior and all its subscales (P<0.05. The findings of multiple linear regressionindicated that child abuse could explain 14% total risk-taking, 25% neurotic personality feature , 14% extroversion, 10% agreeableness, 1% flexibility and 13% conscientiousness (P<0.05. Conclusion: According to the research findings, appropriate behavior with children is of great importance. Therefore, child abuse would form inappropriate personality features and increase risk behaviors among children.

  16. Evaluation of Ethical Attitude Approaches in Midwives and their Relationship with their Demographic Features

    Science.gov (United States)

    Afhami, Narges; Nekuei, Nafisehsadat; Bahadoran, Parvin; Taleghani-Esfahani, HamidReza

    2018-01-01

    Background: Ethical approach is one of the paramount aspects of life. The position of this approach in medical occupations has always been noticed. This study was carried out to analyze the types of ethical approaches in midwives and their relationship with their demographic features. Materials and Methods: The current descriptive-correlation and cross-sectional study was conducted from October to December 2014 using quota random sampling technique. The participants consisted of 189 midwives employed in Isfahan, Iran. The data collection tool was a researcher-made questionnaire. The midwives' attitudes were examined in the four fields of virtue ethics, deontologism, utilitarianism, and religious ethics. Data were analyzed using descriptive and inferential statistics in SPSS software (p ethics with the mean value of 64.36 out of 100. A positive significant relationship was found between deontologism and education level (F = 8.74; p = 0.004), and total ethical approach and workplace (F = 2.60; p = 0.053). There was a reverse significant relationship between age and work experience and virtue ethics (r = −0.15; p = 0.035 and r = −0.20; p = 0.005, respectively). Conclusions: The existing high percentage of religious ethics shows the religious tendency of the participants. The determination of ethical approach among midwives as one of the important medical groups and creation and improvement of the most appropriate attitude among them based on the present regulations and requirements in society are principles that we should attempt to achieve.

  17. Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

    Science.gov (United States)

    Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

    2018-03-01

    Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

  18. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Science.gov (United States)

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  19. [Immunomorphologic features of epithelial-stromal relationships at hyperplasia and endometrial carcinoma].

    Science.gov (United States)

    Bantysh, B B; Paukov, v S; Kogan, E A

    2012-01-01

    The results of a immunomorphologic comprehensive study of epithelial-stromal relationships in the uterus hyperplasia and endometrial cancer suggest that the suppressor gene of cancer (PTEN) plays a key role in the process of neoplastic transformation of endometrial hyperplasia and adenocarcinoma development. For the first time the existence of two highly differentiated endometrial adenocarcinoma immunophenotype were detected The first one is a PTEN-negative endometrial aedenocarcinoma, characterized by an almost complete inhibition of tumor suppressor gene PTEN in the epithelium of the glands and stromal cell of the tumor The second type is a PTEN-positive endometrial adenocarcinoma, in which epithelial and stromal tumor suppressor gene PTEN activity has retained Based on these results we have formulated a hypothesis about the different types of endometrial hyperplasia morphogenesis and its possible transfer to cervical cancer associated with features of tumor suppressor gene PTEN.

  20. Relationship between the latest activity of mare volcanism and topographic features of the Moon

    Science.gov (United States)

    Kato, Shinsuke; Morota, Tomokatsu; Yamaguchi, Yasushi; Watanabe, Sei-ichiro; Otake, Hisashi; Ohtake, Makiko

    2016-04-01

    Lunar mare basalts provide insights into compositions and thermal history of lunar mantle. According to crater counting analysis with remote sensing data, the model ages of mare basalt units indicate a second peak of magma activity at the end of mare volcanism (~2 Ga), and the latest eruptions were limited in the Procellarum KREEP Terrane (PKT), which has high abundances of heat-producing elements. In order to understand the mechanism for causing the second peak and its magma source, we examined the correlation between the titanium contents and eruption ages of mare basalt units using compositional and chronological data updated by SELENE/Kaguya. Although no systematic relationship is observed globally, a rapid increase in mean titanium (Ti) content occurred at 2.3 Ga in the PKT, suggesting that the magma source of mare basalts changed at that time. The high-Ti basaltic eruption, which occurred at the late stage of mare volcanism, can be correlated with the second peak of volcanic activity at ~2 Ga. The latest volcanic activity can be explained by a high-Ti hot plume originated from the core-mantle boundary. If the hot plume was occurred, the topographic features formed by the hot plume may be remained. We calculated the difference between topography and selenoid and found the circular feature like a plateau in the center of the PKT, which scale is ~1000 km horizontal and ~500 m vertical. We investigated the timing of ridge formation in the PKT by using stratigraphic relationship between mare basalts and ridges. The ridges were formed before and after the high-Ti basaltic eruptions and seem to be along with the plateau. These results suggest that the plateau formation is connected with the high-Ti basaltic eruptions.

  1. Features of the brainstem and tentorial foramen relationship and their practical value

    Directory of Open Access Journals (Sweden)

    O. V. Redyakina

    2016-11-01

    Full Text Available Objective. Establish the morphological features and practical significance of the tentorial-stem relationship from the position of individual anatomical variability. Methods: head morphometry, macro and microscopic examination of the brainstem, morphometry of the brainstem and its departments, tentorial aperture morphometry, foramen magnum craniometry, manufacture of corrosion molds of the posterior cranial fossa, statistical processing of the results, computer-graphic modeling of the brainstem and surrounding formations. Results.  In the course of the study, the features of the individual variability of the tentorial foramen form were established, namely: shortened-expanded and oval-convex forms were defined in brachycephalic; in dolichocephalic - oblong-narrowed and elongated-conical. At the same time, a number of existing sizes and forms of the tentorial-stem spaces were noted. Among them, four main ones are described: front, side (right and left and rear. They have individual characteristics. Thus, in the brachycephalic we define lateral holes, due to the convexity of the tentorial margins. In dolichocephalic - front and back gaps, depending on the characteristics of their elongations. The obtained data are of great importance for the craniotopographic justification of the tentorial-stem wedges, which are formed with tumors which located here. In our opinion, tumors have the greatest possibility of passage through the left or right lateral intervals in people with a brachymorph form of the head, and through the anterior and posterior intervals - in people with meso- and dolichomorph forms of the head.

  2. Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

    Science.gov (United States)

    Onay, Aytun; Onay, Melih; Abul, Osman

    2017-04-01

    Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Investigation of the relationship between dermoscopic features and histopathological prognostic indicators in patients with cutaneous melanoma

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    Özlem Özbağçıvan

    2015-09-01

    Full Text Available Background and Design: Dermoscopy has an important role in the diagnosis of melanoma nowadays. Dermoscopic findings of melanoma had been associated with Breslow thickness and invasion status in previous studies but the relationship between dermatoscopic findings and other histopathological prognostic indicators has not been investigated until today. In this study, our aim is to investigate the relationship between dermatoscopic findings and histopathologic prognostic indicators such as Breslow thickness, invasion status, mitotic rate, lymphovascular invasion (LVI, ulceration and regression in patients who had been diagnosed with melanoma due to their clinical, dermatoscopic and histopatological findings. Materials and Methods: Dermoscopic and histopathological findings of 47 cases of melanoma who applied to our clinic between the years 2000 and 2014 were evaluated. The relationship between the dermoscopic findings which had been reported to be observed in melanomas in previous research and the histopathologic prognostic indicators such as Breslow thickness, invasion status, mitotic rate, lymphovascular invasion, ulceration and regression were investigated. Results: Irregular dots/globules, atypical pigment network, multifocal hypopigmentation, radial streaks and moth-eaten borders have been associated with good prognostic indicators whereas comedo like openings, regular blotch, exophytic papillary structures, dotted, glomerular, lineer irregular vessels, pink/red and blue/gray colors were associated with poor prognostic indicators. Additionally some dermatoscopic findings which are more observed in benign lesions such as multiple milia-like cysts, comedo like openings, moth-eaten borders, regular blotch, exophytic papillary structures and finger print areas have been observed in melanomas in our study. Conclusion: Many dermoscopic findings have demonstrated statistically significant association with the histopathological prognostic indicators

  4. Severe visceral leishmaniasis in children: the relationship between cytokine patterns and clinical features

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    Monica Elinor Alves Gama

    2013-12-01

    Full Text Available Introduction The relationship between severe clinical manifestations of visceral leishmaniasis (VL and immune response profiles has not yet been clarified, despite numerous studies on the subject. This study aimed to investigate the relationship between cytokine profiles and the presence of immunological markers associated with clinical manifestations and, particularly, signs of severity, as defined in a protocol drafted by the Ministry of Health (Brazil. Methods We conducted a prospective, descriptive study between May 2008 and December 2009. This study was based on an assessment of all pediatric patients with VL who were observed in a reference hospital in Maranhão. Results Among 27 children, 55.5% presented with more than one sign of severity or warning sign. Patients without signs of severity or warning signs and patients with only one warning sign had the highest interferon-gamma (IFN-γ levels, although their interleukin 10 (IL-10 levels were also elevated. In contrast, patients with the features of severe disease had the lowest IFN-γ levels. Three patients who presented with more than two signs of severe disease died; these patients had undetectable interleukin 2 (IL-2 and IFN-γ levels and low IL-10 levels, which varied between 0 and 36.8pg/mL. Conclusions Our results showed that disease severity was associated with low IFN-γ levels and elevated IL-10 levels. However, further studies with larger samples are needed to better characterize the relationship between disease severity and cytokine levels, with the aim of identifying immunological markers of active-disease severity.

  5. Evaluation of ethical attitude approaches in midwives and their relationship with their demographic features

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    Narges Afhami

    2018-01-01

    Full Text Available Background: Ethical approach is one of the paramount aspects of life. The position of this approach in medical occupations has always been noticed. This study was carried out to analyze the types of ethical approaches in midwives and their relationship with their demographic features. Materials and Methods: The current descriptive-correlation and cross-sectional study was conducted from October to December 2014 using quota random sampling technique. The participants consisted of 189 midwives employed in Isfahan, Iran. The data collection tool was a researcher-made questionnaire. The midwives' attitudes were examined in the four fields of virtue ethics, deontologism, utilitarianism, and religious ethics. Data were analyzed using descriptive and inferential statistics in SPSS software (p < 0.050. Results: The highest score belonged to religious ethics with the mean value of 64.36 out of 100. A positive significant relationship was found between deontologism and education level (F = 8.74; p = 0.004, and total ethical approach and workplace (F = 2.60; p = 0.053. There was a reverse significant relationship between age and work experience and virtue ethics (r = −0.15; p = 0.035 and r = −0.20; p = 0.005, respectively. Conclusions: The existing high percentage of religious ethics shows the religious tendency of the participants. The determination of ethical approach among midwives as one of the important medical groups and creation and improvement of the most appropriate attitude among them based on the present regulations and requirements in society are principles that we should attempt to achieve.

  6. Clinical and radiologic features and their relationships with neurofunctional scores in patients with acute cerebellar infarct

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    Hasan Huseyin Kozak

    2016-01-01

    Full Text Available Background: Cerebellar infarct is a rare condition with very nonspecific clinical features. The aim of this study was to assess the full spectrum of the clinical characteristics, neuroimaging findings and neurofunctional analyses of cerebellar infarction, and the relationship between them. Materials and Methods: Data were collected from 59 patients admitted to our department during an 8-year period. We retrospectively analyzed the relationship between demographic characteristics, clinical symptomatology, etiological factors, functional condition, vascular distribution, frequency of subcortical white matter lesions (WMLs, and concomitant lesion outside the cerebellum in patients with acute cerebellar infarct (ACI at time of admission. Results: The mean age in our series was 65.2 years, with most being male (57.6%. The posterior inferior cerebellar (PICA artery was the most commonly affected territory at 62.7%. There was concomitant lesion outside the cerebellum in 45.7%. The main etiology in PICA was cardioembolism. While mean National Institutes of Health Stroke Scale on admission was 2.08 ± 1.67 in study group, modified Rankin Scale (mRS on admission was detected to be mRS1 (n: 44, 74.5% and mRS2 (n: 12, 20.3% most frequently. Fourteen (35% patients were detected to be in Fazekas stage 0; 11 (27.5% patients in Fazekas stage 1; 6 (15% patients in Fazekas stage 2; and 9 (22.5% patients in Fazekas stage 3. Conclusion: Cerebellar infarct is very heterogeneous. The other cerebral area infarcts which accompany ACI negatively affect neurologic functional scores. Although it is difficult to detect the relationship between WMLs and neurologic functional severity, timely detection of risk factors and their modulation may be associated with prevention and treatability of WMLs, and this may be one of the important points for prevention of stroke-related disability.

  7. Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features.

    Science.gov (United States)

    Saberioon, Mohammadmehdi; Císař, Petr; Labbé, Laurent; Souček, Pavel; Pelissier, Pablo; Kerneis, Thierry

    2018-03-29

    The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout ( Oncorhynchus mykiss ) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k -Nearest neighbours ( k -NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k -NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.

  8. Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss Classification Using Image-Based Features

    Directory of Open Access Journals (Sweden)

    Mohammadmehdi Saberioon

    2018-03-01

    Full Text Available The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss were fed either a fish-meal based diet (80 fish or a 100% plant-based diet (80 fish and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF, Support vector machine (SVM, Logistic regression (LR and k-Nearest neighbours (k-NN. The SVM with radial based kernel provided the best classifier with correct classification rate (CCR of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40% classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet’s effects on fish skin.

  9. SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

    Energy Technology Data Exchange (ETDEWEB)

    Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

  10. A dense microseismic monitoring network in Korea for uncovering relationship between seismic activity and neotectonic features

    Science.gov (United States)

    Kang, T.; Lee, J. M.; Kim, W.; Jo, B. G.; Chung, T.; Choi, S.

    2012-12-01

    A few tens of surface traces indicating movements in Quaternary were found in the southeastern part of the Korean Peninsula. Following both the geological and engineering definitions, those features are classified into "active", in geology, or "capable", in engineering, faults. On the other hand, the present-day seismicity of the region over a couple of thousand years is indistinguishable on the whole with the rest of the Korean Peninsula. It is therefore of great interest whether the present seismic activity is related to the neotectonic features or not. Either of conclusions is not intuitive in terms of the present state of seismic monitoring network in the region. Thus much interest in monitoring seismicity to provide an improved observation resolution and to lower the event-detection threshold has increased with many observations of the Quaternary faults. We installed a remote, wireless seismograph network which is composed of 20 stations with an average spacing of 10 km. Each station is equipped with a three-component Trillium Compact seismometer and Taurus digitizer. Instrumentation and analysis advancements are now offering better tools for this monitoring. This network is scheduled to be in operation over about one and a half year. In spite of the relatively short observation period, we expect that the high density of the network enables us to monitor seismic events with much lower magnitude threshold compared to the preexisting seismic network in the region. Following the Gutenberg-Richter relationship, the number of events with low magnitude is logarithmically larger than that with high magnitude. Following this rule, we can expect that many of microseismic events may reveal behavior of their causative faults, if any. We report the results of observation which has been performed over a year up to now.

  11. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

    Science.gov (United States)

    Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

    2018-03-01

    Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining

  12. Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

    Science.gov (United States)

    Othman, Arsalan; Gloaguen, Richard

    2015-04-01

    Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

  13. Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

    Science.gov (United States)

    Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

    2018-06-01

    With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

  14. IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

    Directory of Open Access Journals (Sweden)

    N Lance Hepler

    2014-09-01

    Full Text Available Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab, determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license, documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

  15. The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

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    Jin-peng Liu

    2017-07-01

    Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

  16. Time feature of Chinese military personnel’s suicide ideation and its relationship with psychosomatic health

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    Li-yi ZHANG

    2012-07-01

    Full Text Available Objective To investigate the time feature of Chinese military personnel's suicide ideation and its relationship with psychosomatic health to provide scientific basis for formulation of mental health policy and intervention of related psychological crisis. Methods By random cluster sampling, a total of 11 362 military personnel including army, navy and air-force (1100 in 1980s, 8000 in 1990s, 2262 in year 2000 were tested by Chinese Psychosomatic Health Scale (CPSHS. SPSS statistic 17.0 program was used for data analysis, i.e., χ2-test, T-test and stepwise regression analysis. Results The incidence rate of military personnel's suicide ideation in the three decades from 1980 to 2000 was 10.27%, 7.09% and 2.83% respectively, which revealed a decreasing trend (P 0.05. Suicide ideation was selected into the regression equation of mental health, physical health, and total psychosomatic health scores, which could positively predict the level of military personnel's psychosomatic health (P=0.05 or 0.01. Conclusions Military personnel's suicide ideation presents a decreasing trend; the psychosomatic health of military personnel who have suicide ideation is worse than that of personnel without suicide ideation.

  17. The relationship between radiological features and clinical manifestation and dental expenses of keratocystic odontogenic tumor

    International Nuclear Information System (INIS)

    Min, Jung Hyun; Huh, Kyung Heo; Heo, Min Suk; Choi, Soon Chul; Lee, Sam Sun; Bae, Kwang Hak; Choi, Jin Woo

    2013-01-01

    This study was performed to identify correlations between keratocystic odontogenic tumor (KCOT) data from CT sections, and data on the KCOT clinical manifestation and resulting dental expenses. Following local Institutional Review Board (IRB) approval, a seven-years of retrospective study was performed regarding patients with KCOTs treated at the Seoul National University Dental Hospital. A total of 180 KCOT were included in this study. The following information was collected: age, gender, location and size of the lesion, radiological features, surgical treatment provided and dental expenses. There was no significant association between the size of the KCOT and age, gender, and presenting preoperative symptoms. In both jaws, it was unusual to find KCOTs under 10 mm. The correlation between the number of teeth removed and the size of the KCOT in the tooth bearing area was statistically significant in the mandible, whereas in the maxilla, no significant relationship was found. Dental expenses compared with the size of the KCOT were found to be significant in both jaws. The size of KCOT was associated with a significant increase in dental expenses for both jaws and the number of teeth removed from the mandible. These findings emphasize the importance of routine examinations and early detection of lesions, which in turn helps preserving anatomical structures and reducing dental expenses.

  18. Exploring the relationships among service quality features, perceived value and customer satisfaction

    Directory of Open Access Journals (Sweden)

    Azman Ismail

    2009-07-01

    Full Text Available The purpose of this paper is to explore the relationships among service quality features (responsiveness, assurance, and empathy, perceived value and customer satisfaction in the context of Malaysia. The empirical data are drawn from 102 members of an academic staff of a Malaysian public institution of higher learning using a survey questionnaire. The results indicate three important findings: firstly, the interaction between perceived value and responsiveness was not significantly correlated with customer satisfaction. Secondly, the interaction between perceived value and assurance also did not correlate significantly with customer satisfaction. Thirdly, the interaction between perceived value and empathy correlated significantly with customer satisfaction. Thus the results demonstrate that perceived value had increased the effect of empathy on customer satisfaction, but it had not increased the effect of responsiveness and assurance on customer satisfaction. In sum, this study confirms that perceived value act as a partial moderating variable in the service quality models of the organizational sample. In addition, implications and limitations of this study, as well as directions for future research are discussed.

  19. [The relationship of attachment features and multi-impulsive symptoms in eating disorders].

    Science.gov (United States)

    Szalai, Tamás Dömötör

    2017-07-01

    Attachment dysfunctions determine borderline personality disorder, which is a frequent background factor of multi-impulsivity; however, the relationship between attachment and multi-impulsive eating disorders is almost unexplored. To compare attachment features of multi-impulsive and classical eating disorder patients with individuals without eating disorders, and to test attachment as a predictor of multi-impulsivity. A cross-sectional survey (148 females, mean age: 30.9 years) investigated maternal, paternal and adult attachment, depression, anxiety, eating disorder and multi-impulsive symptoms in these groups. Altogether 41.3% of the individuals without eating disorders, 17.6% of classical and 11.8% of multi-impulsive eating disorder patients had secure attachment. Multi-impulsive patients had the most severe eating disorder symptoms (F (2) = 17.733) and the lowest paternal care (F (2) = 3.443). Preoccupied and fearful attachment explained 14.5% of multi-impulsive symptoms; however, with adjustment for depression only latter one remained the predictor of multi-impulsivity (t = 5.166, peating disorder patients from the aspects of both symptoms and attachment. Handling their negative moods may hold therapeutic potentials. Longitudinal studies are required to investigate the therapeutic value of paternal care, attachment preoccupation and fearfulness. Orv Hetil. 2017; 158(27): 1058-1066.

  20. The clinical features of EDNOS: relationship to mood, health status and general functioning.

    Science.gov (United States)

    Turner, Hannah; Bryant-Waugh, Rachel; Peveler, Robert

    2010-04-01

    Eating disorder not otherwise specified (EDNOS) remains poorly evaluated in terms of eating disorder features and relationship to mood, health status and general functioning. This study investigated the clinical profiles of a sample of EDNOS patients, and how they compared to patients with anorexia nervosa (AN) and bulimia nervosa (BN). The sample consisted of 178 patients. All completed the Eating Disorder Examination, Beck Depression Inventory, Work and Social Adjustment Scale and Sf-36. ANOVAs were conducted to explore group differences. No differences were found for depression. No differences were found between BN and EDNOS on measures of health status and general functioning. AN patients reported greater role limitations due to physical health and experienced greater physical pain compared with BN or EDNOS patients, and reported poorer social functioning, lower vitality and higher functional impairment compared with EDNOS patients. EDNOS patients are generally no less clinically impaired than those with BN. However AN patients may be more impaired in some aspects of general functioning compared with BN or EDNOS patients. Copyright 2009 Elsevier Ltd. All rights reserved.

  1. Inter-relationships between objective handwriting features and executive control among children with developmental dysgraphia

    Science.gov (United States)

    2018-01-01

    Objective To describe handwriting and executive control features and their inter-relationships among children with developmental dysgraphia, in comparison to controls. Method Participants included 64 children, aged 10–12 years, 32 with dysgraphia based on the Handwriting Proficiency Screening Questionnaire (HPSQ) and 32 matched controls. Children copied a paragraph onto paper affixed to a digitizer that supplied handwriting process objective measures (Computerized Penmanship Evaluation Tool (ComPET). Their written product was evaluated by the Hebrew Handwriting Evaluation (HHE). Parents completed the Behavior Rating Inventory of Executive Function (BRIEF) questionnaire about their child's executive control abilities. Results Significant group differences were found for handwriting performance measures (HHE and ComPET) and executive control domains (BRIEF). Based on one discriminate function, including handwriting performance and executive control measures, 98.4% of the participants were correctly classified into groups. Significant correlations were found in each group between working memory and legibility as well as for other executive domains and handwriting measures. Furthermore, twenty percent of the variability of the mean pressure applied towards the writing surface among children with was explained by their 'emotional control' (BRIEF). Conclusion The results strongly suggest consideration of executive control domains to obtain better insight into handwriting impairment characteristics among children with dysgraphia to improve their identification, evaluation and the intervention process. PMID:29689111

  2. Inter-relationships between objective handwriting features and executive control among children with developmental dysgraphia.

    Science.gov (United States)

    Rosenblum, Sara

    2018-01-01

    To describe handwriting and executive control features and their inter-relationships among children with developmental dysgraphia, in comparison to controls. Participants included 64 children, aged 10-12 years, 32 with dysgraphia based on the Handwriting Proficiency Screening Questionnaire (HPSQ) and 32 matched controls. Children copied a paragraph onto paper affixed to a digitizer that supplied handwriting process objective measures (Computerized Penmanship Evaluation Tool (ComPET). Their written product was evaluated by the Hebrew Handwriting Evaluation (HHE). Parents completed the Behavior Rating Inventory of Executive Function (BRIEF) questionnaire about their child's executive control abilities. Significant group differences were found for handwriting performance measures (HHE and ComPET) and executive control domains (BRIEF). Based on one discriminate function, including handwriting performance and executive control measures, 98.4% of the participants were correctly classified into groups. Significant correlations were found in each group between working memory and legibility as well as for other executive domains and handwriting measures. Furthermore, twenty percent of the variability of the mean pressure applied towards the writing surface among children with was explained by their 'emotional control' (BRIEF). The results strongly suggest consideration of executive control domains to obtain better insight into handwriting impairment characteristics among children with dysgraphia to improve their identification, evaluation and the intervention process.

  3. The relationship between radiological features and clinical manifestation and dental expenses of keratocystic odontogenic tumor

    Energy Technology Data Exchange (ETDEWEB)

    Min, Jung Hyun; Huh, Kyung Heo; Heo, Min Suk; Choi, Soon Chul; Lee, Sam Sun; Bae, Kwang Hak [Dept. of School of Dentistry, Seoul National University, Seoul (Korea, Republic of); Choi, Jin Woo [Dept. of Oral and Maxillofacial Radiology, Dankook University College of Dentistry, Cheonan (Korea, Republic of)

    2013-06-15

    This study was performed to identify correlations between keratocystic odontogenic tumor (KCOT) data from CT sections, and data on the KCOT clinical manifestation and resulting dental expenses. Following local Institutional Review Board (IRB) approval, a seven-years of retrospective study was performed regarding patients with KCOTs treated at the Seoul National University Dental Hospital. A total of 180 KCOT were included in this study. The following information was collected: age, gender, location and size of the lesion, radiological features, surgical treatment provided and dental expenses. There was no significant association between the size of the KCOT and age, gender, and presenting preoperative symptoms. In both jaws, it was unusual to find KCOTs under 10 mm. The correlation between the number of teeth removed and the size of the KCOT in the tooth bearing area was statistically significant in the mandible, whereas in the maxilla, no significant relationship was found. Dental expenses compared with the size of the KCOT were found to be significant in both jaws. The size of KCOT was associated with a significant increase in dental expenses for both jaws and the number of teeth removed from the mandible. These findings emphasize the importance of routine examinations and early detection of lesions, which in turn helps preserving anatomical structures and reducing dental expenses.

  4. RELATIONSHIP AMONG BRAIN HEMISPHERIC DOMINANCE, ATTITUDE TOWARDS L1 AND L2, GENDER, AND LEARNING SUPRASEGMENTAL FEATURES

    Directory of Open Access Journals (Sweden)

    Mohammad Hadi Mahmoodi

    2016-07-01

    Full Text Available Oral skills are important components of language competence. To have good and acceptable listening and speaking, one must have good pronunciation, which encompasses segmental and suprasegmental features. Despite extensive studies on the role of segmental features and related issues in listening and speaking, there is paucity of research on the role of suprasegmental features in the same domain. Conducting studies which aim at shedding light on the issues related to learning suprasegmental features can help language teachers and learners in the process of teaching/learning English as a foreign language. To this end, this study was designed to investigate the relationship among brain hemispheric dominance, gender, attitudes towards L1 and L2, and learning suprasegmental features in Iranian EFL learners. First, 200 Intermediate EFL learners were selected from different English language teaching institutes in Hamedan and Isfahan, two provinces in Iran, as the sample. Prior to the main stage of the study, Oxford Placement Test (OPT was used to homogenize the proficiency level of all the participants. Then, the participants were asked to complete the Edinburgh Handedness Questionnaire to determine their dominant hemisphere. They were also required to answer two questionnaires regarding their attitudes towards L1 and L2. Finally, the participants took suprasegmental features test. The results of the independent samples t-tests indicated left-brained language learners’ superiority in observing and learning suprasegmental features. It was also found that females are better than males in producing suprasegmental features. Furthermore, the results of Pearson Product Moment Correlations indicated that there is significant relationship between attitude towards L2 and learning suprasegmental features. However, no significant relationship was found between attitude towards L1 and learning English suprasegmental features. The findings of this study can

  5. Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of false- negative and false-positive regions

    Science.gov (United States)

    Pietrzyk, Mariusz W.; Donovan, Tim; Brennan, Patrick C.; Dix, Alan; Manning, David J.

    2011-03-01

    Aim: To optimize automated classification of radiological errors during lung nodule detection from chest radiographs (CxR) using a support vector machine (SVM) run on the spatial frequency features extracted from the local background of selected regions. Background: The majority of the unreported pulmonary nodules are visually detected but not recognized; shown by the prolonged dwell time values at false-negative regions. Similarly, overestimated nodule locations are capturing substantial amounts of foveal attention. Spatial frequency properties of selected local backgrounds are correlated with human observer responses either in terms of accuracy in indicating abnormality position or in the precision of visual sampling the medical images. Methods: Seven radiologists participated in the eye tracking experiments conducted under conditions of pulmonary nodule detection from a set of 20 postero-anterior CxR. The most dwelled locations have been identified and subjected to spatial frequency (SF) analysis. The image-based features of selected ROI were extracted with un-decimated Wavelet Packet Transform. An analysis of variance was run to select SF features and a SVM schema was implemented to classify False-Negative and False-Positive from all ROI. Results: A relative high overall accuracy was obtained for each individually developed Wavelet-SVM algorithm, with over 90% average correct ratio for errors recognition from all prolonged dwell locations. Conclusion: The preliminary results show that combined eye-tracking and image-based features can be used for automated detection of radiological error with SVM. The work is still in progress and not all analytical procedures have been completed, which might have an effect on the specificity of the algorithm.

  6. Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

    Directory of Open Access Journals (Sweden)

    Amir Salimi

    2018-04-01

    Full Text Available The curse of dimensionality resulted from insufficient training samples and redundancy is considered as an important problem in the supervised classification of hyperspectral data. This problem can be handled by Feature Subset Selection (FSS methods and Support Vector Machine (SVM. The FSS methods can manage the redundancy by removing redundant spectral bands. Moreover, kernel based methods, especially SVM have a high ability to classify limited-sample data sets. This paper mainly aims to assess the capability of a FSS method and the SVM in curse of dimensional circumstances and to compare results with the Artificial Neural Network (ANN, when they are used to classify alteration zones of the Hyperion hyperspectral image acquired from the greatest Iranian porphyry copper complex. The results demonstrated that by decreasing training samples, the accuracy of SVM was just decreased 1.8% while the accuracy of ANN was highly reduced i.e. 14.01%. In addition, a hybrid FSS was applied to reduce the dimension of Hyperion. Accordingly, among the 165 useable spectral bands of Hyperion, 18 bands were only selected as the most important and informative bands. Although this dimensionality reduction could not intensively improve the performance of SVM, ANN revealed a significant improvement in the computational time and a slightly enhancement in the average accuracy. Therefore, SVM as a low-sensitive method respect to the size of training data set and feature space can be applied to classify the curse of dimensional problems. Also, the FSS methods can improve the performance of non-kernel based classifiers by eliminating redundant features. Keywords: Curse of dimensionality, Feature Subset Selection, Hydrothermal alteration, Hyperspectral, SVM

  7. Intraseasonal variability of organized convective systems in the Central Andes: Relationship to Regional Dynamical Features

    Science.gov (United States)

    Mohr, K. I.; Slayback, D. A.; Nicholls, S.; Yager, K.

    2013-12-01

    The Andes extend from the west coast of Colombia (10N) to the southern tip of Chile (53S). In southern Peru and Bolivia, the Central Andes is split into separate eastern and western cordilleras, with a high plateau (≥ 3000 m), the Altiplano, between them. Because 90% of the Earth's tropical mountain glaciers are located in the Central Andes, our study focuses on this region, defining its zonal extent as 7S-21S and the meridional extent as the terrain 1000 m and greater. Although intense convection occurs during the wet season in the Altiplano, it is not included in the lists of regions with frequent or the most intense convection. The scarcity of in-situ observations with sufficient density and temporal resolution to resolve individual storms or even mesoscale-organized cloud systems and documented biases in microwave-based rainfall products in poorly gauged mountainous regions have impeded the development of an extensive literature on convection and convective systems in this region. With the tropical glaciers receding at unprecedented rates, leaving seasonal precipitation as an increasingly important input to the water balance in alpine valley ecosystems and streams, understanding the nature and characteristics of the seasonal precipitation becomes increasingly important for the rural economies in this region. Previous work in analyzing precipitation in the Central Andes has emphasized interannual variability with respect to ENSO, this is the first study to focus on shorter scale variability with respect to organized convection. The present study took advantage of the University of Utah's Precipitation Features database compiled from 14 years of TRMM observations (1998-2012), supplemented by field observations of rainfall and streamflow, historical gauge data, and long-term WRF-simulations, to analyze the intraseasonal variability of precipitating systems and their relationship regional dynamical features such as the Bolivian High. Through time series and

  8. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.

    Science.gov (United States)

    Sadeghi-Tehran, Pouria; Virlet, Nicolas; Sabermanesh, Kasra; Hawkesford, Malcolm J

    2017-01-01

    Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  9. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

    Directory of Open Access Journals (Sweden)

    Pouria Sadeghi-Tehran

    2017-11-01

    Full Text Available Abstract Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1 comparison with ground-truth images, (2 variation along a day with changes in ambient illumination, (3 comparison with manual measurements and (4 an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  10. 2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Fereshteh Shiri

    2010-08-01

    Full Text Available In the present work, support vector machines (SVMs and multiple linear regression (MLR techniques were used for quantitative structure–property relationship (QSPR studies of retention time (tR in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLRand SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD. The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r2 and q2 are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described.

  11. The Relationships between Ball Throwing Velocity and Physical-Psychomotor Features for Talent Identification in Physical Education

    Science.gov (United States)

    Karadenizli, Zeynep Inci

    2016-01-01

    The aim of this study is to investigate the relationships between ball throwing velocity (BTV), and physical features and anaerobic power (AP) for talent identification in team handball players. Players (n: 54) at 21,91 ± 4,94 age, training experience 11,19 ± 4,46 years participated voluntarily to study. These players consist of 54 Turkish…

  12. HyFlex EDM: superficial features, metallurgical analysis and fatigue resistance of innovative electro discharge machined NiTi rotary instruments.

    Science.gov (United States)

    Pirani, C; Iacono, F; Generali, L; Sassatelli, P; Nucci, C; Lusvarghi, L; Gandolfi, M G; Prati, C

    2016-05-01

    To evaluate the surface and microstructural alterations of new and used HyFlex EDM prototypes and to test their fatigue resistance. Fifteen HyFlex EDM prototypes were used for in vitro instrumentation of severely curved root canals. Surface and microstructural characteristics of new and used files were compared by ESEM analysis equipped with energy dispersive X-ray spectrophotometry (EDS) and optical metallographic imaging. Usage-induced degradation was assessed. Thirty additional HyFlex EDM prototypes and 20 standard manufactured HyFlex CM files were subjected to cyclic fatigue tests. Time to fracture was recorded, and results were validated using the Kruskal-Wallis test (α-level 0.05). Fatigued files were analysed by ESEM for fractographic evaluation. Surface and microstructural characterization of EDM prototypes revealed the typical spark-machined surface of a NiTi EDM alloy. No fractures were registered during root canal instrumentation. No evident surface alterations and minor degradation were observed between new and used instruments. The metallographic analysis of new and used files disclosed a homogeneous structure, mostly composed of lenticular martensite grains, and some residual austenite. The cyclic fatigue test showed an increase of fatigue resistance up to 700% on the EDM compared to CM files. Spark-machined peculiar surface is the main feature of HyFlex EDM. Low degradation was observed after multiple canal instrumentations. Prototypes exhibited surprising high values of cyclic fatigue resistance and a safe in vitro use in severely curved canals. © 2015 International Endodontic Journal. Published by John Wiley & Sons Ltd.

  13. The effect of website features in online relationship marketing: A case of online hotel booking

    OpenAIRE

    Bilgihan, A.

    2015-01-01

    The primary objective of this research is to develop a theory-based model of utilitarian and hedonic website features, customer commitment, trust, and e-loyalty in an online hotel booking context. Structural Equation Modeling was deployed to test research hypotheses. Findings highlight the importance of creating loyalty by focusing on both hedonic and utilitarian features. Affective commitment is more influenced by hedonic features whereas calculative commitment is driven by utilitarian ones....

  14. Neither separate nor equivalent : Relationships between feature representations within bound objects

    NARCIS (Netherlands)

    Morey, Candice C.; Guerard, Katherine; Tremblay, Sebastien

    2013-01-01

    Evidence suggests that binding, or encoding a feature with respect to other features in time and space, can convey cognitive advantages. However, evidence across many kinds of stimuli and paradigms presents a mixed picture, alternatively showing cognitive costs or cognitive advantages associated

  15. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

    Science.gov (United States)

    Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  16. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis.

    Science.gov (United States)

    Oguntunde, Philip G; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P  1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  17. Feature Analysis of the “Customer Relationship Management” Systems for Higher Education Institutions

    Directory of Open Access Journals (Sweden)

    Hugo de Juan-Jordán

    2018-03-01

    This article summarizes the features a CRM system should possess to make educational institutions thrive in the current digital era, and points out the future trends on this topic. The final objective is neither an analysis of the applications available on the market nor a selection guide, but a recommendation for the end users to utilize a CRM system when considering achieving some of the business needs implied in the features available on these CRMs.

  18. Identification of Migratory Insects from their Physical Features using a Decision-Tree Support Vector Machine and its Application to Radar Entomology.

    Science.gov (United States)

    Hu, Cheng; Kong, Shaoyang; Wang, Rui; Long, Teng; Fu, Xiaowei

    2018-04-03

    Migration is a key process in the population dynamics of numerous insect species, including many that are pests or vectors of disease. Identification of insect migrants is critically important to studies of insect migration. Radar is an effective means of monitoring nocturnal insect migrants. However, species identification of migrating insects is often unachievable with current radar technology. Special-purpose entomological radar can measure radar cross-sections (RCSs) from which the insect mass, wingbeat frequency and body length-to-width ratio (a measure of morphological form) can be estimated. These features may be valuable for species identification. This paper explores the identification of insect migrants based on the mass, wingbeat frequency and length-to-width ratio, and body length is also introduced to assess the benefit of adding another variable. A total of 23 species of migratory insects captured by a searchlight trap are used to develop a classification model based on decision-tree support vector machine method. The results reveal that the identification accuracy exceeds 80% for all species if the mass, wingbeat frequency and length-to-width ratio are utilized, and the addition of body length is shown to further increase accuracy. It is also shown that improving the precision of the measurements leads to increased identification accuracy.

  19. Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design

    International Nuclear Information System (INIS)

    Zhao, Hongxia; Zhang, Xiuhui; Ji, Lin; Hu, Haixiang; Li, Qianshu

    2014-01-01

    Highlights: • Nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. • Descriptors for QSAR model were selected by principal component analysis. • Binding energy was taken as one of the descriptors for QSAR model. • Acidic solution and protonation of the inhibitor were considered. - Abstract: The inhibition performance of nineteen amino acids was studied by theoretical methods. The affection of acidic solution and protonation of inhibitor were considered in molecular dynamics simulation and the results indicated that the protonated amino-group was not adsorbed on Fe (1 1 0) surface. Additionally, a nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. The correlation coefficient was 0.97 and the root mean square error, the differences between predicted and experimental inhibition efficiencies (%), was 1.48. Furthermore, five new amino acids were theoretically designed and their inhibition efficiencies were predicted by the built QSAR model

  20. The effect of specifi c relationship between material and coating on tribological and protective features of the product

    Directory of Open Access Journals (Sweden)

    B. Sovilj

    2012-01-01

    Full Text Available Today, parts and tools are increasingly made of composite materials. Realization of specifi c connection between basic material and coating is very important. The quality of coating on products, in terms of wear and resistance to destruction, has a large impact on productivity and reliability of production processes, in particular their life. In this paper, based on experimental investigations, the effect of specific relationship between the base material and coating on tribological and protective features of the product is analyzed.

  1. The toll of stalking: the relationship between features of stalking and psychopathology of victims.

    NARCIS (Netherlands)

    Blaauw, E.; Winkel, F.W.; Arensman, E.; Sheridan, L.; Freeve, A.

    2002-01-01

    Information on the psychological consequences of stalking on victims is scarce. The present study aimed to investigate whether stalking victims have a heightened prevalence of psychopathology and the extent to which symptom levels are associated with stalking features. Stalking victims (N = 241)

  2. Psychopathic Features Moderate the Relationship between Harsh and Inconsistent Parental Discipline and Adolescent Antisocial Behavior

    Science.gov (United States)

    Edens, John F.; Skopp, Nancy A.; Cahill, Melissa A.

    2008-01-01

    Although the quality of parenting predicts externalizing behavior problems generally, ineffective parenting may be less relevant to explaining the behavior problems of children high in callous-unemotional traits. This study tested the potential moderating role of psychopathic features among juvenile offenders (n = 76). Youths were administered the…

  3. Risk estimation using probability machines

    Science.gov (United States)

    2014-01-01

    Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

  4. [Re-signification of the human in the context of the "ciborgzation": a look at the human being-machine relationship in intensive care].

    Science.gov (United States)

    Vargas, Mara Ambrosina de O; Meyer, Dagmar Estermann

    2005-06-01

    This study discusses the human being-machine relationship in the process called "cyborgzation" of the nurse who works in intensive care, based on post-structuralist Cultural Studies and highlighting Haraway's concept of cyborg. In it, manuals used by nurses in Intensive Care Units have been examined as cultural texts. This cultural analysis tries to decode the various senses of "human" and "machine", with the aim of recognizing processes that turn nurses into cyborgs. The argument is that intensive care nurses fall into a process of "technology embodiment" that turns the body-professional into a hybrid that makes possible to disqualify, at the same time, notions such as machine and body "proper", since it is the hybridization between one and the other that counts there. Like cyborgs, intensive care nurses learn to "be with" the machine, and this connection limits the specificity of their actions. It is suggested that processes of "cyborgzation" such as this are useful for questioning - and to deal with in different ways - the senses of "human" and "humanity" that support a major part of knowledge/action in health.

  5. Structural Features of Sibling Dyads and Attitudes toward Sibling Relationships in Young Adulthood

    Science.gov (United States)

    Riggio, Heidi R.

    2006-01-01

    This study examined sibling-dyad structural variables (sex composition, age difference, current coresidence, position adjacency, family size, respondent and/or sibling ordinal position) and attitudes toward adult sibling relationships. A sample of 1,053 young adults (M age = 22.1 years) described one sibling using the Lifespan Sibling Relationship…

  6. [Application of support vector machine-recursive feature elimination algorithm in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases].

    Science.gov (United States)

    Zhang, Haipeng; Fu, Tong; Zhang, Zhiru; Fan, Zhimin; Zheng, Chao; Han, Bing

    2014-08-01

    To explore the value of application of support vector machine-recursive feature elimination (SVM-RFE) method in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases. Fresh breast tissue samples of 168 patients (all female; ages 22-75) were obtained by routine surgical resection from May 2011 to May 2012 at the Department of Breast Surgery, the First Hospital of Jilin University. Among them, there were 51 normal tissues, 66 benign and 51 malignant breast lesions. All the specimens were assessed by Raman spectroscopy, and the SVM-RFE algorithm was used to process the data and build the mathematical model. Mahalanobis distance and spectral residuals were used as discriminating criteria to evaluate this data-processing method. 1 800 Raman spectra were acquired from the fresh samples of human breast tissues. Based on spectral profiles, the presence of 1 078, 1 267, 1 301, 1 437, 1 653, and 1 743 cm(-1) peaks were identified in the normal tissues; and 1 281, 1 341, 1 381, 1 417, 1 465, 1 530, and 1 637 cm(-1) peaks were found in the benign and malignant tissues. The main characteristic peaks differentiating benign and malignant lesions were 1 340 and 1 480 cm(-1). The accuracy of SVM-RFE in discriminating normal and malignant lesions was 100.0%, while that in the assessment of benign lesions was 93.0%. There are distinct differences among the Raman spectra of normal, benign and malignant breast tissues, and SVM-RFE method can be used to build differentiation model of breast lesions.

  7. Relationship of renal insufficiency and clinical features or comorbidities with clinical outcome in patients hospitalised for acute heart failure syndromes.

    Science.gov (United States)

    Kajimoto, Katsuya; Sato, Naoki; Takano, Teruo

    2017-12-01

    Renal insufficiency is a well-known predictor of adverse events in patients with acute heart failure syndromes (AHFS). However, it remains unclear whether there are subgroups of AHFS patients in whom renal insufficiency is related to a higher risk of adverse events because of the heterogeneity of this patient population. Therefore, we investigated the relationship between renal insufficiency, clinical features or comorbidities, and the risk of adverse events in patients with AHFS. Of 4842 patients enrolled in the Acute Decompensated Heart Failure Syndromes (ATTEND) registry, 4628 patients (95.6%) were evaluated in the present study in order to assess the relationship of renal insufficiency and clinical features or comorbidities with all-cause mortality after admission. Renal insufficiency was defined as an estimated creatinine clearance of ⩽40 mL/min (calculated by the Cockcroft-Gault formula) at admission. The median follow-up period after admission was 524 (391-789) days. The all-cause mortality rate after admission was significantly higher in patients with renal insufficiency (36.7%) than in patients without renal insufficiency (14.4%). Stratified analysis was performed in order to explore the heterogeneity of the influence of renal insufficiency on all-cause mortality. This analysis revealed that an ischaemic aetiology and a history of diabetes, atrial fibrillation, serum sodium, and anaemia at admission had significant influences on the relationship between renal insufficiency and all-cause mortality. The present study demonstrated that the relationship between renal insufficiency and all-cause mortality of AHFS patients varies markedly with clinical features or comorbidities and the mode of presentation due to the heterogeneity of this patient population.

  8. Theodore and the fantasy of the self, or afective relationships with machines that look like humans, and humans that also look like humans

    Directory of Open Access Journals (Sweden)

    Baltasar Fernández-Ramírez

    2014-04-01

    Full Text Available Our models for affective relationships can be found in cultural products as novels, poetry and films. How does a relation begin, continue, and end? Which are the usual characters, or the plot points which structure its development? These are questions that make part of narratives produced and reproduced in our cultural background. From a narrative psychology perspective, affective relationships fit a dynamic in which different characters propose lines of development (intrigues, which in turn prescribe how our participation should be, in which we result embodied, creating the illusion of a “real” relation that it’s no more than a virtual fantasy, being these last terms interchangeable. The fantasy of the self is a strategic narration that we impose to the other and to ourselves for the relation to be and take sense. I use the film Her (Spike Jonze, 2013 to reflect on these topics, using the doubts about the possibility/virtuality of the human-machine relationship for extending the metaphoric of the fantasy of the self in the affective relationship. In the tradition of the romantic and science-fiction narrative, the machine is that we try to impose on our demiurgic fantasy, imperfect perfection of human beings, and that finally transcends us, raising doubts about the reality/virtuality of our own presence in the narrations in which we lived

  9. The relationship between electronic gaming machine accessibility and police-recorded domestic violence: A spatio-temporal analysis of 654 postcodes in Victoria, Australia, 2005-2014.

    Science.gov (United States)

    Markham, Francis; Doran, Bruce; Young, Martin

    2016-08-01

    An emerging body of research has documented an association between problem gambling and domestic violence in a range of study populations and locations. Yet little research has analysed this relationship at ecological scales. This study investigates the proposition that gambling accessibility and the incidence of domestic violence might be linked. The association between police-recorded domestic violence and electronic gaming machine accessibility is described at the postcode level. Police recorded family incidents per 10,000 and domestic-violence related physical assault offenses per 10,000 were used as outcome variables. Electronic gaming machine accessibility was measured as electronic gaming machines per 10,000 and gambling venues per 100,000. Bayesian spatio-temporal mixed-effects models were used to estimate the associations between gambling accessibility and domestic violence, using annual postcode-level data in Victoria, Australia between 2005 and 2014, adjusting for a range of covariates. Significant associations of policy-relevant magnitudes were found between all domestic violence and EGM accessibility variables. Postcodes with no electronic gaming machines were associated with 20% (95% credibility interval [C.I.]: 15%, 24%) fewer family incidents per 10,000 and 30% (95% C.I.: 24%, 35%) fewer domestic-violence assaults per 10,000, when compared with postcodes with 75 electronic gaming machine per 10,000. The causal relations underlying these associations are unclear. Quasi-experimental research is required to determine if reducing gambling accessibility is likely to reduce the incidence of domestic violence. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  11. The relationship between psychosocial features of emerging adulthood and substance use change motivation in youth.

    Science.gov (United States)

    Goodman, Ilana; Henderson, Joanna; Peterson-Badali, Michele; Goldstein, Abby L

    2015-05-01

    Despite the peak prevalence of substance use and comorbid mental health problems during emerging adulthood little research has focused on understanding behavior change processes during this transitional period. This study extended Arnett's (2004) theory of the psychosocial features of emerging adulthood to explore how they may relate to treatment motivation (e.g., readiness to comply with treatment) and motivation to change (e.g., problem recognition and taking steps towards change). One hundred sixty-four youth presenting to outpatient substance abuse treatment completed questionnaires investigating problematic substance use, mental health, psychosocial features of emerging adulthood and motivation. Results of hierarchical regression analyses indicated that youth who perceived themselves as having greater responsibility towards others were more intrinsically motivated, recognized their substance use as problematic and were taking steps towards change. None of the other dimensions of emerging adulthood accounted for significant variance beyond relevant controls. Limitations, directions for future research and treatment implications are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Relationship between the Expression of Matrix Metalloproteinase and Clinicopathologic Features in Oral Squamous Cell Carcinoma

    Directory of Open Access Journals (Sweden)

    Amir Hossein Jafarian

    2015-05-01

    Full Text Available Introduction: Squamous cell carcinoma of the oral cavity is one of the most important and common types of head and neck malignancy, with an estimated rate of 4% among all human malignancies. The aim of this study was to determine the association between expression of matrix metalloproteinase 2 and 9 and the clinicopathological features of oral squamous cell carcinoma (OSCC.   Materials and Methods: One hundred existing samples of formalin-fixed paraffin embedded specimens of OSCC were evaluated by immunohistochemistry staining for matrix metalloproteinase 2 and 9 antibodies. Samples were divided into four groups: negative, 50%. Patient records were assessed for demographic characteristics such as age and gender, smoking and family history of OSCC as well as tumor features including location, differentiation, stage and lymph node involvement.   Results: In this study, 58 patients (58% were male and 42 (42% female. The mean age of patients was 60.38±14.07 years. The average number of lymph nodes involved was 8.9±3.8. Tumoral grade, tumoral stage, lymphatic metastasis and history of smoking were significantly related to MMP2 and MMP9 expression.   Conclusion:  Our study demonstrated that MMP2 and MMP9 expression are important in the development of OSCC.

  13. Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer

    International Nuclear Information System (INIS)

    Montemurro, Filippo; Redana, Stefania; Aglietta, Massimo; Martincich, Laura; Bertotto, Ilaria; Cellini, Lisa; Sarotto, Ivana; Ponzone, Riccardo; Sismondi, Piero; Regge, Daniele

    2007-01-01

    We studied whether dynamic contrast-enhanced MRI (DCE-MRI) could identify histopathological characteristics of breast cancer. Seventy-five patients with breast cancer underwent DCE-MRI followed by core biopsy. DCE-MRI findings were evaluated following the scoring system published by Fischer in 1999. In this scoring system, five DCE-MRI features, three morphological (shape, margins, enhancement kinetic) and two functional (initial peak of signal intensity (SI) increase and behavior of signal intensity curve), are defined by 14 parameters. Each parameter is assigned points ranging from 0 to 1 or 0 to 2, with higher points for those that are more likely to be associated with malignancy. The sum of all the points defines the degree of suspicion of malignancy, with a score 0 representing the lowest and 8 the highest degree of suspicion. Associations between DCE-MRI features and tumor histopathological characteristics assessed on core biopsies (histological type, grading, estrogen and progesterone receptor status, Ki67 and HER2 status) were studied by contingency tables and logistic regression analysis. We found a significant inverse association between the Fischer's score and HER2-overexpression (odds ratio-OR 0.608, p = 0.02). Based on our results, we suggest that lesions with intermediate-low suspicious DCE-MRI parameters may represent a subset of tumor with poor histopathological characteristics. (orig.)

  14. Relationship between external and histologic features of progressive stages of caries in the occlusal fossa

    DEFF Research Database (Denmark)

    Ekstrand, K R; Kuzmina, I; Bjørndal, L

    1995-01-01

    highly correlated (rs = 0.90). Dentinal changes were also highly correlated with enamel changes (rs = 0.85). The histologic classifications in conjunction with the macroscopical observations made it possible to demonstrate a clear relationship between the external degree of caries progression......) and 8 (M) classification criteria, ranging from 'sound' to 'cavitation with dentine involvement'. Six radiographic scores were used in the classification. Sections 250 microns in thickness were cut in buccolingual direction through the central fossa, and the fossa section with the most extensive...

  15. ESR signal features of 60Co γ-ray irradiated bone tissue and its dose response relationship

    International Nuclear Information System (INIS)

    Wu Ke; Sun Zunpu; Shi Yuanming

    1993-01-01

    Electron spin resonance (ESR) technique was used to study the radiation-induced ESR signal features of different paramagnetic species of 60 Co γ-ray irradiated bone tissue. The results showed that the intensity of an ESR signal at that the intensity of an ESR signal at g 2.0022 of human bones exposed to a dose range of 0-50 Gy had linear dose response relationships. The lower limit of detectable dose was about 2 Gy and the detecting error was about 10%. The signal was stable at room temperature during 60 days, and the effect of radiation dose rate of 0.5-8.0 Gy/min could be neglected. This signal was insensitive to microwave power and temperature, which was suitable for rapid and direct detection with ESR technique. These features suggest that human bones could be used for radiation accident dose evaluation by ESR

  16. An asymptotical machine

    Science.gov (United States)

    Cristallini, Achille

    2016-07-01

    A new and intriguing machine may be obtained replacing the moving pulley of a gun tackle with a fixed point in the rope. Its most important feature is the asymptotic efficiency. Here we obtain a satisfactory description of this machine by means of vector calculus and elementary trigonometry. The mathematical model has been compared with experimental data and briefly discussed.

  17. Porencephaly in dogs and cats: relationships between magnetic resonance imaging (MRI) features and hippocampal atrophy.

    Science.gov (United States)

    Hori, Ai; Hanazono, Kiwamu; Miyoshi, Kenjirou; Nakade, Tetsuya

    2015-07-01

    Porencephaly is the congenital cerebral defect and a rare malformation and described few MRI reports in veterinary medicine. MRI features of porencephaly are recognized the coexistence with the unilateral/bilateral hippocampal atrophy, caused by the seizure symptoms in human medicine. We studied 2 dogs and 1 cat with congenital porencephaly to characterize the clinical signs and MRI, and to discuss the associated MRI with hippocampal atrophy. The main clinical sign was the seizure symptoms, and all had hippocampal atrophy at the lesion side or the larger defect side. There is association between hippocampal atrophy or the cyst volume and the severe of clinical signs, and it is suggested that porencephaly coexists with hippocampal atrophy as well as humans in this study.

  18. Coastal zone color scanner pigment concentrations in the southern ocean and relationships to geophysical surface features

    Science.gov (United States)

    Comiso, J. C.; Mcclain, C. R.; Sullivan, C. W.; Ryan, J. P.; Leonard, C. L.

    1993-01-01

    Climatological data on the distribution of surface pigment fields in the entire southern ocean over a seasonal cycle are examined. The occurrence of intense phytoplankton blooms during austral summer months and during other seasons in different regions is identified and analyzed. The highest pigment concentrations are observed at high latitudes and over regions with water depths usually less than 600 m. Basin-scale pigment distribution shows a slightly asymmetric pattern of enhanced pigment concentrations about Antarctica, with enhanced concentrations extending to lower latitudes in the Atlantic and Indian sectors than in the Pacific sector. A general increase in pigment concentrations is evident from the low latitudes toward the Antarctic circumpolar region. Spatial relationships between pigment and archived geophysical data reveal significant correlation between pigment distributions and both bathymetry and wind stress, while general hemispheric scale patterns of pigment distributions are most coherent with the geostrophic flow of the Antarctic Circumpolar Current.

  19. Features of an effective operative dentistry learning environment: students' perceptions and relationship with performance.

    Science.gov (United States)

    Suksudaj, N; Lekkas, D; Kaidonis, J; Townsend, G C; Winning, T A

    2015-02-01

    Students' perceptions of their learning environment influence the quality of outcomes they achieve. Learning dental operative techniques in a simulated clinic environment is characterised by reciprocal interactions between skills training, staff- and student-related factors. However, few studies have examined how students perceive their operative learning environments and whether there is a relationship between their perceptions and subsequent performance. Therefore, this study aimed to clarify which learning activities and interactions students perceived as supporting their operative skills learning and to examine relationships with their outcomes. Longitudinal data about examples of operative laboratory sessions that were perceived as effective or ineffective for learning were collected twice a semester, using written critical incidents and interviews. Emergent themes from these data were identified using thematic analysis. Associations between perceptions of learning effectiveness and performance were analysed using chi-square tests. Students indicated that an effective learning environment involved interactions with tutors and peers. This included tutors arranging group discussions to clarify processes and outcomes, providing demonstrations and constructive feedback. Feedback focused on mistakes, and not improvement, was reported as being ineffective for learning. However, there was no significant association between students' perceptions of the effectiveness of their learning experiences and subsequent performance. It was clear that learning in an operative technique setting involved various factors related not only to social interactions and observational aspects of learning but also to cognitive, motivational and affective processes. Consistent with studies that have demonstrated complex interactions between students, their learning environment and outcomes, other factors need investigation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. [A new machinability test machine and the machinability of composite resins for core built-up].

    Science.gov (United States)

    Iwasaki, N

    2001-06-01

    A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

  1. The relationship between uranium distribution and some major crustal features in Canada

    International Nuclear Information System (INIS)

    Darnley, A.G.

    1982-01-01

    The availability of reconnaissance scale geochemical maps for large areas of Canada enables spatial associations between major crustal structures and surface uranium content to be identified. Maps of the distribution of uranium for an area greater than 2 million km 2 compiled from airborne gamma-ray spectrometry data are supplemented by maps for uranium, based on stream and lake sediment and some bore hole sampling. These are examined in relation to gravity, aeromagnetic and geological maps. The radioelement distribution can be related in detail to exposed bedrock and surface geology, but in addition there is evidence of the control of uranium distribution by major structural features which are marked by granitoids containing elevated levels of radioelements; several of these granitoids are associated with large negative Bouguer gravity anomalies. The distribution of such granitoids appears to be related to 'megashears', as in the case of the South Mountain batholith in Nova Scotia, or zones of tension. A belt of uranium enrichment, the Athabasca axis which is characterized by uraniferous granitoids with negative Bouguer gravity anomalies and associated tension faulting extends 2500 km northeastward from Edmonton, Alberta to the Melville Peninsula. This structure passes under the Athabasca basin which contains many large uranium deposits. (author)

  2. MRI features of ovarian fibromas: emphasis on their relationship to the ovary

    International Nuclear Information System (INIS)

    Oh, S.N.; Rha, S.E.; Byun, J.Y.; Lee, Y.J.; Jung, S.E.; Jung, C.K.; Kim, M.R.

    2008-01-01

    Aim: To evaluate the magnetic resonance (MR) imaging features of ovarian fibromas, emphasizing the presence and shape of the ovary on the same side of the fibroma. Materials and methods: MR images from 23 patients with 24 histologically proven ovarian fibromas were reviewed by two radiologists. Eleven were pre-menopausal and 12 were postmenopausal. The presence and shape of the ovarian tissue on the same side of the fibroma were evaluated on T2-weighted MR images. Results: In 11 (46%) of the 24 ovarian fibromas, the ipsilateral ovary was detected on T2-weighted images. The ovary was crescent-shaped along the periphery of the fibroma in six (55%) of 11 fibromas and had a normal, oval shape in five (45%). Of these five tumours, the ovary was connected to the fibromas by a pedicle-like structure in three and was closely attached to the periphery of the fibromas in two. The ipsilateral ovary was detected in 10 (83%) of 12 fibromas in pre-menopausal patients, and in one (8%) of 12 fibromas in postmenopausal patients. There was a statistically significant difference (p = 0.001) in the presence of detectable ipsilateral ovary between pre-menopausal and postmenopausal women. Conclusions: Detection of the remaining ovary on the same side as the fibroma is not unusual on MRI, especially in pre-menopausal women, and the shape of the ovary may be normal in cases of fibromas with exophytic growth from the periphery of the ovary

  3. The complete chloroplast genome sequence of Aster spathulifolius (Asteraceae); genomic features and relationship with Asteraceae.

    Science.gov (United States)

    Choi, Kyoung Su; Park, SeonJoo

    2015-11-10

    Aster spathulifolius, a member of the Asteraceae family, is distributed along the coast of Japan and Korea. This plant is used for medicinal and ornamental purposes. The complete chloroplast (cp) genome of A. sphathulifolius consists of 149,473 bp that include a pair of inverted repeats of 24,751 bp separated by a large single copy region of 81,998 bp and a small single copy region of 17,973 bp. The chloroplast genome contains 78 coding genes, four rRNA genes and 29 tRNA genes. When compared to other cpDNA sequences of Asteraceae, A. spathulifolius showed the closest relationship with Jacobaea vulgaris, and its atpB gene was found to be a pseudogene, unlike J. vulgaris. Furthermore, evaluation of the gene compositions of J. vulgaris, Helianthus annuus, Guizotia abyssinica and A. spathulifolius revealed that 13.6-kb showed inversion from ndhF to rps15, unlike Lactuca of Asteraceae. Comparison of the synonymous (Ks) and nonsynonymous (Ka) substitution rates with J. vulgaris revealed that synonymous genes related to a small subunit of the ribosome showed the highest value (0.1558), while nonsynonymous rates of genes related to ATP synthase genes were highest (0.0118). These findings revealed that substitution has occurred at similar rates in most genes, and the substitution rates suggested that most genes is a purified selection. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Uncovering the features of negotiation in developing the patient-nurse relationship.

    Science.gov (United States)

    Stoddart, Kathleen; Bugge, Carol

    2012-02-01

    This article describes a study that set out to explore the interaction between patients and nurses in community practice settings, in order to understand the social meanings and understandings brought to the interaction and at play within it. The study used a grounded theory methodology with traditional procedures. Driven by constant comparative analysis, data were collected by non-participant observation and informal and semi-structured interviews in four community health centres. Eighteen patients and 18 registered practice nurses participated. Negotiation was found to be a fundamental process in patient- nurse interaction. Navigation, socio-cultural characteristics and power and control were found to be key properties of negotiation. The negotiation processes for developing understanding required patients and nurses to draw upon social meanings and understandings generated from within and beyond their current interaction. Social meanings and understandings created within and beyond the health-care setting influence negotiation. The developmental nature of negotiation in interaction is an important dimension of the patient- nurse relationship in community practice.

  5. Work Productivity in Rheumatoid Arthritis: Relationship with Clinical and Radiological Features

    Directory of Open Access Journals (Sweden)

    Rafael Chaparro del Moral

    2012-01-01

    Full Text Available Objective. To assess the relationship between work productivity with disease activity, functional capacity, life quality and radiological damage in patients with rheumatoid arthritis (RA. Methods. The study included consecutive employed patients with RA (ACR'87, aged over 18. Demographic, disease-related, and work-related variables were determined. The reduction of work productivity was assessed by WPAI-RA. Results. 90 patients were evaluated, 71% women. Age average is 50 years old, DAS28 4, and RAQoL 12. Median SENS is 18 and HAQ-A 0.87. Mean absenteeism was of 14%, presenting an average of 6.30 work hours wasted weekly. The reduction in performance at work or assistance was of 38.4% and the waste of productivity was of 45%. Assistance correlated with DAS28 (r = 0.446; P 18 showed lower work productivity than those with SENS < 18 (50 versus 34; P=0.04. In multiple regression analysis, variables associated with reduction of total work productivity were HAQ-A and RAQoL. Conclusion. RA patients with higher disease severity showed higher work productivity compromise.

  6. Work productivity in rheumatoid arthritis: relationship with clinical and radiological features.

    Science.gov (United States)

    Chaparro Del Moral, Rafael; Rillo, Oscar Luis; Casalla, Luciana; Morón, Carolina Bru; Citera, Gustavo; Cocco, José A Maldonado; Correa, María de Los Ángeles; Buschiazzo, Emilio; Tamborenea, Natalia; Mysler, Eduardo; Tate, Guillermo; Baños, Andrea; Herscovich, Natalia

    2012-01-01

    Objective. To assess the relationship between work productivity with disease activity, functional capacity, life quality and radiological damage in patients with rheumatoid arthritis (RA). Methods. The study included consecutive employed patients with RA (ACR'87), aged over 18. Demographic, disease-related, and work-related variables were determined. The reduction of work productivity was assessed by WPAI-RA. Results. 90 patients were evaluated, 71% women. Age average is 50 years old, DAS28 4, and RAQoL 12. Median SENS is 18 and HAQ-A 0.87. Mean absenteeism was of 14%, presenting an average of 6.30 work hours wasted weekly. The reduction in performance at work or assistance was of 38.4% and the waste of productivity was of 45%. Assistance correlated with DAS28 (r = 0.446; P productivity was noticed in higher levels of activity and functional disability. Patients with SENS > 18 showed lower work productivity than those with SENS work productivity were HAQ-A and RAQoL. Conclusion. RA patients with higher disease severity showed higher work productivity compromise.

  7. The relationship between anaphor features and antecedent retrieval: Comparing Mandarin ziji and ta-ziji

    Directory of Open Access Journals (Sweden)

    Brian eDillon

    2016-01-01

    Full Text Available In the present study we report two self-paced reading experiments that investigate antecedent retrieval processes in sentence comprehension by contrasting the real-time processing behavior of two different reflexive anaphors in Mandarin Chinese. Previous work has suggested that comprehenders initially evaluate the fit between the morphologically simple long-distance reflexive 'ziji' and the closest available subject position, only subsequently considering more structurally distant antecedents (Dillon et al., 2014; Gao et al., 2005; Liu, 2009; Li & Zhou, 2010; cf. Chen et al., 2012. In this paper, we investigate whether this locality bias effect obtains for other reflexive anaphors in Mandarin Chinese, or if it is associated specifically with the morphologically simple reflexive ziji. We do this by comparing the processing of ziji to the processing of the morphologically complex reflexive ta-ziji (lit. s/he-self. In Experiment 1, we investigate the processing of ziji, and replicate the finding of a strong locality bias effect for ziji in self-paced reading measures. In Experiment 2, we investigate the processing of the morphologically complex reflexive ta-ziji in the same structural configurations as Experiment 1. A comparison of our experiments reveals that ta-ziji shows a significantly weaker locality bias effect than ziji does. We propose that this results from the difference in the number of morphological and semantic features on the anaphor ta-ziji relative to ziji. Specifically, we propose that the additional retrieval cues associated with ta-ziji reduce interference from irrelevant representations in memory, allowing it to more reliably access an antecedent regardless its linear or structural distance. This reduced interference in turn leads to a diminished locality bias effect for the morphologically complex anaphor ta-ziji.

  8. Electrical machines & drives

    CERN Document Server

    Hammond, P

    1985-01-01

    Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

  9. Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

    OpenAIRE

    Shan, Min

    2017-01-01

    With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

  10. Relationship between imaging and pathological features and clinical factors in surgical cases of temporal lobe epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Uesugi, Hideji; Matsuda, Hiroshi; Onuma, Teiichi [National Hospital for Mental, Nervous and Muscular Disorders, National Center of Neurology and Psychiatry, Kodaira, Tokyo (Japan); Shimizu, Hiroyuki; Arai, Nobutaka; Nakayama, Hiroshi; Maehara, Taketoshi; Yanashita, Akira

    1998-03-01

    The relationships between imaging, pathology and presumed causes in surgical cases of temporal lobe epilepsy (TLE) was studied. The subject was 62 patients. MRI, PET and SPECT were performed. Hematoxylin and eosin was used for pathological judgement. On MRI, mesial temporal sclerosis (MTS) was detected in 48 of 52 patients (92%); 32 (62%) had high-signal intensity on T2-weighted images; 31 (60%) had atrophy {l_brace}23 (44%) had high-signal intensity on T2+atrophy{r_brace}; 5 (10%) had calcified lesions; and 2 (4%) had cystic lesions. On PET and SPECT, abnormal cerebral blood flow was noted in 33 of 36 (92%). On pathological findings (61 cases), Ammon`s horn sclerosis (AHS), tumors, gliosis in lateral temporal and meningeal inflammatory finding were detected in 42 (69%), 10 (23%) and 8 (13%) cases, respectively, whereas 2 showed no abnormalities. The 2 patients with normal pathology showed both high-signal intensity and atrophy on MRI. The presumed causes of TLE were encephalitis/meningitis and/or suspected of these diseases in 15 patients (24%), injuries at birth in 5 (8%), and none in 42 (68%). The presumed causes in the 43 patients with AHS were encephalitis/meningitis in 11, injuries at birth in 3, and none in 29. Of the 15 patients in whom encephalitis/meningitis was estimated as the causes of TLE, only 6 (40%) had pathological evidence of meningeal inflammatory finding. Of the 42 patients in whom cause could not be determined, 2 had pathological evidence of meningeal inflammatory finding. (K.H.)

  11. Features of Parent-Child Relationship of Mothers with Teenage Children in the Conditions of Late Motherhood

    Directory of Open Access Journals (Sweden)

    Zakharova E.I.,

    2015-02-01

    Full Text Available The author's attention is attracted by one of the features of modern Russian family: the tendency to increase the frequency of childbirth by women of older reproductive age. The article presents the results of a comparative analysis of the mothers’ parent position, who had children at different periods of adulthood (middle, late. The aim of the study was to investigate the features of the parent-child relationship of mothers with teenage children in the conditions of late motherhood. Mothers of adolescents who participated in the study were divided into two groups: "young" mothers who gave birth to the first child before the age of 30 years, and "late" mothers who gave birth to their first child after being 30 years old. It turned out that the strategies of education and interaction between the "young" and "late" mothers, reflecting the value orientation of personality, are significantly different. Focusing on the emotional closeness with the child and creativity, education strategy of "late" mothers has a high emotional involvement, soft and inconsistent parenting. The features of maternal parenting strategies are adequately reflected by the teenagers who follow their mothers in priority of the values of family and work, or material well-being and the pursuit of hedonistic values.

  12. The Relationship Between Sociodemographic Characteristics and Clinical Features in Burning Mouth Syndrome.

    Science.gov (United States)

    Adamo, Daniela; Celentano, Antonio; Ruoppo, Elvira; Cucciniello, Claudia; Pecoraro, Giuseppe; Aria, Massimo; Mignogna, Michele D

    2015-11-01

    To compare sociodemographic and clinical characteristics in patients with burning mouth syndrome (BMS) and their relationship with pain. Cross-sectional clinical study. University-Hospital. 75 BMS patients were enrolled. The study was conducted between September 2011 and March 2012 at the "Federico II" University of Naples. Demographic characteristics and clinical information including age, sex, educational level, marital status, job status, age at disease onset, oral symptoms, and triggers were collected via questionnaire interviews. To assess pain intensity the visual analogue scale (VAS) was administered. Descriptive statistics were collected, and Pearson Chi-square tests, Kruskal-Wallis nonparametric tests and the Spearman bivariate correlation were performed. The mean age was 61.17 (±11.75, female/male ratio = 3:1). The mean age at disease onset was 56.75 (±12.01). A low educational level (8.57 ± 4.95) and 80% of unemployment were found. Job status and age at disease onset correlated with the VAS scale (P = 0.019 and P = 0.015, respectively). Tongue morphology changes, taste disturbances, and intraoral foreign body sensation have a significant dependence on gender (P = 0.049, 0.001, and 0.045, respectively); intraoral foreign body sensation has a significant dependence on marital status (P = 0.033); taste disturbances have a significant dependence on job status. (P = 0.049); xerostomia has a significant dependence on age (P = 0.039); and tongue color changes and a bitter taste have a significant dependence on educational level (P = 0.040 and 0.022, respectively). Marital status and educational level have a significant dependence on the triggers (P = 0.036 and 0.049, respectively). The prevalence of BMS is higher in women, and in married, unemployed, and less highly educated patients. Burning is the most frequent symptom while stressful life events are the most frequent trigger reported. Wiley Periodicals, Inc.

  13. Dimorphic changes of some features of loving relationships during long-term use of antidepressants in depressed outpatients.

    Science.gov (United States)

    Marazziti, Donatella; Akiskal, Hagop S; Udo, Mieko; Picchetti, Michela; Baroni, Stefano; Massimetti, Gabriele; Albanese, Francesco; Dell'Osso, Liliana

    2014-09-01

    The present study aimed at investigating the possible changes of some features of loving relationships during long-term treatment of depression with both selective serotonin reuptake inhibitors (SSRIs) and tricyclics (TCAs), by means of a specifically designed test, the so-called "Sex, Attachment, Love" (SALT) questionnaire. The sample was composed by 192 outpatients (123 women and 69 men, mean age±SD: 41.2±10.2 years), suffering from mild or moderate depression, according to DSM-IV-TR criteria, that were selected if they were treated with one antidepressant only for at least six months and were involved in a loving relationship. The results showed that SSRIs had a significant impact on the feelings of love and attachment towards the partner especially in men, while women taking TCAs complained of more sexual side effects than men. These data were supported also by the detection of a significant interaction between drug and sex on the "Love" and "Sex" domains. The present findings, while demonstrating a dimorphic effect of antidepressants on some component of loving relationships, need to be deepened in future studies. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Relationship Between Quantitative Adverse Plaque Features From Coronary Computed Tomography Angiography and Downstream Impaired Myocardial Flow Reserve by 13N-Ammonia Positron Emission Tomography: A Pilot Study.

    Science.gov (United States)

    Dey, Damini; Diaz Zamudio, Mariana; Schuhbaeck, Annika; Juarez Orozco, Luis Eduardo; Otaki, Yuka; Gransar, Heidi; Li, Debiao; Germano, Guido; Achenbach, Stephan; Berman, Daniel S; Meave, Aloha; Alexanderson, Erick; Slomka, Piotr J

    2015-10-01

    We investigated the relationship of quantitative plaque features from coronary computed tomography (CT) angiography and coronary vascular dysfunction by impaired myocardial flow reserve (MFR) by (13)N-Ammonia positron emission tomography (PET). Fifty-one patients (32 men, 62.4±9.5 years) underwent combined rest-stress (13)N-ammonia PET and CT angiography scans by hybrid PET/CT. Regional MFR was measured from PET. From CT angiography, 153 arteries were evaluated by semiautomated software, computing arterial noncalcified plaque (NCP), low-density NCP (NCP<30 HU), calcified and total plaque volumes, and corresponding plaque burden (plaque volumex100%/vessel volume), stenosis, remodeling index, contrast density difference (maximum difference in luminal attenuation per unit area in the lesion), and plaque length. Quantitative stenosis, plaque burden, and myocardial mass were combined by boosted ensemble machine-learning algorithm into a composite risk score to predict impaired MFR (MFR≤2.0) by PET in each artery. Nineteen patients had impaired regional MFR in at least 1 territory (41/153 vessels). Patients with impaired regional MFR had higher arterial NCP (32.4% versus 17.2%), low-density NCP (7% versus 4%), and total plaque burden (37% versus 19.3%, P<0.02). In multivariable analysis with 10-fold cross-validation, NCP burden was the most significant predictor of impaired MFR (odds ratio, 1.35; P=0.021 for all). For prediction of impaired MFR with 10-fold cross-validation, receiver operating characteristics area under the curve for the composite score was 0.83 (95% confidence interval, 0.79-0.91) greater than for quantitative stenosis (0.66, 95% confidence interval, 0.57-0.76, P=0.005). Compared with stenosis, arterial NCP burden and a composite score combining quantitative stenosis and plaque burden from CT angiography significantly improves identification of downstream regional vascular dysfunction. © 2015 American Heart Association, Inc.

  15. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  16. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  17. Development of Correlations for Windage Power Losses Modeling in an Axial Flux Permanent Magnet Synchronous Machine with Geometrical Features of the Magnets

    Directory of Open Access Journals (Sweden)

    Alireza Rasekh

    2016-11-01

    Full Text Available In this paper, a set of correlations for the windage power losses in a 4 kW axial flux permanent magnet synchronous machine (AFPMSM is presented. In order to have an efficient machine, it is necessary to optimize the total electromagnetic and mechanical losses. Therefore, fast equations are needed to estimate the windage power losses of the machine. The geometry consists of an open rotor–stator with sixteen magnets at the periphery of the rotor with an annular opening in the entire disk. Air can flow in a channel being formed between the magnets and in a small gap region between the magnets and the stator surface. To construct the correlations, computational fluid dynamics (CFD simulations through the frozen rotor (FR method are performed at the practical ranges of the geometrical parameters, namely the gap size distance, the rotational speed of the rotor, the magnet thickness and the magnet angle. Thereafter, two categories of formulations are defined to make the windage losses dimensionless based on whether the losses are mainly due to the viscous forces or the pressure forces. At the end, the correlations can be achieved via curve fittings from the numerical data. The results reveal that the pressure forces are responsible for the windage losses for the side surfaces in the air-channel, whereas for the surfaces facing the stator surface in the gap, the viscous forces mainly contribute to the windage losses. Additionally, the results of the parametric study demonstrate that the overall windage losses in the machine escalate with an increase in either the rotational Reynolds number or the magnet thickness ratio. By contrast, the windage losses decrease once the magnet angle ratio enlarges. Moreover, it can be concluded that the proposed correlations are very useful tools in the design and optimizations of this type of electrical machine.

  18. Relationship between local deformation behavior and crystallographic features of as-quenched lath martensite during uniaxial tensile deformation

    International Nuclear Information System (INIS)

    Michiuchi, M.; Nambu, S.; Ishimoto, Y.; Inoue, J.; Koseki, T.

    2009-01-01

    Electron backscattering diffraction patterns were used to investigate the relationship between local deformation behavior and the crystallographic features of as-quenched lath martensite of low-carbon steel during uniform elongation in tensile tests. The slip system operating during the deformation up to a strain of 20% was estimated by comparing the crystal rotation of each martensite block after deformation of 20% strain with predictions by the Taylor and Sachs models. The results indicate that the in-lath-plane slip system was preferentially activated compared to the out-of-lath-plane system up to this strain level. Further detailed analysis of crystal rotation at intervals of approximately 5% strain confirmed that the constraint on the operative slip system by the lath structure begins at a strain of 8% and that the local strain hardening of the primary slip systems occurred at approximately 15% strain.

  19. Relationship between Features of Desks and Chairs and Prevalence of Skeletal Disorders in Primary School Students in Abadan

    Directory of Open Access Journals (Sweden)

    Yadollah Zakeri

    2016-11-01

    Full Text Available BackgroundSitting on inappropriate benches, as well as the poor posture (body position during the years of growth, can lead to spinal disorders, fatigue and discomfort in students. This study aimed to investigate the relationship between features of desks and chairs and prevalence of some musculoskeletal disorders in primary school students in Abadan.Materials and MethodsThis cross-sectional study was conducted in 2015 in the city of Abadan- South West of Iran; for which, 383 primary school students were selected and studied through cluster sampling method. Data were collected by the checkered board and researcher-made questionnaire. Features and dimensions of desks and chairs of students were recorded and evaluated based on their condition (being standard or not. Statistical analysis was conducted using SPSS, version 22; and then, descriptive statistics and Chi-square test were conducted.ResultsStudy results showed that about 56.1% of the desks and chairs in under study schools were non-standard. It found that drooping shoulder (85.4% and scoliosis (81.7% were the more prevalent disorders and back straight (1.6% was the least frequent disorder. There was a significant relationship between the variable of non-standard desks and chairs and prevalence of drooping shoulders (P=0.001, scoliosis (P= 0.04, kyphosis (P=0.007 and lordosis (P=0.002 disorders in students.ConclusionThe non-standard-sized desks and chairs increase the prevalence of skeletal disorders in schoolchildren. Therefore, it is essential to pay attention to design and build standard classroom desks and chairs, which are best, adjust to students’ physics.

  20. Adaptive Machine Aids to Learning.

    Science.gov (United States)

    Starkweather, John A.

    With emphasis on man-machine relationships and on machine evolution, computer-assisted instruction (CAI) is examined in this paper. The discussion includes the background of machine assistance to learning, the current status of CAI, directions of development, the development of criteria for successful instruction, meeting the needs of users,…

  1. Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

    Science.gov (United States)

    Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

    2017-09-09

    Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

  2. A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

    International Nuclear Information System (INIS)

    Burzynski, T; Papini, M

    2012-01-01

    A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask–target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times. (paper)

  3. Introduction to machine learning.

    Science.gov (United States)

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  4. Adolescents' hypochondriacal fears and beliefs: Relationship with demographic features, psychological distress, well-being and health-related behaviors.

    Science.gov (United States)

    Sirri, Laura; Ricci Garotti, Maria Grazia; Grandi, Silvana; Tossani, Eliana

    2015-10-01

    There is little previous literature on hypochondriacal attitudes in teens. We examined the relationship between adolescents' hypochondriacal fears and beliefs, demographic features, psychological distress and well-being, and health-related behaviors. Nine hundred and forty-eight students (53.4% males), aged 14-19years (mean 15.8±1.3years), completed the Illness Attitude Scales, the Symptom Questionnaire, and the Psychological Well-Being scales. Demographic features and health-related behaviors (smoking, alcohol consumption, illicit substance use, and sedentary, eating and sleep habits) were also collected. Hypochondriacal concerns were significantly higher among females and correlated with increased psychological distress and reduced well-being. One hundred and forty-nine participants (15.7% of the sample) reached the threshold of the "hypochondriacal responses", identified by Kellner as a screening method for clinically significant hypochondriacal symptoms. The "hypochondriacal responses" were significantly associated with higher levels of psychological distress, decreased well-being, and some unhealthy behaviors: smoking, use of illicit substances, physical inactivity, and short sleep. Female gender, physical inactivity, and higher levels of hostility independently predicted the "hypochondriacal responses" pattern. A substantial percentage of adolescents experience significant concerns about health. Excessive illness fears are associated with less healthy behaviors. A thorough assessment of illness-related concerns may be crucial for the prevention of both the development of more structured forms of abnormal illness behavior (e.g., severe health anxiety) and the engagement in some unhealthy lifestyles in adolescents. However, it may also be that unhealthy behaviors lead to increased preoccupation with one's own health through adolescents' implicit knowledge about possible consequences of such behaviors. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Superconducting machines. Chapter 4

    International Nuclear Information System (INIS)

    Appleton, A.D.

    1977-01-01

    A brief account is given of the principles of superconductivity and superconductors. The properties of Nb-Ti superconductors and the method of flux stabilization are described. The basic features of superconducting d.c. machines are illustrated by the use of these machines for ship propulsion, steel-mill drives, industrial drives, aluminium production, and other d.c. power supplies. Superconducting a.c. generators and their design parameters are discussed. (U.K.)

  6. Relationships between abstract features and methodological quality explained variations of social media activity derived from systematic reviews about psoriasis interventions.

    Science.gov (United States)

    Ruano, J; Aguilar-Luque, M; Isla-Tejera, B; Alcalde-Mellado, P; Gay-Mimbrera, J; Hernandez-Romero, José Luis; Sanz-Cabanillas, J L; Maestre-López, B; González-Padilla, M; Carmona-Fernández, P J; Gómez-García, F; García-Nieto, A Vélez

    2018-05-24

    The aim of this study was to describe the relationship among abstract structure, readability, and completeness, and how these features may influence social media activity and bibliometric results, considering systematic reviews (SRs) about interventions in psoriasis classified by methodological quality. Systematic literature searches about psoriasis interventions were undertaken on relevant databases. For each review, methodological quality was evaluated using the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) tool. Abstract extension, structure, readability, and quality and completeness of reporting were analyzed. Social media activity, which consider Twitter and Facebook mention counts, as well as Mendeley readers and Google scholar citations were obtained for each article. Analyses were conducted to describe any potential influence of abstract characteristics on review's social media diffusion. We classified 139 intervention SRs as displaying high/moderate/low methodological quality. We observed that abstract readability of SRs has been maintained high for last 20 years, although there are some differences based on their methodological quality. Free-format abstracts were most sensitive to the increase of text readability as compared with more structured abstracts (IMRAD or 8-headings), yielding opposite effects on their quality and completeness depending on the methodological quality: a worsening in low quality reviews and an improvement in those of high-quality. Both readability indices and PRISMA for Abstract total scores showed an inverse relationship with social media activity and bibliometric results in high methodological quality reviews but not in those of lower quality. Our results suggest that increasing abstract readability must be specially considered when writing free-format summaries of high-quality reviews, because this fact correlates with an improvement of their completeness and quality, and this may help to achieve broader

  7. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  8. Novel feature selection method based on Stochastic Methods Coupled to Support Vector Machines using H- NMR data (data of olive and hazelnut oils

    Directory of Open Access Journals (Sweden)

    Oscar Eduardo Gualdron

    2014-12-01

    Full Text Available One of the principal inconveniences that analysis and information processing presents is that of the representation of dataset. Normally, one encounters a high number of samples, each one with thousands of variables, and in many cases with irrelevant information and noise. Therefore, in order to represent findings in a clearer way, it is necessary to reduce the amount of variables. In this paper, a novel variable selection technique for multivariable data analysis, inspired on stochastic methods and designed to work with support vector machines (SVM, is described. The approach is demonstrated in a food application involving the detection of adulteration of olive oil (more expensive with hazelnut oil (cheaper. Fingerprinting by H NMR spectroscopy was used to analyze the different samples. Results show that it is possible to reduce the number of variables without affecting classification results.

  9. Research on intelligent machine self-perception method based on LSTM

    Science.gov (United States)

    Wang, Qiang; Cheng, Tao

    2018-05-01

    In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

  10. Power Electronics and Electric Machines Facilities | Transportation

    Science.gov (United States)

    Research | NREL Facilities Power Electronics and Electric Machines Facilities NREL's power electronics and electric machines thermal management experimentation facilities feature a wide range of four researchers in discussion around a piece of laboratory equipment. Power electronics researchers

  11. Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

    Directory of Open Access Journals (Sweden)

    Khader Shameer

    2010-06-01

    Full Text Available 3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neuro-degenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping.

  12. A systematic review of the relationship between subchondral bone features, pain and structural pathology in peripheral joint osteoarthritis.

    Science.gov (United States)

    Barr, Andrew J; Campbell, T Mark; Hopkinson, Devan; Kingsbury, Sarah R; Bowes, Mike A; Conaghan, Philip G

    2015-08-25

    Bone is an integral part of the osteoarthritis (OA) process. We conducted a systematic literature review in order to understand the relationship between non-conventional radiographic imaging of subchondral bone, pain, structural pathology and joint replacement in peripheral joint OA. A search of the Medline, EMBASE and Cochrane library databases was performed for original articles reporting association between non-conventional radiographic imaging-assessed subchondral bone pathologies and joint replacement, pain or structural progression in knee, hip, hand, ankle and foot OA. Each association was qualitatively characterised by a synthesis of the data from each analysis based upon study design, adequacy of covariate adjustment and quality scoring. In total 2456 abstracts were screened and 139 papers were included (70 cross-sectional, 71 longitudinal analyses; 116 knee, 15 hip, six hand, two ankle and involved 113 MRI, eight DXA, four CT, eight scintigraphic and eight 2D shape analyses). BMLs, osteophytes and bone shape were independently associated with structural progression or joint replacement. BMLs and bone shape were independently associated with longitudinal change in pain and incident frequent knee pain respectively. Subchondral bone features have independent associations with structural progression, pain and joint replacement in peripheral OA in the hip and hand but especially in the knee. For peripheral OA sites other than the knee, there are fewer associations and independent associations of bone pathologies with these important OA outcomes which may reflect fewer studies; for example the foot and ankle were poorly studied. Subchondral OA bone appears to be a relevant therapeutic target. PROSPERO registration number: CRD 42013005009.

  13. An examination of the relationship between childhood emotional abuse and borderline personality disorder features: the role of difficulties with emotion regulation.

    Science.gov (United States)

    Kuo, Janice R; Khoury, Jennifer E; Metcalfe, Rebecca; Fitzpatrick, Skye; Goodwill, Alasdair

    2015-01-01

    Childhood abuse has been consistently linked with borderline personality disorder (BPD) and recent studies suggest that some forms of childhood abuse might be uniquely related to both BPD and BPD features. In addition, difficulties with emotion regulation have been found to be associated with childhood abuse, BPD, as well as BPD features. The present study examined (1) whether frequency of childhood emotional abuse is uniquely associated with BPD feature severity when controlling for other forms of childhood abuse and (2) whether difficulties with emotion regulation accounts for the relationship between childhood emotional abuse and BPD feature severity. A sample of undergraduates (n=243) completed the Childhood Trauma Questionnaire - Short Form, Difficulties in Emotion Regulation Scale, and Borderline Symptom List-23. Multiple regression analyses and Structural Equation Modeling were conducted. Results indicated that frequency of childhood emotional abuse (and not sexual or physical abuse) was uniquely associated with BPD feature severity. In addition, while there was no direct path between childhood emotional abuse, childhood physical abuse, or childhood sexual abuse and BPD features, there was an indirect relationship between childhood emotional abuse and BPD features through difficulties with emotion regulation. These findings suggest that, of the different forms of childhood abuse, emotional abuse specifically, may have a developmental role in BPD pathology. Prevention and treatment of BPD pathology might benefit from the provision of emotion regulation strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Further Interpretation of the Relationship between Faunal Community and Seafloor Geology at Southern Hydrate Ridge, Cascadia Margin: Exploring Machine Learning

    Science.gov (United States)

    Bigham, K.; Kelley, D. S.; Marburg, A.; Delaney, J. R.

    2017-12-01

    In 2011, high-resolution, georeferenced photomoasiacs were taken of Einstein's Grotto, an active methane hydrate seep within the field at Southern Hydrate Ridge located 90 km west of Newport, Oregon at a water depth of 800 m. Methods used to analyze the relationships between the seep site, seafloor geology, and the spatial distribution and abundances of microbial and macrofaunal communities at Einstein's Grotto were expanded to three other sites over the 200 by 300 m active seep field. These seeps were documented in the same survey in 2011 conducted by the remotely operated vehicle ROPOS on board the R/V Thompson. Over 10,000 high definition images allowed for the further quantification and characterization of the diversity and structure of the faunal community at this seep field. The new results support the study's initial findings of high variability in the distribution and abundance of seep organisms across the field, with correlation to seafloor geology. The manual classification of organisms was also used to train a series of convolutional neural networks in Nvidia DIGITS and Google Tensorflow environments for automated identification. The developed networks proved highly accurate at background/non-background segmentation ( 96%) and slightly less reliable for fauna identification ( 89%). This study provides a baseline for the faunal community at the Southern Hydrate Ridge methane seeps and a more efficient computer assisted method for processing follow on studies.

  15. 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

    Science.gov (United States)

    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.

  16. Analysis on the Relationship Between Layout and Consumption of Face Cutters on Hard Rock Tunnel Boring Machines (TBMs)

    Science.gov (United States)

    Geng, Qi; Bruland, Amund; Macias, Francisco Javier

    2018-01-01

    The consumption of TBM disc cutters is influenced by the ground conditions (e.g. intact rock properties, rock mass properties, etc.), the TBM boring parameters (e.g. thrust, RPM, penetration, etc.) and the cutterhead design parameters (e.g. cutterhead shape, cutter layout). Previous researchers have done much work on the influence of the ground conditions and TBM boring parameters on cutter consumption; however, limited research has been found on the relationship between the cutterhead design and cutter consumption. The purpose of the present paper is to study the influence of layout on consumption for the TBM face cutters. Data collected from six tunnels (i.e. the Røssåga Headrace Tunnel in Norway, the Qinling Railway Tunnel in China, tubes 3 and 4 of the Guadarrama Railway Tunnel in Spain, the parallel tubes of the Vigo-Das Maceiras Tunnel in Spain) were used for analysis. The cutter consumption shape curve defined as the fitted function of the normalized cutter consumption versus the cutter position radius is found to be uniquely determined by the cutter layout and was used for analysis. The straightness and smoothness indexes are introduced to evaluate the quality of the shape curves. The analytical results suggest that the spacing of face cutters in the inner and outer parts of cutterhead should to be slightly larger and smaller, respectively, than the average spacing, and the difference of the position angles between the neighbouring cutters should be constant among the cutter positions. The 2-spiral layout pattern is found to be better than other layout patterns in view of cutter consumption and cutterhead force balance.

  17. Relationships between self-reported childhood traumatic experiences, attachment style, neuroticism and features of borderline personality disorders in patients with mood disorders.

    Science.gov (United States)

    Baryshnikov, Ilya; Joffe, Grigori; Koivisto, Maaria; Melartin, Tarja; Aaltonen, Kari; Suominen, Kirsi; Rosenström, Tom; Näätänen, Petri; Karpov, Boris; Heikkinen, Martti; Isometsä, Erkki

    2017-03-01

    Co-occurring borderline personality disorder (BPD) features have a marked impact on treatment of patients with mood disorders. Overall, high neuroticism, childhood traumatic experiences (TEs) and insecure attachment are plausible aetiological factors for BPD. However, their relationship with BPD features specifically among patients with mood disorders remains unclear. We investigated these relationships among unipolar and bipolar mood disorder patients. As part of the Helsinki University Psychiatric Consortium study, the McLean Screening Instrument (MSI), the Experiences in Close Relationships-Revised (ECR-R), the Short Five (S5) and the Trauma and Distress Scale (TADS) were filled in by patients with mood disorders (n=282) in psychiatric care. Correlation coefficients between total scores of scales and their dimensions were estimated, and multivariate regression (MRA) and mediation analyses were conducted. Spearman's correlations were strong (rho=0.58; pchildhood traumatic experiences and Attachment Anxiety also among patients with mood disorders. Independent predictors for BPD features include young age, frequency of childhood traumatic experiences and high neuroticism. Insecure attachment may partially mediate the relationship between childhood traumatic experiences and borderline features among mood disorder patients. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Mechanical design of machine components

    CERN Document Server

    Ugural, Ansel C

    2015-01-01

    Mechanical Design of Machine Components, Second Edition strikes a balance between theory and application, and prepares students for more advanced study or professional practice. It outlines the basic concepts in the design and analysis of machine elements using traditional methods, based on the principles of mechanics of materials. The text combines the theory needed to gain insight into mechanics with numerical methods in design. It presents real-world engineering applications, and reveals the link between basic mechanics and the specific design of machine components and machines. Divided into three parts, this revised text presents basic background topics, deals with failure prevention in a variety of machine elements and covers applications in design of machine components as well as entire machines. Optional sections treating special and advanced topics are also included.Key Features of the Second Edition:Incorporates material that has been completely updated with new chapters, problems, practical examples...

  19. Simple machines

    CERN Document Server

    Graybill, George

    2007-01-01

    Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

  20. Relationships between anthropometric features, body composition, and anaerobic alactic power in elite post-pubertal and mature male taekwondo athletes

    Directory of Open Access Journals (Sweden)

    Boraczyński Michał

    2017-12-01

    Full Text Available Purpose. The paper describes the relationships between anthropometric features, body composition, and anaerobic alactic power (AAP in elite post-pubertal and mature male taekwondo athletes. Methods. The sample of 41 taekwondo athletes was divided into two groups: post-pubertal (P-P, n = 19, Mage = 15.6 ± 1.1 years and mature (M, n = 22, Mage = 20.7 ± 2.8 years. Anthropometric features (WB-150, ZPU Tryb-Wag, Poland, body composition (BC-418 MA, Tanita, Japan, maturational status (Pubertal Maturational Observational Scale, and AAP (10-s version of the Wingate Anaerobic Test were assessed. Results. Post-hoc testing revealed significant between-group differences (3.2-20.4%, p < 0.01 in all anthropometric and body composition measures, with effect sizes (ES between −0.79 and −1.25 (p < 0.001, except for fat content and percentage of skeletal muscle mass (SMM (p ≥ 0.05. In group M, the maximal power output (Pmax was greater (ES = −1.15, p < 0.001 and the time of its attainment shorter (ES = 0.59, p < 0.001 than in group P-P. Correlation analyses indicated notably strong associations between body mass (BM and Pmax in group P-P (r = 0.950 [95% CI, 0.85-0.98], p < 0.001 and M (r = 0.926 [95% CI, 0.81-0.97], p < 0.001, and similar-sized strong correlations between fat-free mass (FFM and Pmax in group P-P (r = 0.955 [95% CI, 0.86-0.99], p < 0.001 and M (r = 0.924 [95% CI, 0.82-0.96], p < 0.001. Additionally, a strong correlation was found between body height and Pmax in groups P-P and M (r = 0.805 [95% CI, 0.54-0.92], p < 0.001 and r = 0.819 [95% CI, 0.58-0.93], p < 0.001, respectively. Linear regression analyses demonstrated that FFM, BM, and absolute SMM best explained the variance in Pmax in both groups (r, 0.939-0.951; r2, 0.882-0.909. Conclusions. The strong correlations observed in both groups between BM, FFM, SMM, and Pmax demonstrate the significant effects of body size and composition on AAP. By determining the current levels of these

  1. DNA-based machines.

    Science.gov (United States)

    Wang, Fuan; Willner, Bilha; Willner, Itamar

    2014-01-01

    The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

  2. Face machines

    Energy Technology Data Exchange (ETDEWEB)

    Hindle, D.

    1999-06-01

    The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

  3. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  4. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  5. Nonplanar machines

    International Nuclear Information System (INIS)

    Ritson, D.

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

  6. A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines.

    Science.gov (United States)

    Markham, Francis; Young, Martin; Doran, Bruce; Sugden, Mark

    2017-05-23

    Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2  ≥ 0.97; R 2  ≤ 0.01). The

  7. A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

    Directory of Open Access Journals (Sweden)

    Francis Markham

    2017-05-01

    Full Text Available Abstract Background Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. Methods A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Results Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by

  8. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  9. A Study of the Subject Categorization of the MIS-related Journals in the ISI Databases Using Topical Features in the Text Content and Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Sung-Chien Lin

    2015-07-01

    Full Text Available In this study we analyzed and discussed that the MIS-related journals under the ISI subject category of IS&LS are simultaneously given with subject category Management, using methods of topic modeling, journal clustering and subject category prediction. In the experiment of journal clustering, all journals under subject category Management and other journals also having similar topical features can be gathered into a cluster, and “management” is their common and the most distinct topic. Because the journals belonged to this cluster are almost same to those in the MIS clusters generated by the previous studies, we considered it as the MIS cluster in this study. In the second experiment, we used the classification and regression tree (CART technique to predict assignment of subject category with that the journals in the original subject category Management and in the MIS cluster produced in this study as positive examples, respectively. The trees generated by the two tests both used the occurring probabilities of the topic “management” as the main classification rule. However, in the latter test, we did not only obtain a simpler classification tree but also had a result with less predicting errors. This means that if all journals in the MIS cluster could be given with subject category Management, the retrieval results can be more effective and complete.

  10. Advances in Machine Technology.

    Science.gov (United States)

    Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

    2018-01-01

    Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

  11. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  12. Machine Learning for Medical Imaging.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

    2017-01-01

    Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

  13. Machine translation

    Energy Technology Data Exchange (ETDEWEB)

    Nagao, M

    1982-04-01

    Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

  14. [Features of adaptive responses in right-handers and left-handers, and their relationship to the functional activity of the brain].

    Science.gov (United States)

    Barkar, A A; Markina, L D

    2014-01-01

    In the article there is considered the relationship between adaptation state of the organism and features of bioelectric activity of the brain in right-handers and left-handers. Practically healthy persons of both genders, 23-45 years of age, with the chronic stress disorder were examined. Adaptation status was evaluated with a computer software "Anti-stress", features of bioelectric brain activity were detected by means of spectral and coherent EEG analysis, also the character of motor and sensory asymmetries was determined. The obtained data showed that the response of the organism to excitators of varying strength is a system one and manifested at different levels; adaptation status and bioelectrical activity in right-handers and left-handers have features.

  15. The mediational significance of negative/depressive affect in the relationship of childhood maltreatment and eating disorder features in adolescent psychiatric inpatients.

    Science.gov (United States)

    Hopwood, C J; Ansell, E B; Fehon, D C; Grilo, C M

    2011-03-01

    Childhood maltreatment is a risk factor for eating disorder and negative/depressive affect appears to mediate this relation. However, the specific elements of eating- and body-related psychopathology that are influenced by various forms of childhood maltreatment remain unclear, and investigations among adolescents and men/boys have been limited. This study investigated the mediating role of negative affect/depression across multiple types of childhood maltreatment and eating disorder features in hospitalized adolescent boys and girls. Participants were 148 adolescent psychiatric inpatients who completed an assessment battery including measures of specific forms of childhood maltreatment (sexual, emotional, and physical abuse), negative/depressive affect, and eating disorder features (dietary restriction, binge eating, and body dissatisfaction). Findings suggest that for girls, negative/depressive affect significantly mediates the relationships between childhood maltreatment and eating disorder psychopathology, although effects varied somewhat across types of maltreatment and eating disorder features. Generalization of mediation effects to boys was limited.

  16. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  17. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  18. Machine Learning of Fault Friction

    Science.gov (United States)

    Johnson, P. A.; Rouet-Leduc, B.; Hulbert, C.; Marone, C.; Guyer, R. A.

    2017-12-01

    We are applying machine learning (ML) techniques to continuous acoustic emission (AE) data from laboratory earthquake experiments. Our goal is to apply explicit ML methods to this acoustic datathe AE in order to infer frictional properties of a laboratory fault. The experiment is a double direct shear apparatus comprised of fault blocks surrounding fault gouge comprised of glass beads or quartz powder. Fault characteristics are recorded, including shear stress, applied load (bulk friction = shear stress/normal load) and shear velocity. The raw acoustic signal is continuously recorded. We rely on explicit decision tree approaches (Random Forest and Gradient Boosted Trees) that allow us to identify important features linked to the fault friction. A training procedure that employs both the AE and the recorded shear stress from the experiment is first conducted. Then, testing takes place on data the algorithm has never seen before, using only the continuous AE signal. We find that these methods provide rich information regarding frictional processes during slip (Rouet-Leduc et al., 2017a; Hulbert et al., 2017). In addition, similar machine learning approaches predict failure times, as well as slip magnitudes in some cases. We find that these methods work for both stick slip and slow slip experiments, for periodic slip and for aperiodic slip. We also derive a fundamental relationship between the AE and the friction describing the frictional behavior of any earthquake slip cycle in a given experiment (Rouet-Leduc et al., 2017b). Our goal is to ultimately scale these approaches to Earth geophysical data to probe fault friction. References Rouet-Leduc, B., C. Hulbert, N. Lubbers, K. Barros, C. Humphreys and P. A. Johnson, Machine learning predicts laboratory earthquakes, in review (2017). https://arxiv.org/abs/1702.05774Rouet-LeDuc, B. et al., Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning (2017), AGU Fall Meeting Session S025

  19. Family Perspectives on Siblings' Conflict Goals in Middle Childhood: Links to Hierarchical and Affective Features of Sibling Relationships

    Science.gov (United States)

    Recchia, Holly E.; Witwit, Ma-ab

    2017-01-01

    This study examined parents' and children's descriptions of older and younger siblings' conflict goals in the late preschool and middle childhood years, and how these attributions were related to sibling relationship quality. Parents and 4- to 10-year-old children from 62 families were interviewed separately about siblings' motivations in two…

  20. Relationship between the feature of gravity and magnetic fields and uranium mineralization in the south piedmont of Tianshan mountain

    International Nuclear Information System (INIS)

    Cui Huanmin; Luo Juecheng.

    1988-01-01

    The figures of Bouguer anomalies and vertical magnetic anomalies obtained at the scales of 1:1000 000, 1:500 000 and 1:100 000 were processed and the Moho depths were calculated. The feature of gravity and magnetic fields over uranium deposits was determined through interpreting cmprehensive data from uranium deposits No. 504 and No. 509 and potential areas of uranium mineralization were predicted

  1. Age at onset of major depressive disorder in Han Chinese women: Relationship with clinical features and family history☆

    Science.gov (United States)

    Yang, Fuzhong; Li, Yihan; Xie, Dong; Shao, Chunhong; Ren, Jianer; Wu, Wenyuan; Zhang, Ning; Zhang, Zhen; Zou, Ying; Zhang, Jiulong; Qiao, Dongdong; Gao, Chengge; Li, Youhui; Hu, Jian; Deng, Hong; Wang, Gang; Du, Bo; Wang, Xumei; Liu, Tiebang; Gan, Zhaoyu; Peng, Juyi; Wei, Bo; Pan, Jiyang; Chen, Honghui; Sun, Shufan; Jia, Hong; Liu, Ying; Chen, Qiaoling; Wang, Xueyi; Cao, Juling; Lv, Luxian; Chen, Yunchun; Ha, Baowei; Ning, Yuping; Chen, YiPing; Kendler, Kenneth S.; Flint, Jonathan; Shi, Shenxun

    2011-01-01

    Background Individuals with early-onset depression may be a clinically distinct group with particular symptom patterns, illness course, comorbidity and family history. This question has not been previously investigated in a Han Chinese population. Methods We examined the clinical features of 1970 Han Chinese women with DSM-IV major depressive disorder (MDD) between 30 and 60 years of age across China. Analysis of linear, logistic and multiple logistic regression models was used to determine the association between age at onset (AAO) with continuous, binary and discrete characteristic clinical features of MDD. Results Earlier AAO was associated with more suicidal ideation and attempts and higher neuroticism, but fewer sleep, appetite and weight changes. Patients with an earlier AAO were more likely to suffer a chronic course (longer illness duration, more MDD episodes and longer index episode), increased rates of MDD in their parents and a lower likelihood of marriage. They tend to have higher comorbidity with anxiety disorders (general anxiety disorder, social phobia and agoraphobia) and dysthymia. Conclusions Early AAO in MDD may be an index of a more severe, highly comorbid and familial disorder. Our findings indicate that the features of MDD in China are similar to those reported elsewhere in the world. PMID:21782247

  2. Age at onset of major depressive disorder in Han Chinese women: relationship with clinical features and family history.

    Science.gov (United States)

    Yang, Fuzhong; Li, Yihan; Xie, Dong; Shao, Chunhong; Ren, Jianer; Wu, Wenyuan; Zhang, Ning; Zhang, Zhen; Zou, Ying; Zhang, Jiulong; Qiao, Dongdong; Gao, Chengge; Li, Youhui; Hu, Jian; Deng, Hong; Wang, Gang; Du, Bo; Wang, Xumei; Liu, Tiebang; Gan, Zhaoyu; Peng, Juyi; Wei, Bo; Pan, Jiyang; Chen, Honghui; Sun, Shufan; Jia, Hong; Liu, Ying; Chen, Qiaoling; Wang, Xueyi; Cao, Juling; Lv, Luxian; Chen, Yunchun; Ha, Baowei; Ning, Yuping; Chen, Yiping; Kendler, Kenneth S; Flint, Jonathan; Shi, Shenxun

    2011-12-01

    Individuals with early-onset depression may be a clinically distinct group with particular symptom patterns, illness course, comorbidity and family history. This question has not been previously investigated in a Han Chinese population. We examined the clinical features of 1970 Han Chinese women with DSM-IV major depressive disorder (MDD) between 30 and 60 years of age across China. Analysis of linear, logistic and multiple logistic regression models was used to determine the association between age at onset (AAO) with continuous, binary and discrete characteristic clinical features of MDD. Earlier AAO was associated with more suicidal ideation and attempts and higher neuroticism, but fewer sleep, appetite and weight changes. Patients with an earlier AAO were more likely to suffer a chronic course (longer illness duration, more MDD episodes and longer index episode), increased rates of MDD in their parents and a lower likelihood of marriage. They tend to have higher comorbidity with anxiety disorders (general anxiety disorder, social phobia and agoraphobia) and dysthymia. Early AAO in MDD may be an index of a more severe, highly comorbid and familial disorder. Our findings indicate that the features of MDD in China are similar to those reported elsewhere in the world. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. [Hygienic assessment of student's nutrition through vending machines (fast food)].

    Science.gov (United States)

    Karelin, A O; Pavlova, D V; Babalyan, A V

    2015-01-01

    The article presents the results of a research work on studying the nutrition of students through vending machines (fast food), taking into account consumer priorities of students of medical University, the features and possible consequences of their use by students. The object of study was assortment of products sold through vending machines on the territory of the First Saint-Petersburg Medical University. Net calories, content of proteins, fats and carbohydrates, glycemic index, glycemic load were determined for each product. Information about the use of vending machines was obtained by questionnaires of students 2 and 4 courses of medical and dental faculties by standardized interview method. As was found, most sold through vending machines products has a high energy value, mainly due to refined carbohydrates, and was characterized by medium and high glycemic load. They have got low protein content. Most of the students (87.3%) take some products from the vending machines, mainly because of lack of time for canteen and buffets visiting. Only 4.2% students like assortment of vending machines. More than 50% students have got gastrointestinal complaints. Statistically significant relationship between time of study at the University and morbidity of gastrointestinal tract, as well as the number of students needing medical diet nutrition was found. The students who need the medical diet use fast food significantly more often (46.6% who need the medical diet and 37.7% who don't need it).

  4. Using Machine Learning to Predict Student Performance

    OpenAIRE

    Pojon, Murat

    2017-01-01

    This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

  5. Attention: A Machine Learning Perspective

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2012-01-01

    We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

  6. The impact of childhood traumas, depressive and anxiety symptoms on the relationship between borderline personality features and symptoms of adult attention deficit hyperactivity disorder in Turkish university students.

    Science.gov (United States)

    Dalbudak, Ercan; Evren, Cuneyt

    2015-01-01

    Previous studies reported that there is a significant association between attention deficit hyperactivity disorder (ADHD) in childhood and borderline personality disorder (BPD) in adulthood. The aim of this study is to investigate the relationship of borderline personality features (BPF) and ADHD symptoms while controlling the effect of childhood traumas, symptoms of depression and anxiety in adulthood on this relationship in Turkish university students. A total of 271 Turkish university students participated in this study. The students were assessed through the Turkish version of the Borderline Personality Inventory (BPI), the Adult ADHD Self-Report Scale (ASRS), the Childhood Trauma Questionnaire (CTQ-28), the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). Correlation analyses have revealed that severity of BPF is related with adult ADHD symptoms, emotional, physical abuse and depression scores. Hierarchical regression analysis has indicated that depressive symptoms, emotional and physical abuse and the severity of ADHD symptoms are the predictors for severity of BPF. Findings of the present study suggests that clinicians must carefully evaluate these variables and the relationship between them to understand BPF and ADHD symptoms in university students better. Together with depressive symptoms, emotional and physical abuse may play a mediator role on this relationship. Further studies are needed to evaluate causal relationship between these variables in both clinical and non-clinical populations.

  7. Machine Protection

    International Nuclear Information System (INIS)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

  8. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  9. Machine Protection

    CERN Document Server

    Zerlauth, Markus; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  10. Machine Protection

    Energy Technology Data Exchange (ETDEWEB)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  11. Machine vision systems using machine learning for industrial product inspection

    Science.gov (United States)

    Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

    2002-02-01

    Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

  12. Teletherapy machine

    International Nuclear Information System (INIS)

    Panyam, Vinatha S.; Rakshit, Sougata; Kulkarni, M.S.; Pradeepkumar, K.S.

    2017-01-01

    Radiation Standards Section (RSS), RSSD, BARC is the national metrology institute for ionizing radiation. RSS develops and maintains radiation standards for X-ray, beta, gamma and neutron radiations. In radiation dosimetry, traceability, accuracy and consistency of radiation measurements is very important especially in radiotherapy where the success of patient treatment is dependent on the accuracy of the dose delivered to the tumour. Cobalt teletherapy machines have been used in the treatment of cancer since the early 1950s and India had its first cobalt teletherapy machine installed at the Cancer Institute, Chennai in 1956

  13. Monitoring machining conditions by infrared images

    Science.gov (United States)

    Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

    2001-03-01

    During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

  14. The relationship between the MRI features of mild osteoarthritis in the patellofemoral and tibiofemoral compartments of the knee

    International Nuclear Information System (INIS)

    Kornaat, Peter R.; Watt, Iain; Bloem, Johan L.; Riyazi, Naghmeh; Kloppenburg, Margreet

    2005-01-01

    The aim of this work was to demonstrate the relationship between osteoarthritic changes seen on magnetic resonance (MR) images of the patellofemoral (PF) or tibiofemoral (TF) compartments in patients with mild osteoarthritis (OA) of the knee. MR images of the knee were obtained in 105 sib pairs (210 patients) who had been diagnosed with OA at multiple joints. Entry criteria included that the degree of OA in the knee examined should be between a Kellgren and Lawrence score of 2 or 3. MR images were analyzed for the presence of cartilaginous lesions, bone marrow edema (BME) and meniscal tears. The relationship between findings in the medial and lateral aspects of the PF and TF compartments was examined. The number of cartilaginous defects on either side of the PF compartment correlated positively with number of cartilaginous defects in the ipsilateral TF compartment (odds ratio, OR, 55, confidence interval, CI, 7.8-382). The number of cartilaginous defects in the PF compartment correlated positively with ipsilateral meniscal tears (OR 3.7, CI 1.0-14) and ipsilateral PF BME (OR 17, CI 3.8-72). Cartilaginous defects in the TF compartment correlated positively with ipsilateral meniscal tears (OR 9.8, CI 2.5-38) and ipsilateral TF BME (OR 120, CI 6.5-2,221). Osteoarthritic defects lateralize or medialize in the PF and TF compartments of the knee in patients with mild OA. (orig.)

  15. The relationship between the MRI features of mild osteoarthritis in the patellofemoral and tibiofemoral compartments of the knee

    Energy Technology Data Exchange (ETDEWEB)

    Kornaat, Peter R.; Watt, Iain; Bloem, Johan L. [Leiden University Medical Center, Department of Radiology, Leiden (Netherlands); Riyazi, Naghmeh; Kloppenburg, Margreet [Leiden University Medical Center, Department of Rheumatology, Leiden (Netherlands)

    2005-08-01

    The aim of this work was to demonstrate the relationship between osteoarthritic changes seen on magnetic resonance (MR) images of the patellofemoral (PF) or tibiofemoral (TF) compartments in patients with mild osteoarthritis (OA) of the knee. MR images of the knee were obtained in 105 sib pairs (210 patients) who had been diagnosed with OA at multiple joints. Entry criteria included that the degree of OA in the knee examined should be between a Kellgren and Lawrence score of 2 or 3. MR images were analyzed for the presence of cartilaginous lesions, bone marrow edema (BME) and meniscal tears. The relationship between findings in the medial and lateral aspects of the PF and TF compartments was examined. The number of cartilaginous defects on either side of the PF compartment correlated positively with number of cartilaginous defects in the ipsilateral TF compartment (odds ratio, OR, 55, confidence interval, CI, 7.8-382). The number of cartilaginous defects in the PF compartment correlated positively with ipsilateral meniscal tears (OR 3.7, CI 1.0-14) and ipsilateral PF BME (OR 17, CI 3.8-72). Cartilaginous defects in the TF compartment correlated positively with ipsilateral meniscal tears (OR 9.8, CI 2.5-38) and ipsilateral TF BME (OR 120, CI 6.5-2,221). Osteoarthritic defects lateralize or medialize in the PF and TF compartments of the knee in patients with mild OA. (orig.)

  16. Origin of pingo-like features on the Beaufort Sea shelf and their possible relationship to decomposing methane gas hydrates

    Science.gov (United States)

    Paull, C.K.; Ussler, W.; Dallimore, S.R.; Blasco, S.M.; Lorenson, T.D.; Melling, H.; Medioli, B.E.; Nixon, F.M.; McLaughlin, F.A.

    2007-01-01

    The Arctic shelf is currently undergoing dramatic thermal changes caused by the continued warming associated with Holocene sea level rise. During this transgression, comparatively warm waters have flooded over cold permafrost areas of the Arctic Shelf. A thermal pulse of more than 10??C is still propagating down into the submerged sediment and may be decomposing gas hydrate as well as permafrost. A search for gas venting on the Arctic seafloor focused on pingo-like-features (PLFs) on the Beaufort Sea Shelf because they may be a direct consequence of gas hydrate decomposition at depth. Vibracores collected from eight PLFs had systematically elevated methane concentrations. ROV observations revealed streams of methane-rich gas bubbles coming from the crests of PLFs. We offer a scenario of how PLFs may be growing offshore as a result of gas pressure associated with gas hydrate decomposition. Copyright 2007 by the American Geophysical Union.

  17. Studies on the relationship of pleiotrophin and MMP2 with the clinicopathological features of invasive breast carcinoma

    Directory of Open Access Journals (Sweden)

    Bo ZHANG

    2012-08-01

    Full Text Available Objective To study the correlation between the expressions of both pleitropin (PTN and matrix metalloproteinase-2 (MMP2 to the clinicopathological features of patients with breast cancer. Methods The pathological specimens were collected from 103 cases of invasive breast cancer, including 51 cases of triple negative breast cancer (TNBC, i.e. all the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 were negatively expressed and 52 cases of non-TNBC. Ten specimens of paraneoplastic tissue were also collected as controls. The expressions of PTN and MMP2 were detected with immunohistochemical method, and the correlation of PTN and MMP2 expressions to the clinicopathological features of breast cancer (age, tumor size, histopathological grading and axillary lymph node metastases was assessed. Results Among the 103 patients with breast cancer, no statistical difference was found between TNBC group and non-TNBC group in age of onset, tumor size and the axillary lymph node metastasis (P > 0.05, but significant difference was found in histopathological grading (P < 0.05. The positive rate of PTN expression was 83.5% (86/103, and of MMP2 expression was 68% (70/103, and no significant difference was found between TNBC group and non-TNBC group. The expressions of PTN and MMP2 were correlated with the age of onset, histopathological grading and axillary lymph node metastasis, but showed poor consistency in breast cancer (Kappa coefficient=0.1817, 95% CI=-0.0091-0.3726; Z=2.0212, P=0.0433. Conclusions The expression of PTN and MMP2 is correlated with the age, histopathological grading and axillary lymph node metastasis of patients with invasive breast cancer, and not correlated with TNBC. The expression of PTN and MMP2 shows poor consistency in invasive breast cancer.

  18. Maternal Alexithymia and Attachment Style: Which Relationship with Their Children’s Headache Features and Psychological Profile?

    Directory of Open Access Journals (Sweden)

    Samuela Tarantino

    2018-01-01

    Full Text Available IntroductionA growing body of literature has shown an association between somatic symptoms and insecure “attachment style.” In a recent study, we found a relationship between migraine severity, ambivalent attachment style, and psychological symptoms in children/adolescents. There is evidence that caregivers’ attachment styles and their way of management/expression of emotions can influence children’s psychological profile and pain expression. To date, data dealing with headache are scarce. Our aim was to study the role of maternal alexithymia and attachment style on their children’s migraine severity, attachment style, and psychological profile.Materials and methodsWe enrolled 84 consecutive patients suffering from migraine without aura (female: 45, male: 39; mean age 11.8 ± 2.4 years. According to headache frequency, children/adolescents were divided into two groups: (1 high frequency (patients reporting from weekly to daily attacks, and (2 low frequency (patients having ≤3 episodes per month. We divided headache attacks intensity into two groups (mild and severe pain. SAFA “Anxiety,” “Depression,” and “Somatization” scales were used to explore children’s psychological profile. To evaluate attachment style, the semi-projective test SAT for patients and ASQ Questionnaire for mothers were employed. Maternal alexithymia traits were assessed by TAS-20.ResultsWe found a significant higher score in maternal alexithymia levels in children classified as “ambivalent,” compared to those classified as “avoiding” (Total scale: p = 0.011. A positive correlation has been identified between mother’s TAS-20 Total score and the children’s SAFA-A Total score (p = 0.026. In particular, positive correlations were found between maternal alexithymia and children’s “Separation anxiety” (p = 0.009 and “School anxiety” (p = 0.015 subscales. Maternal “Externally-oriented thinking” subscale

  19. Design of a 10 MJ fast discharging homopolar machine

    International Nuclear Information System (INIS)

    Stillwagon, R.E.; Thullen, P.

    1977-01-01

    The design of a fast discharging homopolar machine is described. The machine capacity is 10 MJ with a 30 ms energy delivery time. The salient features of the machine are relatively high terminal voltage, fast discharge time, high power density and high efficiency. The machine integrates several new technologies including high surface speeds, large superconducting magnets and current collection at high density

  20. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  1. Relationship between dysfunctional breathing patterns and ability to achieve target heart rate variability with features of "coherence" during biofeedback.

    Science.gov (United States)

    Courtney, Rosalba; Cohen, Marc; van Dixhoorn, Jan

    2011-01-01

    Heart rate variability (HRV) biofeedback is a self-regulation strategy used to improve conditions including asthma, stress, hypertension, and chronic obstructive pulmonary disease. Respiratory muscle function affects hemodynamic influences on respiratory sinus arrhythmia (RSA), and HRV and HRV-biofeedback protocols often include slow abdominal breathing to achieve physiologically optimal patterns of HRV with power spectral distribution concentrated around the 0.1-Hz frequency and large amplitude. It is likely that optimal balanced breathing patterns and ability to entrain heart rhythms to breathing reflect physiological efficiency and resilience and that individuals with dysfunctional breathing patterns may have difficulty voluntarily modulating HRV and RSA. The relationship between breathing movement patterns and HRV, however, has not been investigated. This study examines how individuals' habitual breathing patterns correspond with their ability to optimize HRV and RSA. Breathing pattern was assessed using the Manual Assessment of Respiratory Motion (MARM) and the Hi Lo manual palpation techniques in 83 people with possible dysfunctional breathing before they attempted HRV biofeedback. Mean respiratory rate was also assessed. Subsequently, participants applied a brief 5-minute biofeedback protocol, involving breathing and positive emotional focus, to achieve HRV patterns proposed to reflect physiological "coherence" and entrainment of heart rhythm oscillations to other oscillating body systems. Thoracic-dominant breathing was associated with decreased coherence of HRV (r = -.463, P = .0001). Individuals with paradoxical breathing had the lowest HRV coherence (t(8) = 10.7, P = .001), and the negative relationship between coherence of HRV and extent of thoracic breathing was strongest in this group (r = -.768, P = .03). Dysfunctional breathing patterns are associated with decreased ability to achieve HRV patterns that reflect cardiorespiratory efficiency and

  2. Machine rates for selected forest harvesting machines

    Science.gov (United States)

    R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

    2002-01-01

    Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

  3. Genome-wide study of correlations between genomic features and their relationship with the regulation of gene expression.

    Science.gov (United States)

    Kravatsky, Yuri V; Chechetkin, Vladimir R; Tchurikov, Nikolai A; Kravatskaya, Galina I

    2015-02-01

    The broad class of tasks in genetics and epigenetics can be reduced to the study of various features that are distributed over the genome (genome tracks). The rapid and efficient processing of the huge amount of data stored in the genome-scale databases cannot be achieved without the software packages based on the analytical criteria. However, strong inhomogeneity of genome tracks hampers the development of relevant statistics. We developed the criteria for the assessment of genome track inhomogeneity and correlations between two genome tracks. We also developed a software package, Genome Track Analyzer, based on this theory. The theory and software were tested on simulated data and were applied to the study of correlations between CpG islands and transcription start sites in the Homo sapiens genome, between profiles of protein-binding sites in chromosomes of Drosophila melanogaster, and between DNA double-strand breaks and histone marks in the H. sapiens genome. Significant correlations between transcription start sites on the forward and the reverse strands were observed in genomes of D. melanogaster, Caenorhabditis elegans, Mus musculus, H. sapiens, and Danio rerio. The observed correlations may be related to the regulation of gene expression in eukaryotes. Genome Track Analyzer is freely available at http://ancorr.eimb.ru/. © The Author 2015. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

  4. EBSD Analysis of Relationship Between Microstructural Features and Toughness of a Medium-Carbon Quenching and Partitioning Bainitic Steel

    Science.gov (United States)

    Li, Qiangguo; Huang, Xuefei; Huang, Weigang

    2017-12-01

    A multiphase microstructure of bainite, martensite and retained austenite in a 0.3C bainitic steel was obtained by a novel bainite isothermal transformation plus quenching and partitioning (B-QP) process. The correlations between microstructural features and toughness were investigated by electron backscatter diffraction (EBSD), and the results showed that the multiphase microstructure containing approximately 50% bainite exhibits higher strength (1617 MPa), greater elongation (18.6%) and greater impact toughness (103 J) than the full martensite. The EBSD analysis indicated that the multiphase microstructure with a smaller average local misorientation (1.22°) has a lower inner stress concentration possibility and that the first formed bainitic ferrite plates in the multiphase microstructure can refine subsequently generated packets and blocks. The corresponding packet and block average size decrease from 11.9 and 2.3 to 8.4 and 1.6 μm, respectively. A boundary misorientation analysis indicated that the multiphase microstructure has a higher percentage of high-angle boundaries (67.1%) than the full martensite (57.9%) because of the larger numbers and smaller sizes of packets and blocks. The packet boundary obstructs crack propagation more effectively than the block boundary.

  5. Architectural features of the Kayenta formation (Lower Jurassic), Colorado Plateau, USA: relationship to salt tectonics in the Paradox Basin

    Science.gov (United States)

    Bromley, Michael H.

    1991-09-01

    Fluvial sandstones of the Kayenta Formation were analyzed using architectural element analysis. Paleocurrent trends, the distribution of lacustrine facies and local silcrete development indicate that synsedimentary movement of evaporites in the underlying Paradox Basin created an unstable basin floor beneath the Kayenta fluvial system. This instability resulted in deflection of fluvial axes, local basin development and local areas of interrupted fluvial deposition with eolian dunes. Paleocurrent trends in the Kayenta system reflect periodic interruptions of southwesterly flow. Salt migrating laterally out of a rim syncline into an adjacent salt anticline resulted in a rim syncline of slight topographic relief. The resulting basin was probably rapidly filled, allowing the resumption of southwesterly flow. Differential movement of salt (incipient solution collapse features (?)) resulted in the formation of small centripetal basins in which playa mudstones formed. A laterally extensive resistant ledge underlies a horizontal surface, suggestive of deflation to the water table of an exposed section of valley fill. A channel scour in the top of one of these surfaces has margins much steeper ( > 60°) than the angle of repose for unconsolidated sand. Early cementation of the exposed floodplain could account for this resistance.

  6. Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

    Science.gov (United States)

    Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

    2014-03-01

    The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  7. RELATIONSHIPS BETWEEN ANATOMICAL FEATURES AND INTRA-RING WOOD DENSITY PROFILES IN Gmelina arborea APPLYING X-RAY DENSITOMETRY

    Directory of Open Access Journals (Sweden)

    Mario Tomazelo-Filho

    2007-12-01

    Full Text Available Four annual tree-rings (2 of juvenile wood and 2 of mature wood were sampled from fast-growth plantations ofGmelina arborea in two climatic conditions (dry and wet tropical in Costa Rica. Each annual tree-ring was divided in equal parts ina radial direction. For each part, X-ray density as well as vessel percentage, length and width fiber, cell wall thickness and lumendiameter were measured. Wood density and profile patterns of cell dimension demonstrated inconsistency between juvenile andmature wood and climatic conditions. The Pearson correlation matrix showed that intra-ring wood density was positively correlatedwith the cell wall thickness and negatively correlated with vessel percentage, fiber length, lumen diameter and width. The forwardstepwise regressions determined that: (i intra-ring wood density variation could be predicted from 76 to 96% for anatomicalvariation; (ii cell wall thickness was the most important anatomical feature to produce intra-ring wood density variation and (iii thevessel percentage, fiber length, lumen diameter and width were the second most statically significant characteristics to intra-ring wooddensity, however, with low participation of the determination coefficient of stepwise regressions.

  8. Non-small cell lung cancer: evaluation of the relationship between fibrosis and washout feature at dynamic contrast enhanced CT

    International Nuclear Information System (INIS)

    Ye Xiaodan; Yuan Zheng; Ye Jianding; Li Huimin; Zhu Yuzhao; Zhang Shunmin; Liu Shiyuan; Xiao Xiangsheng

    2010-01-01

    Objective: To correlate dynamic parameters at contrast enhanced CT and interstitial fibrosis grade of' non-small cell lung cancer (NSCLC). Methods: Twenty-nine patients with NSCLC were evaluated by multi-slice CT. Images were obtained before and at 20, 30, 45, 60, 75, 90, 120, 180, 300, 540, 720, 900 and 1200 s after the injection of contrast media, which was administered at a rate of 4 ml/s for a total of 420 mg I/kg body weight. Washout parameters were calculated. Lung cancer specimens were stained with hematoxylin-eosin stain and collagen and elastic double stain. Spearman test was made to analyze correlation between dynamic parameters and interstitial fibrosis grade of tumor. Results: Twenty- nine NSCLC demonstrated washout at 20 min 12.1 (0.32-58.0) HU, washout ratio at 20 minutes 15.3% (0.3%-39.2%), slope of washout at 20 minutes 0.0152%/s (0.0007%/s-0.0561%/s). Interstitial fibrosis of 29 lesions was graded as grade Ⅰ (10), grade Ⅱ (14) and grade Ⅲ (5). There were significant correlation between washout at 20 min (r=-0.402, P<0.05), washout ratio at 20 min (r= -0.372, P<0.05), slope of washout ratio (r=-0.459, P<0.05) and interstitial fibrosis grade in tumors. Conclusion: NSCLC washout features at dynamic multi-detector CT correlates with interstitial fibrosis in the tumor. (authors)

  9. How do disease perception, treatment features, and dermatologist–patient relationship impact on patients assuming topical treatment? An Italian survey

    Directory of Open Access Journals (Sweden)

    Burroni AG

    2015-02-01

    Full Text Available Anna Graziella Burroni,1 Mariella Fassino,2 Antonio Torti,3 Elena Visentin4 1IRCCS University Hospital San Martino, IST National Institute for Cancer Research, Genoa, Italy; 2Department of Psychology, Specialization School in Clinical Psychology, University of Turin, Turin, Italy; 3Dermatology practice, Milan, Italy; 4HTA and Scientific Support, CSD Medical Research Srl, Milan, Italy Background: Psoriasis largely affects daily activities and social interactions and has a strong impact on patients’ quality of life. Psoriatic patients have different attitudes toward their condition. Topical medications are essential for the treatment of psoriasis, but the majority of patients do not adhere to these therapies. Objective: The history of treatment success or failure seems to influence patient attitude toward topical therapy. Therefore, it is important to understand the psychological, experiential, and motivational aspects that could be critical for treatment adherence, and to describe the different attitudes toward topical treatment. Furthermore, the physician–patient relationship and the willingness to trust the dermatologist may have a substantial role in encouraging or discouraging patients’ attitudes toward topical therapy. Methods: A survey was designed to collect aspects that could be relevant to understanding different patient attitudes toward psoriasis and its treatments. A total of 495 self-administered questionnaires compiled by psoriatic patients were analyzed from 20 Italian specialized hospital centers in order to provide a nationwide picture. Results: Psoriatic patients have different perceptions and experiences in relation to their condition: half of them consider psoriasis as a disease, while the other half consider psoriasis as a disorder or a nuisance. Topical therapy is the most widely used treatment, even though it is not considered the most effective one and often perceived to be cosmetic. The main findings are: 1

  10. On the relationship between retrospective childhood ADHD symptoms and adult BPD features: the mediating role of action-oriented personality traits.

    Science.gov (United States)

    Carlotta, Davide; Borroni, Serena; Maffei, Cesare; Fossati, Andrea

    2013-10-01

    A number of studies have reported data suggestive of a significant association between ADHD and BPD, nevertheless, the nature of this relation has not been fully understood yet. In our study, we tried to evaluate if the relationship between retrospectively assessed ADHD symptoms and adult BPD features could mediated by selected temperament/personality traits. Four hundred forty-seven in- and outpatients consecutively admitted to the Clinical Psychology and Psychotherapy Unit of the Scientific Institute H San Raffaele of Milan, Italy, were administered the Italian versions of the following instruments: Structured Clinical Interview for DSM-IV Axis II Personality Disorders, Version 2.0 (SCID-II), Wender Utah Rating Scale (WURS), Temperament and Character Inventory-Revised (TCI-R), Barratt Impulsiveness Scale-11 (BIS-11), and Aggression Questionnaire (AQ). Our mediation analyses showed that the combination of impulsivity, aggression, novelty seeking, and juvenile conduct problems completely mediate the relationship between retrospectively assessed ADHD symptoms and current BPD features. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. The relationship between the clinical features of idiopathic burning mouth syndrome and self-perceived quality of life.

    Science.gov (United States)

    Braud, Adeline; Boucher, Yves

    2016-01-01

    In this descriptive study, we investigated the relationship between the clinical characteristics of idiopathic burning mouth syndrome (iBMS) and the quality of life. Eighteen iBMS patients were interviewed about their experience with pain, oral-associated complaints, cognitive status, and self-perceived quality of life using the French versions of the Hospital Anxiety and Depression Scale (HADS) and the Global Oral Health Assessment Index (GOHAI). The Spearman coefficient was used to analyze correlations. The level of significance was fixed at P burning sensations with other oral complaints, including dry mouth (77.8%), tactile abnormalities (66.7%), thermal abnormalities (44.5%), and taste disturbances (38.9%). HAD-anxiety scores were higher than 10 in 38.8% of iBMS patients and HAD-depression scores were higher than 10 in 33.3% of patients. A significant correlation was found between the number of associated complaints and HAD-depression scores. The mean GOHAI-add score was 37.9 ± 9.6 (mean ± SD), and 94.5% of iBMS patients had a score lower than 50. GOHAI-add scores strongly correlated with pain intensity, which was calculated using a visual analog scale and duration of pain. Our findings indicate a strong correlation between iBMS-related pain and self-perceived oral health-related quality of life. In addition, a correlation was observed between iBMS-associated oral complaints and cognitive status.(J Oral Sci 58, 475-481, 2016).

  12. Remote filter handling machine for Sizewell B

    International Nuclear Information System (INIS)

    Barker, D.

    1993-01-01

    Two Filter Handling machines (FHM) have been supplied to Nuclear Electric plc for use at Sizewell B Power Station. These machines have been designed and built following ALARP principles with the functional objective being to remove radioactive filter cartridges from a filter housing and replace them with clean filter cartridges. Operation of the machine is achieved by the prompt of each distinct task via an industrial computer or the prompt of a full cycle using the automatic mode. The design of the machine features many aspects demonstrating ALARP while keeping the machine simple, robust and easy to maintain. (author)

  13. The severity of Internet addiction risk and its relationship with the severity of borderline personality features, childhood traumas, dissociative experiences, depression and anxiety symptoms among Turkish university students.

    Science.gov (United States)

    Dalbudak, Ercan; Evren, Cuneyt; Aldemir, Secil; Evren, Bilge

    2014-11-30

    The aim of this study was to investigate the relationship of Internet addiction (IA) risk with the severity of borderline personality features, childhood traumas, dissociative experiences, depression and anxiety symptoms among Turkish university students. A total of 271 Turkish university students participated in this study. The students were assessed through the Internet Addiction Scale (IAS), the Borderline Personality Inventory (BPI), the Dissociative Experiences Scale (DES), the Childhood Trauma Questionnaire (CTQ-28), the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). The rates of students were 19.9% (n=54) in the high IA risk group, 38.7% (n=105) in the mild IA risk group and 41.3% (n=112) in the group without IA risk. Correlation analyses revealed that the severity of IA risk was related with BPI, DES, emotional abuse, CTQ-28, depression and anxiety scores. Univariate covariance analysis (ANCOVA) indicated that the severity of borderline personality features, emotional abuse, depression and anxiety symptoms were the predictors of IAS score, while gender had no effect on IAS score. Among childhood trauma types, emotional abuse seems to be the main predictor of IA risk severity. Borderline personality features predicted the severity of IA risk together with emotional abuse, depression and anxiety symptoms among Turkish university students. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  14. Electric machines

    CERN Document Server

    Gross, Charles A

    2006-01-01

    BASIC ELECTROMAGNETIC CONCEPTSBasic Magnetic ConceptsMagnetically Linear Systems: Magnetic CircuitsVoltage, Current, and Magnetic Field InteractionsMagnetic Properties of MaterialsNonlinear Magnetic Circuit AnalysisPermanent MagnetsSuperconducting MagnetsThe Fundamental Translational EM MachineThe Fundamental Rotational EM MachineMultiwinding EM SystemsLeakage FluxThe Concept of Ratings in EM SystemsSummaryProblemsTRANSFORMERSThe Ideal n-Winding TransformerTransformer Ratings and Per-Unit ScalingThe Nonideal Three-Winding TransformerThe Nonideal Two-Winding TransformerTransformer Efficiency and Voltage RegulationPractical ConsiderationsThe AutotransformerOperation of Transformers in Three-Phase EnvironmentsSequence Circuit Models for Three-Phase Transformer AnalysisHarmonics in TransformersSummaryProblemsBASIC MECHANICAL CONSIDERATIONSSome General PerspectivesEfficiencyLoad Torque-Speed CharacteristicsMass Polar Moment of InertiaGearingOperating ModesTranslational SystemsA Comprehensive Example: The ElevatorP...

  15. Charging machine

    International Nuclear Information System (INIS)

    Medlin, J.B.

    1976-01-01

    A charging machine for loading fuel slugs into the process tubes of a nuclear reactor includes a tubular housing connected to the process tube, a charging trough connected to the other end of the tubular housing, a device for loading the charging trough with a group of fuel slugs, means for equalizing the coolant pressure in the charging trough with the pressure in the process tubes, means for pushing the group of fuel slugs into the process tube and a latch and a seal engaging the last object in the group of fuel slugs to prevent the fuel slugs from being ejected from the process tube when the pusher is removed and to prevent pressure liquid from entering the charging machine. 3 claims, 11 drawing figures

  16. Homopolar machine design

    International Nuclear Information System (INIS)

    Thullen, P.

    1978-01-01

    A general conceptual design for a disc-type homopolar machine is presented. This machine uses a superconducting, air-core, solenoidal field winding with a peak field of 8 T. A total energy of 500 MJ is stored in two counter-rotating disc rotors that operate at a surface speed of 200 m/s. Terminal voltages of 500 to 2000 V are obtained over the range of designs studied. Brush systems to collect 3 MA are investigated. Various brush materials are discussed to determine their usefulness in this application. Sufficient information on operating characteristics in high-power applications is only available for copper-graphite brushes. The use of sliding brushes for terminal voltage regulation is discussed. This feature cannot provide a great deal of flexibility in this particular application although it may be useful during start-up. The brush system is the most demanding feature of this design. Few systems in the million ampere range have been constructed, consequently, it is not possible to predict the behavior of this brush system with great certainty. A detailed design of the brushes should be undertaken. It is estimated that the cost of such a machine will range from 0.5 to 1.5 cents per joule

  17. Genesis machines

    CERN Document Server

    Amos, Martyn

    2014-01-01

    Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

  18. The Relationship between Brachycephalic Head Features in Modern Persian Cats and Dysmorphologies of the Skull and Internal Hydrocephalus.

    Science.gov (United States)

    Schmidt, M J; Kampschulte, M; Enderlein, S; Gorgas, D; Lang, J; Ludewig, E; Fischer, A; Meyer-Lindenberg, A; Schaubmar, A R; Failing, K; Ondreka, N

    2017-09-01

    Cat breeders observed a frequent occurrence of internal hydrocephalus in Persian cats with extreme brachycephalic head morphology. To investigate a possible relationship among the grade of brachycephaly, ventricular dilatation, and skull dysmorphologies in Persian cats. 92 Persian-, 10 Domestic shorthair cats. The grade of brachycephaly was determined on skull models based on CT datasets. Cranial measurements were examined with regard to a possible correlation with relative ventricular volume, and cranial capacity. Persians with high (peke-face Persians) and lower grades of brachycephaly (doll-face Persians) were investigated for the presence of skull dysmorphologies. The mean cranial index of the peke-face Persians (0.97 ± 0.14) was significantly higher than the mean cranial index of doll-face Persians (0.66 ± 0.04; P < 0.001). Peke-face Persians had a lower relative nasal bone length (0.15 ± 0.04) compared to doll-face (0.29 ± 0.08; P < 0.001). The endocranial volume was significantly lower in doll-face than peke-face Persians (89.6 ± 1.27% versus 91.76 ± 2.07%; P < 0.001). The cranial index was significantly correlated with this variable (Spearman's r: 0.7; P < 0.0001). Mean ventricle: Brain ratio of the peke-face group (0.159 ± 0.14) was significantly higher compared to doll-face Persians (0.015 ± 0.01; P < 0.001). High grades of brachycephaly are also associated with malformations of the calvarial and facial bones as well as dental malformations. As these dysmorphologies can affect animal welfare, the selection for extreme forms of brachycephaly in Persian cats should be reconsidered. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

  19. Introduction: Minds, Bodies, Machines

    Directory of Open Access Journals (Sweden)

    Deirdre Coleman

    2008-10-01

    Full Text Available This issue of 19 brings together a selection of essays from an interdisciplinary conference on 'Minds, Bodies, Machines' convened last year by Birkbeck's Centre for Nineteenth-Century Studies, University of London, in partnership with the English programme, University of Melbourne and software developers Constraint Technologies International (CTI. The conference explored the relationship between minds, bodies and machines in the long nineteenth century, with a view to understanding the history of our technology-driven, post-human visions. It is in the nineteenth century that the relationship between the human and the machine under post-industrial capitalism becomes a pervasive theme. From Blake on the mills of the mind by which we are enslaved, to Carlyle's and Arnold's denunciation of the machinery of modern life, from Dickens's sooty fictional locomotive Mr Pancks, who 'snorted and sniffed and puffed and blew, like a little labouring steam-engine', and 'shot out […]cinders of principles, as if it were done by mechanical revolvency', to the alienated historical body of the late-nineteenth-century factory worker under Taylorization, whose movements and gestures were timed, regulated and rationalised to maximize efficiency; we find a cultural preoccupation with the mechanisation of the nineteenth-century human body that uncannily resonates with modern dreams and anxieties around technologies of the human.

  20. Representational Machines

    DEFF Research Database (Denmark)

    Photography not only represents space. Space is produced photographically. Since its inception in the 19th century, photography has brought to light a vast array of represented subjects. Always situated in some spatial order, photographic representations have been operatively underpinned by social...... to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological...... possibilities, and genre distinctions. Presenting several distinct ways of producing space photographically, this book opens a new and important field of inquiry for photography research....

  1. Shear machines

    International Nuclear Information System (INIS)

    Astill, M.; Sunderland, A.; Waine, M.G.

    1980-01-01

    A shear machine for irradiated nuclear fuel elements has a replaceable shear assembly comprising a fuel element support block, a shear blade support and a clamp assembly which hold the fuel element to be sheared in contact with the support block. A first clamp member contacts the fuel element remote from the shear blade and a second clamp member contacts the fuel element adjacent the shear blade and is advanced towards the support block during shearing to compensate for any compression of the fuel element caused by the shear blade (U.K.)

  2. Assessment of use of specific features of subcutaneous insulin infusion systems and their relationship to metabolic control in patients with type 1 diabetes.

    Science.gov (United States)

    Quirós, Carmen; Patrascioiu, Ioana; Giménez, Marga; Vinagre, Irene; Vidal, Mercè; Jansà, Margarita; Conget, Ignacio

    2014-01-01

    Patients with type 1 diabetes (T1DM) treated with continuous subcutaneous insulin infusion (CSII) have available several specific features of these devices. The aim of this study was to evaluate the relationship between real use of them and the degree of glycemic control in patients using this therapy. Forty-four T1DM patients on CSII therapy with or without real-time continuous glucose monitoring (CGM) were included. Data from 14 consecutive days were retrospectively collected using the therapy management software CareLink Personal/Pro(®) and HbA1c measurement performed at that period. The relationship between the frequency of usie of specific features of insulin pumps (non-sensor augmented or sensor-augmented) and glycemic control was analyzed. Mean HbA1c in the group was 7.5 ± .8%. Mean daily number of boluses administered was 5.1 ± 1.8, with 75.4% of them being bolus wizards (BW). Daily number of boluses was significantly greater in patients with HbA1c 7.5% (5.3 ± 1.6 vs. 4.3 ± 1.6, P=.056). There was a trend to greater use of BW in patients with better control (82.8 ± 21.4% vs. 69.9 ± 29.1%, P=.106). HbA1c was lower in patients using CGM (n=8) as compared to those not using sensor-augmented pumps (7.6 ± .8 vs 7.1 ± .7, P=.067), but the difference was not statistically significant. More frequent use of BW appears to be associated to better metabolic control in patients with T1DM using pump therapy. In standard clinical practice, augmentation of insulin pump with CGM may be associated to improved glycemic control. Copyright © 2013 SEEN. Published by Elsevier Espana. All rights reserved.

  3. Electricity of machine tool

    International Nuclear Information System (INIS)

    Gijeon media editorial department

    1977-10-01

    This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

  4. Environmentally Friendly Machining

    CERN Document Server

    Dixit, U S; Davim, J Paulo

    2012-01-01

    Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

  5. Magnesium-to-calcium ratio in tap water, and its relationship to geological features and the incidence of calcium-containing urinary stones.

    Science.gov (United States)

    Kohri, K; Kodama, M; Ishikawa, Y; Katayama, Y; Takada, M; Katoh, Y; Kataoka, K; Iguchi, M; Kurita, T

    1989-11-01

    We examined the relationship among magnesium and calcium content in tap water, the geological features and urinary stone incidence in Japan. The magnesium-to-calcium ratio in tap water correlated negatively with the incidence of urolithiasis. There was no correlation between calcium and magnesium concentration in tap water and urinary stone incidence. Geological features in Japan were classified into 5 groups. The magnesium-to-calcium ratio in the basalt areas was higher than in the other areas, while ratio in the granite areas was low. In the sedimentary rock areas calcium and magnesium concentrations were high; the magnesium-to-calcium ratio in these areas was between those of the basalt and granite areas. The limestone areas had a much higher calcium concentration. The incidence of urinary stones in the sedimentary rock and basalt areas was lower than that of the granite areas, while that in the limestone areas was the highest. Thus, the incidence of urinary stone is related to the magnesium-to-calcium ratio in tap water and the geological area.

  6. Machine Protection

    CERN Document Server

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an ...

  7. CANDU 9 fuelling machine carriage

    Energy Technology Data Exchange (ETDEWEB)

    Ullrich, D J; Slavik, J F [Atomic Energy of Canada Ltd., Saskatoon, SK (Canada)

    1997-12-31

    Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs.

  8. CANDU 9 fuelling machine carriage

    International Nuclear Information System (INIS)

    Ullrich, D.J.; Slavik, J.F.

    1996-01-01

    Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs

  9. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

  10. Toroidal field coils for the PDX machine

    International Nuclear Information System (INIS)

    Bushnell, C.W.

    1975-01-01

    This paper describes the engineering design features of the TF coils for the PDX machine. Included are design details of the electrical insulation, water cooling, and coil segment joint which allows access to the central machine area. A discussion of the problems anticipated in the manufacture and the planned solutions are presented

  11. Refueling machine for a nuclear reactor

    International Nuclear Information System (INIS)

    Kowalski, E.F.; Hornak, L.P.; Swidwa, K.J.

    1981-01-01

    An improved refuelling machine for inserting and removing fuel assemblies from a nuclear reactor is described which has been designed to increase the reliability of such machines. The system incorporates features which enable the refuelling operation to be performed more efficiently and economically. (U.K.)

  12. 基于特征选择支持向量机的柱塞泵智能诊断%Intelligent Fault Diagnosis for Plunger Pump Based on Features Selection and Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    崔英; 杜文辽; 孙旺; 李彦明

    2013-01-01

    柱塞泵是工程机械的关键部件,其性能好坏将直接影响整个设备的正常工作。针对柱塞泵提出基于特征选择支持向量机的智能诊断方法。对采集的振动信号基于小波包分解提取能量特征,然后利用Fisher准则函数选择对智能诊断最有利的特征,利用支持向量机进行训练,并将每个二类支持向量机按二叉树的组织形式构成系统的诊断模型。以汽车起重机柱塞泵为研究对象,其6种故障形式,包括正常、轴承内圈故障、滚动体故障、柱塞故障、配流盘故障、斜盘故障,用于检验所提算法的诊断能力,并与传统的BP神经网络和最近的蚁群神经网络方法进行对比。诊断结果表明:所提出的算法优于另外两种方法,具有较好的诊断效果。%In truck crane,the plunger pump is the key equipment,and the quality of the pump affects directly the performance of whole mechanical system. A novel intelligent diagnosis method based on features selection and support vector machine (SVM)was proposed for plunger pump in truck crane. Based on the wavelet packet decompose,the wavelet packet energy was extracted from the original vibration signal to represent the condition of equipment. Then,the Fisher criterion was utilized to select the most suitable fea-tures for diagnosis. Finally,each two-class SVM with binary tree architecture was trained to recognize the condition of mechanism. The proposed method was employed in the diagnosis of plunger pump in truck crane. The six states,including normal state,bearing inner race fault,bearing roller fault,plunger fault,thrust plate wear fault,and swash plate wear fault,were used to test the classification performance of the proposed Fisher-SVMs model,which was compared with the classical and the latest models,such as BP ANN,ANT ANN,respectively. The experimental results show that the Fisher-SVMs is superior to the other two models,and gets a promising re-sult.

  13. Analysis of machining and machine tools

    CERN Document Server

    Liang, Steven Y

    2016-01-01

    This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

  14. Machine Protection

    International Nuclear Information System (INIS)

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an interlock system providing the glue between these systems. The most recent accelerator, the LHC, will operate with about 3 × 10 14 protons per beam, corresponding to an energy stored in each beam of 360 MJ. This energy can cause massive damage to accelerator equipment in case of uncontrolled beam loss, and a single accident damaging vital parts of the accelerator could interrupt operation for years. This article provides an overview of the requirements for protection of accelerator equipment and introduces the various protection systems. Examples are mainly from LHC, SNS and ESS

  15. A topological insight into restricted Boltzmann machines

    NARCIS (Netherlands)

    Mocanu, D.C.; Mocanu, E.; Nguyen, H.P.; Gibescu, M.; Liotta, A.

    Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative

  16. Machine terms dictionary

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1979-04-15

    This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

  17. Relationship of tumor grade to other pathologic features and to treatment outcome for patients with early-stage breast cancer treated with breast-conserving therapy

    Energy Technology Data Exchange (ETDEWEB)

    Nixon, Asa J; Gage, Irene; Connolly, James L; Schnitt, Stuart; Silver, Barbara; Hetelekidis, Stella; Recht, Abram; Harris, Jay R

    1995-07-01

    Purpose: To study the relationship of tumor grade to the distribution of pathologic features and to the risk of local and distant recurrence following breast-conserving therapy in patients with pure infiltrating ductal carcinoma, and to explore the differences between this type and tubular carcinoma. Materials and Methods: Between 1968 and 1986, 1624 patients were treated for clinical Stage I or II invasive breast cancer with a complete gross excision and {>=}60 Gy to the tumor bed. The original slides were reviewed in 1337 cases (82%). Of these, 1081 were pure infiltrating ductal carcinoma and 28 were tubular carcinoma and these constitute the study population. Fifty-five patients (5%) have been lost to followup after 7-181 months. Median followup for 742 survivors is 134 months (7-278 mos.). We evaluated the following features: histologic grade (modified Bloom-Richardson system), the presence of nodal metastases (in 891 pts. (80%) undergoing axillary dissection [pLN+]), an extensive intraductal component (EIC), lymphatic vessel invasion (LVI), mononuclear cellular response (MCR), and necrosis. We analyzed the incidence of clinical and pathologic characteristics as a function of histology and histologic grade (Table 1). We also examined the 10-year crude rates of first failure for evaluable patients (Table 2) and calculated actuarial curves for regional nodal failure or distant metastasis (RNF/DM) at any time during followup (Figure 1). Results: Conclusions: 1) The proportion of tumors with LVI, EIC, or lymph node involvement did not vary significantly by histologic grade. Low grade tumors tended to be smaller and exhibit less MCR and necrosis; 2) Grade did not predict for local recurrence. Distant recurrence rates were significantly higher in patients with grade II or III as compared with grade I tumors, although recurrence rates continued to rise for grade I tumors through 10 years of followup; 3) Although patient numbers are small, tubular breast carcinomas

  18. Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients

    Directory of Open Access Journals (Sweden)

    Stephen ShingFan Yip

    2016-03-01

    Full Text Available Purpose: Although change in SUV measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Methods: Fifty-four esophageal cancer patients received PET/CT scans before and after chemo-radiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two co-occurrence matrix (Entropy and Homogeneity, two run-length-matrix (High-gray-run-emphasis and Short-run-high-gray-run-emphasis, and two size-zone-matrix (High-gray-zone-emphasis and Short-zone-high-gray-emphasis textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC and Kaplan-Meier statistics respectively. Results: All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC=0.79, p=1.7x10^-4 and partial (AUC=0.71, p=0.01 responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run length and size zone matrix textures (AUC=0.71‒0.76, p≤0.02. Homogeneity, SUVmax and SUVmean failed to differentiate between any of the responders (AUC=0.50‒0.57, p≥0.46. However, none of the measures were found to significantly distinguish between complete and partial responders with AUC0.25. Conclusions: For the patients studied, temporal change in Entropy and all Run length matrix were better correlated with pathological response and survival than the SUV

  19. The use of fluoride as a natural tracer in water and the relationship to geological features: Examples from the Animas River Watershed, San Juan Mountains, Silverton, Colorado

    Science.gov (United States)

    Bove, D.J.; Walton-Day, K.; Kimball, B.A.

    2009-01-01

    Investigations within the Silverton caldera, in southwestern Colorado, used a combination of traditional geological mapping, alteration-assemblage mapping, and aqueous geochemical sampling that showed a relationship between geological and hydrologic features that may be used to better understand the provenance and evolution of the water. Veins containing fluorite, huebnerite, and elevated molybdenum concentrations are temporally and perhaps genetically associated with the emplacement of high-silica rhyolite intrusions. Both the rhyolites and the fluorite-bearing veins produce waters containing elevated concentrations of F-, K and Be. The identification of water samples with elevated F/Cl molar ratios (> 10) has also aided in the location of water draining F-rich sources, even after these waters have been diluted substantially. These unique aqueous geochemical signatures can be used to relate water chemistry to key geological features and mineralized source areas. Two examples that illustrate this relationship are: (1) surface-water samples containing elevated F-concentrations (> 1.8 mg/l) that closely bracket the extent of several small high-silica rhyolite intrusions; and (2) water samples containing elevated concentrations of F-(> 1.8 mg/ l) that spatially relate to mines or areas that contain late-stage fluorite/huebnerite veins. In two additional cases, the existence of high F-concentrations in water can be used to: (1) infer interaction of the water with mine waste derived from systems known to contain the fluorite/huebnerite association; and (2) relate changes in water quality over time at a high elevation mine tunnel to plugging of a lower elevation mine tunnel and the subsequent rise of the water table into mineralized areas containing fluorite/huebnerite veining. Thus, the unique geochemical signature of the water produced from fluorite veins indicates the location of high-silica rhyolites, mines, and mine waste containing the veins. Existence of high F

  20. Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients.

    Science.gov (United States)

    Yip, Stephen S F; Coroller, Thibaud P; Sanford, Nina N; Mamon, Harvey; Aerts, Hugo J W L; Berbeco, Ross I

    2016-01-01

    Although change in standardized uptake value (SUV) measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Fifty-four esophageal cancer patients received PET/CT scans before and after chemoradiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two cooccurrence matrix (Entropy and Homogeneity), two run-length matrix (RLM) (high-gray-run emphasis and Short-run high-gray-run emphasis), and two size-zone matrix (high-gray-zone emphasis and short-zone high-gray emphasis) textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC) and Kaplan-Meier statistics, respectively. All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC = 0.79, p = 1.7 × 10(-4)) and partial (AUC = 0.71, p = 0.01) responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run-length and size-zone matrix textures (AUC = 0.71-0.76, p ≤ 0.02). Homogeneity, SUVmax, and SUVmean failed to differentiate between any of the responders (AUC = 0.50-0.57, p ≥ 0.46). However, none of the measures were found to significantly distinguish between complete and partial responders with AUC textures significantly discriminated patients with good and poor survival (log-rank p textures and survival were poorly related

  1. Discriminating Induced-Microearthquakes Using New Seismic Features

    Science.gov (United States)

    Mousavi, S. M.; Horton, S.

    2016-12-01

    We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

  2. Addiction Machines

    Directory of Open Access Journals (Sweden)

    James Godley

    2011-10-01

    Full Text Available Entry into the crypt William Burroughs shared with his mother opened and shut around a failed re-enactment of William Tell’s shot through the prop placed upon a loved one’s head. The accidental killing of his wife Joan completed the installation of the addictation machine that spun melancholia as manic dissemination. An early encryptment to which was added the audio portion of abuse deposited an undeliverable message in WB. Wil- liam could never tell, although his corpus bears the in- scription of this impossibility as another form of pos- sibility. James Godley is currently a doctoral candidate in Eng- lish at SUNY Buffalo, where he studies psychoanalysis, Continental philosophy, and nineteenth-century litera- ture and poetry (British and American. His work on the concept of mourning and “the dead” in Freudian and Lacanian approaches to psychoanalytic thought and in Gothic literature has also spawned an essay on zombie porn. Since entering the Academy of Fine Arts Karlsruhe in 2007, Valentin Hennig has studied in the classes of Sil- via Bächli, Claudio Moser, and Corinne Wasmuht. In 2010 he spent a semester at the Dresden Academy of Fine Arts. His work has been shown in group exhibi- tions in Freiburg and Karlsruhe.

  3. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  4. Identifying product order with restricted Boltzmann machines

    Science.gov (United States)

    Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

    2018-03-01

    Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

  5. Application of Machine Learning to Rotorcraft Health Monitoring

    Science.gov (United States)

    Cody, Tyler; Dempsey, Paula J.

    2017-01-01

    Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

  6. [CH4 emission features of leading super-rice varieties and their relationships with the varieties growth characteristics in Yangtze Delta of China].

    Science.gov (United States)

    Yan, Xiao-Jun; Wang, Li-Li; Jiang, Yu; Deng, Ai-Xing; Tian, Yun-Lu; Zhang, Wei-Jian

    2013-09-01

    A pot experiment was conducted to study the CH4 emission features of fourteen leading super-rice varieties (six Japonica rice varieties and eight Indica hybrid rice varieties) and their relationships with the varieties growth characteristics in Yangtze Delta. Two distinct peaks of CH4 emission were detected during the entire growth period of the varieties, one peak occurred at full-tillering stage, and the other appeared at booting stage. The average total CH4 emission of Japonica rice varieties was 37.6% higher than that of the Indica hybrid rice varieties (Price types occurred at the post-anthesis phase. For all the varieties, there was a significant positive correlation between the total CH4 emission and the maximum leaf area, but the correlations between the CH4 emission and the other growth characteristics varied with variety type. The total CH4 emission of Japonica rice varieties had a significant positive correlation with plant height, while the correlations between the total CH4 emission of Indica hybrid rice varieties and their plant height were not significant. The total CH4 emission of Indica hybrid rice varieties had significant negative correlations with the total aboveground biomass, grain yield, and harvest index, but the correlations were not significant for Japonica rice varieties. The lower CH4 emission of Indica hybrid rice varieties was likely due to their significantly higher root biomass, as compared with Japonica rice varieties.

  7. Relationship between morphological features and kinetic patterns of enhancement of the dynamic breast magnetic resonance imaging and clinico-pathological and biological factors in invasive breast cancer

    International Nuclear Information System (INIS)

    Fernández-Guinea, Oscar; Andicoechea, Alejandro; González, Luis O; González-Reyes, Salomé; Merino, Antonio M; Hernández, Luis C; López-Muñiz, Alfonso; García-Pravia, Paz; Vizoso, Francisco J

    2010-01-01

    To investigate the relationship between the magnetic resonance imaging (MRI) features of breast cancer and its clinicopathological and biological factors. Dynamic MRI parameters of 68 invasive breast carcinomas were investigated. We also analyzed microvessel density (MVD), estrogen and progesterone receptor status, and expression of p53, HER2, ki67, VEGFR-1 and 2. Homogeneous enhancement was significantly associated with smaller tumor size (T1: < 2 cm) (p = 0.015). Tumors with irregular or spiculated margins had a significantly higher MVD than tumors with smooth margins (p = 0.038). Tumors showing a maximum enhancement peak at two minutes, or longer, after injecting the contrast, had a significantly higher MVD count than those which reached this point sooner (p = 0.012). The percentage of tumors with vascular invasion or high mitotic index was significantly higher among those showing a low percentage (≤ 150%) of maximum enhancement before two minutes than among those ones showing a high percentage (>150%) of enhancement rate (p = 0.016 and p = 0.03, respectively). However, there was a significant and positive association between the mitotic index and the peak of maximum intensity (p = 0.036). Peritumor inflammation was significantly associated with washout curve type III (p = 0.042). Variations in the early phase of dynamic MRI seem to be associated with parameters indicatives of tumor aggressiveness in breast cancer

  8. Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

    Science.gov (United States)

    Li, Yang; Wu, Yanbin; Yan, Aixia

    2017-07-01

    HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replication infection. In this work, 274 LEDGF/p75-IN inhibitors were collected as the dataset. Support Vector Machine (SVM), Decision Tree (DT), Function Tree (FT) and Random Forest (RF) were applied to build several computational models for predicting whether a compound is an active or weakly active LEDGF/p75-IN inhibitor. Each compound is represented by MACCS fingerprints and CORINA Symphony descriptors. The prediction accuracies for the test sets of all the models are over 70 %. The best model Model 3B built by FT obtained a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 81.08 % and 0.62 on test set, respectively. We found that the hydrogen bond and hydrophobic interactions are important for the bioactivity of an inhibitor. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Directory of Open Access Journals (Sweden)

    Cai Wingfield

    2017-09-01

    Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  10. Machine drawing

    CERN Document Server

    Narayana, KL; Reddy, K Venkata

    2006-01-01

    About the Book: Written by three distinguished authors with ample academic and teaching experience, this textbook, meant for diploma and degree students of Mechanical Engineering as well as those preparing for AMIE examination, incorporates the latest standards. The new edition includes the features of assembly drawings, part drawings and computer-aided drawings to cater to the needs of students pursuing various courses. The text of the new edition has been thoroughly revised to include new concepts and practices in the subject. It should prove an ideal textbook. Contents: Introduction

  11. INVESTIGATION OF MAGNESIUM ALLOYS MACHINABILITY

    Directory of Open Access Journals (Sweden)

    Berat Barıs BULDUM

    2013-01-01

    Full Text Available Magnesium is the lightest structural metal. Magnesium alloys have a hexagonal lattice structure, which affects the fundamental properties of these alloys. Plastic deformation of the hexagonal lattice is more complicated than in cubic latticed metals like aluminum, copper and steel. Magnesium alloy developments have traditionally been driven by industry requirements for lightweight materials to operate under increasingly demanding conditions. Magnesium alloys have always been attractive to designers due to their low density, only two thirds that of aluminium and its alloys [1]. The element and its alloys take a big part of modern industry needs. Especially nowadays magnesium alloys are used in automotive and mechanical (trains and wagons manufacture, because of its lightness and other features. Magnesium and magnesium alloys are the easiest of all metals to machine, allowing machining operations at extremely high speed. All standard machining operations such as turning, drilling, milling, are commonly performed on magnesium parts.

  12. Mineral mining machines

    Energy Technology Data Exchange (ETDEWEB)

    Mc Gaw, B H

    1984-01-01

    A machine for mining minerals is patented. It is a cutter loader with a drum actuating element of the worm type equipped with a multitude of cutting teeth reinforced with tungsten carbide. A feature of the patented machine is that all of the cutting teeth and holders on the drum have the identical design. This is achieved through selecting a slant angle for the cutting teeth which is the mean between the slant angle of the conventional radial teeth and the slant angle of the advance teeth. This, in turn, is provided thanks to the corresponding slant of the holders relative to the drum and (or) the slant of the cutting part of the teeth relative to their stems. Thus, the advance teeth projecting beyond the surface of the drum on the face side and providing upper and lateral clearances have the same angle of attack as the radial teeth, that is, from 20 to 35 degrees. A series of modifications of the cutting teeth is patented. One of the designs allows the cutting tooth to occupy a varying position relative to the drum, from the conventional vertical to an inverted, axially projecting position. In the last case the tooth in the extraction process provides the upper and lateral clearances for the drum on the face side. Among the different modifications of the cutting teeth, a design is proposed which provides for the presence of a stem which is shaped like a truncated cone. This particular stem is designed for use jointly with a wedge which unfastens the teeth and is placed in a holder. The latter is completed in a transverse slot thanks to which the rear end of the stem is compressed, which simplifies replacement of a tooth. Channels are provided in the patented machine for feeding water to the worm spiral, the holders and the cutting teeth themselves in order to deal with dust.

  13. Inverse analysis of turbidites by machine learning

    Science.gov (United States)

    Naruse, H.; Nakao, K.

    2017-12-01

    This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small

  14. Machine technology: a survey

    International Nuclear Information System (INIS)

    Barbier, M.M.

    1981-01-01

    An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

  15. Machine Shop Lathes.

    Science.gov (United States)

    Dunn, James

    This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

  16. Superconducting rotating machines

    International Nuclear Information System (INIS)

    Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

    1975-01-01

    The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

  17. High intensity focused ultrasound treatment of adenomyosis: The relationship between the features of magnetic resonance imaging on T2 weighted images and the therapeutic efficacy

    Energy Technology Data Exchange (ETDEWEB)

    Gong, Chunmei [State Key Laboratory of Ultrasound Engineering in Medicine Co-founded by Chongqing and the Ministry of Science and Technology, Chongqing Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Haifu Hospital, College of Biomedical Engineering, Chongqing Medical University, Chongqing (China); Setzen, Raymond [Department of Obstetrics and Gynecology, Chris Hani Baragwanath Academic Hospital, Johannesburg (South Africa); Liu, Zhongqiong; Liu, Yunchang [State Key Laboratory of Ultrasound Engineering in Medicine Co-founded by Chongqing and the Ministry of Science and Technology, Chongqing Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Haifu Hospital, College of Biomedical Engineering, Chongqing Medical University, Chongqing (China); Xie, Bin [Department of Ultrasound, Huanggang Central Hospital, Huanggang City, Hubei 438000 (China); Aili, Aixingzi, E-mail: 1819483078@qq.com [Shanghai First Maternity and Infant Health Hospital, Shanghai (China); Zhang, Lian, E-mail: lianwzhang@yahoo.com [State Key Laboratory of Ultrasound Engineering in Medicine Co-founded by Chongqing and the Ministry of Science and Technology, Chongqing Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Haifu Hospital, College of Biomedical Engineering, Chongqing Medical University, Chongqing (China)

    2017-04-15

    Objectives: To investigate the relationship between the features of magnetic resonance imaging (MRI) on T2 weighted images (T2WI) and the therapeutic efficacy of high intensity focused ultrasound (HIFU) on adenomyosis. Materials and methods: From January 2011 to November 2015, four hundred and twenty-eight patients with symptomatic adenomyosis were treated with HIFU. Based on the signal intensity and the number of hyperintense foci in the adenomyotic lesions on T2WI, the patients were classified into groups. The day after HIFU ablation patients underwent contrast-enhanced MRI and a comparison was made of non-perfused volume (NPV) ratio, energy efficiency factor (EEF), treatment time, sonication time, and adverse effects. Results: No significant difference in terms of HIFU treatment settings and results was observed between the group of patients with hypointense adenomyotic lesions and the group with isointense adenomyotic lesions (P > 0.05). However, the sonication time and EEF were significantly higher in the group with multiple hyperintense foci compared to the group with few hyperintense foci. The NPV ratio achieved in the lesions with multiple hyperintenese foci was significantly lower than that in the lesions with few hyperintense foci (P < 0.05). No significant difference was observed in the rate of adverse effects between the two groups. Conclusions: Based on our results, the response of the adenomyotic lesions to HIFU treatment is not related to the signal intensity of adenomyotic lesions on T2WI. However, the number of the high signal intensity foci in the adenomyotic lesions on T2WI can be considered as a predictive factor to help select patients for HIFU treatment.

  18. Probabilistic hazard assessment for skin sensitization potency by dose–response modeling using feature elimination instead of quantitative structure–activity relationships

    Science.gov (United States)

    McKim, James M.; Hartung, Thomas; Kleensang, Andre; Sá-Rocha, Vanessa

    2016-01-01

    Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose–response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimension-ality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals’ potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced "false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets. PMID:26046447

  19. High intensity focused ultrasound treatment of adenomyosis: The relationship between the features of magnetic resonance imaging on T2 weighted images and the therapeutic efficacy

    International Nuclear Information System (INIS)

    Gong, Chunmei; Setzen, Raymond; Liu, Zhongqiong; Liu, Yunchang; Xie, Bin; Aili, Aixingzi; Zhang, Lian

    2017-01-01

    Objectives: To investigate the relationship between the features of magnetic resonance imaging (MRI) on T2 weighted images (T2WI) and the therapeutic efficacy of high intensity focused ultrasound (HIFU) on adenomyosis. Materials and methods: From January 2011 to November 2015, four hundred and twenty-eight patients with symptomatic adenomyosis were treated with HIFU. Based on the signal intensity and the number of hyperintense foci in the adenomyotic lesions on T2WI, the patients were classified into groups. The day after HIFU ablation patients underwent contrast-enhanced MRI and a comparison was made of non-perfused volume (NPV) ratio, energy efficiency factor (EEF), treatment time, sonication time, and adverse effects. Results: No significant difference in terms of HIFU treatment settings and results was observed between the group of patients with hypointense adenomyotic lesions and the group with isointense adenomyotic lesions (P > 0.05). However, the sonication time and EEF were significantly higher in the group with multiple hyperintense foci compared to the group with few hyperintense foci. The NPV ratio achieved in the lesions with multiple hyperintenese foci was significantly lower than that in the lesions with few hyperintense foci (P < 0.05). No significant difference was observed in the rate of adverse effects between the two groups. Conclusions: Based on our results, the response of the adenomyotic lesions to HIFU treatment is not related to the signal intensity of adenomyotic lesions on T2WI. However, the number of the high signal intensity foci in the adenomyotic lesions on T2WI can be considered as a predictive factor to help select patients for HIFU treatment.

  20. Giro form reading machine

    Science.gov (United States)

    Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

    1995-08-01

    Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

  1. [Comparison of machinability of two types of dental machinable ceramic].

    Science.gov (United States)

    Fu, Qiang; Zhao, Yunfeng; Li, Yong; Fan, Xinping; Li, Yan; Lin, Xuefeng

    2002-11-01

    In terms of the problems of now available dental machinable ceramics, a new type of calcium-mica glass-ceramic, PMC-I ceramic, was developed, and its machinability was compared with that of Vita MKII quantitatively. Moreover, the relationship between the strength and the machinability of PMC-I ceramic was studied. Samples of PMC-I ceramic were divided into four groups according to their nucleation procedures. 600-seconds drilling tests were conducted with high-speed steel tools (Phi = 2.3 mm) to measure the drilling depths of Vita MKII ceramic and PMC-I ceramic, while constant drilling speed of 600 rpm and constant axial load of 39.2 N were used. And the 3-point bending strength of the four groups of PMC-I ceramic were recorded. Drilling depth of Vita MKII was 0.71 mm, while the depths of the four groups of PMC-I ceramic were 0.88 mm, 1.40 mm, 0.40 mm and 0.90 mm, respectively. Group B of PMC-I ceramic showed the largest depth of 1.40 mm and was statistically different from other groups and Vita MKII. And the strength of the four groups of PMC-I ceramic were 137.7, 210.2, 118.0 and 106.0 MPa, respectively. The machinability of the new developed dental machinable ceramic of PMC-I could meet the need of the clinic.

  2. Development of piezoelectric ceramics driven fatigue testing machine for small specimens

    International Nuclear Information System (INIS)

    Saito, S.; Kikuchi, K.; Onishi, Y.; Nishino, T.

    2002-01-01

    A new fatigue testing machine with piezoelectric ceramics actuators was developed and a prototype was manufactured for high-cycle fatigue tests with small specimens. The machine has a simple mechanism and is compact. These features make it easy to set up and to maintain the machine in a hot cell. The excitation of the actuator can be transmitted to the specimen using a lever-type testing jig. More than 100 μm of displacement could be prescribed precisely to the specimen at a frequency of 50 Hz. This was sufficient performance for high-cycle bend fatigue tests on specimens irradiated at the SINQ target in Paul Scherrer Institute. The relationship of a displacement applied to the specimen and the strain of the necking part were obtained by experimental methods and by finite element method (FEM) calculations. Both results showed good agreement. This fact makes it possible to evaluate the strain of irradiated specimens by FEM simulations

  3. The Improved Relevance Voxel Machine

    DEFF Research Database (Denmark)

    Ganz, Melanie; Sabuncu, Mert; Van Leemput, Koen

    The concept of sparse Bayesian learning has received much attention in the machine learning literature as a means of achieving parsimonious representations of features used in regression and classification. It is an important family of algorithms for sparse signal recovery and compressed sensing....... Hence in its current form it is reminiscent of a greedy forward feature selection algorithm. In this report, we aim to solve the problems of the original RVoxM algorithm in the spirit of [7] (FastRVM).We call the new algorithm Improved Relevance Voxel Machine (IRVoxM). Our contributions...... and enables basis selection from overcomplete dictionaries. One of the trailblazers of Bayesian learning is MacKay who already worked on the topic in his PhD thesis in 1992 [1]. Later on Tipping and Bishop developed the concept of sparse Bayesian learning [2, 3] and Tipping published the Relevance Vector...

  4. Managing virtual machines with Vac and Vcycle

    Science.gov (United States)

    McNab, A.; Love, P.; MacMahon, E.

    2015-12-01

    We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

  5. Archetypal Analysis for Machine Learning

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    2010-01-01

    Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

  6. Adaptive machine and its thermodynamic costs

    Science.gov (United States)

    Allahverdyan, Armen E.; Wang, Q. A.

    2013-03-01

    We study the minimal thermodynamically consistent model for an adaptive machine that transfers particles from a higher chemical potential reservoir to a lower one. This model describes essentials of the inhomogeneous catalysis. It is supposed to function with the maximal current under uncertain chemical potentials: if they change, the machine tunes its own structure fitting it to the maximal current under new conditions. This adaptation is possible under two limitations: (i) The degree of freedom that controls the machine's structure has to have a stored energy (described via a negative temperature). The origin of this result is traced back to the Le Chatelier principle. (ii) The machine has to malfunction at a constant environment due to structural fluctuations, whose relative magnitude is controlled solely by the stored energy. We argue that several features of the adaptive machine are similar to those of living organisms (energy storage, aging).

  7. Machine tool structures

    CERN Document Server

    Koenigsberger, F

    1970-01-01

    Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

  8. THE GARDEN AND THE MACHINE

    DEFF Research Database (Denmark)

    Clemmensen, Thomas Juel

    2012-01-01

    The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape as infrastructure...... in relation to the environmental problems being addressed, and that we need gardens of reflection, interrogation and doubt, in order to engage with the deeper complexities of territorial transformations....

  9. The Garden and the Machine

    DEFF Research Database (Denmark)

    Clemmensen, Thomas Juel

    2014-01-01

    The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape ‘as’ infrastructure...... in relation to the environmental problems being addressed, and that we need gardens of reflection, interrogation and doubt, in order to engage with the deeper complexities of territorial transformations....

  10. Model-Agnostic Interpretability of Machine Learning

    OpenAIRE

    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

    2016-01-01

    Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

  11. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer

    International Nuclear Information System (INIS)

    Gao, Xuan; Chu, Chunyu; Li, Yingci; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

    2015-01-01

    Highlights: • Three support vector machine classifiers were constructed from PET-CT images. • The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. • The areas under curves for maximum short diameter and SUV max were 0.684 and 0.652, respectively. • The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes. - Abstract: Objectives: In clinical practice, image analysis is dependent on simply visual perception and the diagnostic efficacy of this analysis pattern is limited for mediastinal lymph nodes in patients with lung cancer. In order to improve diagnostic efficacy, we developed a new computer-based algorithm and tested its diagnostic efficacy. Methods: 132 consecutive patients with lung cancer underwent 18 F-FDG PET/CT examination before treatment. After all data were imported into the database of an on-line medical image analysis platform, the diagnostic efficacy of visual analysis was first evaluated without knowing pathological results, and the maximum short diameter and maximum standardized uptake value (SUV max ) were measured. Then lymph nodes were segmented manually. Three classifiers based on support vector machine (SVM) were constructed from CT, PET, and combined PET-CT images, respectively. The diagnostic efficacy of SVM classifiers was obtained and evaluated. Results: According to ROC curves, the areas under curves for maximum short diameter and SUV max were 0.684 and 0.652, respectively. The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. Conclusion: The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes

  12. An Individual Claims History Simulation Machine

    Directory of Open Access Journals (Sweden)

    Andrea Gabrielli

    2018-03-01

    Full Text Available The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

  13. Operating System For Numerically Controlled Milling Machine

    Science.gov (United States)

    Ray, R. B.

    1992-01-01

    OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

  14. Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses.

    Science.gov (United States)

    Iqbal, Abdullah; Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

    2010-03-01

    Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of b*, which indicates blue to yellow in L*a*b* colour space] and three textural features [entropy of b*, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R, mean of H, std. deviation of a*, which indicates green to red in L*a*b* colour space) and two textural features [contrast of B, contrast of L* (luminance or lightness in L*a*b* colour space)] for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-valuetexture appearance and acceptability), a dichotomous logistic regression model using the best image features was able to explain the variability of consumers' responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams. Copyright 2009 Elsevier Ltd. All rights reserved.

  15. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  16. MITS machine operations

    International Nuclear Information System (INIS)

    Flinchem, J.

    1980-01-01

    This document contains procedures which apply to operations performed on individual P-1c machines in the Machine Interface Test System (MITS) at AiResearch Manufacturing Company's Torrance, California Facility

  17. Brain versus Machine Control.

    Directory of Open Access Journals (Sweden)

    Jose M Carmena

    2004-12-01

    Full Text Available Dr. Octopus, the villain of the movie "Spiderman 2", is a fusion of man and machine. Neuroscientist Jose Carmena examines the facts behind this fictional account of a brain- machine interface

  18. Applied machining technology

    CERN Document Server

    Tschätsch, Heinz

    2010-01-01

    Machining and cutting technologies are still crucial for many manufacturing processes. This reference presents all important machining processes in a comprehensive and coherent way. It includes many examples of concrete calculations, problems and solutions.

  19. Machining with abrasives

    CERN Document Server

    Jackson, Mark J

    2011-01-01

    Abrasive machining is key to obtaining the desired geometry and surface quality in manufacturing. This book discusses the fundamentals and advances in the abrasive machining processes. It provides a complete overview of developing areas in the field.

  20. Understanding Legacy Features with Featureous

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

  1. Machine protection systems

    CERN Document Server

    Macpherson, A L

    2010-01-01

    A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

  2. Dictionary of machine terms

    International Nuclear Information System (INIS)

    1990-06-01

    This book has introduction of dictionary of machine terms, and a compilation committee and introductory remarks. It gives descriptions of the machine terms in alphabetical order from a to Z and also includes abbreviation of machine terms and symbol table, way to read mathematical symbols and abbreviation and terms of drawings.

  3. Mankind, machines and people

    Energy Technology Data Exchange (ETDEWEB)

    Hugli, A

    1984-01-01

    The following questions are addressed: is there a difference between machines and men, between human communication and communication with machines. Will we ever reach the point when the dream of artificial intelligence becomes a reality. Will thinking machines be able to replace the human spirit in all its aspects. Social consequences and philosophical aspects are addressed. 8 references.

  4. A Universal Reactive Machine

    DEFF Research Database (Denmark)

    Andersen, Henrik Reif; Mørk, Simon; Sørensen, Morten U.

    1997-01-01

    Turing showed the existence of a model universal for the set of Turing machines in the sense that given an encoding of any Turing machine asinput the universal Turing machine simulates it. We introduce the concept of universality for reactive systems and construct a CCS processuniversal...

  5. HTS machine laboratory prototype

    DEFF Research Database (Denmark)

    machine. The machine comprises six stationary HTS field windings wound from both YBCO and BiSCOO tape operated at liquid nitrogen temperature and enclosed in a cryostat, and a three phase armature winding spinning at up to 300 rpm. This design has full functionality of HTS synchronous machines. The design...

  6. Your Sewing Machine.

    Science.gov (United States)

    Peacock, Marion E.

    The programed instruction manual is designed to aid the student in learning the parts, uses, and operation of the sewing machine. Drawings of sewing machine parts are presented, and space is provided for the student's written responses. Following an introductory section identifying sewing machine parts, the manual deals with each part and its…

  7. Art in the Age of Machine Intelligence

    Directory of Open Access Journals (Sweden)

    Blaise Agüera y Arcas

    2017-09-01

    Full Text Available In this wide‐ranging essay, the leader of Google’s Seattle AI group and founder of the Artists and Machine Intelligence program discusses the long‐standing and complex relationship between art and technology. The transformation of artistic practice and theory that attended the 19th century photographic revolution is explored as a parallel for the current revolution in machine intelligence, which promises not only to mechanize (or democratize the means of reproduction, but also of production.

  8. Feature Article

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education. Feature Article. Articles in Resonance – Journal of Science Education. Volume 1 Issue 1 January 1996 pp 80-85 Feature Article. What's New in Computers Windows 95 · Vijnan Shastri · More Details Fulltext PDF. Volume 1 Issue 1 January 1996 pp 86-89 Feature ...

  9. Nano Mechanical Machining Using AFM Probe

    Science.gov (United States)

    Mostofa, Md. Golam

    Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces

  10. Quantum machine learning.

    Science.gov (United States)

    Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

    2017-09-13

    Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

  11. Asynchronized synchronous machines

    CERN Document Server

    Botvinnik, M M

    1964-01-01

    Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

  12. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  13. What is the machine learning?

    Science.gov (United States)

    Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

    2018-03-01

    Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

  14. THE RELATIONSHIP BETWEEN SOCIAL, POLICY AND PHYSICAL VENUE FEATURES AND SOCIAL COHESION ON CONDOM USE FOR PREGNANCY PREVENTION AMONG SEX WORKERS: A SAFER INDOOR WORK ENVIRONMENT SCALE

    Science.gov (United States)

    Duff, Putu; Shoveller, Jean; Dobrer, Sabina; Ogilvie, Gina; Montaner, Julio; Chettiar, Jill; Shannon, Kate

    2015-01-01

    Background This study aims to: report on a newly developed ‘Safer Indoor Work Environmental Scale’ that characterizes the social, policy and physical features of indoor venues and social cohesion; and using this scale, longitudinally evaluate the association between these features on sex workers’ (SWs’) condom use for pregnancy prevention. Methods Drawing on a prospective open cohort of female SWs working in indoor venues, a newly-developed ‘Safer Indoor Work Environment Scale’ was used to build six multivariable models with generalized estimating equations (GEE), to determine the independent effects of social, policy and venue-based features and social cohesion on condom use. Results Of 588 indoor SWs, 63.6% used condoms for pregnancy prevention in the last month. In multivariable GEE analysis, the following venue-based features were significantly correlated with barrier contraceptive use for pregnancy prevention: managerial practices and venue safety policies (Adjusted Odds Ratio (AOR)=1.09; 95% Confidence Interval (95%CI) 1.01–1.17) access to sexual and reproductive health services/supplies (AOR=1.10; 95%CI 1.00–1.20) access to drug harm reduction (AOR=1.13; 95%CI 1.01–1.28), and social cohesion among workers (AOR=1.05; 95%CI 1.03–1.07). Access to security features was marginally associated with condom use (AOR=1.13; 95%CI 0.99–1.29). Conclusion The findings of the current study highlight how work environment and social cohesion among SWs are related to improved condom use. Given global calls for the decriminalization of sex work, and potential legislative reforms in Canada, this study points to the critical need for new institutional arrangements (e.g., legal and regulatory frameworks; labour standards) to support safer sex workplaces. PMID:25678713

  15. The relationship between social, policy and physical venue features and social cohesion on condom use for pregnancy prevention among sex workers: a safer indoor work environment scale.

    Science.gov (United States)

    Duff, Putu; Shoveller, Jean; Dobrer, Sabina; Ogilvie, Gina; Montaner, Julio; Chettiar, Jill; Shannon, Kate

    2015-07-01

    This study aims to report on a newly developed Safer Indoor Work Environmental Scale that characterises the social, policy and physical features of indoor venues and social cohesion; and using this scale, longitudinally evaluate the association between these features on sex workers' (SWs') condom use for pregnancy prevention. Drawing on a prospective open cohort of female SWs working in indoor venues, a newly developed Safer Indoor Work Environment Scale was used to build six multivariable models with generalised estimating equations (GEE), to determine the independent effects of social, policy and physical venue-based features and social cohesion on condom use. Of 588 indoor SWs, 63.6% used condoms for pregnancy prevention in the last month. In multivariable GEE analysis, the following venue-based features were significantly correlated with barrier contraceptive use for pregnancy prevention: managerial practices and venue safety policies (adjusted OR (AOR)=1.09; 95% CI 1.01 to 1.17), access to sexual and reproductive health services/supplies (AOR=1.10; 95% CI 1.00 to 1.20), access to drug harm reduction (AOR=1.13; 95% CI 1.01 to 1.28) and social cohesion among workers (AOR=1.05; 95% CI 1.03 to 1.07). Access to security features was marginally associated with condom use (AOR=1.13; 95% CI 0.99 to 1.29). The findings of the current study highlight how work environment and social cohesion among SWs are related to improved condom use. Given global calls for the decriminalisation of sex work, and potential legislative reforms in Canada, this study points to the critical need for new institutional arrangements (eg, legal and regulatory frameworks; labour standards) to support safer sex workplaces. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  16. Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

    Science.gov (United States)

    Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

    2012-08-07

    Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

  17. Virtual Machine Language

    Science.gov (United States)

    Grasso, Christopher; Page, Dennis; O'Reilly, Taifun; Fteichert, Ralph; Lock, Patricia; Lin, Imin; Naviaux, Keith; Sisino, John

    2005-01-01

    Virtual Machine Language (VML) is a mission-independent, reusable software system for programming for spacecraft operations. Features of VML include a rich set of data types, named functions, parameters, IF and WHILE control structures, polymorphism, and on-the-fly creation of spacecraft commands from calculated values. Spacecraft functions can be abstracted into named blocks that reside in files aboard the spacecraft. These named blocks accept parameters and execute in a repeatable fashion. The sizes of uplink products are minimized by the ability to call blocks that implement most of the command steps. This block approach also enables some autonomous operations aboard the spacecraft, such as aerobraking, telemetry conditional monitoring, and anomaly response, without developing autonomous flight software. Operators on the ground write blocks and command sequences in a concise, high-level, human-readable programming language (also called VML ). A compiler translates the human-readable blocks and command sequences into binary files (the operations products). The flight portion of VML interprets the uplinked binary files. The ground subsystem of VML also includes an interactive sequence- execution tool hosted on workstations, which runs sequences at several thousand times real-time speed, affords debugging, and generates reports. This tool enables iterative development of blocks and sequences within times of the order of seconds.

  18. BADMINTON TRAINING MACHINE WITH IMPACT MECHANISM

    Directory of Open Access Journals (Sweden)

    B. F. YOUSIF

    2011-02-01

    Full Text Available In the current work, a newly machine was designed and fabricated for badminton training purpose. In the designing process, CATIA software was used to design and simulate the machine components. The design was based on direct impact method to launch the shuttle using spring as the source of the impact. Hook’s law was used theoretically to determine the initial and the maximum lengths of the springs. The main feature of the machine is that can move in two axes (up and down, left and right. For the control system, infra-red sensor and touch switch were adapted in microcontroller. The final product was locally fabricated and proved that the machine can operate properly.

  19. Salivary gland scintigraphy with 99mTc-pertechnetate in Sjoegren's syndro Relationship to clinicopathologic features of salivary and lacrimal glands

    International Nuclear Information System (INIS)

    Saito, Tohru; Fukuda, Hiroshi; Horikawa, Masa-aki; Ohmori, Kei-ichi; Shindoh, Masanobu; Amemiya, Akira

    1997-01-01

    Salivary gland scintigraphy was performed on 52 patients who were suspected of having Sjoegren's syndrome (SS), and the results were compared with clinicopathologic features of the salivary and lacrimal glands. The time-activity curves which were obtained from computer-assisted analysis of 99m Tc-pertechnetate ( 99m Tc) scintigraphy were classified into four types (normal, median, flat and sloped types). The stimulated parotid flow rate decreased and the incidence of SS-related sialographic and histopathologic findings increased significantly as the scintigraphic abnormality advanced. In addition, the lacrimal gland function decreased and the proportion of patients diagnosed as having keratoconjunctivitis sicca (KCS) increased significantly as the scintigraphic abnormality advanced. These results indicate that the results of scintigraphy are related not only to the clinicopathologic features of the salivary glands but also to the lacrimal gland functions in SS. (au) 25 refs

  20. Machines for lattice gauge theory

    International Nuclear Information System (INIS)

    Mackenzie, P.B.

    1989-05-01

    The most promising approach to the solution of the theory of strong interactions is large scale numerical simulation using the techniques of lattice gauge theory. At the present time, computing requirements for convincing calculations of the properties of hadrons exceed the capabilities of even the most powerful commercial supercomputers. This has led to the development of massively parallel computers dedicated to lattice gauge theory. This talk will discuss the computing requirements behind these machines, and general features of the components and architectures of the half dozen major projects now in existence. 20 refs., 1 fig

  1. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  2. The Newest Machine Material

    International Nuclear Information System (INIS)

    Seo, Yeong Seop; Choe, Byeong Do; Bang, Meong Sung

    2005-08-01

    This book gives descriptions of machine material with classification of machine material and selection of machine material, structure and connection of material, coagulation of metal and crystal structure, equilibrium diagram, properties of metal material, elasticity and plasticity, biopsy of metal, material test and nondestructive test. It also explains steel material such as heat treatment of steel, cast iron and cast steel, nonferrous metal materials, non metallic materials, and new materials.

  3. Introduction to machine learning

    OpenAIRE

    Baştanlar, Yalın; Özuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...

  4. Machinability of advanced materials

    CERN Document Server

    Davim, J Paulo

    2014-01-01

    Machinability of Advanced Materials addresses the level of difficulty involved in machining a material, or multiple materials, with the appropriate tooling and cutting parameters.  A variety of factors determine a material's machinability, including tool life rate, cutting forces and power consumption, surface integrity, limiting rate of metal removal, and chip shape. These topics, among others, and multiple examples comprise this research resource for engineering students, academics, and practitioners.

  5. Machining of titanium alloys

    CERN Document Server

    2014-01-01

    This book presents a collection of examples illustrating the resent research advances in the machining of titanium alloys. These materials have excellent strength and fracture toughness as well as low density and good corrosion resistance; however, machinability is still poor due to their low thermal conductivity and high chemical reactivity with cutting tool materials. This book presents solutions to enhance machinability in titanium-based alloys and serves as a useful reference to professionals and researchers in aerospace, automotive and biomedical fields.

  6. Bionic machines and systems

    Energy Technology Data Exchange (ETDEWEB)

    Halme, A.; Paanajaervi, J. (eds.)

    2004-07-01

    of bio-structures. Today's robotics research is directed towards solving the problems of the third generation intelligent robots. Most of them are not any more intended for working in production lines, as their second generation predecessors do, but for serving in different tasks related to natural environment or urban structures. Many of them are supposed to work in close cooperation with humans as a member of their community. One of the basic features needed is mobility, capability to go to the work, because works are not any more coming to the machine - as they do in factories - but the machines have to move. This, in turn, implies need for other primary functions, such as localization and navigation. Further, because the environment and details of the task are usually not known beforehand, the control system of the robot has to relay on perceptive information through sensors and senses in order to complete satisfactorily the task. Biological species have developed a large variety of solutions for all these primary functions. The variety in motion control methods provides also many interesting solutions, like walking, swimming and flying, which all are worth of mimicking in robotics. Learning is still in its infancy in intelligent robotics, especially regarding skilled tasks done with hands or tools. Biological research offers many interesting results on animal learning, which, while being a complex process in its own right, is still simpler than the corresponding human learning and thus easier to mimic. The report is based on the presentations given by the participants. The material has been collected from published references in literature and Web. Besides written material, also a video file archive has been collected and is available as an appendix to this report. The presentation order follows in a way bottom up hierarchy of subsystems in biological machines. Chapter 2 introduces the background of biological energy. Chapter 3 deals with motions, motion

  7. Tribology in machine design

    CERN Document Server

    Stolarski, Tadeusz

    1999-01-01

    ""Tribology in Machine Design is strongly recommended for machine designers, and engineers and scientists interested in tribology. It should be in the engineering library of companies producing mechanical equipment.""Applied Mechanics ReviewTribology in Machine Design explains the role of tribology in the design of machine elements. It shows how algorithms developed from the basic principles of tribology can be used in a range of practical applications within mechanical devices and systems.The computer offers today's designer the possibility of greater stringen

  8. Induction machine handbook

    CERN Document Server

    Boldea, Ion

    2002-01-01

    Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

  9. Chaotic Boltzmann machines

    Science.gov (United States)

    Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

    2013-01-01

    The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425

  10. Nanocomposites for Machining Tools

    Directory of Open Access Journals (Sweden)

    Daria Sidorenko

    2017-10-01

    Full Text Available Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance.

  11. Machine listening intelligence

    Science.gov (United States)

    Cella, C. E.

    2017-05-01

    This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

  12. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2013-01-01

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

  13. Rotating electrical machines

    CERN Document Server

    Le Doeuff, René

    2013-01-01

    In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

  14. Relationship between the cortisol awakening response and other features of the diurnal cortisol rhythm: The Multi-Ethnic Study of Atherosclerosis

    OpenAIRE

    Golden, Sherita Hill; Sánchez, Brisa N.; Wu, Meihua; Champaneri, Shivam; Diez Roux, Ana V.; Seeman, Teresa; Wand, Gary S.

    2013-01-01

    Cumulative cortisol burden is known to influence neuropsychiatric and metabolic disorders. To better understand the relationship between daily cortisol exposure and measures of the diurnal circadian cortisol rhythm, we examined the cross-sectional association of the cortisol awakening response (CAR) with wake-up cortisol, bedtime cortisol, diurnal slope, and total cortisol area under the curve (AUC). Up to 18 salivary cortisol samples were collected over 3 days from 935 White, Hispanic, and B...

  15. The relationships between morphological features and social signalling behaviours in juvenile dogs: the effect of early experience with dogs of different morphotypes.

    Science.gov (United States)

    Kerswell, Keven J; Butler, Kym L; Bennett, Pauleen; Hemsworth, Paul H

    2010-09-01

    Research on dog communication has tended to focus on breed differences and the use of lupine signals by the domestic dog. However, the relationship between morphological change and communication has received little empirical study. The link between morphology and behavioural selection in a canid undergoing domestication, the silver fox (Vulpes vulpes), has been well documented. Therefore, it is reasonable to propose a similar link may be present in another canid species that has undergone domestication, namely the domestic dog (Canis familiaris). Inter-morphotype interactions (587 interactions) of 115 juvenile dogs aged 8-20 weeks from over 30 breeds and various hybrids, enrolled in veterinary "Puppy Socialisation Classes", were video taped. Each signal that could be sent, was recorded, and the sending and the intended receiving dog identified. The frequencies with which a dog sent each category of signal, and the frequency with which each category of signal was directed at the dog (elicited), were calculated. The relationship between these frequencies and the morphology of the dog was then studied using generalized linear models. Overall morphology of the dog was not related to either the sending or eliciting of any social signaling behaviours (social signals). However, snout length was related to both the signals sent by a dog, and especially the signals that were directed to a dog (elicited). Relationships to eye cover and coat length were also found. Possible explanations for the results are discussed, and avenues for further research are indicated. Copyright 2010 Elsevier B.V. All rights reserved.

  16. Are there intelligent Turing machines?

    OpenAIRE

    Bátfai, Norbert

    2015-01-01

    This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...

  17. Machinability of structural steels with calcium addition

    International Nuclear Information System (INIS)

    Pytel, S.; Zadecki, M.

    2003-01-01

    The machinability of the plain carbon and low alloy structural steels with carbon content of 0.1-0.6% is briefly discussed in the first part of the paper. In the experimental part a dependence between the addition of calcium and some changes in sulphide and oxide inclusions morphology is presented. The Volvo test for measurement of machinability index B i has been applied. Using the multiple regression methods two relationships between machinability index B i and stereological parameters of non-metallic inclusions as well as hardness of the steels have been calculated. The authors have reached the conclusion that owing to the changes in inclusion chemical composition and geometry as result of calcium addition the machinability index of the steel can be higher. (author)

  18. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  19. Predicting drug-target interactions using restricted Boltzmann machines.

    Science.gov (United States)

    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  20. Optimization of machining fixture layout for tolerance requirements ...

    African Journals Online (AJOL)

    Dimensional accuracy of workpart under machining is strongly influenced by the layout of the fixturing elements like locators and clamps. Setup or geometrical errors in locators result in overall machining error of the feature under consideration. Therefore it is necessary to ensure that the layout is optimized for the desired ...

  1. Using High Resolution Commercial Satellite Imagery to Quantify Spatial Features of Urban Areas and their Relationship to Quality of Life Indicators in Accra, Ghana

    Science.gov (United States)

    Sandborn, A.; Engstrom, R.; Yu, Q.

    2014-12-01

    Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.

  2. Fantastic Journey through Minds and Machines.

    Science.gov (United States)

    Muir, Michael

    Intended for learners with a basic familiarity with the Logo programming language, this manual is designed to introduce them to artificial intelligence and enhance their programming capabilities. Nine chapters discuss the following features of Logo: (1) MAZE.MASTER, a look at robots and how sensors make machines aware of their environment; (2)…

  3. High speed operation of permanent magnet machines

    Science.gov (United States)

    El-Refaie, Ayman M.

    investigated. A 6kW, 36slot/30pole prototype SPM machine has been designed and built. Experimental measurements have been used to verify the analytical and FEA results. These test results have demonstrated that wide constant-power speed range can be achieved. Other important machine features such as the near-sinusoidal back-emf, high efficiency, and low cogging torque have also been demonstrated.

  4. What is the machine learning.

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. In this talk, I explore a procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus non-linear nature of the boundaries between signal and background. I will demonstrate these features in both an easy to understand toy model and an idealized LHC resonance scenario.

  5. Microsoft Azure machine learning

    CERN Document Server

    Mund, Sumit

    2015-01-01

    The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

  6. The Hooey Machine.

    Science.gov (United States)

    Scarnati, James T.; Tice, Craig J.

    1992-01-01

    Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)

  7. Nanocomposites for Machining Tools

    DEFF Research Database (Denmark)

    Sidorenko, Daria; Loginov, Pavel; Mishnaevsky, Leon

    2017-01-01

    Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials...

  8. A nucleonic weighing machine

    International Nuclear Information System (INIS)

    Anon.

    1978-01-01

    The design and operation of a nucleonic weighing machine fabricated for continuous weighing of material over conveyor belt are described. The machine uses a 40 mCi cesium-137 line source and a 10 litre capacity ionization chamber. It is easy to maintain as there are no moving parts. It can also be easily removed and reinstalled. (M.G.B.)

  9. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2015-01-01

    Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

  10. The deleuzian abstract machines

    DEFF Research Database (Denmark)

    Werner Petersen, Erik

    2005-01-01

    To most people the concept of abstract machines is connected to the name of Alan Turing and the development of the modern computer. The Turing machine is universal, axiomatic and symbolic (E.g. operating on symbols). Inspired by Foucault, Deleuze and Guattari extended the concept of abstract...

  11. Human Machine Learning Symbiosis

    Science.gov (United States)

    Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

    2017-01-01

    Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

  12. Feature Extraction

    CERN Document Server

    CERN. Geneva

    2015-01-01

    Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

  13. Solar Features

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes a variety of solar feature datasets contributed by a number of national and private solar observatories located worldwide.

  14. Site Features

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

  15. Marketing and vending machine; Marketing to jido hanbaiki

    Energy Technology Data Exchange (ETDEWEB)

    Onzo, N. [Waseda University, Tokyo (Japan)

    1999-08-10

    Vending machines in Japan have made original progress and have developed into big business. Annual sales by vending machines are 6 trillion 700 billion yen, which exceeds 6 trillion 100 billion yen sales by convenience stores. Research on vending machines may have advanced on the technical side but almost none on the marketing. In a vending machine that made an appearance in 1980 with the feature of a lottery, the winning probability was approximately one in fifty. In addition to a simple vending function, these machines have a promotion function. Some other machines have an electrical display of a commercial for products inside the machine for the purpose of attracting attention of passersby. This is an advertising function of the machines. In other words, one vending machine is capable of various marketing functions. This precisely means the subjects are numerous in the marketing research on vending machines. In contrast to the present century in which technical innovations have been made for vending machines, the coming 21st century may turn out to be the one in which marketing innovations are the mainstream for them. (NEDO)

  16. Precision machining commercialization

    International Nuclear Information System (INIS)

    1978-01-01

    To accelerate precision machining development so as to realize more of the potential savings within the next few years of known Department of Defense (DOD) part procurement, the Air Force Materials Laboratory (AFML) is sponsoring the Precision Machining Commercialization Project (PMC). PMC is part of the Tri-Service Precision Machine Tool Program of the DOD Manufacturing Technology Five-Year Plan. The technical resources supporting PMC are provided under sponsorship of the Department of Energy (DOE). The goal of PMC is to minimize precision machining development time and cost risk for interested vendors. PMC will do this by making available the high precision machining technology as developed in two DOE contractor facilities, the Lawrence Livermore Laboratory of the University of California and the Union Carbide Corporation, Nuclear Division, Y-12 Plant, at Oak Ridge, Tennessee

  17. LHC Report: machine development

    CERN Multimedia

    Rogelio Tomás García for the LHC team

    2015-01-01

    Machine development weeks are carefully planned in the LHC operation schedule to optimise and further study the performance of the machine. The first machine development session of Run 2 ended on Saturday, 25 July. Despite various hiccoughs, it allowed the operators to make great strides towards improving the long-term performance of the LHC.   The main goals of this first machine development (MD) week were to determine the minimum beam-spot size at the interaction points given existing optics and collimation constraints; to test new beam instrumentation; to evaluate the effectiveness of performing part of the beam-squeezing process during the energy ramp; and to explore the limits on the number of protons per bunch arising from the electromagnetic interactions with the accelerator environment and the other beam. Unfortunately, a series of events reduced the machine availability for studies to about 50%. The most critical issue was the recurrent trip of a sextupolar corrector circuit –...

  18. Virtual NC machine model with integrated knowledge data

    International Nuclear Information System (INIS)

    Sidorenko, Sofija; Dukovski, Vladimir

    2002-01-01

    The concept of virtual NC machining was established for providing a virtual product that could be compared with an appropriate designed product, in order to make NC program correctness evaluation, without real experiments. This concept is applied in the intelligent CAD/CAM system named VIRTUAL MANUFACTURE. This paper presents the first intelligent module that enables creation of the virtual models of existed NC machines and virtual creation of new ones, applying modular composition. Creation of a virtual NC machine is carried out via automatic knowledge data saving (features of the created NC machine). (Author)

  19. Fuel element load/unload machine for the PEC reactor

    International Nuclear Information System (INIS)

    Clayton, K.F.

    1984-01-01

    GEC Energy Systems Limited are providing two fuel element load/unload machines for use in the Italian fast reactor programme. One will be used in the mechanism test facility (IPM) at Casaccia, to check the salient features of the machine operating in a sodium environment prior to the second machine being installed in the PEC Brasimone Reactor. The machine is used to handle fuel elements, control rods and other reactor components in the sodium-immersed core of the reactor. (U.K.)

  20. The distribution and inter-relationships of radiologic features of osteoarthrosis of the hip. A survey of 4151 subjects of the Copenhagen City Heart Study: the Osteoarthrosis Substudy

    DEFF Research Database (Denmark)

    Jacobsen, Steffen; Sonne-Holm, Stig; Søballe, Kjeld

    2004-01-01

    -relationships and correlations to age, sex, body mass index (BMI) and occupational exposure to repeated lifting. RESULTS: Overall, subchondral sclerosis, cysts and osteophytes were more frequently recorded in male hip joints compared to female hip joints, while a decrease in minimum JSW by age was more pronounced...... and progressive in women after the fifth decade compared to men. Applying logistic regression analyses, only age was found to be significantly associated to pathologically reduced minimum JSW (cut off value set at osteophytes and subchondral cysts in both sexes (P ranging from 0.......00 to 0.03). Minimum JSW subchondral cysts, osteophytes and sclerosis were found to be significantly inter-related to minimum JSW

  1. Gram staining with an automatic machine.

    Science.gov (United States)

    Felek, S; Arslan, A

    1999-01-01

    This study was undertaken to develop a new Gram-staining machine controlled by a micro-controller and to investigate the quality of slides that were stained in the machine. The machine was designed and produced by the authors. It uses standard 220 V AC. Staining, washing, and drying periods are controlled by a timer built in the micro-controller. A software was made that contains a certain algorithm and time intervals for the staining mode. One-hundred and forty smears were prepared from Escherichia coli, Staphylococcus aureus, Neisseria sp., blood culture, trypticase soy broth, direct pus and sputum smears for comparison studies. Half of the slides in each group were stained with the machine, the other half by hand and then examined by four different microbiologists. Machine-stained slides had a higher clarity and less debris than the hand-stained slides (p stained slides, some Gram-positive organisms showed poor Gram-positive staining features (p Gram staining with the automatic machine increases the staining quality and helps to decrease the work load in a busy diagnostic laboratory.

  2. Machine Learning and Radiology

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

  3. Machine learning and radiology.

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M

    2012-07-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

  4. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

    This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

  5. Delayed small intestinal transit in patients with long-standing type 1 diabetes mellitus: investigation of the relationships with clinical features, gastric emptying, psychological distress, and nutritional parameters.

    Science.gov (United States)

    Faria, Mariza; Pavin, Elizabeth João; Parisi, Maria Cândida Ribeiro; Lorena, Sônia Letícia Silva; Brunetto, Sérgio Quirino; Ramos, Celso Dario; Pavan, Célia Regina; Mesquita, Maria Aparecida

    2013-01-01

    Studies on small intestinal transit in type 1 diabetes mellitus have reported contradictory results. This study assessed the orocecal transit time (OCTT) in a group of patients with type 1 diabetes mellitus and its relationships with gastrointestinal symptoms, glycemic control, chronic complications of diabetes, anthropometric indices, gastric emptying, small intestinal bacterial overgrowth (SIBO), and psychological distress. Twenty-eight patients with long-standing (>10 years) type 1 diabetes mellitus (22 women, six men; mean age, 39 ± 9 years) participated in the study. The lactulose hydrogen breath test was used to determine OCTT and the occurrence of SIBO. The presence of anxiety and depression was assessed by the Hospital Anxiety and Depression scale. Gastric emptying was measured by scintigraphy. Anthropometric indices included body mass index, percentage body fat, midarm circumference, and arm muscle area. There was a statistically significant increase in OCTT values in diabetes patients (79 ± 41 min) in comparison with controls (54 ± 17 min) (P=0.01). Individual analysis showed that OCTT was above the upper limit (mean+2 SD) in 30.8% of patients. All anthropometric parameters were significantly decreased (Pdiabetic retinopathy, glycated hemoglobin, delayed gastric emptying, SIBO, anxiety, or depression. Small bowel transit may be delayed in about one-third of patients with long-standing type 1 diabetes mellitus. This abnormality seems to have a negative effect on nutritional status in these patients.

  6. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

    Directory of Open Access Journals (Sweden)

    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  7. Molecular spectroscopic features of protein in newly developed chickpea: Relationship with protein chemical profile and metabolism in the rumen and intestine of dairy cows

    Science.gov (United States)

    Sun, Baoli; Khan, Nazir Ahmad; Yu, Peiqiang

    2018-05-01

    The first aim of this study was to investigate the nutritional value of crude protein (CP) in CDC [Crop Development Centre (CDC), University of Saskatchewan] chickpea varieties (Frontier kabuli and Corinne desi) in comparison with a CDC barley variety in terms of: 1) CP chemical profile and subfractions; (2) in situ rumen degradation kinetics and intestinal digestibility of CP; 2) metabolizable protein (MP) supply to dairy cows; and (3) protein molecular structure characteristics using advanced molecular spectroscopy. The second aim was to quantify the relationship between protein molecular spectral characteristics and CP subfractions, in situ rumen CP degradation characteristics, intestinal digestibility of CP, and MP supply to dairy cows. Samples (n = 4) of each variety, from two consecutive years were analyzed. Chickpeas had higher (P content (21.71-22.11 vs 12.96% DM), with higher (P content, and any of the measured in situ degradation and molecular spectral characteristics of protein. The content of RUP was positively (r = 0.94, P content of CP (R2 = 0.91) D-fraction (R2 = 0.82), RDP (R2 = 0.77), RUP (R2 = 0.77), TDP (R2 = 0.98), MP (R2 = 0.80), and FMV (R2 = 0.80) can be predicted from amide II peak height. Despite extensive ruminal degradation, chickpea is a good source of MP for dairy cows, and molecular spectroscopy can be used to rapidly characterize feed protein molecular structures and predict their digestibility and nutritive value.

  8. Molecular spectroscopic features of protein in newly developed chickpea: Relationship with protein chemical profile and metabolism in the rumen and intestine of dairy cows.

    Science.gov (United States)

    Sun, Baoli; Khan, Nazir Ahmad; Yu, Peiqiang

    2018-05-05

    The first aim of this study was to investigate the nutritional value of crude protein (CP) in CDC [Crop Development Centre (CDC), University of Saskatchewan] chickpea varieties (Frontier kabuli and Corinne desi) in comparison with a CDC barley variety in terms of: 1) CP chemical profile and subfractions; (2) in situ rumen degradation kinetics and intestinal digestibility of CP; 2) metabolizable protein (MP) supply to dairy cows; and (3) protein molecular structure characteristics using advanced molecular spectroscopy. The second aim was to quantify the relationship between protein molecular spectral characteristics and CP subfractions, in situ rumen CP degradation characteristics, intestinal digestibility of CP, and MP supply to dairy cows. Samples (n=4) of each variety, from two consecutive years were analyzed. Chickpeas had higher (Pmolecular spectral data of chickpeas can be distinguished from the barley. The two chickpeas did not differ in CP content, and any of the measured in situ degradation and molecular spectral characteristics of protein. The content of RUP was positively (r=0.94, Pmolecular spectroscopy can be used to rapidly characterize feed protein molecular structures and predict their digestibility and nutritive value. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

  9. EGFR Mutations in Surgically Resected Fresh Specimens from 697 Consecutive Chinese Patients with Non-Small Cell Lung Cancer and Their Relationships with Clinical Features

    Directory of Open Access Journals (Sweden)

    Yuanyang Lai

    2013-12-01

    Full Text Available We aimed to reveal the true status of epidermal growth factor receptor (EGFR mutations in Chinese patients with non-small cell lung cancer (NSCLC after lung resections. EGFR mutations of surgically resected fresh tumor samples from 697 Chinese NSCLC patients were analyzed by Amplification Refractory Mutation System (ARMS. Correlations between EGFR mutation hotspots and clinical features were also explored. Of the 697 NSCLC patients, 235 (33.7% patients had tyrosine kinase inhibitor (TKIs sensitive EGFR mutations in 41 (14.5% of the 282 squamous carcinomas, 155 (52.9% of the 293 adenocarcinomas, 34 (39.5% of the 86 adenosquamous carcinomas, one (9.1% of the 11 large-cell carcinomas, 2 (11.1% of the 18 sarcomatoid carcinomas, and 2 (28.6% of the 7 mucoepidermoid carcinomas. TKIs sensitive EGFR mutations were more frequently found in female patients (p < 0.001, non-smokers (p = 0.047 and adenocarcinomas (p < 0.001. The rates of exon 19 deletion mutation (19-del, exon 21 L858R point mutation (L858R, exon 21 L861Q point mutation (L861Q, exon 18 G719X point mutations (G719X, including G719C, G719S, G719A were 43.4%, 48.1%, 1.7% and 6.8%, respectively. Exon 20 T790M point mutation (T790M was detected in 3 squamous carcinomas and 3 adenocarcinomas and exon 20 insertion mutation (20-ins was detected in 2 patients with adenocarcinoma. Our results show the rates of EGFR mutations are higher in all types of NSCLC in Chinese patients. 19-del and L858R are two of the more frequent mutations. EGFR mutation detection should be performed as a routine postoperative examination in Chinese NSCLC patients.

  10. Fundamentals of machine design

    CERN Document Server

    Karaszewski, Waldemar

    2011-01-01

    A forum of researchers, educators and engineers involved in various aspects of Machine Design provided the inspiration for this collection of peer-reviewed papers. The resultant dissemination of the latest research results, and the exchange of views concerning the future research directions to be taken in this field will make the work of immense value to all those having an interest in the topics covered. The book reflects the cooperative efforts made in seeking out the best strategies for effecting improvements in the quality and the reliability of machines and machine parts and for extending

  11. Machine Learning for Hackers

    CERN Document Server

    Conway, Drew

    2012-01-01

    If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyz

  12. Creativity in Machine Learning

    OpenAIRE

    Thoma, Martin

    2016-01-01

    Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.

  13. Machine Tool Software

    Science.gov (United States)

    1988-01-01

    A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

  14. Quantitative criticism of literary relationships.

    Science.gov (United States)

    Dexter, Joseph P; Katz, Theodore; Tripuraneni, Nilesh; Dasgupta, Tathagata; Kannan, Ajay; Brofos, James A; Bonilla Lopez, Jorge A; Schroeder, Lea A; Casarez, Adriana; Rabinovich, Maxim; Haimson Lushkov, Ayelet; Chaudhuri, Pramit

    2017-04-18

    Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.

  15. Classifying smoking urges via machine learning.

    Science.gov (United States)

    Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

    2016-12-01

    Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

  16. Development of a coppice planting machine to commercial standards

    Energy Technology Data Exchange (ETDEWEB)

    Turton, J.S.

    2000-07-01

    This report gives details of the development work carried out on the Turton Engineering Coppice Planting machine in order to commercially market it. The background to the machine which plants single rows of cuttings from rods is traced,, and previous development work, design work, production of sub-assemblies and the assembly of modules, inspection and assembly, static trials, and commercial planting are examined. Further machine developments, proving trials, and recommendations for further work are discussed. Appendices address relationships applicable to vertical planting, the Turton short rotation cultivation machine rod format, estimated prices and charges, and a list of main suppliers. (UK)

  17. Relationship between the cortisol awakening response and other features of the diurnal cortisol rhythm: the Multi-Ethnic Study of Atherosclerosis.

    Science.gov (United States)

    Golden, Sherita Hill; Sánchez, Brisa N; Wu, Meihua; Champaneri, Shivam; Diez Roux, Ana V; Seeman, Teresa; Wand, Gary S

    2013-11-01

    Cumulative cortisol burden is known to influence neuropsychiatric and metabolic disorders. To better understand the relationship between daily cortisol exposure and measures of the diurnal circadian cortisol rhythm, we examined the cross-sectional association of the cortisol awakening response (CAR) with wake-up cortisol, bedtime cortisol, diurnal slope, and total cortisol area under the curve (AUC). Up to 18 salivary cortisol samples were collected over 3 days from 935 White, Hispanic, and Black individuals (mean age 65 ± 9.8 years) in the Multi-Ethnic Study of Atherosclerosis. Outcome measures included awakening cortisol, CAR (awakening to 30 min post-awakening), early decline (30 min to 2h post-awakening), late decline (2h post-awakening to bedtime), and the corresponding AUCs. Total cortisol AUC was a summary measure of cumulative cortisol exposure. Higher CAR was associated with significantly lower wake-up cortisol (β=-0.56; 95% CI: -0.59 to -0.53) and a higher early decline AUC (β=0.38; 95% CI: 0.34-0.42) but was not associated with total cortisol AUC (β=0.04; 95% CI: -0.01 to 0.09), or other diurnal cortisol curve components following multivariable adjustment. Total cortisol AUC was significantly and positively associated with wake-up cortisol (β=0.36; 95% CI: 0.32-0.40), bedtime cortisol (β=0.61; 95% CI: 0.58-0.64), and other AUC measures, following multivariable adjustment. Associations were similar by sex, race/ethnicity, and age categories. We conclude that bedtime cortisol showed the strongest correlation with total cortisol AUC, suggesting it may be a marker of daily cortisol exposure. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection

    Directory of Open Access Journals (Sweden)

    Daren Yu

    2011-08-01

    Full Text Available Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with qmin-Redundancy-Max-Relevanceq, qMax-Dependencyq and min-Redundancy-Max-Dependencyq algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.

  19. Better feature acquisition through the use of infrared imaging for human detection systems

    CSIR Research Space (South Africa)

    Kunene, Dumisani C

    2017-09-01

    Full Text Available are used for training the classifiers with infrared samples. The conventional use of support vector machines (SVM) on HOG features is tested against extreme learning machines (ELM) and convolutional neural networks (CNN). The results obtained show...

  20. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  1. Coordinate measuring machines

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceabilit...... and uncertainty during coordinate measurements, 3) Digitalisation and Reverse Engineering. This document contains a short description of each step in the exercise and schemes with room for taking notes of the results.......This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceability...

  2. Machine Vision Handbook

    CERN Document Server

    2012-01-01

    The automation of visual inspection is becoming more and more important in modern industry as a consistent, reliable means of judging the quality of raw materials and manufactured goods . The Machine Vision Handbook  equips the reader with the practical details required to engineer integrated mechanical-optical-electronic-software systems. Machine vision is first set in the context of basic information on light, natural vision, colour sensing and optics. The physical apparatus required for mechanized image capture – lenses, cameras, scanners and light sources – are discussed followed by detailed treatment of various image-processing methods including an introduction to the QT image processing system. QT is unique to this book, and provides an example of a practical machine vision system along with extensive libraries of useful commands, functions and images which can be implemented by the reader. The main text of the book is completed by studies of a wide variety of applications of machine vision in insp...

  3. Enter the machine

    Science.gov (United States)

    Palittapongarnpim, Pantita; Sanders, Barry C.

    2018-05-01

    Quantum tomography infers quantum states from measurement data, but it becomes infeasible for large systems. Machine learning enables tomography of highly entangled many-body states and suggests a new powerful approach to this problem.

  4. Introduction to AC machine design

    CERN Document Server

    Lipo, Thomas A

    2018-01-01

    AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...

  5. Metalworking and machining fluids

    Science.gov (United States)

    Erdemir, Ali; Sykora, Frank; Dorbeck, Mark

    2010-10-12

    Improved boron-based metal working and machining fluids. Boric acid and boron-based additives that, when mixed with certain carrier fluids, such as water, cellulose and/or cellulose derivatives, polyhydric alcohol, polyalkylene glycol, polyvinyl alcohol, starch, dextrin, in solid and/or solvated forms result in improved metalworking and machining of metallic work pieces. Fluids manufactured with boric acid or boron-based additives effectively reduce friction, prevent galling and severe wear problems on cutting and forming tools.

  6. Quantum Machine Learning

    OpenAIRE

    Romero García, Cristian

    2017-01-01

    [EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

  7. Human-machine interactions

    Science.gov (United States)

    Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM

    2009-04-28

    Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

  8. Some relations between quantum Turing machines and Turing machines

    OpenAIRE

    Sicard, Andrés; Vélez, Mario

    1999-01-01

    For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

  9. Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: How Science and Technique Work?

    Directory of Open Access Journals (Sweden)

    Raffaele Pisano

    2014-10-01

    Full Text Available This paper is divided into two parts, this being the first one. The second is entitled ‘Historical and Epistemological Reflections on the Culture of Machines around Renaissance: Machines, Machineries and Perpetual Motion’ and will be published in Acta Baltica Historiae et Philosophiae Scientiarum in 2015. Based on our recent studies, we provide here a historical and epistemological feature on the role played by machines and machineries. Ours is an epistemological thesis based on a series of historical examples to show that the relations between theoretical science and the construction of machines cannot be taken for granted, a priori. Our analysis is mainly based on the culture of machines around 15th and 17th centuries, namely the epoch of Late Renaissance and Early Modern Age. For this is the period of scientific revolution and this age offers abundant interesting material for researches into the relations of theoretical science/construction of machines as well. However, to prove our epistemological thesis, we will also exploit examples of machines built in other historical periods. Particularly, a discussion concerning the relationship between science theory and the development of science art crafts produced by non-recognized scientists in a certain historical time is presented. The main questions are: when and why did the tension between science (physics, mathematics and geometry give rise to a new scientific approach to applied discipline such as studies on machines and machineries? What kind of science was used (if at all for projecting machines and machineries? Was science at the time a necessary precondition to build a machine? In the first part we will focus on the difference between Aristotelian-Euclidean and Archimedean approaches and we will outline the heritage of these two different approaches in late medieval and Renaissance science. In the second part, we will apply our reconstructions to some historical and epistemological

  10. Reactor refueling machine simulator

    International Nuclear Information System (INIS)

    Rohosky, T.L.; Swidwa, K.J.

    1987-01-01

    This patent describes in combination: a nuclear reactor; a refueling machine having a bridge, trolley and hoist each driven by a separate motor having feedback means for generating a feedback signal indicative of movement thereof. The motors are operable to position the refueling machine over the nuclear reactor for refueling the same. The refueling machine also has a removable control console including means for selectively generating separate motor signals for operating the bridge, trolley and hoist motors and for processing the feedback signals to generate an indication of the positions thereof, separate output leads connecting each of the motor signals to the respective refueling machine motor, and separate input leads for connecting each of the feedback means to the console; and a portable simulator unit comprising: a single simulator motor; a single simulator feedback signal generator connected to the simulator motor for generating a simulator feedback signal in response to operation of the simulator motor; means for selectively connecting the output leads of the console to the simulator unit in place of the refueling machine motors, and for connecting the console input leads to the simulator unit in place of the refueling machine motor feedback means; and means for driving the single simulator motor in response to any of the bridge, trolley or hoist motor signals generated by the console and means for applying the simulator feedback signal to the console input lead associated with the motor signal being generated by the control console

  11. Computer and machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2012-01-01

    Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-il...

  12. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  13. Thermodynamic database for proteins: features and applications.

    Science.gov (United States)

    Gromiha, M Michael; Sarai, Akinori

    2010-01-01

    We have developed a thermodynamic database for proteins and mutants, ProTherm, which is a collection of a large number of thermodynamic data on protein stability along with the sequence and structure information, experimental methods and conditions, and literature information. This is a valuable resource for understanding/predicting the stability of proteins, and it can be accessible at http://www.gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html . ProTherm has several features including various search, display, and sorting options and visualization tools. We have analyzed the data in ProTherm to examine the relationship among thermodynamics, structure, and function of proteins. We describe the progress on the development of methods for understanding/predicting protein stability, such as (i) relationship between the stability of protein mutants and amino acid properties, (ii) average assignment method, (iii) empirical energy functions, (iv) torsion, distance, and contact potentials, and (v) machine learning techniques. The list of online resources for predicting protein stability has also been provided.

  14. Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

    International Nuclear Information System (INIS)

    Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

    2013-01-01

    The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

  15. The Knife Machine. Module 15.

    Science.gov (United States)

    South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

    This module on the knife machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the knife machine (a single needle or multi-needle machine which sews and cuts at the same time). These components are provided: an introduction, directions, an objective,…

  16. The Buttonhole Machine. Module 13.

    Science.gov (United States)

    South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

    This module on the bottonhole machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers two topics: performing special operations on the buttonhole machine (parts and purpose) and performing special operations on the buttonhole machine (gauged buttonholes). For each topic these components are…

  17. Micro-machined resonator oscillator

    Science.gov (United States)

    Koehler, Dale R.; Sniegowski, Jeffry J.; Bivens, Hugh M.; Wessendorf, Kurt O.

    1994-01-01

    A micro-miniature resonator-oscillator is disclosed. Due to the miniaturization of the resonator-oscillator, oscillation frequencies of one MHz and higher are utilized. A thickness-mode quartz resonator housed in a micro-machined silicon package and operated as a "telemetered sensor beacon" that is, a digital, self-powered, remote, parameter measuring-transmitter in the FM-band. The resonator design uses trapped energy principles and temperature dependence methodology through crystal orientation control, with operation in the 20-100 MHz range. High volume batch-processing manufacturing is utilized, with package and resonator assembly at the wafer level. Unique design features include squeeze-film damping for robust vibration and shock performance, capacitive coupling through micro-machined diaphragms allowing resonator excitation at the package exterior, circuit integration and extremely small (0.1 in. square) dimensioning. A family of micro-miniature sensor beacons is also disclosed with widespread applications as bio-medical sensors, vehicle status monitors and high-volume animal identification and health sensors. The sensor family allows measurement of temperatures, chemicals, acceleration and pressure. A microphone and clock realization is also available.

  18. Machining of Metal Matrix Composites

    CERN Document Server

    2012-01-01

    Machining of Metal Matrix Composites provides the fundamentals and recent advances in the study of machining of metal matrix composites (MMCs). Each chapter is written by an international expert in this important field of research. Machining of Metal Matrix Composites gives the reader information on machining of MMCs with a special emphasis on aluminium matrix composites. Chapter 1 provides the mechanics and modelling of chip formation for traditional machining processes. Chapter 2 is dedicated to surface integrity when machining MMCs. Chapter 3 describes the machinability aspects of MMCs. Chapter 4 contains information on traditional machining processes and Chapter 5 is dedicated to the grinding of MMCs. Chapter 6 describes the dry cutting of MMCs with SiC particulate reinforcement. Finally, Chapter 7 is dedicated to computational methods and optimization in the machining of MMCs. Machining of Metal Matrix Composites can serve as a useful reference for academics, manufacturing and materials researchers, manu...

  19. Design of on-power fuelling machines

    International Nuclear Information System (INIS)

    Jackson, W.H.

    2004-01-01

    In May 1957, CGE was asked to design a fuelling machine for NPD2 Reactor. Two fuelling machines were required, one at each end of the reactor, that could either push the fuel bundles through the reactor or accept the bundles being pushed out. The machines had to connect on to the end fittings of the same tube, seal, fill with heavy water and pressure up to 1000 psi without external leaks. Each machine had to remove the tube seal plug from its end fitting and store it in an indexing magazine, which also had to hold up to six fuel bundles, or retrieve that many, if the magazine was empty. There was also the provision to store a spare plug. When finished moving fuel bundles, the tube plugs were to be replaced and tested for leaks, before the fuelling machines would be detached from the end fittings. This was all to be done by remote control. By late September 1957, sufficient design features were on paper and CGE management made a presentation to AECL at Chalk River Laboratories and this proposal was later accepted

  20. Virtual Machine in Automation Projects

    OpenAIRE

    Xing, Xiaoyuan

    2010-01-01

    Virtual machine, as an engineering tool, has recently been introduced into automation projects in Tetra Pak Processing System AB. The goal of this paper is to examine how to better utilize virtual machine for the automation projects. This paper designs different project scenarios using virtual machine. It analyzes installability, performance and stability of virtual machine from the test results. Technical solutions concerning virtual machine are discussed such as the conversion with physical...

  1. Non-conventional electrical machines

    CERN Document Server

    Rezzoug, Abderrezak

    2013-01-01

    The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

  2. Fall Detection Using Smartphone Audio Features.

    Science.gov (United States)

    Cheffena, Michael

    2016-07-01

    An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

  3. Advanced SLARette delivery machine

    International Nuclear Information System (INIS)

    Bodner, R.R.

    1995-01-01

    SLARette 1 equipment, comprising of a SLARette Delivery Machine, SLAR Tools, SLAR power supplies and SLAR Inspection Systems was designed, developed and manufactured to service fuel channels of CANDU 6 stations during the regular yearly station outages. The Mark 2 SLARette Delivery Machine uses a Push Tube system to provide the axial and rotary movements of the SLAR Tool. The Push Tubes are operated remotely but must be attached and removed manually. Since this operation is performed at the Reactor face, there is radiation dose involved for the workers. An Advanced SLARette Delivery Machine which incorporates a computer controlled telescoping Ram in the place of the Push Tubes has been recently designed and manufactured. Utilization of the Advanced SLARette Delivery Machine significantly reduces the amount of radiation dose picked up by the workers because the need to have workers at the face of the Reactor during the SLARette operation is greatly reduced. This paper describes the design, development and manufacturing process utilized to produce the Advanced SLARette Delivery Machine and the experience gained during the Gentilly-2 NGS Spring outage. (author)

  4. The Bearingless Electrical Machine

    Science.gov (United States)

    Bichsel, J.

    1992-01-01

    Electromagnetic bearings allow the suspension of solids. For rotary applications, the most important physical effect is the force of a magnetic circuit to a high permeable armature, called the MAXWELL force. Contrary to the commonly used MAXWELL bearings, the bearingless electrical machine will take advantage of the reaction force of a conductor carrying a current in a magnetic field. This kind of force, called Lorentz force, generates the torque in direct current, asynchronous and synchronous machines. The magnetic field, which already exists in electrical machines and helps to build up the torque, can also be used for the suspension of the rotor. Besides the normal winding of the stator, a special winding was added, which generates forces for levitation. So a radial bearing, which is integrated directly in the active part of the machine, and the motor use the laminated core simultaneously. The winding was constructed for the levitating forces in a special way so that commercially available standard ac inverters for drives can be used. Besides wholly magnetic suspended machines, there is a wide range of applications for normal drives with ball bearings. Resonances of the rotor, especially critical speeds, can be damped actively.

  5. Asymmetric quantum cloning machines

    International Nuclear Information System (INIS)

    Cerf, N.J.

    1998-01-01

    A family of asymmetric cloning machines for quantum bits and N-dimensional quantum states is introduced. These machines produce two approximate copies of a single quantum state that emerge from two distinct channels. In particular, an asymmetric Pauli cloning machine is defined that makes two imperfect copies of a quantum bit, while the overall input-to-output operation for each copy is a Pauli channel. A no-cloning inequality is derived, characterizing the impossibility of copying imposed by quantum mechanics. If p and p ' are the probabilities of the depolarizing channels associated with the two outputs, the domain in (√p,√p ' )-space located inside a particular ellipse representing close-to-perfect cloning is forbidden. This ellipse tends to a circle when copying an N-dimensional state with N→∞, which has a simple semi-classical interpretation. The symmetric Pauli cloning machines are then used to provide an upper bound on the quantum capacity of the Pauli channel of probabilities p x , p y and p z . The capacity is proven to be vanishing if (√p x , √p y , √p z ) lies outside an ellipsoid whose pole coincides with the depolarizing channel that underlies the universal cloning machine. Finally, the tradeoff between the quality of the two copies is shown to result from a complementarity akin to Heisenberg uncertainty principle. (author)

  6. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  7. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

    Energy Technology Data Exchange (ETDEWEB)

    Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Coufalova, Lucie [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Department of Neurosurgery of First Faculty of Medicine, Charles University in Prague, Military University Hospital, Prague 128 21 (Czech Republic); International Clinical Research Center, St. Anne’s University Hospital Brno, Brno 656 91 (Czech Republic); Lachance, Daniel H. [Department of Neurology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Parney, Ian F. [Department of Neurologic Surgery, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Carter, Rickey E. [Department of Health Sciences Research, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Buckner, Jan C. [Department of Medical Oncology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States)

    2016-06-15

    Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

  8. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

    International Nuclear Information System (INIS)

    Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.; Coufalova, Lucie; Lachance, Daniel H.; Parney, Ian F.; Carter, Rickey E.; Buckner, Jan C.

    2016-01-01

    Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O 6 -methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

  9. Two-Dimensional Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Bo Jia

    2015-01-01

    (BP networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

  10. Fundamental Study on Electrical Discharge Machining

    OpenAIRE

    Uno, Yoshiyuki; Nakajima, Toshikatsu; Endo, Osamu

    1989-01-01

    The generation mechanism of crater in electrical discharge machining is analyzed with a single pulse discharge device for alloy tool steel, black alumina ceramics, cermet and cemented carbide, investigating the gap voltage, the discharge current, the shape of crater, the wear of electrode and so on. The experimental analysis makes it clear that the shape of crater has a characteristic feature for the kind of workpiece. The shape of electrode, which changes with the wear by an electric spark, ...

  11. Human-Machine Communication

    International Nuclear Information System (INIS)

    Farbrot, J.E.; Nihlwing, Ch.; Svengren, H.

    2005-01-01

    New requirements for enhanced safety and design changes in process systems often leads to a step-wise installation of new information and control equipment in the control room of older nuclear power plants, where nowadays modern digital I and C solutions with screen-based human-machine interfaces (HMI) most often are introduced. Human factors (HF) expertise is then required to assist in specifying a unified, integrated HMI, where the entire integration of information is addressed to ensure an optimal and effective interplay between human (operators) and machine (process). Following a controlled design process is the best insurance for ending up with good solutions. This paper addresses the approach taken when introducing modern human-machine communication in the Oskarshamn 1 NPP, the results, and the lessons learned from this work with high operator involvement seen from an HF point of view. Examples of possibilities modern technology might offer for the operators are also addressed. (orig.)

  12. Machines and Metaphors

    Directory of Open Access Journals (Sweden)

    Ángel Martínez García-Posada

    2016-10-01

    Full Text Available The edition La ley del reloj. Arquitectura, máquinas y cultura moderna (Cátedra, Madrid, 2016 registers the useful paradox of the analogy between architecture and technique. Its author, the architect Eduardo Prieto, also a philosopher, professor and writer, acknowledges the obvious distance from machines to buildings, so great that it can only be solved using strange comparisons, since architecture does not move nor are the machines habitable, however throughout the book, from the origin of the metaphor of the machine, with clarity in his essay and enlightening erudition, he points out with certainty some concomitances of high interest, drawing throughout history a beautiful cartography of the fruitful encounter between organics and mechanics.

  13. Machine Learning for Security

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

  14. Chatter and machine tools

    CERN Document Server

    Stone, Brian

    2014-01-01

    Focussing on occurrences of unstable vibrations, or Chatter, in machine tools, this book gives important insights into how to eliminate chatter with associated improvements in product quality, surface finish and tool wear. Covering a wide range of machining processes, including turning, drilling, milling and grinding, the author uses his research expertise and practical knowledge of vibration problems to provide solutions supported by experimental evidence of their effectiveness. In addition, this book contains links to supplementary animation programs that help readers to visualise the ideas detailed in the text. Advancing knowledge in chatter avoidance and suggesting areas for new innovations, Chatter and Machine Tools serves as a handbook for those desiring to achieve significant reductions in noise, longer tool and grinding wheel life and improved product finish.

  15. A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data

    KAUST Repository

    Abusamra, Heba

    2013-05-01

    Microarray technology has enriched the study of gene expression in such a way that scientists are now able to measure the expression levels of thousands of genes in a single experiment. Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification, interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This thesis aims on a comparative study of state-of-the-art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k- nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t- statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used for this study. Different experiments have been applied to compare the performance of the classification methods with and without performing feature selection. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in

  16. Writer identification using curvature-free features

    NARCIS (Netherlands)

    He, Sheng; Schomaker, Lambertus

    2017-01-01

    Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit

  17. Clojure for machine learning

    CERN Document Server

    Wali, Akhil

    2014-01-01

    A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated.This book is intended for Clojure developers who want to explore the area of machine learning. Basic understanding of the Clojure programming language is required, but thorough acquaintance with the standard Clojure library or any libraries are not required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.

  18. Machine learning systems

    Energy Technology Data Exchange (ETDEWEB)

    Forsyth, R

    1984-05-01

    With the dramatic rise of expert systems has come a renewed interest in the fuel that drives them-knowledge. For it is specialist knowledge which gives expert systems their power. But extracting knowledge from human experts in symbolic form has proved arduous and labour-intensive. So the idea of machine learning is enjoying a renaissance. Machine learning is any automatic improvement in the performance of a computer system over time, as a result of experience. Thus a learning algorithm seeks to do one or more of the following: cover a wider range of problems, deliver more accurate solutions, obtain answers more cheaply, and simplify codified knowledge. 6 references.

  19. Machine tool evaluation

    International Nuclear Information System (INIS)

    Lunsford, B.E.

    1976-01-01

    Continued improvement in numerical control (NC) units and the mechanical components used in the construction of today's machine tools, necessitate the use of more precise instrumentation to calibrate and determine the capabilities of these systems. It is now necessary to calibrate most tape-control lathes to a tool-path positioning accuracy of +-300 microinches in the full slide travel and, on some special turning and boring machines, a capability of +-100 microinches must be achieved. The use of a laser interferometer to determine tool-path capabilities is described

  20. Electrical machines & their applications

    CERN Document Server

    Hindmarsh, J

    1984-01-01

    A self-contained, comprehensive and unified treatment of electrical machines, including consideration of their control characteristics in both conventional and semiconductor switched circuits. This new edition has been expanded and updated to include material which reflects current thinking and practice. All references have been updated to conform to the latest national (BS) and international (IEC) recommendations and a new appendix has been added which deals more fully with the theory of permanent-magnets, recognising the growing importance of permanent-magnet machines. The text is so arra