56 research outputs found
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Backpropagation architecture optimization and an application in nuclear power plant diagnostics
This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a root'' network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, and other classifier'' network is trained to recognize the particular transient taking place. these networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at the Iowa Electric Light and Power Company's Duane Arnold Energy Center (DAEC) operator training simulator
Time evolution of in vivo articular cartilage repair induced by bone marrow stimulation and scaffold implantation in rabbits
Purpose: Tissue engineering techniques were used to study cartilage repair over a 12-month period in a rabbit model.
Methods: A full-depth chondral defect along with subchondral bone injury were originated in the knee joint, where a biostable porous scaffold was implanted, synthesized of poly(ethyl acrylate-co-hydroxyethyl acrylate) copolymer. Morphological evolution of cartilage repair was studied 1 and 2 weeks, and 1, 3, and 12 months after implantation by histological techniques. The 3-month group was chosen to compare cartilage repair to an additional group where scaffolds were preseeded with allogeneic chondrocytes before implantation, and also to controls, who underwent the same surgery procedure, with no scaffold implantation.
Results: Neotissue growth was first observed in the deepest scaffold pores 1 week after implantation, which spread thereafter; 3 months later scaffold pores were filled mostly with cartilaginous tissue in superficial and middle zones, and with bone tissue adjacent to subchondral bone. Simultaneously, native chondrocytes at the edges of the defect started to proliferate 1 week after implantation; within a month those edges had grown centripetally and seemed to embed the scaffold, and after 3 months, hyaline-like cartilage was observed on the condylar surface. Preseeded scaffolds slightly improved tissue growth, although the quality of repair tissue was similar to non-preseeded scaffolds. Controls showed that fibrous cartilage was mainly filling the repair area 3 months after surgery. In the 12-month group, articular cartilage resembled the untreated surface.
Conclusions: Scaffolds guided cartilaginous tissue growth in vivo, suggesting their importance in stress transmission to the cells for cartilage repair.This study was supported by the Spanish Ministry of Science and Innovation through MAT2010-21611-C03-00 project (including the FEDER financial support), by Conselleria de Educacion (Generalitat Valenciana, Spain) PROMETEO/2011/084 grant, and by CIBER-BBN en Bioingenieria, Biomateriales y Nanomedicina. The work of JLGR was partially supported by funds from the Generalitat Valenciana, ACOMP/2012/075 project. CIBER-BBN is an initiative funded by the VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the - Instituto de Salud Carlos III with assistance from the European Regional Development Fund.Sancho-Tello Valls, M.; Forriol, F.; Gastaldi, P.; Ruiz Sauri, A.; Martín De Llano, JJ.; Novella-Maestre, E.; Antolinos Turpín, CM.... (2015). 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Physical and Biological Analysis of Polymeric Substrates for Epithelial Cell Growth. Biomacromolecules, 8(8), 2429-2436. doi:10.1021/bm0703012Funayama, A., Niki, Y., Matsumoto, H., Maeno, S., Yatabe, T., Morioka, H., … Toyama, Y. (2008). Repair of full-thickness articular cartilage defects using injectable type II collagen gel embedded with cultured chondrocytes in a rabbit model. Journal of Orthopaedic Science, 13(3), 225-232. doi:10.1007/s00776-008-1220-zKitahara, S., Nakagawa, K., Sah, R. L., Wada, Y., Ogawa, T., Moriya, H., & Masuda, K. (2008). In Vivo Maturation of Scaffold-free Engineered Articular Cartilage on Hydroxyapatite. Tissue Engineering Part A, 14(11), 1905-1913. doi:10.1089/ten.tea.2006.0419Martinez-Diaz, S., Garcia-Giralt, N., Lebourg, M., Gómez-Tejedor, J.-A., Vila, G., Caceres, E., … Monllau, J. C. (2010). In Vivo Evaluation of 3-Dimensional Polycaprolactone Scaffolds for Cartilage Repair in Rabbits. 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Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.
BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362
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Neural network recognition of nuclear power plant transients. Final report, April 15, 1992--April 15, 1995
The objective of this report is to describe results obtained during the second year of funding that will lead to the development of an artificial neural network (A.N.N) fault diagnostic system for the real-time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety-parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the second of three scheduled years for the project. Included herein is a summary of the second year`s results as well as descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period
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High-speed reconstruction of spect images with a tailored piecewise neural network
Artificial neural networks (ANNs) have proven to be highly adept at mapping complex functional relationships. We have previously shown that a standard backpropagation neural network can be trained to reconstruct sections of single photon emission computed tomography (SPECT) images based on the planar image projections as inputs. In this study, we demonstrate that a neural network that utilizes a tailored three-phase piecewise activation function is able to perform high-speed reconstructions of SPECT images after learning the relationship between the planar images and the tomographic reconstructions. In addition, the tailored piecewise neural network produces reconstructions with significantly lower RMS error, and does so in far less training iterations, than a standard backpropagation ANN. The tailored piecewise function used in this research enables the network to train on a continuous range of outputs more efficiently than with a standard sigmoidal function. Based on the results obtained, we hypothesize that the optimal ANN transfer function or functions, are directly related to the statistical distribution of the training set data. As a preliminary demonstration, a neural network with statistically derived activation functions is shown to have better training and generalization characteristics for SPECT reconstruction than either the single sigmoidal or the three- phase sigmoidal activation functions
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SPECT reconstruction using a backpropagation neural network implemented on a massively parallel SIMD computer
In this paper, the feasibility of reconstructing a single photon emission computed tomography (SPECT) image via the parallel implementation of a backpropagation neural network is shown. The MasPar, MP-1 is a single instruction multiple data (SIMD) massively parallel machine. It is composed of a 128 x 128 array of 4-bit processors. The neural network is distributed on the array by dedicating a processor to each node and each interconnection of the network. An 8 x 8 SPECT image slice section is projected into eight planes. It is shown that based on the projections, the neural network can produce the original SPECT slice image exactly. Likewise, when trained on two parallel slices, separated by one slice, the neural network is able to reproduce the center, untrained image to an RMS error of 0.001928
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Error bounds on the outputs of artificial neural networks
Resolving the uncertainties associated with solutions obtained from artificial neural networks (ANNs) is a major concern for ANN researchers. Error bounds on the solutions are important because they are in integral part of verification and validation. In this research, stacked generalization (SG) is applied to provide error bounds for novel solutions obtained from ANNs. This work shows that SG can provide error bounds on ANN results. We have applied SG to nuclear power plant fault detection for verification of diagnoses provided by ANNs
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Neural network recognition of nuclear power plant transients
The objective of this report is to describe results obtained during the first year of funding that will lead to the development of an artificial neural network (ANN) fault - diagnostic system for the real - time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety - parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the first of three scheduled years for the project. Included herein is a summary of the first year's results as, well as individual descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period
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